NTT Technical Review

December 2020 Vol. 18 No. 12

View from the Top Hidemune Sugahara, Senior Executive Vice President, NTT Communications
Front-line Researchers
 Hideki Yamamoto, Senior Distinguished Researcher, NTT Basic Research Laboratories
Feature Articles: Global Business Initiatives
 NTT’s Global Business Initiatives
 A New Training System Using VR Will Rev Up the Baseball Industry Like Never Before Navigating Today’s Computing Model Landscape NTT Pro Cycling: Data-driven Innovation in Action
Feature Articles: Media-processing Technologies for Artificial Intelligence to Support andSubstitute Human Activities
 Researching AI Technologies to Support and Substitute Human Activities
 Media-processing Technologies for Ultimate Private Sound Space Saxe: Text-to-Speech Synthesis Engine Applicable to Diverse Use Cases
 Speech Recognition Technologies for Creating Knowledge Resources from Communications
Knowledge and Language-processing Technology that Supports and Substitutes
Customer-contact Work Spatial-information Processing Technology for Establishing a 4D Digital Platform
Feature Articles: NTT DOCOMO 5G Initiatives for Solving Social Problems and Achieving
Social Transformation
 History of 5G Initiatives
 NTT DOCOMO Contributions to 5G Standardization
 5G Network
Regular Articles
 Soft Error Countermeasure for 10G-EPON ONU
Global Standardization Activities Trends in Standardization of Mapping and Multiplexing Technologies for Optical Transport
Networks

Practical Field Information about Telecommunication Technologies
 Manual for Assisting with Troubleshooting in PHS Digital Cordless Telephone Systems
External Awards/Papers Published in Technical Journals and Conference Proceedings

View from the Top

Start with Yourself, Improve
Together, and Contribute to Society
Hidemune Sugahara
Senior Executive Vice President,
NTT Communications
Overview

Digital transformation is accelerating in a society that has changed dramatically due to the spread of the novel coronavirus. NTT Communications, which recently celebrated its 20th anniversary, has reorganized withthe aim of providing a safe and secure platform for data utilization to customers around the world. The company is striving to contribute to building
a data-driven society on the basis of the concept called 3S (simple, smart,
speedy) + S (secure) while developing technology and systems to implement the Innovative Optical and Wireless Network (IOWN). We asked
Hidemune Sugahara, senior executive vice president of NTT Communications, about his enthusiasm and philosophy as a member of senior management.
Keywords: digital transformation, data utilization, IOWN

Growing into a company that can deepen andexpand its business and contribute to society—How do you approach business as senior executive
vice president?
I would like NTT Communications to become acompany that excites our employees, customers, andpartners. We would like to use our technology to create value and an exciting future for all stakeholders.
We would like to grow into a company that candeepen and expand its business and contribute tosociety.
In July 2019, the overseas business unit of NTT
Communications, Dimension Data, and NTT
Security, were integrated to become NTT Ltd. Whileseamlessly collaborating with NTT Ltd. inside andoutside Japan, NTT Communications has resolved torespond firmly to Japanese customers operating
global business.
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Vol. 18 No. 12 Dec. 2020

In Japan, under the circumstances embodied by the
keywords data utilization, digitization, and datadriven, the management and business of our customers have entered a period of major transformation,and digital transformation (DX) has been attracting
attention. To respond to these trends with the concept
called 3S (simple, smart, speedy) + S (secure), we
reorganized the company in April 2020 to focus on anindustry-specific solution-provision system and platform-service provision system. I am supervising
platform service, technological innovation, information security, and DX areas.
—So, what is 3S + S?
The concept of 3S + S was created to accelerate
efforts toward the actualization of a smart world, andit means delivering simpler and smarter services andsolutions than other companies at the customers’desired pace with more secureness. On the basis of1View from the Top

this concept, we are developing our business as one
team centered on the following four major organizations.
At the Business Solutions Headquarters, we areaiming to create a smart world and support DX of ourcustomers. In particular, through industry-specificconsulting-type sales focused on the financial, public,
manufacturing, distribution, and foreign-capital/
information-technology (IT)-service industries, we
provide optimal solutions that match the needs of ourcustomers’ business, industry, and society. We have
set up the Smart World Promotion Office to envisiona new world—focusing on data utilization—together
with our customers and partners. We are working tosolve social problems by using data from fields suchas education and manufacturing. In fiscal 2020, weplan to start full-scale activities with a team of 200
people and gradually expand into new fields. To be atrue partner who is closest to the customer, theBusiness Solutions Headquarters is promoting collaboration with customers and other NTT Group
companies with the aim of maximizing our capabilities and expanding B2B2X (business-to-business-toX) business.
The Platform Services Headquarters will add morevalue to our core competencies of networks and datacenters and will be responsible for developing andproviding safe and secure services demanded by thedigital society. We will continue to enhance functions, such as data collection, connection, storage,
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and analysis, on our Smart Data Platform (SDPF). In
particular, we are developing access network-related
services such as the introduction of a zero-trust
Secure Access Service Edge model and local fifthgeneration mobile communication networks (5G).
Moreover, we plan to create a new service that offers
network operations while strengthening security ofIT and operational technology (OT) and to provide
these as one-stop managed services. To develop services from the perspective of user experience, it isimportant to have a mechanism to ensure the smart
world and SDPF work in unison and to maintain amarket-oriented mindset.
At the Innovation Center, we are creating new value
and view of the world—based on technology—that
are not bound by conventional business domains. The
four divisions of technology, namely, strategy, management, technology, and design, are creating andpromoting new business through open innovation
with an eye toward the future, five to ten years fromnow. Gathering 200 people, including business planners, digital engineers, and designers as a center of
excellence (CoE), the Innovation Center supports theabove-mentioned Business Solutions Division andPlatform Services Division for proposing and providing services to customers. The Innovation Center isalso promoting open innovation to create a not-yetseen exciting future together with our partners andcustomers while providing a venue for fostering a
co-creation community called C4BASE.
2

View from the Top

The Digital Reform Promotion Department will
provide customers with successful use cases regarding our own DX and expertise in data-driven management. The Innovation Center helps customers develop
the data infrastructure, implement data-driven management through our CoE for data science and information security, reform work styles, and improve
operational efficiency and productivity. To create asmart world, DX must be promoted throughout society. We believe that showcasing our DX efforts will
help drive DX and eventually contribute to society.
The spread of the novel coronavirus has changed
society completely, and we are all trying to do ourbest to meet the expectations of the so-called newnormal society.
Prepare an environment in which employees cando their best
—There is a great responsibility behind the phrase“Let’s do our best.” What are your thoughts on thisphrase?
That’s a difficult question. It is easy to say, “Let’s
do our best.” However, if I don’t do my best, I canhardly tell others to do their best. Even if a problem
is difficult to solve from the standpoint of an employee, it might be possible to solve it from the standpointof senior management, like myself. We, senior management, should do our best to prepare an environment in which employees can do their best. Last year,
we celebrated the 20th anniversary of the company’s
founding and took that opportunity to advocate “Start
with yourself, improve together, and contribute tosociety.” I want to foster an organizational culturethat ensures employees can do their best (take onchallenges), regardless of their positions.
I also think we should support our customers to takeon new challenges. To achieve this, both pioneering
efforts and reading technological and societal trends
are important; however, we must not be too pioneering nor focus too much on the support side. Although
this balancing act is very difficult, we must maintainit. I’m sure we can do it.
In terms of pioneering efforts, researchers at NTT
laboratories are conducting basic research and technological development. I would like them to makepioneering efforts while listening to the voices at thefront lines to anticipate business trends. If cuttingedge sales and cutting-edge researchers collaborate,
we will have no rivals.
I was involved in the satellite-communication business in NTT and have a long-standing relationshipwith NTT laboratories, with whom I have worked on
the development and launch of artificial satellites. I
was also involved in the founding of NTT Resonant,at which time, I worked with about 200 people seconded from NTT laboratories. With this experience, I
understand how fortunate we are to be able to use theresources of the laboratories in our business. By sharing the responsibility that was previously assigned toeach laboratory, we support the commercialization ofresearch results and deliver them to our customers sothat researchers can focus on their research. I’d likethem to make full use of their talents and create something new. Moreover, our employees assigned to theInnovation Center will probe the research results
produced by researchers and set up systems to commercialize them.
—You have high expectations for NTT laboratories,
right?
We are grateful for NTT laboratories, which haveeverything from the world’s most-advanced basic
research to its application and practical implementation. I think that contributing to society by using theirresearch results and converting them into products
and services is our way of responding to those benefits. Going beyond that point, I think it is also

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Vol. 18 No. 12 Dec. 20203

View from the Top

necessary to consider collaboration with customers at
the research phase. To that end, I’d like to actively
discuss such collaboration with the laboratories. Twoof the key technologies that I have recently become
interested in are cloud-based 5G and quantum cryptography. Regarding technology being researched at
NTT laboratories, we are interested in security technology required for data utilization, especially thezero trust model, and are working on such technology
at the Innovation Center.
Our efforts toward the implementation of theInnovative Optical and Wireless Network (IOWN)
has also been underway, and we have created an
IOWN-promotion project team at the Innovation
Center to collaborate with NTT laboratories. In the
project, we are focusing on implementing the AllPhotonics Network and overlay networking solutionsat an early stage. We first expect to apply them to the
network in our datacenters. Moreover, we want tocollaborate with NTT laboratories to create a showcase using IOWN technology at the NTT Musashino
R&D Center in Tokyo. Considering the coming spaceera, we would like to explore space business in collaboration with NTT Space Environment and Energy
Laboratories.
To implement Cognitive Foundation®, a key element of IOWN, it is important to share technology
and architecture within the NTT Group. This sharing
will help us build a foundation for providing one-stop
services to customers. IOWN is a good target for
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standardization, and I’d like to promote that standardization in cooperation with NTT laboratories.
One step forward, one step ahead,
one step outside—Lastly, would you tell us what you have valued inyour work?
“One step forward, one step ahead, one step outside” is my motto. The same phrase is true for individuals and organizations, and the world you see
when you go one step forward, ahead, or outside will
differ. When you experience that difference, different
ideas and actions are born. However, sometimes thatstep is not taken or cannot be taken. That’s why I
cherish this phrase.
Looking back on making mistakes and misjudgments, I should have taken a step forward. Such failure may cause problems for others and regret inmyself. I sometimes think I was not prepared enough.
Some of these problems can be beyond our control. It
is necessary to read the trends in society; however,sometimes you can’t obtain satisfactory results even
if you are fully prepared. That’s why I think it’s
important to believe in yourself and take that step
forward.
Even if you make a mistake, you can always learn
from it. It is important to think about how to learn
from the experience and better prepare for your next4

View from the Top

step. Regarding a recent inconvenience caused to ourcustomers and other stakeholders due to unauthorizedaccess, I regret that it occurred because we were convinced that we had taken all possible measures. From
investigating the cause to dealing with it, however,many issues concerning this incident were addressedby the considerable efforts of our employees, reaffirming their high skill level. Taking advantage of thelessons learned at that time, we would like to support
our customers through our skilled employees—who
are our assets—and grow into a company that excites
our customers and partners. To that end, I want us tobe an attractive company where employees can play
an active and exciting role.

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Interviewee profile
Career highlights
Hidemune Sugahara joined Nippon Telegraphand Telephone Public Corporation (now NTT) in
1987. He became senior vice president of NTT
Communications in 2016, president & CEO of
NTT Com Solutions in 2018, and executive vicepresident of NTT Communications in 2019. He
has been in his current position since June 2020.

5

Front-line Researchers

Unexpected Experimental Results
against Common Beliefs Bring
Opportunities for New Discoveries
Hideki Yamamoto
Senior Distinguished Researcher,
NTT Basic Research Laboratories
Overview
Hideki Yamamoto, Senior Distinguished Researcher at NTT
Basic Research Laboratories, and his co-researchers are creatingnovel materials nonexistent in the natural world and elucidating
the property of such materials. They apply NTT’s unique andstate-of-the-art apparatus and technologies to stack the elements
making up a material through control of their reaction at theatomic and molecular levels. We asked Dr. Yamamoto about his
current research activities and the mindset he adopts as a
researcher.
Keywords: superconductor, magnetic material, thin-film synthesis, molecular beam epitaxy

Synthesis of novel materials and discovery of new
properties through original and unique methods—Dr. Yamamoto, please tell us about your current
research activities.
My research theme is the design and creation ofnovel superconducting and magnetic materials
through thin-film synthesis methods and explanationof their properties (Fig. 1) [1]. The ultimate goals of
this research are to contribute to the (i) developmentof lossless power transmission/supply and wiring, (ii)further reduction in power consumption in devices,and (iii) shift to green-power generation and power
storage.
To be more specific, I am involved in creating novel
materials not seen in the natural world by growing
thin films with a thickness from 0.1 nm (atomic
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monolayer) to 1 μm on a base of a single crystal (acrystalized solid with regularly arranged atoms)
called a substrate. I am also involved in accounting
for various, and sometimes novel, properties emerging in such specimens. We use molecular beam epitaxy (MBE) for growing these thin films. With thismethod, we use ultrahigh-vacuum (UHV) chambers(having a vacuum of one ten-trillionth that of ambientpressure), in which each constituent element of thedesignated compound is supplied in the form ofatoms or molecules to give rise to reaction on heatedsubstrates leading to the formation of thin films [2].
I believe that we are an extremely advanced
research team on a global basis working on the creation of completely new compounds by using MBE.
Specifically, we are conducting research on thin-film
growth of oxides that contain two or more cations(complex oxides) by using a method called oxide
6Front-line Researchers(1) Chemical reactions under low-reaction temperatures and non-equilibrium conditions
Reactions between ultimately small particles such as atoms, molecules, and ions(2) Stabilization of metastable phases through epitaxy (mutual interaction with the underlying crystal substrate)(3) Uniform oxidation in the case of oxides (strong oxidation possible through the use of O3 (ozone) and O)
Tenuity and large surface area to volume ratio of specimens(4) Impurity-free synthesizing environment (ultrahigh vacuum)(5) High-throughput syntheses(6) Resource-saving (usage of small amount of raw material)(7) Specimens in the form of single-crystal thin films, highly compatible with future device fabrication

Fig. 1. A dvantages of searching for and synthesizing new materials by using MBE—a method for growing high-quality thinfilms of known materials.

MBE. Our custom-made MBE apparatus sui generis(Fig. 2) has two key features. First, each atomic/
molecular flux of constituent cations can be suppliedin a stable manner for a prolonged time by monitoring
the flux rates of those elements in real time and feeding the results back to the power supply of the evaporation source. Second, it is capable of strong oxidation in a vacuum by supplying oxygen gas, which
typically exists as O2 (oxygen molecules), in the formof highly reactive atomic O (oxygen) or O3 (ozone)
gas. By taking advantage of these features, we havebeen able to synthesize crystals not found in the natural world.
Using this apparatus, we have been involved forsome time in creating new superconducting materials
and elucidating their properties. In superconducting
materials, electrical resistance becomes zero undercertain conditions. In particular, superconductivity
occurs at low temperatures below a critical temperature (superconducting transition temperature: Tc). In
this state of zero electrical resistance, electricity
flows with no power loss within the superconducting
material. The highest Tc under ambient pressure (1atmosphere) is currently –140ºC, which is about 60ºC
lower than the temperature at which dry ice sublimes
from a solid (approximately –79ºC). However, there
have recently been a series of reports on materials
having a Tc of –70ºC or even –25ºC closer to room
temperature while they can be stable only under ultrahigh pressures (approximately 2 million atmospheres). These reports have led many researchers tobelieve that room-temperature superconductors must
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exist but simply have not yet been discovered andsynthesized. A report on room-temperature superconductivity in a carbonaceous sulfur hydride, though
under 2.7 million atmospheres, has just been published (October 2020) [3]. It strongly suggests thatroom-temperature superconductivity is no longer an
impossible dream.
We, of course, aim to synthesize and discover suchmaterials, but as a prior step, we have been using thethin-film growth methods that I just mentioned to create new superconducting materials having a variety ofproperties. Examples include the discovery of diverse
phenomena such as induction of superconductivity inmaterials previously thought to be insulators andstrain-induced increase in Tc. More recently, we havetaken up the challenge of inducing superconductivity
in an artificial structure (artificial superlattice) consisting of alternating layers of the simplest building
block of cuprate superconductors and oxides containing no copper (Cu); note that the cuprate superconductor family exhibit the highest Tc under ambientpressure. We are now only one step away from superconductivity in such artificial superlattices.
While known as a ferromagnetic metal and neither
a new material nor superconductor, we most recently
prepared the world’s highest quality SrRuO3 (Sr:strontium, Ru: ruthenium) thin film and observed theemergence of an exotic state called a magnetic Weyl
semimetal* [4], the quest of which had so far been*

Magnetic Weyl semimetal: A special state that has only recently
been found to occur in some magnetic materials. Weyl quasi-particles that emerge in this state behave as if they have no mass.

7

Front-line Researchers(a) Schematic diagram

EIES
sensorEvaporant
fluxesEIES
HeaterSubstrate

Electrons

EIES
QCM 1

ED
Shutter

RHEED image

RHE

Emittedlight

Filament
O
O3

PlasmasourceDetectors(PMTs)

QCM 2
10 sources

6 electron
guns
UHV
chamber

Wavelength filters
MovablePC
Signal processor and microcontroller

Feedback

Evaporation source (electron gun) power supply(b) Photo of the MBE apparatus

EIES: electron impact emission spectroscopy
PC: personal computer
PMT: photomultiplier tube

QCM: quartz crystal microbalance
RHEED: reflection high-energy electron diffraction

Fig. 2. Our MBE apparatus for fabricating complex-oxide thin films.

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8

Front-line Researchers

hampered due to difficulties in preparing high-quality
specimens. This accomplishment shows the effectiveness of our high-level thin-film growth technology and measurement techniques for electrical properties under magnetic fields. In future research, we
hope to conduct measurements with which the electronic state can be visualized so that the existence of
this exotic state can be recognized at a glance.
—Your team is achieving world-class results basedon an original method.
The creation of new substances and materials hashelped to advance the natural sciences while simultaneously contributing to the development of highperformance and highly functional devices andbreakthroughs in device design. This is clearly shownby the discovery of superconductivity in ceramicmaterials (1987 Nobel Prize in Physics), discovery offullerenes (molecules consisting of 60 carbon atoms
in a soccer ball structure) (1996 Nobel Prize in Chemistry), fabrication of graphene (ultimately thin graphite having a thickness of a single layer of atoms)(2010 Nobel Prize in Physics), and invention of blue
light-emitting diodes using a nitride semiconductor(2014 Nobel Prize in Physics). There have also beena variety of proposals and initiatives, in which elements and compounds not previously used will be
exploited, for overcoming the limits of miniaturization (the limit of Moore’s law) in Si (silicon) integrated circuits, the basis for modern electronics.
Against this background, we have been pursuing
the creation of new materials using oxide MBE andsuccessfully created not only superconducting materials but also new magnetic materials. A magnet is a
substance whose magnetic property (ferromagnetism) weakens as the temperature increases until
eventually disappearing at a certain temperature(Curie temperature). Accordingly, magnetic materials
having higher Curie temperature allow for a magnet
that can be operated at higher temperatures. With thisin mind, we conducted a materials search, which has
evolved into the synthesis and discovery of the new
ferromagnetic material Sr3OsO6 (Os: osmium) [5].
This novel material exhibits the highest Curie temperature (above 780°C) among insulating materials,
smashing the record for the first time in 88 years. In
addition, Sr3OsO6 is free from iron or cobalt unlike
most magnetic materials in existence today, whichblazes a new trail in the search for magnetic substances. Together with further new materials which, I
believe, will be found along the guidelines obtained
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through the discovery of Sr3OsO6, applying thesematerials to magnetic/spintronic devices that can be
operated stably above room temperature will become
possible.
—Please tell us how you got started in your worldclass research.
In 1987, my former supervisor in this research field
moved from Stanford University to NTT Basic
Research Laboratories with the aspiration of contributing to both basic and applied research and founded
this research unit. About six years later, I entered thelaboratories and became involved in this research
together with the supervisor and another senior colleague forming a three-person team. At that time,
technology for achieving a long-term and stable supply of cation fluxes in a vacuum chamber by providing feedback to the power supply source had not been
seen outside of NTT Basic Research Laboratories. I
found this to be astonishing, and at the same time, I
intuitively felt that this was technology that I should
focus on throughout my career. I was fascinated by
the possibility of creating novel materials not existingin the world by using this technology.
In 2004, I inherited this research from my former
supervisor, and I have since been involved in thiswork for about 15 years. A variety of experiences
have taken place during this time.
We have searched for and synthesized new materials by using the unique thin-film growth techniquesthat have been cultivated and built up over many
years at NTT. However, the manufacturer of the rate
controller, which is indispensable for our technologies of high-precision control of the flux rate of eachelement making up a thin film by electron impact
emission spectroscopy (EIES), discontinued the
product in 2001. Consequently, thinking that it would
be fatal to my research if this technology were to vanish, I consulted with my former supervisor and in theend formulated a specification and ordered a custommade product from a startup company in Silicon Valley, USA. For about one year starting about threemonths after that order, I had the good fortune ofresearching at Stanford University for joint research,which allowed me to visit that company frequently
during breaks in my research. Thanks to this state ofaffairs, I was able to personally participate in thecompletion of this custom-made product from theoperation-verification stage of the first prototype(Fig. 3). This technology has further evolved over the
15 years since the development of the prototype, and9Front-line Researchers

Flux (µmol·m−2·s−1)8Evaporant
fluxesEIES
sensorElectrons
Emittedlight

Filament
First prototype (2004)

6
4
2
0

Detectors

Detectors(PMTs)

Sr
Os

0

5

10
15
Time (min.)20

Simultaneous control of theflux rate of multiple cations
with high precision

Wavelength filters
PC

Signal processor
and microcontroller

Signal processor and microcontroller

Fig. 3. Flux-rate control equipment using EIES (first prototype of custom-made product).

looking at the way that it has supported our research
is deeply moving for me.
When involved in research, there are tough years tocontend with, but also exciting years when discoveries, even if small, occur one after another. My firstthrilling experience occurred in my third year at NTT.
I was able to interpret the experimental results fromthe research that I had taken up immediately after
entering the company and to then publish my first
paper on those results. My good fortune then continued with our discovery of the first new superconductor synthesized using the thin-film growth method
and other achievements. Our research team was givena boost when my senior colleague received the Young
Scientist Presentation Award of the Japan Society ofApplied Physics in 1996 and I the same award thefollowing year. On the other hand, I remember well
how our supervisor at that time admonished us young
researchers excited about our string of successes withthe words “perform your experiments simply andhonestly.” Experimental science is the process ofunderstanding nature by changing experimental conditions in a variety of ways to query nature on its laws
and using the answers received. For this reason, thesignificance of the words “simply and honestly” spoNTT Technical Review

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ken by him sticks with me to this very day and at
every turn.
In subsequent years, more encouragement came myway by receiving the Superconductivity Science andTechnology Award (2016) that I had privately longed
for and by having the opportunity to publish a numberof papers in leading journals. More recently, however,I am frequently called upon to submit scientificmanuscripts as the last author (usually, the person in
charge of those research projects), so it’s more asense of relief than a feeling of joy when we’ve gotten
a paper published. Within the laboratories, I sometimes hear young researchers talk about their tears of
joy over getting a paper published, which I feel a bit
envious about. I guess this is a clear sign of my age!
Setting big goals (high mountains to climb) andtaking a safe and steady approach—How have your views of things and role changed
after becoming a senior distinguished researcher?
I am grateful for being put into a stable position thatallows me to concentrate on my research. I believe I
can take appropriate and legitimate risks for making
10Front-line Researchers

breakthroughs and acquiring high returns by exploiting this position. Taking risks is vital to achieving
breakthroughs in basic research, especially in materials research. However, there are many cases in which
there is no other choice but to select research themes
for which results and papers can be readily produced
to secure the next positions of team members and win
competitive research funding. The reality is that taking risks is not easy. I therefore want to make gooduse of my very favorable position and environment as
an NTT senior distinguished researcher to pursue
research that takes appropriate and legitimate risks.
Among the 11 core technologies for making the
NTT vision of the Innovative Optical and Wireless
Network (IOWN) a reality, 6 are related in some form
to materials research, so in this sense too, I feel theweight of responsibility. Although my theme ofdesign and thin-film synthesis of novel superconductors and magnetic materials with elucidation of theunderlying physics is not necessarily a mainstream
one in IOWN, there are many examples in history ofmajor breakthroughs coming out of non-mainstream
research themes, and I have expectations in thisregard.
I also feel that forming research teams that are
diverse not only in gender, nationality, and language
but also in expertise is important for achieving breakthroughs. It is easy to form and manage a researchteam whose members are in the same field of expertise, but I think it’s difficult for such a team to makemajor breakthroughs. I think it’s great if a young
researcher who joined a research team but with a different field of expertise occasionally thinks, “Why
don’t my seniors know about this?” From a shortterm perspective, many points of difference can makethings all the more difficult, but from a long-term
perspective, the importance of such diversity will be
felt.
—Is there anything that you have kept in mind whensearching for problems or themes to work on?
Since entering NTT Basic Research Laboratories
after receiving my doctorate, setting big goals (highmountains to climb) and taking a safe and steadyapproach is something that I have always kept inmind. For example, the search for room-temperature
superconductors included in my research theme is ahigh mountain to climb and an appropriate theme fora researcher in my present position. The search for
artificial photosynthetic material on par with plants is
likewise a high mountain to climb. However, when
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you’re young and have a dream, you might take on asignificant challenge from the beginning, and I was
the same way. This can lead to a situation of trying topush a massive rock with all one’s might and failingto move it even 1 mm. To avoid taking such an all-ornothing approach, it is advisable to achieve small
milestones and write papers on those results as they
happen. I learned this approach from my seniors at
NTT Basic Research Laboratories.
Basic research does not go well if the theme is one
that the researcher does not truly enjoy. It is notalways a short run to the top of the mountain, and it is
not unusual to have to take detours or pull back asneeded. In such circumstances, it is important toadopt a mindset in which you never quit looking up at
the top of the mountain. Good basic research can havea major impact on creating or changing a certain concept. In this sense, an example of good basic researchthat I’m particularly proud of was our research ofnovel superconducting materials that we synthesizedand discovered over about ten years from 2003. This
research presented counterexamples to previously
established superconductivity emergence conditions
in cuprate superconductors. I felt disheartened when
our paper was initially not accepted by academic
journals, but at least the research was a thrilling experience. Although these materials were established as
novel superconductors, a dispute as to whether ourinterpretation of the superconductivity-emergencemechanism is truly a counterexample to the established theory is continuing. This is also a common
occurrence in basic research, so I maintain a positive
attitude.
Imagining one’s ideal research life given
ample funding—What would you like to say to junior researchers?
Materials informatics has recently appeared as the
fourth paradigm of science after experimentation,
theory, and computational science. At the same time,
the accuracy of predicting the electronic structure
within materials through theoretical computations
has significantly improved. As a result, it has become
possible to conduct materials search in a considerably
more efficient manner than before and optimize thinfilm growth conditions. I would like junior researchers to use such efficient methods and avoid thedetours that I took due to my own lack of ability.
However, the reality is that there are still many things
you won’t understand unless you actually conduct
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Front-line Researchers

experiments in materials science, so I would likejunior researchers to keep in mind the following
advice.
Before doing anything else, construct a high-reliability experimental system that can reproduce
experimental results. Once you complete this to areasonable extent, try conducting some experiments.
It’s important to pursue experiments with good efficiency, but it’s also important to try detours withoutemphasizing only short-distance runs. While thereproduction of results is a prerequisite in science,experimental results that differ from what was
expected or from commonly accepted theory present
a great opportunity. I believe that there’s a strong possibility of encountering an unknown or unexpectedphenomenon through those experimental results.
Moreover, experimental results thought to be a failurein the sense of achieving a certain goal, cannot alone
be judged a failure. For example, while it would certainly be disappointing to fail in preparing a thin filmwith the target level of quality, it is not uncommon forsome hints to be hidden in those experimental results.
I sometimes ask young researchers, “What kind ofresearch life would you like to lead and what kind ofresearch would you like to pursue if your pay wasenough to lead a worry-free life and you could receive
ample funding for research?” The reply to the question should be, “the research that I have a great desire
to pursue.” In the long run, it would be better to pursue a research theme determined in this manner.
As you know, originality is more important thananything else in basic research. On the other hand, aformer supervisor of mine once told me that researchon current technology is not something to detest as
long as it concerns truly important technology. Avoiding researching current technology and falling to atechnological level at which you cannot even reproduce fascinating results that some other researchgroups have provided is worse than researching it. I
believe that you have sound science if multiple toplevel research teams and institutions can reproduce
good basic research results.
—Dr. Yamamoto, can you tell us about your future
research topics?
Until several years ago, the elements that we used
to grow thin films of complex oxides included transition metals on the relatively upper rows of the periodic table such as Cu. Recently, however, we have
expanded our target to transition metals such as Ru,

NTT Technical Review

Vol. 18 No. 12 Dec. 2020

Pd (palladium), and Os that belong to lower rows thanthat of Cu on the periodic table, which, for example,
has led to the discovery of a material having magnetic properties up to the highest temperatures among
insulators. Our next theme is to what extent we cansystematically and strategically (but not haphazardly)
expand our use of these transition metals. In addition,
the discovery of materials that can exist stably only
under ultrahigh pressures of 2 million atmospheres
but make a superconducting transition at temperatures closer to room temperature has been reported
for hydrides, not oxides. Up to now, our target for
growing thin films has been essentially limited tocomplex oxides, so how we should expand our target
beyond oxides is also in my things-to-do list.
Tackling these issues is going to take some time—it
is not something that can be completed within one’s
own generation. Training up-and-coming researchers
who will become the principal investigators of nextgeneration research while continuing to produce
research results is of prime importance. I feel theneed for passing on knowledge as well as technicalskills. This is necessary not only for our researchers
but also for equipment and component manufacturersthat support our research, so I think it would be
worthwhile to develop some means of passing ontechnology on both sides.
References[1]

H. Yamamoto and H. Gotoh, “Overview of Novel Materials Creation
Research at NTT,” NTT Technical Review, Vol. 17, No. 10, pp. 1–6,
2019.
https://www.ntt-review.jp/archive/ntttechnical.php?contents=
ntr201910fa1.html[2] A. Ikeda, Y. Krockenberger, Y. Taniyasu, and H. Yamamoto, “MBE
Growth and Element-distinctive Atomic-resolution Characterizationof High Temperature Superconductors,” NTT Technical Review, Vol.
17, No. 10, pp. 18–23, 2019.
https://www.ntt-review.jp/archive/ntttechnical.php?contents=
ntr201910fa4.html[3] E. Snider, N. Dasenbrock-Gammon, R. McBride, M. Debessai, H.
Vindana, K. Vencatasamy, K. V. Lawler, A. Salamat, and R. P. Dias,“Room-temperature Superconductivity in a Carbonaceous Sulfur
Hydride,” Nature, Vol. 586, 373, Oct. 2020.
https://doi.org/10.1038/s41586-020-2801-z[4] K. Takiguchi, Y. K. Wakabayashi, H. Irie, Y. Krockenberger, T. Otsuka, H. Sawada, S. A. Nikolaev, H. Das, M. Tanaka, Y. Taniyasu, andH. Yamamoto, “Quantum Transport Evidence of Weyl Fermions in an
Epitaxial Ferromagnetic Oxide,” Nat. Commun., Vol. 11, 4969, Oct.
2020.
https://doi.org/10.1038/s41467-020-18646-8[5] Y. K. Wakabayashi, Y. Krockenberger, Y. Taniyasu, and H. Yamamoto,“Creation of Novel Material Sr3OsO6 with the Highest Ferromagnetic
Transition Temperature among Insulators,” NTT Technical Review,Vol. 17, No. 10, pp. 7–11, 2019.
https://www.ntt-review.jp/archive/ntttechnical.php?contents=
ntr201910fa2.html

12Front-line Researchers

Interviewee profile
Hideki Yamamoto
Senior Distinguished Researcher, Supervisor,
Executive Manager of Materials Science
Laboratory, NTT Basic Research Laboratories.
He received a B.S., M.S., and Ph.D. in chemistry from the University of Tokyo in 1990, 1992,
and 1995. He joined NTT in 1995, and his principal research fields are thin-film growth, surface
science, and condensed-matter physics. He was avisiting scholar at the Geballe Laboratory for
Advanced Materials, Stanford University, USA(2004–2005). He received the 2nd Young Scientist
Presentation Award (1997) from the Japan
Society of Applied Physics (JSAP) and the 20th
Superconductivity Science and Technology
Award (2016) from the Forum of Superconduc­tivity Science and Technology, the Society of
Non-traditional Technology. He is a member of
JSAP, the Physical Society of Japan, the Japan
Society of Vacuum and Surface Science, theAmerican Physical Society, and the Materials
Research Society.

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Vol. 18 No. 12 Dec. 202013

Feature Articles: Global Business Initiatives

NTT’s Global Business Initiatives
NTT, Inc.
Abstract

The NTT Group positioned its global business as one of its pillars of growth in the NTT Medium-Term
Management Strategy announced in 2018, and in FY 2019 revenue grew to US$19.5 billion. After active
pursuit of international mergers and acquisitions, approximately one-fourth of the NTT Group’s workforce (135,000 people) now works outside Japan, and the global business has grown in importance. The
NTT Group’s strength lies in its ability to offer full stack support for customer business transformation
through the global collaboration of operating companies. This full stack support encompasses everythingfrom networks, datacenters, hybrid clouds, and other information technology infrastructure to management services and business applications.
Keywords: global, smart world, brand

1. Expanding global business
In November 2018, the global holding company
NTT, Inc. was established under the NTT holding
company’s umbrella, and NTT Communications,
Dimension Data, NTT DATA, NTT Security, andNTT Innovation Institute Inc. (NTTi3) were moved
under NTT, Inc. The aim of this is to improve global
governance, rapidly incorporate into NTT, Inc. management the expertise and experience of talent specialized in global markets, and strengthen NTT’s
global competitive advantage and profitability. In
July 2019, NTT Ltd. was established to promote customer business development and the creation as “OneNTT” (Fig. 1), a global business growth strategy,
which combines support for customer business development with innovative creation, to strengthen thecompetitive strength of NTT’s global business(Fig. 2).
2. Support for customer
business transformation
NTT is using software-defined technology to
deploy IT (information technology) as a service in theform of networks, datacenters, and cybersecurity
essential to customer business development. In addition to these services, NTT is deploying industryspecific advisories and outcome-based solutions thatNTT Technical Review

Vol. 18 No. 12 Dec. 2020

directly contribute to customer value across a variety
of industries. NTT is also cutting total supply costs
across the NTT Group, mainly through a global procurement company.
3. Contributing to the creation of a smart world
Through these activities, NTT is transforming the
NTT Group’s homegrown technology and capabilities developed in collaboration with its clients into
integrated solutions to contribute to the creation of asmart world. In Las Vegas, Nevada, in October 2018,
NTT signed a memorandum of understanding regarding the creation of new use cases in transportation,parks, and roadside monitoring for deployment incities in Nevada, across North America, and throughout the world. NTT’s achievements in public safety
solutions with advanced situational awareness capabilities in cooperation with the City of Las Vegas wasdue to the NTT Group’s ability to fully use its expertise by drawing on its full IT stack that serves as a
one-stop source of innovation and its stance that“customers own their data.”
4. Commitment to innovation
NTT has established a global innovation fund, anorganization that boosts innovative creation, and an
overseas research hub to catalyze its innovative
14

Feature Articles

Aug. 2018

Nov. 2018

July 2019

NTT

NTT

NTT

NTT, Inc.

NTT, Inc.

(New)NTT Communications

NTT Communications

Dimension Data

Dimension Data

NTT Security

NTT Security

NTT DATANTT DATANTT Global Data Centers(New)

NTT Communications(Reorganized)

NTT DATA

Global Innovation Fund
(New)
(NTT Venture Capital)
Global Procurement Company(New)(NTT Global Sourcing)

NTTi3(Reorganized)

NTT Ltd.

NTT Venture Capital

NTTi3

NTT Global Sourcing(New)

NTT Disruption(New)

NTT Research, Inc.

(New)

Fig. 1. Reorganization of global business.

One NTT global growth strategy
Create new innovative solutions

Business modernization

Disruptive innovation

Industry advisory expertise drives
outcome-based solutions

Global innovation fund

Automotive/manufacturing

Communications
Healthcare/mediaAccelerate IT-as-a-Service
with software-defined technologies
Managedinfrastructure

Cybersecurity
services

Cognitive
foundation

Globaledge services

Datacenter asset firm
Improve efficiency of datacenter business

Invest in the next innovativetechnology frontier

PublicsectorVentureecosystem

Integrated capabilities(Enabling a smart world)

Financialservices

Deliver high-value solutions

Digitalstartups

Disruptive innovation approachCo-create with clientsusing advanced technologies
Industry specialized
Business function
Smart world

Technology-led

e.g. AI, robotics, edge computing
NTT global procurement company

Research center (outside Japan)

Global procurement
excellence

Invent world-leading
innovative technologies

GlobalTalent Development

One NTT

GlobalBranding

Fig. 2. One NTT global growth strategy.

initiatives aimed at creating something totally new.
NTT Venture Capital was established in November
2018 as the NTT Group’s global innovation fund to
NTT Technical Review

Vol. 18 No. 12 Dec. 2020

promote synergies and new business creation within
the group. Through NTT Venture Capital, NTT isinvesting primarily in technology sectors that are15

Feature Articles

expected to grow in global markets. NTT Disruption
boosts innovative creation, using cutting-edge technology in artificial intelligence, robotics, and edge
computing, to create new value in collaboration withclients. In particular, the company aims to commercialize new technologies through proofs of conceptthat affirm the value of the technologies with customers. Finally, NTT established NTT Research, Inc., anoverseas research hub that leads the world in researchon innovative technologies.
5. Strengthening the NTT brandTo further enhance its global presence, NTT has
become the leading sponsor of the NTT IndyCar

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Series of top-of-the-line professional motorsports inNorth America. NTT has also signed a technology
partnership agreement with Major League Baseballin the USA and is also an official technology partnerof the Tour de France. Through partnerships like theabove, NTT is working to provide an innovative
experience for sports fans using cutting-edge technology and building the NTT brand through televisionand the web, enhancing its global competitive advantage.
By combining support for customer business development with its commitment to innovative creation,
NTT, with its talents and brand, is enhancing its competitive strength in global business and accelerate
growth as One NTT.

16

Feature Articles: Global Business Initiatives

A New Training System Using VR
Will Rev Up the Baseball Industry
Like Never Before
Tomoko Ara and Hitomi Nakamura
Abstract

By accurately reproducing various pitches of actual pitchers in virtual space and visualizing the movements of athletes using a sensing function, the baseball-simulation system that we provide achieves more
efficient and effective training for athletes. This system also provides baseball fans with a new means ofenjoying the sport by enabling them to stand—in virtual space—in the batter’s box and play against thepitching of a real pitcher. This system represents a new initiative that has never been available before,
and it is generating interest within the industry.
Keywords: virtual reality, baseball, innovation

1. Baseball-simulation system using
virtual reality
The baseball simulation system that we provide
uses virtual-reality (VR) technology to highly accurately reproduce an actual baseball ground and thepitching of actual pitchers in a 360-degree virtual
space by matching the trajectory and speed of theball. This system was developed using the research
results from NTT Media Intelligence Laboratories
and NTT Communication Science Laboratories(Sports Brain Science Project) and commercialized
after several verification tests.
The key feature of this system is its high reproducibility. It is technically possible to reproduce pitching
images with computer graphics (CG). An actual batter standing in the batter’s box focuses on the stanceor even the interval between the pitcher’s movements
and uses that knowledge to decide how to swing thebat or determine the type of pitch that will be thrown.
The system therefore makes full use of actual throwing images to precisely reproduce positioning andtiming when the pitcher releases the ball. By referring
to actual pitching data, the system realistically reproduces the trajectory and speed of the pitch that thepitcher threw in a specific inning of a specific game.
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The same reproduction applies to the baseball groundin the background. By incorporating data taken onsite using a 360-degree camera, the system virtualizes
a situation in which the player (batter) can receive thepitch as if he or she were standing on home plate. In
an actual baseball ground, the atmosphere in the stadium and the appearance of the pitching differ
between night and day games. This system can reproduce such environments in the VR space. By changing the background according to the purpose, it ispossible to experience a more realistic space (Figs. 1
and 2).
2. Sports × VR
Although it may seem difficult to imagine using VR
in the sports industry, VR can be advantageous for
sports. Video analysis and the utilization of data aregradually becoming widespread in all sports disciplines. However, in many cases, such data are twodimensional information, so they are understoodusing one’s intellect. VR makes it possible to experience things through the body in a highly immersive
three-dimensional space, which may lead to more
effective training. It is also possible to practicerepeatedly with different ball types and speeds that17

Feature Articles

First-person-view sports-synthesis technology

HMD/tracking sensor

Pitching data
Process pitching data
and reproduce with CG

Pitching-video data

The ball is hidden in
the pitching video.

Pitching data aresynthesized with CG
at the correct positionin virtual space.
The pitching video data and360-degree video data areplaced in the correct positionand stitched in VR space.

360-degree video
data of surroundings

LaptopVR
content

Watch VR content
with HMD

HMD: head-mounted display

Fig. 1. The baseball-simulation system.

Fig. 2. Reproduction of baseball ground with the baseball-simulation system.

are difficult to reproduce in the real world. Since thenecessary equipment is compact, full-scale training ispossible regardless of time, place, or weather.
3. Expansion to Japanese and
US professional teams
The system is currently being offered for the purpose of training (improving performance of athletes)and entertainment (engaging with fans). There are
two use cases regarding training. The first case isimagery training before a game. Reproducing, in VR
space, the balls thrown by the starting pitcher of the
opposing team in the previous game enables the batter to understand the pitching tendency of the pitcher
and create a more concrete swing image. As menNTT Technical Review

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tioned above, more and more professional baseballteams are checking game videos and pitching data of
opponents on tablets and other devices before a game.
It can be said that being able to experience such datawith the body, as opposed to understanding it with themind only, is very effective just before a game.
Although this system is basically for batters, we have
had many requests from pitchers. Pitchers only havea limited number of ways to evaluate their pitching in
the real world; however, with VR, it is possible toactually stand at the home plate and see what theirpitching looks like from the batter’s perspective.
The other use case is visualization of a player’s
performance. By monitoring the movement of theplayer (batter) via sensors while watching the pitch in
VR space, it is possible to visualize the movement
18Feature Articles

ySensors

Bat

HMD
Back

x

z

Fig. 3. Swing movement is measured in three directions by using sensors.

and timing of the swing. In addition to the headmounted display (HMD) worn on the batter’s head,
sensors are attached to the lower back and bat tomonitor the swing movement in three axial directions(X, Y, and Z) (Fig. 3). For example, by comparing thepitching timing in the VR space with the movementof the swing, it is possible to determine, for example,
delay and hesitation in the swing with objective data
(Fig. 4). In this manner, it becomes possible to visualize—with objective data—things that are recognized
as good or bad, which was previously difficult toverbalize, and athletes and coaches can better embody
the training athletes need to overcome their weaknesses. In addition, by monitoring using VR for
observing fixed points, it will be possible to determine the effects of training and certain changes.
The system was initially introduced through verification testing to the Rakuten Eagles, a Japanese professional baseball team, in 2017. At that time, thepurpose was to conduct imagery training to beat thepitcher of the opposing team before the game. However, we received feedback stating that “As well as
watching the pitching, I want to check my swing
movement while watching,” accordingly, we developed a sensing function for visualizing the batter’sswing movement while watching the pitching in VR
space and added it to the system. We then focused on
introducing the system to Major League Baseball(MLB) in the United States in consideration ofMLB’s market size and environmental conditions.
The basis of the system for entertainment is thesame as that for training, and the aim is to reproduce
NTT Technical Review

Vol. 18 No. 12 Dec. 2020a ball thrown by an actual pitcher in an actual gamewith high accuracy. In the real world, it is unlikely aperson will ever experience standing in the batter’s
box in a real baseball stadium and receiving a ballthrown by a professional pitcher in a real game; however, this is possible in VR space. Being neither abatting cage nor a video game, we believe the system
will provide a new means to enjoy baseball.
We launched a service for fans of Rakuten Eagles
from the 2018 season onwards and a similar service
for fans of Hiroshima Toyo Carp in 2019. The system
is attracting attention as an effective tool for maintaining the relationship between teams and their fansin circumstances in which it is difficult to directly
contact with fans during off-season or due to the current COVID-19 pandemic (Figs. 5 and 6).
4. Differences between Japanese andUS sports markets
If baseball alone is looked at, we see that the market
size in the United States is more than five times
larger than in Japan, and the number of teams in MLB
is 30 (that in Nippon Professional Baseball (NPB) is12). Although that fact alone makes MLB valuable asa potential market, environmental conditions have amajor impact on introduction of the system. Specialized staff, such as data analysts, are employed in all
MLB teams. On top of that, if the system stands achance of making a team stronger, even if it is a newtool that has never been available in the industry,
MLB teams will actively try and find ways to use the19Feature Articles

Time when the ball passes
home base

Bat
1.0
0.8
0.6
0.4
0.2
0.0
−0.2

Bad timing (early)
Swing before the ball passes
Time when the ball passes
home base

Bat
Swing
0.0

0.2

1.0

0.8
0.4
Time
0.6

Good timing
Swing when the ball passes

0.4

Swing

0.2
0.0
−0.2

Time when the ball passes
home base
0.2
0.4
0.6
0.8
Bat
Time
0.4 Bad timing (late)
0.2 Swing after the ball passes

0.0

0.0
−0.2
−0.4

Swing

−0.6
0.0

0.2

0.4
Time

0.6

0.8

Fig. 4. Sample results.

Beginner

Intermediate

Advanced

Start thegame

Fig. 5. Top screen of entertainment version.

system with the mindset of first giving it a try. As aresult of their efforts, we were also able to compile
effective use cases, acquire knowledge, and identify
issues. On the basis of the usage record of the systemin the United States, we are considering deploying the
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system to other teams such as more NPB teams.

20

Feature Articles

Straight ballFig. 6. Illustration of a play in the entertainment version.

5. Future developments: bringing change to thebaseball industry
On the basis of our efforts thus far, we are currently
redefining the system’s effectiveness, simplifying it
to expand it to more NPB teams, and building andverifying a new service model. The VR equipment is
not manufactured under the assumption that it will be
used by sportspeople, so we want to optimize it as the
manufacturer improves the VR equipment.
The introduction of the system to amateur baseballteams is also one of our goals. Compared to professional teams, amateur teams have a limited training
environment and time. The types and speeds ofpitches that can be experienced in actual games arealso limited. Training using our system makes it possible to repeatedly practice for ball types and speedsthat are difficult to train for in the real world. In theactual verification tests, we heard the comment that
players who trained at a ball speed of 150 km/h in VR
space began to feel that the ball speeds of 130 km/h
and 140 km/h were slow in real practice, and it
became easier for them to adjust their timing.
The ultimate goal of this project is to establish aplatform that can visualize and manage player performance. The evaluation of a player’s performance has
traditionally been highly opaque. An evaluation index
for a batter is the batting average—the number of hits
divided by the number of at-bats—which, however, isa comparison under different conditions such as

NTT Technical Review

Vol. 18 No. 12 Dec. 2020

opponent pitcher, time, and weather. In contrast, thereaction data for pitching in VR space is measured
under constant conditions, including ball type andspeed, so that data can be used to compare players.
By managing the performance data of players fromamateurs to NPB and MLB players in the form
requested by each user, it is possible to support thedevelopment of young players within the team andthe formulation of game strategy. It will also be possible to use the system to provide new reference data
for scouting and drafting players, which used to belargely dependent on individuals. With these possibilities in mind, we would like to continue discussions with the individuals concerned.
This year’s National High School Baseball Championship, which is held in the spring and summer
every year in Japan, was canceled due to the COVID19 pandemic. Thus, it is necessary to establish a newformat for the draft that provides amateur players apath to becoming professional players. As can beseen from the introduction of a limited number ofpitched balls per pitcher per week at the National
High School Baseball Championship, attention is
also being paid to the viewpoint of a player’s career
length. We thus believe that visualizing the player
performance and making training more efficient will
become even more important. We will contribute to
the further development of the baseball industry byusing new technologies and ideas regarding VR.

21

Feature Articles

Tomoko Ara

Deputy Manager, NTT DATA Corporation.
She received a B.A. from the Department of
Humanities and Social Sciences, Keio University, Kanagawa, in 2004 and an MBA from Waseda
University, Tokyo, and University of Washington, USA, in 2016. She joined NTT DATA in
2004. Her main interest is how to create andcommercialize new business with technologies
developed by NTT R&D. She has been researching immersive technology including VR since2016 and is currently conducting field experiments of the VR Batting Training system in theJapanese and US markets.

NTT Technical Review

Vol. 18 No. 12 Dec. 2020

Hitomi Nakamura

Assistant Manager, NTT DATA Corporation.

22

Feature Articles: Global Business Initiatives

Navigating Today’s Computing
Model LandscapeNadeem Ahmad
Abstract

We are witnessing a shift in the computing model landscape. We are increasingly seeing enterprises
move away from on-premise hardware deployments while seeing growth in cloud and hyperscale providers. Enterprises are now dealing with a hybrid computing environment encompassing four different
computing architectures and multiple cloud-deployment services. This article recaps a recent review by
the Innovation and Technology group at NTT Ltd. on the shift in the computing model landscape and thefour main computing architectures being deployed within enterprise environments today. This article
also covers where NTT Ltd. have helped clients navigate the decision-making process around deploying
the various computing models and finally touches on our focus around providing high-value services tocontinue enabling and supporting clients with their hybrid computing challenges.
Keywords: cloud, application, data analytics

1. Changing dynamics in the market
The market is going through multiple transitions
with a strong drive for enterprise clients to consider
and adopt cloud computing models. In the datacenterand more traditional datacenter infrastructure space(servers, storage, and datacenter networking), there is
no doubt that the new paradigm will consist of a
hybrid computing model, incorporating a combination of on-premise computing resources (as per theclassic bare metal servers and infrastructure), private
cloud platforms (on-premise and/or hosted), and public cloud platforms.
2. Four computing models are challenging theenterprise application landscapeThe market is also seeing new computing architectures emerging in quick succession, creating further
challenges for enterprises as to which models andarchitectures to pursue. There are four distinct computing models evolving at the same time: physicalserver, virtual server, container-based architecture,and serverless computing. This results in enterprises
having to support a hybrid computing environment
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Vol. 18 No. 12 Dec. 2020

where the more diverse the mix, the more complex
and expensive operations and service continuity
become (Fig. 1).
In 2019, less than 50% of the physical servers globally have been virtualized, yet we are already seeing
a rise in container-based architectures and serverlesscomputing models. Each of these models has a relevant place and fit, and it is highly likely that enterprises will have to consider, plan, and invest in allthese architectures. The answer as to which computing model or computing architecture to pursue isfound in the application in question.
Many applications cannot be re-platformed to run
on virtual machine (VM) architectures and willremain on a bare metal and dedicated infrastructure,most likely for years to come, as these are core business applications that will have to be rewritten to
move to more modern architectures, and in manycases this is not worth the return. There are many
applications that have been re-platformed to run on
VM architectures, and many of the modern cloud
platforms originated from these architectures andcontinue to grow. The next transition is defined by container-based architectures, and most applications will
have to be rewritten to fully leverage container-based
23

Feature Articles

Enterprise clientsneed to support all
four architecture
modelsArchitectures

Multi-cloud services• Physical server
architecture

PublicPrivate

IaaS

laaS

PaaS

PaaS

SaaS

SaaS

• Virtualization• Containerization

Enterprise clients
will use acombination ofthese cloudservices• Serverless computing

The more diverse the mix, the more complex and expensive operations and service continuity become
IaaS: infrastructure as a service
PaaS: platform as a service
SaaS: software as a service

Fig. 1. Hybrid computing environment.

architectures.
At the same time, we are seeing the strong emergence of yet another computing architecture in theform of serverless computing, where value is provided to cloud-based functions, the functions are
executed, and value is returned for use. Serverlesscomputing will require further adjustment in computing architecture, especially in the context of data
platforms and data abstraction, which implies anentirely different application architecture. Not all
applications will be suitable for serverless computing, so the expectation is that certain applications will
be suitable or execute optimally on specific computing models and architectures, resulting in a situationin which enterprises will have to invest in all thesemodels and architectures to truly benefit fromadvances in technology.
This article distinguishes between computing models such as physical/dedicated infrastructure models,cloud-based models, such as infrastructure as a service (IaaS), and computing architectures such as
NTT Technical Review

Vol. 18 No. 12 Dec. 2020

dedicated server, VM, container, and serverless. It
should be noted that hybrids occur within and acrossboth computing models and computing architectures,
resulting in a complex service challenge for mostenterprises. This complexity is also contributing to
the desire for an increasing number of enterprises toleverage applications in a software as a service (SaaS)
service type to avoid the complexity of the underlying
infrastructure or rewriting applications. This is notalways possible, and an increasing complex hybrid isa reality.
3. Where are all the workloads(and money) going?
Morgan Stanley estimates 44% of computing workloads will be orchestrated in the cloud by the end of2021, up from 21% in 2018 [1]. Meanwhile, every 1
USD of revenue growth for the largest cloud service
providers (i.e. hyperscalers) has resulted in about 3
USD of revenue decline for the major legacy non-cloud24

Feature Articles

infrastructure-technology providers. Data from otherindustry analysts and views from key market players
support this trend. It is clear there is a market shift
coming across three critical areas:• The shift in information technology (IT) spending growth focused on digital transformation• The change in the mix of enterprise workloads
by an environment showing non-cloud shrinking• The rapid growth and adoption of softwaredefined infrastructure
There are three clear leaders in the IaaS and platform as a service (PaaS) hyperscale market, Amazon
Web Services (AWS), Microsoft, and Google.
• AWS is still the market leader. The rate of innovation and expansion of their services is significant, and part of their success with Amazon has
been to compete where needed with razor thin
margins, which could be dangerous as the market
matures and competitive pressures rise.
• Microsoft has focused almost every part of its
business on winning in IaaS and PaaS as well as
making the most use of its SaaS offerings (predominantly Office 365) to drive utilization of
Azure and its rich partner ecosystem to drive
adoption and utilization. Microsoft also have thelargest incentive in this group to drive true richness in hybridcloud linking to their partner ecosystem and massive presence and breadth onpremise at the enterprise.
• Google is a relatively late entrant; however, they
are investing heavily in enterprise, have significant capacity worldwide, strength in high growthand consumption areas of data and artificial
intelligence, and machine learning, and clear
ambition to win.
Though hyperscale cloud providers have seen tremendous growth, they are not satisfied and continue
to expand the number, variety, and scale of the services that they offer to customers to meet theirdemand. The number of AWS services rose from 1 in2006 to over 140 services by 2018. To us, this represents a shift away from traditional servers and buying
patterns for some legacy applications and certainly
for new initiatives. The applications and scale points
to the fact that AWS’ (and other hyperscalers’) support for migration to cloud continues to expand, supporting larger instances, more dedicated configurations with the same programmability as their heritage, and customer demand. This is reflected in thegrowth in their revenue.
Today, enterprises are facing challenges within andcompetition from external and new entrants looking
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to disrupt their business and even their entire industry.
As a result, digital transformation and speed of innovation is top priority for both business and technology
leaders. Increasingly, the line between IaaS and PaaS
is blurring, and the speed and efficiency to consume
one from the other is driving the shift to hyperscale
providers. Cloud applications that are written to takeadvantage of instant scaling, latest DevOps (development & operations) practices, and toolchains andcontainers are often public cloud first workloads,
skipping the enterprise datacenter all together. While
there is a risk of provider lock-in for enterprises, thespeed and richness of solutions is incredibly attractive. The stickiness with a single hyperscale provider
is high for a solution given that many PaaS components are proprietary and designed to work ‘bettertogether’ with other parts of their own ecosystem andpartners.
4. The struggle to maintain relevance is real:enter programmability
Considering the complexity and scale of the computing models and architectures and the adjacentinfrastructures such as connectivity, the modern
workplace, and diverse business models, it goes without saying that there is a major challenge to operateand service these complex environments and remain
relevant in the industry, especially in the light of the
speed of technology transitions. The industry does
not produce enough skilled human resources to perform the operational services to maintain these environments. The problem is multi-facetted in that there
is a shortage of skilled human resources, resulting inthese environments becoming so complex anddynamic that humans can no longer perform theoperations effectively or efficiently. All operationalmodels are now underpinned by a large degree ofautomation, which in turn requires all these infrastructure and architectural platforms to be fully programmable.
Many infrastructure vendors have been redeveloping their products to be programmable, and one must
consider the life cycle of the installed base, legacy ofmultiple platforms, operating systems, product families, and lack of modern software architectures. Thereis an increasing demand for all vendor products to be‘API (application programming interface)-first,’
implying that products need to be programmable firstand foremost and console or command line configurability secondary, if at all.
Companies that have a true API-first model will25

Feature Articles

most often have their console access via the verysame APIs, whereas companies that have to transition
from a console or command-line-first approach to an
API have to do so via complicated stages. Typically,
one finds that not all the configurational items areaccessible via their APIs, which results in a complex
hybrid model. The latter is not desirable as it requires‘old’ and ‘new’ skills and does not support the automation that operational model demand, and all of thisbecomes costly and lethargic. Considering the current
market and key technology vendors, it goes without
saying that those who transition to API-first and truly
programmable models will flourish, be preferred in
the market, and accelerate their growth, and those
who do not move swiftly enough will see market
share losses and face severe market challenges.
As information and communication technology(ICT) providers consider their objective to be leading
IT and services companies for their clients, they will
need, if they have not already, to establish a preference for vendor partners that offer API-first technology that is fully programmable. At the same time,they will need to proactively remove technology
products from their portfolio that do not provide programmability, since there is no longer a sustainablebusiness model for non-programmable infrastructure
for ICT providers or their clients.
5. With no end in sight—maturity andexpansion of SaaS continues
The mass adoption of enterprise SaaS continues
across a wide range of application categories, andeach of these applications previously required servers
and storage inside the enterprise and are now consumed as a service. There are SaaS providers in thefollowing major categories:• Productivity: Office 365, Google G Suite, Atlassian Confluence and JIRA• File store: OneDrive for Business, Dropbox,
Box, Google Drive• Collaboration: WebEx, Zoom, High Five, Slack,
MS Teams, Yammer• Enterprise resource planning (ERP): SAP Hana
& cloud variants, Oracle, Dynamics 365, Intacct,
Intuit, NetSuite• People and payroll: Workday, ADP• Service/sales/customer relationship management (CRM): ServiceNow, Salesforce.com,
SiebelPaaS and serverless computing are poised to makefurther inroads into reducing the requirement for
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enterprise clients to buy servers, storage, and networking infrastructure, not to mention the consulting,professional, and technical services that went into
their design, build, and operation.
6. Hybrid computing environment in practiceThis concept of four computing architectures is notjust theory or an academic exercise. At NTT Ltd., wehave practical use cases of implementing a hybrid
mix of architectures. However, decision-making forclients is not always centered around the best technical approach. Often, the factors that go into choosing
to implement one architecture over another or a mixof architectures are a matter of operational challenges, financial discipline, and risk tolerance. Let us
explore one of our practical examples of our multiarchitectural approach to meet the needs of one of ourmore well-known clients.
7. Implementation of four computing
architectures for Tour de France
NTT Ltd.’s Advanced Technology Group focus ison using sports as a technology incubator withinNTT. They are focusing on the strategic innovation
projects that NTT are executing with clients. The
Amaury Sports Organization (A.S.O.) organizes theTour de France, a bicycle race that takes place over 21days around France involving riders traveling about
200 kilometers every day. Supporting the race is what
is commonly referred to as a traveling circus as alarge number of support and infrastructure vehicles
are required. When we started and first partnered with
the A.S.O., there was a real concern around the levelof connectivity, e.g., just basic supply of items suchas electricity, in many of the remote locations the tour
visits.
Our first solution was using an enormous truck, i.e.,a traveling datacenter. The truck traveled hundreds ofkilometers each day and night to support the race.
With this truck, we collected the data from the bicycles and riders, provided a large amount of analytics,
and provided the summary and result of data analytics to the broadcasters. Our truck would pull up next
to the broadcasters’ trucks, and a cable would be
plugged in from one truck to the next. We had aphysical infrastructure, i.e., physical servers in thetruck. This worked, but it was quite challenging as
one had to take all the technical staff on the road toensure everything worked smoothly.
In the 2016 race, we had a stage of the race located26Feature Articles

Live data
streamReference data

and timing data

API

Official results

Real-time analytics
platform

Lowlatency

Live TV

Enriched live data
Live AI models</>

Race center
websites

Data store
Reference data
Historical data
Results data
Predictive dataAPI
VIP screensHighlatency

Mobile app

Observability

Weather data

Logging, monitoring, alerts, dashboard
Enrichment data

Fig. 2. Data platform.

at a mountain called Mont Ventoux, which is known
for having extremely windy conditions and severe
storms. During that stage, a severe storm camethrough. It was so bad that the finish line, which wasmeant to be at the ski resort at the top of the mountain,
had to be changed, and they finished the race only
halfway up the mountain.
This does not seem like a significant operational
challenge, but all support trucks that were going to beparked in the large parking lot up at the ski resort
were distributed at schools, fields, and supermarket
parking lots across several villages at the bottom of
the mountain, and our truck was no longer parked
next to the broadcasters’ trucks. In fact, it was two
and a half kilometers away.
Therefore, the decision was made at around two
o’clock in the morning that we basically had to takeeverything that we have been running on physicalservers and shift to running on VMs in the NTT cloud
overnight, and we were actually up and running forthe next day. We ran successfully on that model forthe rest of the race, and that convinced everyone
including our client that the applications and functions running on physical servers could run in a virtual world in a virtualized environment, and we
stayed with that virtualized environment until 2018.
Over the last eight months or so, we have been making some very fundamental architectural changes,
which are in line with what we have been talking
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about thus far, i.e., the four computing models. If welook at Fig. 2, we see a representation of our data
structure, which is the core of our solution. Verybroadly, we obtain data from a range of sources on theleft side, carry out some data processing and analytics
in the middle, and distribute data to a variety of consumers on the right side. The real-time data analytics
platform is grouped into four main areas because
these areas were essentially built as different platforms and have different drivers in each area.
As an example, we need a robustly managed API.
However, we had a hard look at our previously built
API and through the course of last year, as much as
everyone prefers to do it themselves, we realized that
there was actually nothing about our API that wasdifferent from anyone else’s. There was no additional
benefit to building it ourselves, and what we could
bring would be only additional cost, complexity, andthe management burden across the platform and solution.
Therefore, we ended up going with a set of PaaS
services and serverless functions for the API. We useda third party’s API, which has the advantage in that
we do not need to worry about any of the infrastructure or mechanics of the API infrastructure and simply plug in our functions and transformations wewant and structure the data that we want to process.
This has significantly narrowed the scope, focus,
and effort of that workstream. It also means that the27

Feature Articles

support requirement on our end significantly decreases. We do not need to worry about servers. We werepreviously running an API that can scale to support a
million users and requires a very significant infrastructure and large amount of looking after, but using
a third party’s API basically bypassed that whole lot.
The last race of the Tour de France in which we usedsuch APIs, was uneventful for the people looking
after the API infrastructure because it just worked. At
the end of the day, this is the desired result for all IT
operational support people both on the client andprovider sides.
The real learning has been trying to understand thespecific features of the hyperscalers’ solution. One
needs to get into such specifics, and we found the best
way of doing this is running proofs of concept andproving out the technologies because one needs to not
only make decisions in terms of technology but also
in terms of costing models.
Previously, the approach was just about obtaining
the right technologies, then we would run proofs ofconcept and end up with a choice of technology that
supports the desired functionally, however, the cost of
the technology solution was quite high due to ourlarge amount of data. Today, with most hyperscalers,
if there are multiple overlapping options in many
areas, we can switch to another option that is functionally just as effective and have a far better pricing
model. This is something to consider, particularly
when one starts looking up at the higher end of thestack of solutions from the hyperscalers.
Thus far, we have seen operational challenges moveour architecture from physical to virtual. We then saw
that financial considerations cause a need to adopt
serverless and “as a service” models from hyperscalers. Technologies are not always the deciding factor
into which a computing model will work for our client environments.
Looking at Fig. 2 again, the data store is primarilyan SQL (structured query language) server; it is just adata store. It has a large amount of data, and we havemade the choice, in this case, to stay with the VMbased solution not because there are no other alternatives or because it is the best option. It is just that in
our case for 2020, we have several other parts of thesolution changing, so we did not want to also change
our data store architecture. This represents anotheritem for organizations to consider and is their tolerance of risk. We have deliberately chosen not to do
anything with that data store and chose to leave it ona VM because we have enough technology risk elsewhere in the stack and do not need to change it. We
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will probably change it next year because there aresome advantages to shifting to PaaS solutions, but it
is not an imperative this year. We now have two computing models incorporated into the environment,i.e., we have serverless functions on the API and VMs
on the data store.
The last area is the real-time analytics platform,
which is where NTT Ltd. brings a large amount of its
intellectual property into the services that we provide
to the A.S.O. and broadcasters. It is where we take thesensor data from the bicycles and enrich the speed
and location data we obtain from the bikes and apply
machine learning to turn a couple of data points intoover fifty data points that then support a range ofconsumers and consumption models. Therefore, as
opposed to the above third party’s API, it made sense
for us to build our own data analytics platform.
However, building our own real-time analytics platform is complex, and we want to be able to continue
to implement changes and rapidly evolve that solution. In this case, therefore, we have gone with acontainerized solution that is built across a range ofDocker containers, which makes deployments mucheasier. One might ask, “How does running on containers make things easier?” It is because one does
not need to deploy an entire VM, operating system,
plus everything else. One just deploys the container
so what one needs to run on is much smaller and simpler in terms of footprint and resources. We candescribe this in code, which means we can say wewant a container that is this big and looks like this andhas these items in it much more easily in code. Thismeans we can make a deployment directly from code.
The code for the infrastructure can be managed, justlike application code, and run just like the rest of theapplication. In fact, it becomes a single package—thecontainer and everything that is in it. We can thenmake deployments multiple times a day, whichmeans that we can make changes and test the changes
rapidly, giving us the velocity that we really wantaround the development in that space. The above gave
a bit of context and insight in the three computing
models being implemented. Remember, we moved
away from an entirely physical infrastructure to VMs,
serverless computing, and container-based architectures.
There is one last point around the complexity of
having all three architectures in play. Referring to
Fig. 2 once again, there is a small box at the bottom
called “Observability.” Running hybrid architecture
solutions like this introduces much complexity. The
challenge is that if one changes one piece of the28

Feature Articles

puzzle, another piece somewhere else can entirely
break or malfunction, and it can become too complicated to understand all the ways the system can fail.
While one can put in place alerts, one can be guaranteed that what will break is not something one has
placed an alert on.
What we have built across the whole solution is anobservability platform for visualizing the outputs ofall the layers then all the functions of the layers. This
allows us to monitor the outputs and understand what
functions are working or not working. Complexity isdefinitely a challenge and is the reason we have
implemented that fourth observability component inour solution to address and manage that challenge.
The Tour de France platform we built provides an
excellent example and provides interesting insights
into the factors considered when implementing a
hybrid mix of computing architectures. We started
out with a traditional physical infrastructure then, due
to operational challenges, we had to pivot to VM
implementation. Due to financial considerations, weincorporated serverless computing, and further operational, portability, and efficiency considerations
drove the move to a container-based architecture.
The lesson here is not always a matter of the best
technology but the best fit of technology driven bybusiness considerations, aside from the technology
itself. In the Tour de France example, the team had toaddress considerations such as operational challenges, financial considerations, security, and of course
risk tolerance.
8. Summary and high-value services
Unlike physical computing, virtual and containerbased computing make it easy to move applications,as they are decoupled from the underlying computing
infrastructure. The use cases for these models are
becoming clearer and adoption is on the rise, andthere is now the promise of serverless computing,which offers even greater agility and cost savings
because applications do not have to be deployed on aserver. Instead, functions run from a cloud provider’splatform, return outputs, and immediately release theassociated resources.
We have four distinct computing models evolving
simultaneously: physical server, virtual server, container-based architecture, and serverless computing.
This raises interesting questions for organizations:
What do we do with our applications? Which workloads should move to the cloud? What must stay onpremise? Can I re-platform some applications to run
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on VMs? Can I rewrite others for the container orserverless world? Most importantly, how long will ittake to get this done so I can unlock the benefits ofthese new models? The computing-architecture decision has a direct relationship with the future of eachapplication, in fact, it may even dictate the future
direction of each application.
This article reviewed some of these decisions andconsiderations during a practical client implementation of all four computing architectures where factors
such as operational challenges, financial considerations, portability, efficiency, and risk tolerance all
played a role in making the decision to deploy each of
the four computing architecture. ICT service providers, including NTT Ltd., are adapting their go-tomarket and partner strategies given the rapid growthof hybrid and hyperscale deployments. Hyperscale
providers have such a breadth of service offerings
that staying up to date across multiple clouds will
require a massive investment on the part of enterpriseclients as well as some complexity. In this regard,
ICT service providers can provide true value andguidance to their clients.
What is also evident is the need for ICT providers
to drive high-value service offers to enterprise clientsto guide and support them on the journey ahead. This
is why NTT Ltd. is now focused on selling and architecting over forty high-value services. Some relevant
services include:• Infrastructure consulting services encompassing several services focused on optimization andtransformation wherever our clients are in theirtransformation journey. Whether it is resolving
ageing infrastructure, managing complexity,
securing infrastructure, moving to a multi-cloudenvironment, or improving user experience, we
can help our clients evaluate their current operating state and transform and improve the performance of their infrastructure while optimizing
their spending.
• Cloud consulting services including a hybrid
migration assessment to obtain clear insights
into a client’s current infrastructure and applications to create a roadmap to managed services ona hybrid cloud. Cloud consulting services also
include cloud migration services, which are endto-end services beginning with auditing workloads for suitability for migration, planning the
migration to the best-fit venue, then migratingthese workloads to the cloud without disrupting
daily operations and business.
• M anaged Hybrid Infrastructure Services
29

Feature Articles

offering comprehensive management and monitoring of on-premise, cloud, and hybrid IT infrastructures to improve operational agility, reducerisk, and optimize cloud and technology infrastructure investments. With our highly trained
vendor-accredited people, processes, and tools
necessary to manage the day-to-day operationsof our clients’ multi-platform multi-site IT infrastructures, we ensure the resilience and performance of such infrastructures so one can focus
on achieving business outcomes.
• M anaged application services around theimplementation and management of ERP and
CRM applications on premise, in the cloud, or in
hybrid IT operating environments with consistent governance, security, and management practices.
• Finally, our Hybrid and Multicloud service
allowing our clients to use the best platform foreach workload and taking away the difficulty ofmanaging a hybrid IT environment. With systems spread out across multiple environments,
our client’s need a way to manage all these environments from a single interface. With our ‘Solution Insight’ cloud management platform, we
enable our clients to gain insight into how all
their systems work together. This helps them bet-

ter understand which applications can be moved
to the cloud and which need to stay in their current environments.
This short list is not an all-inclusive one, as we haveseveral other high-value services we are emphasizingin our engagements with clients across digital transformation, workplace, datacenter, networking, andsecurity.
The shift in the computing landscape is upon us asenterprise clients need to support four different architecture models at the same time. Enterprises will use
a combination of cloud services across public andprivate clouds. The more diverse the mix, the more
complex and expensive operations and service continuity become. Through our high-value service offers,
we can help enterprise clients navigate decisions andconsiderations when embarking on a hybrid computing-environment-implementation journey.
Reference[1]

Morgan Stanley, https://www.morganstanley.com/ideas/ithardware-2018

Trademark notes

All brand, product, and company/organization names that appear in thisarticle are trademarks or registered trademarks of their respective owners.

Nadeem Ahmad

Group Senior Vice President, Technology
Strategy, NTT Ltd.
Nadeem Ahmad is a technology professionalwith 25 years of ICT industry experience and is asocial constructivist at heart. He believes technology does not determine human action but thathuman action shapes technology. He joined NTT
Ltd. (previously Dimension Data) over twenty
years ago initially leading technical delivery andoperations teams within the United Kingdom,
Europe, and Americas but has spent the bulk of
his time in the Group CTO Office (now the Innovation and Technology group) focused on business and technology innovation initiatives acrossthe Group.
His role at the Innovation and Technology
group involves defining NTT Ltd.’s technology
strategy and positioning, in collaboration withgo-to-market practice leaders, across relevant
technology domains. This includes identifying
and monitoring key technology trends andemerging domains, assessing their impact onenterprise clients and their potential to affect new
solutions or services, as well as providing input
into new offer creation. This extends to providing
group guidance on emerging technology domains
and translating key trends and market developments into business impact via thought leadership, internally and externally.
LinkedIn profile: https://www.linkedin.com/in/
nadeemahmad1/

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Vol. 18 No. 12 Dec. 202030

Feature Articles: Global Business Initiatives

NTT Pro Cycling: Data-driven
Innovation in Action
Nadeem Ahmad
Abstract

With thousands of riders and hundreds of races to choose from each year, the NTT Pro Cycling teamuses data and advanced analytics to optimize their performance by ensuring they recruit the best possible
riders for their budget and for selected races. Training is then optimized for every individual rider toensure they are in peak condition for their target races, giving them the best chance to maximize UCI(Union Cycliste Internationale) points. NTT Ltd. supports the team through innovative technology thatmonitors and manages the performance of the riders. We also use advanced data analytics and data science to help the team make key decisions. Working together within a support ecosystem, we give theteam every possible advantage to be a high-performing pro sports team, both on and off the bike.
Keywords: data analytics, technology, NTT Pro Cycling

1. The NTT Pro Cycling team and its mission
In the world of professional cycling, NTT Pro
Cycling is unique. It is the first team with roots in
Africa competing on the world stage and that is riding
to uplift communities in Africa through their supportof the Qhubeka charity. Qhubeka is an African notfor-profit organization that donates bicycles as part of
the World Bicycle Relief’s charity program and has
supplied more than 75,000 bicycles through fundraising programs targeted at children in rural African
communities to make it easier for them to get to andfrom school, helping to improve their attendance andperformance.
The team strives to be the benchmark within the
sport through being a performance-driven, technology-enabled team underpinned by their unique purpose-led approach in their support of the Qhubeka
charity. Their commitment to changing lives together
with Qhubeka remains stronger than ever, and they
will be looking to do so with even greater enthusiasm
in the years to come. Together with staff, riders, andvalued partners, this core belief remains a foundationof the team. This truly is a team that was born inAfrica but created for the world.
As the NTT Pro Cycling team’s technology innovaNTT Technical Review

Vol. 18 No. 12 Dec. 2020tion partner, NTT Ltd. helps the team to be the most
purpose-led, performance-driven, and technologyenabled team in pro cycling today. We work together
to fulfill their ambition of being ranked among the top
10 cycling teams in the world. Racing in the Union
Cycliste Internationale (UCI) World Tour, the premier men’s elite road cycling tour, means competing
against some of the best funded teams in the most
iconic cycling races. To achieve this, the team leverages the power of advanced data analytics and data
platforms. This enables them to harness all theirresources in the best possible way, based on real data,to perform at the highest level possible to win as
many points as possible, retaining World Tour status
and delivering value for their sponsors who provide
their primary revenue stream.
2. Creating a technology-enabled data-driven
pro-cycling teamIn 2020, the team adopted a fully integrated athlete
performance management solution for training analysis, health and wellness tracking, in-race hydrationand nutrition management, injury reporting, andoverall athlete condition (health and mental wellness)monitoring. Additionally, advanced data analytics
31Feature Articles

NTT ProCycling

Race results of all pro races over 8 years
Rider profile and recruitment
Race and stage profile and indexing
Race – rider matching and team selection

Nutritional and hydration data
In-race diet and hydration optimization,
improved recovery
Rider health and wellness daily data
Training program optimization

UCI race program and point table
Calendar optimization for maximum points

Fig. 1. NTT Pro Cycling’s data-driven approach.

help the team with regards to talent recruitment andretention as well as establishing a race calendar andteam selection to maximize the potential UCI points
and race results (Fig. 1).
The team uses data science to drive key decisionmaking to meet the following three critical performance criteria:• Recruiting and retaining the best talent to create
the best team within the available budget• Optimizing athlete performance so they can perform at their best
• Constructing the most effective race calendar
and team selection to maximize the potential
UCI points and race results
NTT Ltd. provides several technologies and services to help NTT Pro Cycling meet these performance criteria. These technologies cut acrossadvanced data analytics, data platform, hybrid cloud,and artificial intelligence and machine learning. We
provide a range of services in support of these technologies to address the team’s performance criteria
including:• Digital and analytics consulting• Data platform and data architecture definition• Data science services• Systems integration• Application managed services• Program management
Over the past year and a half, NTT Pro Cycling, inconjunction with NTT Ltd., has been working on acomprehensive data platform that helps the teamclimb the UCI rankings. The solution supports theteam through innovative technology focused on
advanced data science, which is used to help makekey decisions faster.
Innovative technology not only supports NTT Pro
Cycling’s performance but also its business processNTT Technical Review

Vol. 18 No. 12 Dec. 2020

es. Data analytics help the team select riders, prepare
the race calendar, and assign the right riders to theright races. Such technology also helps the teammonitor the health of its riders by tracking physiological data through a health and wellness application.
To compete at the top of cycling’s World Tour, theteam is looking to drastically improve and force its
way into the top 10 of the World Tour team ranking.
The team is challenging themselves to be better. Theyleverage innovation, technology, and data in new
ways to enhance their performance daily and speed
up and improve decision making. Like all other organizations, professional sports teams have embarked
on a digital transformation journey to remain relevant
and competitive. It is only through this that they
improve their efficiencies and become more effective
organizations. For NTT Pro Cycling, this is no different.
3. Rider recruitment
Every pro cycling team knows their riders are theirmost important assets. Having the right riders with arange of skills and capabilities is an important foundation for building a successful cycling team and tosecure those all-important race victories. NTT Pro
Cycling uses data to analyze just about every pro
cyclist in the world. Using a combination of historical
data and performance metrics, they can identify
which riders they want to recruit. By leveraging data
analytics, team management can make more informed
decisions to build the best possible team while still
staying within budget.
Over the past 18 months, NTT Pro Cycling and theNTT Ltd. Advanced Technology Group have jointly
developed several statistical and analytical models forclassifying different riders to study career trajectories
32

Feature Articles

Race results of all pro races over 8 years
Rider profile and recruitment

Race and stage profile and indexing
Race – rider matching and team selection

Fig. 2. Rider recruitment data.

Fig. 3. Example of NTT Pro Cycling rider dashboard.

and form. Built upon data from ProCyclingStats.com,
the analytics package allows the team to distil every
rider down to a matrix of strengths and weaknesses.
It is a process that runs daily in NTT Ltd.’s cloud
platform, scraping required data, calculating all the
key metrics, then encapsulating them into a dashboard that team management can use to analyze different groups of riders, deep-dive into individual rider
profiles, and calculate the trajectory of that rider inthe future (Fig. 2).
3.1 Building a team
To give the team the intelligence they need, NTT
Ltd.’s advanced data analytics solution analyzed overeight years’ worth of race results and team performances. The application programming interface
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(API) from the Pro Cycling Stats platform creates aview of the current composition of the team (seeFig. 3 for an example). This is then used to identify
the specific strengths and weaknesses within the teamacross five categories of general, sprint, depth, stability, and mountain (example in Fig. 4).
Starting with this overall understanding of theteam’s performance data and metrics, team management can work out which riders work best together.
They can then use this to create a ‘team stability’score.
3.2 Gap analysis
Once the status of the current team is established,team management can identify any gaps the riders
need to fill (see Fig. 5). By using the analysis of the33

Feature Articles

Team profile

Team stability

Fig. 4. Team strengths, weaknesses, and stability.

Fig. 5. Rider ranking and gap analysis.

global pool of pro cyclists, they can see which riders
would deliver the biggest ‘bang for buck.’ In addition
to examining the strength of the individual riders
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against the needs of the team, the data analysis solution incorporates the full pro cycling calendar, including the number of points available and the historic
34

Feature Articles

strength of the field. This enables team management
to identify opportunities to score the maximum
amount of points along with which riders and combinations are most likely to be successful.
3.3 Performance analysis
As well as using analytics to establish which riders
are the best in any given situation, NTT Pro Cycling
uses them to review the global pool of riders. The
team understands that discovering the right resourcesat the right time is critical to success both in businessand on the road.
With their understanding of the team’s requirements, team management and performance coaches
work together to narrow down the thousands ofcyclists, creating a shortlist of potential talent. Factors taken into consideration include rider type, race
victories, age, and recent performance, to name a few.
From that information, the analytics package’s algorithm deducts the likelihood of riders earning everimportant World Tour points in races to come, i.e., the‘win quotient.’ Together with data-driven rider profiles, management has a view of each rider’s strengths
and weaknesses, allowing them to identify the skills
needed to fill the gaps.
3.4 Integrate, monitor, and report
The final phase is actual recruitment. With a holistic view of the current team, the gaps they need to fill,
and the potential talent available from our data andanalytics solution, team management are able to
make quicker, more informed decisions, taking someof the guesswork out of the process. These tools allow
decision-makers to make objective choices to help
them achieve their goals. All this information allows
team management to sign riders and be assured that,together with their vast industry knowledge and experience, technology will enable them to improve theirperformance. To monitor and evaluate performance,
NTT Ltd. has integrated multiple technologies, wearables, Internet-of-Things devices, platforms, andtools to provide both detailed performance analytics
and a holistic snapshot for management.
One final note on rider recruitment. NTT Ltd.’ssolution allows the team to look beyond the big
names, to the true ‘moneyball’ performers, i.e., theriders who have previously been overlooked but provide a unique value proposition. Using a combinationof historical data and performance metrics, experienced sports directors can supplement the process byidentifying riders for specific roles to help fill out theroster.
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Vol. 18 No. 12 Dec. 2020

UCI race program and point table
Calendar optimization for maximum points

Fig. 6. Point data for calendar optimization.

4. Data-driven calendar optimization
In addition to the 36 compulsory World Tour
events, there are hundreds of races for teams tochoose from, so making the decision about whichraces to participate in with which riders is critical tosuccess. For this reason, NTT Pro Cycling uses data
analytics to help optimize their race calendar and use
their resources most effectively. The team participates in 82 races on 256 days across five continents
every year with up to three different races on a single
day. This creates not just logistical challenges but
challenges in ensuring optimal team performance. To
obtain the best possible results, the right riders need
to be at the right races at the right time. The logisticalissue is that there are 29 riders, 54 management andsupport staff members, over 200 bicycles, 250 sets ofcarbon wheels, 22 vehicles, over 900 airplane tickets,
3.5 tons of nutrition, and dozens of partners andassets across the globe. This makes up the complex
team infrastructure that is NTT Pro Cycling. As partof NTT Pro Cycling’s drive to secure World Tour
points, the team must make tough decisions about
which races to participate in. The calendar needs tocreate a balance among logistical feasibility, theopportunity to earn points, rider schedules, theirstrengths, and their training and race load (Fig. 6).
At the top level of cycling—the UCI World Tour—securing enough points is critical to the team’s survival. With different tiers of races and different point
scales, it is essential to know where the best opportunities to score points are. Data and analytics tools
help the team identify this and from that, build a race
calendar.
The team uses an algorithm to take into account the
points available by race, calculate field strengths
based on the caliber of riders who have competed in
the past, and create a points-per-race-day weighted
by the field strength indicator. This shows the best35Feature Articles

Fig. 7. Race calendar ranking and the biggest “bang for buck.”value races where they are likely to achieve the best
results, therefore earn the most points.
From there, the scoring, together with the logisticalconstraints of time and distance, helps the team build
out the race calendar. Coupled with our rider andteam composition analyses, they can pinpoint who
should go to which races. The system produces multiple dashboards (see Fig. 7 for an example), which
team management can use along with other key information such as rider programs and team logistics, indetermining which races they should attend.
One of the main findings from this analysis is that
it is four times easier to win points in Asia Tour .HC(now ProTour) races than in European World Tour
races. It also shows that the team’s riders can win
more points in one-day races than stage races. There
are more points on offer at World Tour races, but they
are statistically more difficult to earn. The analysis
proved its worth early on with strong results acrossthe globe:• Eight top-2 finishes with six different riders on
three continents• Stage wins at both Étoile de Bessèges and Tourde Langkawi (Ben O’Connor and Max Walscheid)• Leader of the points classification at the Tour ofLangkawi (Max Walscheid)• National Championships of South Africa (Ryan
Gibbons)
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Vol. 18 No. 12 Dec. 2020

5. Rider training, health, and wellness
Training, preparation, nutrition, and health are key
ingredients for any successful pro-cyclist. NTT Pro
Cycling understands this as they use a combination oftechnology solutions from their ecosystem of partners including NTT Ltd., Alcatel Lucent Enterprise,
Lumin Sports, Today’s Plan, Garmin, and Rotor, tomonitor and optimize the riders training and race
preparation.
Phila (an Nguni word meaning ‘to live’) is a healthand wellness mobile application that provides amorning and evening questionnaire to check on therider’s location, sleep, fatigue, mood, soreness, illness, injury, training, and race feedback. It allows thecoach to provide immediate feedback on the day’s
training plans (Fig. 8).
Assisting in optimizing hydration and nutrition forrecovery, the team uses the Hydration and Nutrition
Guidance (HANG), an automated application for use
at races. The app is linked with Bluetooth Smart
Scales to capture morning, pre-, and post-race
weights to track hydration levels and provide personalized recommendations to each rider following therace. The team uses a centralized athlete data reporting tool called ARC, which integrates all the training
data, health and wellness data, HANG data, and race
reporting to provide the coaches and team management with performance dashboards on key metrics,
36

Feature Articles

Nutritional and hydration data
In-race diet and hydration optimization,
improved recovery

Rider health and wellness daily data
Training program optimization

Fig. 8. Rider health and nutritional data.

including the optimal training load for each rider andrace analysis and modelling training load based onrider race schedules.
6. Summary and results
For NTT Pro Cycling, innovative and advanced
data science is used to help make key decisions fasterto not only support the team performance but also
their business processes. Data analytics help themselect riders, prepare their race calendar, and assign
the right riders to the right races. Innovative ecosystem technologies also help them monitor the health of
its riders by tracking physiological data through ahealth and wellness application.
It would be naive to think that NTT Pro Cycling is
the only team looking at gaps in its line-up and trying
to recruit to fill those gaps. It is also unlikely to be theonly team with a data analytics solution of some kindto help with the process. However, the team is far
ahead of the curve in the road cycling space in thelevel of sophistication and detail of performancemonitoring and management solution to provide
access to their capabilities and all the crucial data via
one unified data analytics platform. With a holistic
view of the current team, the gaps they need to fill,
and the potential talent available from the data andanalytics solution, team management is able to makequicker more informed decisions, taking some of theguesswork out of the process.
Using data-driven recruiting, based on team performance, 2020 NTT Pro Cycling’s selected riders
should be expected to deliver 25% more UCI points

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Vol. 18 No. 12 Dec. 2020

than the 2019 team did, which would result in themmoving up 7 positions in the UCI World ranking. To
move into the top 15 cycling teams in the world, theyrequire their top 10 riders to win 25% more points
than in 2019. To achieve a top 10 ranking would
require double the points they accumulated in 2019.
Through optimizing the race calendar alone, there ispotential to increase the available points they can win
by 20–30%.
6.1 Initial results
At the time of the writing of this article (before theTour de France that began in August), the 2020 racing
season had already proven to be very successful,reaping the benefits from a technology-enabled, dataand-performance-driven team. With an increase of
18% more UCI points, a ground-breaking 200%
increase in wins and 114% more podiums compared
to last year’s results, the solution in place has alreadyproven to be of immense value to the team and its
management.
When NTT Ltd. built the data and analytics platform, the team committed to use the data, applications, and platforms that are collecting the data, so
algorithms can be applied to analyze the data to makeaccurate data-driven decisions. The key is that they
trust the data and monitor the outcomes, adjustingalong the way, to refine the solution over time andachieve the goal of becoming a top-10 World Tour
cycling team.
Trademark notes
All brand, product, and company/organization names that appear in thisarticle are trademarks or registered trademarks of their respective owners.

37

Feature Articles

Nadeem Ahmad

Group Senior Vice President, Technology
Strategy, NTT Ltd.
Nadeem Ahmad is a technology professionalwith 25 years of ICT industry experience and is asocial constructivist at heart. He believes technology does not determine human action but thathuman action shapes technology. He joined NTT
Ltd. (previously Dimension Data) over twenty
years ago initially leading technical delivery andoperations teams within the United Kingdom,
Europe, and America, but has spent the bulk of
his time in the Group CTO Office (now the Innovation and Technology group) focused on business and technology innovation initiatives acrossthe Group.
His role at the Innovation and Technology
group involves defining NTT Ltd.’s technology
strategy and positioning, in collaboration withgo-to-market practice leaders, across relevant
technology domains. This includes identifying
and monitoring key technology trends andemerging domains, assessing their impact onenterprise clients and their potential to affect new
solutions or services, as well as providing input
into new offer creation. This extends to providing
group guidance on emerging technology domains
and translating key trends and market developments to business impact via thought leadership,
internally and externally.
LinkedIn profile: https://www.linkedin.com/in/
nadeemahmad1/

NTT Technical Review

Vol. 18 No. 12 Dec. 202038

Feature Articles: Media-processing Technologies for Artificial
Intelligence to Support and Substitute Human Activities

Researching AI Technologies to
Support and Substitute Human
Activities
Hidenori Tanaka, Masaki Kitahara,
and Yoshinori Kusachi
Abstract

The coming of digital transformation has been gaining interest, and its development seems to be accelerating during the COVID-19 pandemic. Competition in artificial intelligence (AI) technologies is also
intensifying, and platformers are accumulating massive amounts of data as learning data. Amid thesetrends, NTT Media Intelligence Laboratories is promoting the research and development (R&D) of AI
technologies to support and substitute human activities by taking advantage of the technologies andexpertise that it has cultivated. This article introduces this R&D initiative.
Keywords: support of human activities, substitute for human activities, AI technology

1. Introduction
NTT Media Intelligence Laboratories has been
involved in the research and development (R&D) of
technologies for processing various forms of media,
such as voice, audio, language, images, and video,
and has put several of these technologies into practice. More recently, it has made a number of contributions to business such as operator support at contact
centers [1], provision of artificial intelligence (AI)agents [2, 3], sound-collection technology for emergency call systems [4], and video-compression
devices for 4K/8K broadcasting [5].
However, today’s market environment is going
through major changes and competition is intensifying. Every industry is experiencing digital transformation (DX), which is revolutionizing business models through digitalization, and the development of
DX is expected to accelerate to deal with the COVID19 pandemic. The advent of deep learning has ignited
a third AI boom, and the basic algorithms of AI technologies are now available to anyone. Moreover,learning data that contributes to the improved performance of AI systems is now being collected on a large
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Vol. 18 No. 12 Dec. 2020

scale by platformers, such as Google, Apple, Facebook, and Amazon, so a world in which AI performance increases daily is becoming a reality.
In response to this external environment, the NTT
Group is promoting initiatives such as B2B2X (business-to-business-to-X) and DX toward the creation ofa smart world as part of its Medium-Term Management Strategy. It is also promoting the concept of theInnovative Optical and Wireless Network (IOWN) tobuild a smart world through innovative technologies.
Against this background, NTT Media Intelligence
Laboratories is leveraging the technologies andexpertise cultivated through R&D activities in media
processing to pursue the R&D of AI technologies forsupporting and substituting human activities—thesource of all value—and the R&D of Digital Twin
Computing (DTC) to create new value from a medium- to long-term perspective [6]. In this article, we
introduce the R&D of AI technologies toward an
application domain in which AI supports and substitutes human activities.

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Feature Articles

2. Overview of AI technologies to support andsubstitute human activities
Several possible scenarios can be considered whentalking about an application domain in which AI supports and substitutes human activities. For example,
in terms of increasing efficiency, AI can not only
improve the productivity of operators at contact centers and AI agents but also improve business processes and productivity in offices and improve thequality of life as new value. We expect the COVID-19
pandemic to drive the penetration of teleworking andonline meetings and transform the way in which theyshould be carried out.
However, applying current AI technologies to such
scenarios is difficult. For example, when attempting
to support and substitute human activities on a more
personal level based on a person’s environment, there
would be a need to obtain data on that person andenvironment, but there are situations in which a large
amount of data cannot be obtained. Under such conditions, it would be difficult to achieve the full potential of AI. Taking speech-recognition technology as
an example, differences in its application would arise
in telephone calls, meetings, etc. depending onwhether only speech-to-text processing is sufficientor whether processing as far as speaker identificationis necessary. In the case of online meetings, there may
even be more differences in expected performanceand other requirements.
NTT Media Intelligence Laboratories has begun toaddress these issues by focusing its efforts on achieving efficient learning with a relatively small amountof data, producing new AI technologies, and achieving breakthroughs in the performance of current
technologies.
3. Current initiatives in AI technologies tosupport and substitute human activities
The Feature Articles in this issue introduce a groupof technologies that we are currently developing.
First, in the article “Media-processing Technologies
for Ultimate Private Sound Space” [7], the focus is on
sound as a major element of technologies that can
produce new AI technologies, such as for understanding surrounding conditions from sound, listening to
only the person you want to hear, and eliminating
sound you do not want to hear. Our aim with thesetechnologies is to achieve an ultimate private spacethat can be expected in applications such as teleworking.
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Vol. 18 No. 12 Dec. 2020

Next is “Saxe: Text-to-Speech Synthesis Engine
Applicable to Diverse Use Cases” [8]. Regarding
speech-synthesis technology for generating speech
for virtual announcers, AI agents, etc., this article
introduces technology for high-accuracy reading ofheteronyms according to context and deep-neuralnetwork speech-synthesis technology for reproducing a variety of speaker characteristics at low cost.
These technologies are aimed at achieving breakthroughs in the performance of current technologies
and efficient learning with a relatively small amountof data.
In “Speech Recognition Technologies for Creating
Knowledge Resources from Communications” [9],we envision speech recognition in scenarios such as
meetings and face-to-face customer service. In addition to techniques for improving current speech-totext technology, this article introduces technologies
for producing new AI technologies such as for
extracting the gender, emotions, and other characteristics of the speaker from sound.
In “Knowledge and Language-processing Technology that Supports and Substitutes Customer-contact
Work” [10], we introduce technologies for achieving
breakthroughs in the performance of current technologies and producing new AI technologies. These
technologies include document summarizing with acertain specified length according to the application
scenario and response analysis for improving the productivity of an operator involved in inside (remote)sales.
Finally, to achieve a fourth-dimensional (4D) digital platform [11] that integrates various types of sensing data in real time and enables a variety of future
predictions, the article “Spatial-information Processing Technology for Establishing a 4D Digital Platform” [12] introduces technology for digitalizing
real-world space and point-cloud-coding technology
for efficiently compressing 3D data that include temporal changes. These are technologies for efficientlearning with a small amount of data and achieving
breakthroughs in the performance of current technologies.
4. Anticipated use cases
We now introduce use cases of AI technologies forsupporting and substituting human activities that take
into account the recent COVID-19 pandemic (Fig. 1).
In creating personal space, the aim is to construct a
pseudo personal space for teleworking and a spacethat ensures privacy within one’s home. In online
40

Feature Articles

Work-stylereformWork-stylereformCreate personal space

Use AI technology to
Automate taking ofminutes and summaries convert proceedings

Construct a pseudo personal
space that ensures privacy tofacilitate teleworking

Work-stylereformof an online meeting
to text, summarize
the text, andtranslate it to ease
the time/space
Real-time translation
constraints inbased on high-accuracy people’s traditional
speech recognition
work styles
Distribution
reform

Support and take over work

Let AI do the work that traditionallycould only be done by people such as
announcing, thereby achieving quick
work processes requiring no contact
among fellow workers

Support collaborative work

High-accuracy 3D
space database

Promptly deliver what is needed to the person
who needs it
Deliveryby drones

Delivery by
self-driving
vehiclesIdentify goods inshort supply andassociated locations
from SNSs, etc. andautomatically deliver
them by recognizing
3D urban structures

Fig. 1. Use cases of AI technologies to support and substitute human activities.

meetings, AI technologies will be used to support
innovative collaborative work by converting the proceedings of a meeting to text, summarizing and translating the meeting, and easing the time/space constraints in people’s traditional work styles. Additionally, by letting AI do the work traditionally done bypeople, such as announcing, we anticipate quick
work processes that require no contact among fellow
workers. Finally, from the viewpoint of distribution
reform, we envision the possibility of identifying
goods in short supply and associated locations fromsocial networking services (SNSs), etc. and automatically delivering those goods by recognizing 3D
urban structures.
5. Future outlook
The surrounding environment is changing rapidly.
To provide various means of supporting and substituting human activities, technology must deal accordingly with these changes. To this end, we plan topromote R&D with a flexible frame of mind while
understanding both the macro and micro aspects ofthese changes.
References[1][2]

ForeSight Voice Mining, https://www.ntt-tx.com/products/foresight_vm/
Website of NTT Communications’ AI services, https://www.ntt.com/

NTT Technical Review

Vol. 18 No. 12 Dec. 2020en/services/ai.html
Website of NTT DOCOMO’s my daiz AI agent service (in Japanese),https://www.nttdocomo.co.jp/service/mydaiz/[4] Press release issued by NTT on Feb. 19, 2018 (in Japanese).
https://www.ntt.co.jp/news2018/1802/180219c.html[5] Press release issued by NTT, “NTT Develops the Worlds’ Best Performance 8K HEVC Real-time Encoder through Dedicated LSI Use,”Feb. 15, 2016.
https://www.ntt.co.jp/news2016/1602e/160215a.html[6] White paper on Digital Twin Computing, https://www.ntt.co.jp/
svlab/e/DTC/DTC_Whitepaper_en_2_0_0.pdf[7] M. Fukui, S. Saito, and K. Kobayashi, “Media-processing Technologies for Ultimate Private Sound Space,” NTT Technical Review, Vol.
18, No. 12, pp. 43–47, 2020.
https://www.ntt-review.jp/archive/ntttechnical.php?contents=
ntr202012fa6.html[8] Y. Ijima, N. Kobayashi, H. Yabushita, and T. Nakamura, “Saxe: Textto-Speech Synthesis Engine Applicable to Diverse Use Cases,” NTT
Technical Review, Vol. 18, No. 12, pp. 48–52, 2020.
https://www.ntt-review.jp/archive/ntttechnical.php?contents=
ntr202012fa7.html[9] Y. Nakazawa, T. Mori, Y. Yamaguchi, Y. Shinohara, and N. Miyazaki,“Speech Recognition Technologies for Creating Knowledge Resources from Communications,” NTT Technical Review, Vol. 18, No. 12,
pp. 53–58, 2020.
https://www.ntt-review.jp/archive/ntttechnical.php?contents=
ntr202012fa8.html[10] K. Nishida, K. Saito, T. Amakasu, K. Iso, and S. Nishioka, “Knowledge and Language-processing Technology that Supports and Substitutes Customer-contact Work,” NTT Technical Review, Vol. 18, No.
12, pp. 59–63, 2020.
https://www.ntt-review.jp/archive/ntttechnical.php?contents=
ntr202012fa9.html[11] Press release issued by NTT, “4D Digital PlatformTM”; Integrates
Various Sensing Data in Real Time and Enables Future Predictions,”
Mar. 26, 2020.
https://www.ntt.co.jp/news2020/2003e/200326c.html[12] Y. Yao, K. Kurata, N. Ito, S. Ando, J. Shimamura, M. Watanabe, R.
[3]

41

Feature Articles

Tanida, and H. Kimata, “Spatial-information Processing Technology
for Establishing a 4D Digital Platform,” NTT Technical Review, Vol.
18, No. 12, pp. 64–70, 2020.

Hidenori Tanaka

Senior Research Engineer/Producer, Planning
Section, NTT Media Intelligence Laboratories.
He received a B.E., M.E., and Ph.D. in scienceand technology from Keio University, Kanagawa, in 2004, 2006, and 2011. After joining NTT
in 2006, he engaged in research on computer
vision and pattern recognition. He has been
involved in technology marketing and R&D
management.

https://www.ntt-review.jp/archive/ntttechnical.php?contents=
ntr202012fa10.html

Yoshinori Kusachi

Chief Producer, Planning Section, NTT MediaIntelligence Laboratories.
He received a B.E. in computer science from
Kyoto University in 1995 and an M.E. in information science and Ph.D. in information engineering from Nara Institute of Science andTechnology in 1997 and 2007. He joined NTT in
1997 and engaged in research and practical application development in the fields of image processing, computer vision, and pattern recognition. He has also been involved in R&D management.

Masaki Kitahara

Senior Research Engineer, Scene Analysis
Technology Group, Universe Data Handling
Laboratory, NTT Media Intelligence Laboratories.
He received a B.E. and M.E. in industrial andmanagement engineering from Waseda University, Tokyo, in 1999 and 2001. After joining NTT
in 2001, he engaged in the development of compression and rendering algorithms for 3D video.
He transferred to NTT Advanced Technology(NTT-AT) and managed various software development projects. In 2009, he returned to NTT
and has been engaged in the development ofvideo encoding and AI technologies and R&D
planning. He is a member of the Institute ofElectronics, Information and Communication
Engineers (IEICE).

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Vol. 18 No. 12 Dec. 202042

Feature Articles: Media-processing Technologies for Artificial
Intelligence to Support and Substitute Human Activities

Media-processing Technologies for
Ultimate Private Sound Space
Masahiro Fukui, Shoichiro Saito,
and Kazunori Kobayashi
Abstract

NTT Media Intelligence Laboratories has been researching and developing media-processing technologies to create ultimate private spaces for promoting digital transformation in diverse spaces, including work-style reform. This article focuses on sound, which is one of the most important elements inachieving private space and introduces our aims in establishing the following three technologies: technology to understand surroundings from sound (event-detection and scene-identification techniques),
active-sound-control technology to make the sound heard only by listeners who want to hear, and activenoise-control technology to eliminate undesired sounds. This article describes our, the authors’, efforts
regarding these technologies.
Keywords: event detection, active sound control, active noise control

1. Introduction
Due to the work-style reforms promoted by theJapanese government and the impact of the novel
coronavirus, the traditional way of working that
requires employees to come to the office has beenreconsidered, and flexible work styles that are notbound by location or time have been attracting attention. An important part of this new work style is theability to create a comfortable working environment,
no matter where one is.
Let us consider telecommuting (remote working).
There are various types of noise in the home, such as
from the air conditioner, traffic outside, and sometimes chimes to announce deliveries. If there are family members present, there may be their voices andsounds from the television (TV). Such noise includes
sounds that telecommuters do not want to hear. However, in some situations, chimes and a baby crying
may be sounds one wants to hear. When a conference
call is held at home, we do not want the noise generated near us to reach the other party. We also do notwant those near us to hear the voice from the otherparty. If we can create an ultimate private sound spacein which telecommuters can hear only what they want
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to hear, e.g., voices from their telecommunication
partners, they will be able to work comfortably at
home.
The goal of NTT Media Intelligence Laboratories
is to create the ultimate private sound space, which
we call a Personalized Sound Zone (PSZ), by accurately collecting sound information and understanding the situation. NTT Media Intelligence Laboratories has accumulated a large amount of knowledge onsound-collection technology and is currently working
on further developing sound situational understanding and control technologies. The following describes
the three major technologies (Fig. 1).
2. Technology to understand surroundings fromsound (event-detection andscene-identification techniques)
People want to hear different sounds depending on
the situation. For example, a user may want to hear
his/her dog bark when at home, but may not want tohear the barking of other dogs when away from home.
In such a case, if we can detect the sound of a dog
barking while the user is away from home, we canjudge that the sound as undesired.
43

Feature Articles

Use scenes

Remote work

Technological development

Active noise
control

Value creation

Active soundcontrol

Event detection

PSZ

ed
esir
Und undso

De

Danger scene

sire

ds

oun

d

Meeting

Watching over

Composition of private space

Event detection andscene identification
Remote work

Teleconference

Teleconference

Fig. 1. Concept of Personalized Sound Zone (PSZ).

Thus, to achieve a PSZ, it is important to selectively convey sounds and situations to the user
according to the situation, rather than simply suppressing all ambient sounds. To do so, it is necessary
to be aware of the environment surrounding the user.
For this purpose, we are developing an event-detection technique for simultaneous estimation of information, such as “when,” “what,” and “where,” and ascene-identification technique for estimating themeaning of information such as “in what situation”
and “why.”
One of the difficulties with event detection is thatthe sound changes in various ways depending on thesurrounding environment, even if the sound reaches
the same location. For example, deep neural networks(DNNs) have recently become the mainstream method for determining where sounds are generated, but
due to the diversity of the environment, even DNNs
cannot be 100% effective. We are working on improving estimation accuracy by using the spatial symmetry of the sound field and combining it with physicalquantity estimation methods [1–3]. We are also
investigating a method for satisfying the requirementof detecting only specific events with fast computation and low complexity [4].
The goal with the scene-identification technique isto estimate the information about the user’s situation
as higher-level information than events and soundsource locations. For example, this technique will
estimate not only the sound of a car running but also
the user’s situation; therefore, it can judge whether
the sound should be suppressed or alert the user
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Vol. 18 No. 12 Dec. 2020

according to his/her situation. We are currently developing a sound-description-generation technique [5]for describing sound signals in natural language as anelemental technology of the scene-identification
technique.
3. Active-sound-control technology to make thesound heard only by listeners who want to hear
Listeners have been using earphones and headphones to listen to sound without affecting their surroundings. However, there are many problems, suchas the inconvenience of wearing/carrying a device,possibility of fatigue and hearing loss due to prolonged use, and difficulty in detecting the situationand possible danger around when wearing/carrying
such a device. Therefore, if localized sound-zone
generation is possible so that only the target listener
can hear without using earphones or headphones,
these problems can be solved (Fig. 2). NTT MediaIntelligence Laboratories is engaged in research anddevelopment (R&D) of both software and hardware
to develop active-sound-control technology for generating a localized sound zone. The following sections discuss issues and approaches regarding thistechnology.
3.1 Efforts to improve software performance
Although localized sound-zone generation requires
multiple loudspeakers, its control is implemented
using software and uses signal processing called
active sound control. This technology requires more
44

Feature Articles

Localized sound-zone generation
Playing sound toaudio listener onlyMusic

Localized
sound zone

Navi

Navigation voicethat only driver
can hearHeadphone-free
airplane seat

No soundleakage during
web conference

No soundleakage inhome theater

Fig. 2. Application range of active-sound-control technology.

loudspeakers when the upper frequency range is set
higher. Also, there may be restrictions on the placement of each speaker. PSZs are designed for individual users, and the number and degree of freedom of
loudspeaker placement are severely restricted, unlike
general problem settings. For example, at home, thespace where the speakers can be installed is limited,such as around a computer desk. The aim with PSZs
is to achieve localized sound-zone generation with asmall number of loudspeakers and limited space.
Active-sound-control technology identifies all theconditions necessary for filter design and optimizes
the overall filter to satisfy all conditions simultaneously.
3.2 Efforts to improve hardware performance
In addition to researching signal processing, we arealso investigating the loudspeaker arrangement thatminimizes sound leakage with a limited number ofloudspeakers and installation locations as well as
hardware that has a greater attenuation of sound level
as it becomes further away from the loudspeakers. We
are also engaged in R&D of small speakers for bass
reproduction. Bass is important to maintain highsound quality. The physical size of the loudspeaker
body is necessary to reproduce bass at a sufficientvolume. However, as described above, it is not practical to use a large loudspeaker because of the spaceconstraint of PSZs. We aim to improve the hardware
so that the bass limit of small speakers can be lowered
more than ever before.

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4. Active-noise-control technology to eliminate
undesired sounds
A PSZ uses the event-detection and scene-identification techniques to discriminate between incoming
sounds to create a space where undesired sounds are
not heard. Currently, noise cancellation in earphones
and other widely used devices is easy to achieve
because the space to muffle the sound is small andfixed. However, wearing earphones for a long periodcauses discomfort, such as earache. If we can develop
technology to muffle undesired sounds using a device
that does not need to be worn, it will be more convenient and expand the range of applications (Fig. 3).
Active-noise-control technology that muffles soundin a space consists of a control loudspeaker that generates the control sound, error microphone that
detects the error signal at the control point, reference
microphone that references the noise signal, and controller that uses an adaptive algorithm to produce thecontrol sound. If the error signal detected with theerror microphone is small, undesired sounds are
reduced.
The more loudspeakers there are that produce controlled sounds, the more points can be controlled andthe easier it is for active noise control to eliminate
undesired sounds. However, when considering use in
the home, a small number of speakers is desirable.
Considering the housing conditions in Japan, theseloudspeakers and microphones will be located close
to each other. Therefore, problems may occur, such asdegradation in performance due to the control soundgoing around the reference microphones, which was45

Feature Articles

Earphone- and headphone-free noise cancellation
Quiet zone

Currently

Quiet zone

Quiet zone

Canceling noisein large space
Quiet zone
Airplane and train seats Automotive seat

Shared workspace

Fig. 3. Application area of active-noise-control technology.

not envisioned in the previous active-noise-control
technology.
Undesired sound depends on the individual andenvironment. For example, people prefer not to hear
the sound of a car when they are indoors, but a pedestrian feels safer if he/she can hear car noise. After
detecting the sound with the event-detection andscene-identification techniques, technology is alsoneeded to determine the necessity of sound according
to the situation, for example, whether the sound isnecessary at the moment. To achieve a space where
the listener can hear only what the listener wants tohear, multiple technologies are needed, and they must
be coordinated at a high level.
5. Future prospects
This article outlined PSZs and described the current
status of its elemental technologies, such as technology to understand surroundings from sound (eventdetection and scene-identification techniques),
active-sound-control technology, and active-noisecontrol technology. There are still many technicalissues to be addressed, and NTT Media Intelligence

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Vol. 18 No. 12 Dec. 2020

Laboratories will continue to conduct R&D on this
topic. We will also collaborate with other organizations both inside and outside the group to achieve
PSZs.
References
[1]

[2]

[3]

[4]

[5]

L. Mazzon, Y. Koizumi, M. Yasuda, and N. Harada, “First Order
Ambisonics Domain Spatial Augmentation for DNN-based Directionof Arrival Estimation,” Proc. of the 4th Workshop on Detection andClassification of Acoustic Scenes and Events (DCASE 2019), New
York, USA, Oct. 2019.
M. Yasuda, Y. Koizumi, S. Saito, H. Uematsu, and K. Imoto, “Sound
Event Localization Based on Sound Intensity Vector Refined by
DNN-based Denoising and Source Separation,” Proc. of the 45th
International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2020), May 2020.
R. Sato, K. Niwa, and K. Kobayashi, “Ambisonic Domain DNN Preserving Physical Symmetry and Its Application to Sound Event Detection and Direction of Arrival Estimation,” The 2020 Autumn meetingof the Acoustical Society of Japan, Sept. 2020 (in Japanese).
S. Murata, S. Saito, K. Kobayashi, and A. Nakagawa, “A Study ofLightweight Sound Event Detection Method Based on Decision,” The2020 Autumn meeting of the Acoustical Society of Japan, Sept. 2020
(in Japanese).
Y. Koizumi, R. Masumura, K. Nishida, M. Yasuda, and S. Saito, “A
Transformer-based Audio Captioning Model with Keyword Estimation,” Proc. of INTERSPEECH 2020, Oct. 2020.

46

Feature Articles

Masahiro Fukui

Senior Research Engineer, Cognitive Information Processing Laboratory, NTT Media Intelligence Laboratories.
He received a B.E., M.E., and Ph.D. in information science from Ritsumeikan University,
Shiga, in 2002, Nara Institute of Science andTechnology in 2004, and Ritsumeikan University
in 2018. Since joining NTT in 2004, he has been
engaged in research on acoustic echo cancellers
and speech coding. He received the best paper
award of the ICCE conference and the technicaldevelopment award from the Acoustic Society ofJapan (ASJ) in 2014. He is a member of theInstitute of Electrical and Electronics Engineers(IEEE) and the ASJ.

Kazunori Kobayashi

Senior Research Engineer, Cognitive Information Processing Laboratory, NTT Media Intelligence Laboratories.
He received a B.E., M.E., and Ph.D. in electrical and electronic system engineering from
Nagaoka University of Technology, Niigata, in
1997, 1999, and 2003. Since joining NTT in
1999, he has been engaged in research on microphone arrays, acoustic echo cancellers, andhands-free systems. He is a member of IEICE
and ASJ.

Shoichiro Saito

Senior Research Engineer, Cognitive Information Processing Laboratory, NTT Media Intelligence Laboratories.
He received a B.E. and M.E. from the University of Tokyo in 2005 and 2007. Since joining
NTT in 2007, he has been engaging in researchand development of acoustic signal processing
systems, including acoustic echo cancellers,
hands-free telecommunication, and anomaly
detection in sound. He is a member of the Institute of Electronics, Information and Communication Engineers (IEICE), IEEE, and ASJ.

NTT Technical Review

Vol. 18 No. 12 Dec. 202047

Feature Articles: Media-processing Technologies for Artificial
Intelligence to Support and Substitute Human Activities

Saxe: Text-to-Speech Synthesis
Engine Applicable to Diverse
Use Cases
Yusuke Ijima, Nozomi Kobayashi, Hiroko Yabushita,
and Takashi Nakamura
Abstract

Technological advances such as deep learning and changes in the social structure where artificial intelligence supports and substitutes human activities have recently been accompanied by changes in use
cases requiring speech-synthesis technology and in the functions and performance they require. Key
issues in implementing these new use cases are understanding according to context, reproduction of
speaker characteristics, and support for diverse operation environments. NTT Media Intelligence Laboratories is developing a speech-synthesis engine (development codename “Saxe”) based on deep neuralnetworks to address these issues. This article provides a technological overview of Saxe along withseveral application examples and touches upon future developments.
Keywords: speech synthesis, deep learning, diversity

1. Introduction
Speech synthesis is technology for generating
speech from input text, therefore it is sometimes
called text-to-speech synthesis. NTT has a long history of research and development in the field ofspeech synthesis and has developed a variety ofspeech-synthesis technologies that have found widespread use in services with the purpose of conveying
information accurately. These include the telephoneservices web171 (disaster emergency message board)and 177 (weather forecast telephone service) andIVR (interactive voice response) systems.
There have also been a variety of technologicaladvances typified by deep learning and changes in thesocial structure where artificial intelligence (AI) is
increasingly being used to support and substitutehuman activities. These developments are being
accompanied by changes in use cases requiring
speech-synthesis technology and in the functions andperformance they require. Uses cases that aimed toconvey information accurately have required the genNTT Technical Review

Vol. 18 No. 12 Dec. 2020

eration of speech for standard text in a specificspeaker’s voice. However, use cases that support andsubstitute human activities require the generation of
speech for all kinds of text in a user-desired speaker’s
voice in diverse operation environments. NTT MediaIntelligence Laboratories is developing a speechsynthesis engine (development codename “Saxe”)
based on deep neural networks (DNNs) in response tothese issues and is promoting its practical applicationin diverse use cases. In this article, we outline thetechnologies behind this engine, introduce several
application examples, and touch upon future developments.
2. Technology overview
2.1 H
 igh-accuracy reading of heteronyms according to context
Speech synthesis can be broadly divided into a textanalysis section that infers the reading and accents ofcharacters from input text and a speech-synthesis section that generates synthesized speech from the48

Feature Articles

Japan beat Australia 1 to 0
to win the championship!

Japan beat Australia 1 to 0
to win the championship!

Character information
有難う

Text-analysis
sectionSpeechsynthesis
sectionSpeech signala ri ga tou

a ri ga tou

arigatou

Fig. 1. Overview of speech-synthesis technology.

この店のカレーは辛いだけではない(The curry at this shop is not only spicy…)

Current technology: readalways as “tsurai (difficult)”
Obtain words targeted for
assigning a reading from acandidate list

New technology: determine
reading based on context
辛い: karai/tsurai

Obtain ambiguous
word candidates

Reading
candidate list
• Category is food → karai: 2 points• Category is incident → tsurai: 2 points

If a rule exists for eachreading candidate, score eachaccordingly and select thereading with the highest score.

Resolve ambiguity

Disambiguation
rules

この

店

の

カレー

は

辛い

だけ

では

ない

kono

mise

no

karee

wa

karai

dake

dewa

nai

Fig. 2. Overview of word-reading disambiguation technology.

inferred reading and accents (Fig. 1). In the textanalysis section, an erroneous inference of the reading or accents of characters can result in the transmission of incorrect information to the listener of thatsynthesized speech, so there is a need for high-accuracy inference of reading and accents with respect to
input text. The Japanese language, however, includes
heteronyms, which are words for which reading andaccents differ according to context despite being written the same. For example, the word “辛い” can bepronounced “karai (spicy)” or “tsurai (difficult)” andthe word “寒気” can be pronounced “samuke(chills)” or “kanki (cold air).” For this reason, achieving high-accuracy inference of reading and accentshas become a major objective.
To this end, we developed word-reading-disambiguation technology that infers the correct reading
against an obvious misreading. This technology caninfer the reading of a word having ambiguity throughNTT Technical Review

Vol. 18 No. 12 Dec. 2020

the use of dictionaries and rules that make use of linguistic knowledge. For example, a rule can be prepared beforehand that says if the word “カレー,”
which is pronounced “karee (curry),” should appear
near the word “辛い,” points are given to reading thelatter as “karai (spicy).” Consequently, the word “辛い” appearing in the sentence “この店のカレーは辛いだけではない” (The curry at this shop is not
only spicy…) would then be correctly read as “karai”(Fig. 2). Since it would be difficult to exhaustively
write out all words considered to be associated with“karai (spicy),” an alternative means would be aframework that uses word categories (e.g., “food”) as
rules. Therefore, we can reduce the number of rules
while improving comprehensiveness. This technology can infer readings with high accuracy while savingon memory.

49

New technology

Current technology

Feature Articles

Synthesized
speechDBDB
Trainingaiueo

Prepares a large amount of training
Readingdata for a specific speaker
AccentTraining DB(speaker #1)

ai

Training DB(speaker #2)

ue

Training DB(speaker #3)oai

SpeechReadings not in training datacannot be synthesized.
aiueo
aiueo

Reading
Accent+ speaker

SpeechDiverse and high-quality speech
can be synthesized even if eachspeaker’s database is small.

aiueo
Readings can besynthesized among
speakers in a complementary manner.

Fig. 3. DNN speech-synthesis technology for reproducing diverse speaker characteristics.

2.2 D
 NN speech-synthesis technology for reproducing diverse speaker characteristics at low
cost
In contrast to the text-analysis section that isrequired to infer the reading and accents of input text
with high accuracy, the speech-synthesis section isrequired to reproduce with high accuracy the voice ofa desired speaker according to the wishes of the customer or other factors. However, achieving highquality speech synthesis for a desired speakerrequires a large amount of speech data uttered by thatspeaker. For example, in Cralinet [1], a unit-selection
speech-synthesis system, up to 20 hours of speech
data are needed to generate high-quality synthesized
speech. As a result, achieving speech synthesis of avariety of speaker characteristics in an interactive
speech system requires speech recordings and database (DB) construction, which can be a major issue
due to the costs involved.
In response to this issue, we have achieved highquality speech synthesis of a desired speaker from
20–30 minutes of speech data (about 2 hours ofspeech recordings) by using a previously constructed
multi-speaker speech DB together with a DNN. A key
feature of this system is that the speech data of multiple speakers can be modeled with a single DNN(Fig. 3). The information needed to generate speech(readings and accents) is learned from previously
prepared multi-speaker speech data, and features ofthe desired speaker, such as voice quality and speaking style, are learned from the speech data of thatdesired speaker. Therefore, we have achieved highNTT Technical Review

Vol. 18 No. 12 Dec. 2020

quality speech synthesis even with a relatively small
amount of speech data of the desired speaker [2].
Additionally, by combining this system with generative adversarial networks that have been found to beeffective in image generation and other tasks, wehave achieved improvements in the quality of synthesized speech and in the reproducibility of a speaker’s
voice [3].
2.3 D
 NN speech-synthesis technology for diverse
environments
Depending on the environment in which speechsynthesis is actually being used, a variety of constraints (e.g., no network access, need for high-speed
response) require that the system be operated not on
a computer server having abundant computing
resources (central processing unit (CPU), read only
memory (ROM), random access memory (RAM),etc.) but rather on devices such as smartphones orrobots that are very limited in such resources. To
address this issue, we developed an embedded type of
DNN speech-synthesis library that can operate atpractical speeds on devices with limited computing
resources while maintaining the quality of synthesized speech to the extent possible. Specifically, we
created a lineup of three types of libraries. In addition
to the server-specific library, we developed a library
for resource-limited terminals for operation on devices such as smartphones and tablets and a library for
ultra-resource-limited terminals for devices such as
microcomputers, home appliances, and high-grade
toys that are greatly limited in computing resources
50

Feature Articles

Lineup of 3 versions according to resources, specifications, application, etc.
Server version

Resource-limited version

About 1/10
computingpower

ServerFeatures

CPU
MemoryUltra-resource-limited version
About 1/10
computingpower

Smartphones, tablets,
single-board computers

Microcomputers, home appliances, toys

High-quality speech synthesis,
multilingual support (Japanese,
English, Chinese, Korean)

Lightweight speech-synthesis
library enabling high-quality
speech synthesis for
smartphones, etc.

Ultra-lightweight speech-synthesis
library enabling operation on lowend CPUs having only integer
arithmetic

x86_64

ARM Cortex-A series

ARM9, MIPS1, etc.

2GB (ROM)/4GB (RAM)

Applications Content creation, robots,
interactive speech, entertainment,
information processing, and more

About 10 MB (ROM/RAM)Up to 10 MB

Robots requiring high-speed
response, use in environmentswith no network access

Use on inexpensive, low-end
devices4

Fig. 4. Speech-synthesis engine operable on diverse devices.

(Fig. 4).
Many microcomputers and similar devices have nobuilt-in floating-point unit (FPU), so the issue was
how to speed up DNN-inference processing. In thelibrary for ultra-resource-limited terminals, we used
fixed-point arithmetic to achieve high-speed DNNinference processing without using floating-point
operations. Additionally, by incorporating speedenhancement measures into the text-analysis section,
the library for ultra-resource-limited terminals canoperate at high speeds with small memory (ROM: 7
MB or greater) even on devices equipped with no FPU.
3. Application examples
3.1 Application to news-reading speech spoken bycomputer-generated (CG) announcer
FutureVoice Crayon [4] from NTT TechnoCross
provides the text-to-speech-synthesis technology wedeveloped as a service. It has been used since February 2020 for synthesizing the speech of TV Asahi’s“AI×CG Announcer Hanasato Yuina” [5]. This is anews program, so there is a need for rich expressive
power close to that of a human announcer as well as
the ability to correctly read news copy in various categories. The word-reading-disambiguation technology described above automatically assigns readingsthat fit the category of that news, which helps toreduce the work involved in correcting readings andaccents that up to now has been performed by humans.
Note that the voice of the CG announcer in thisexample took form by mixing the voices of several
announcers at TV Asahi. This project therefore creNTT Technical Review

Vol. 18 No. 12 Dec. 2020

ated an original voice independent of the rights of a
specific person, which demonstrates new possibilitiesof speech-synthesis technology.
3.2 docomo AI Agent API
NTT DOCOMO’s “docomo AI Agent API” [6] isan interactive-type AI-based application service provider service that packages a speech/text user interface (UI). This service incorporates our speech-synthesis technology as its speech-synthesis engine. The
application programming interface (API) includes
more than 50 types of voices as preset speakers,
which enables a user to implement voice UIs that fit
a variety of characters and environments without having to go through the trouble of recording speech or
managing rights for each UI. The DNN speech-synthesis technology described above enables the reproduction of speaker characteristics with much variation ranging from the voices of elementary school
students to those of the elderly.
3.3 Disaster Mitigation Communication System
NTT DATA’s Disaster Mitigation Communication
System [7] is an announcement broadcasting system
for transmitting administrative and disaster-prevention information from local governments to residents.
It delivers this information from a transmission system
inside a government building or from remote operation terminals to outdoor speaker equipment installed
within the community and to tablets, smartphones,
mobile phones, and other devices. The informationdelivered in this manner is then used as a basis for
conducting speech synthesis from the outdoor speaker51

Feature Articles

equipment and each device, such as a tablet or housespecific receiver, then conveying that information to
local residents with synthesized speech.
4. Future developments
This article described the recent technologicaldevelopments in speech-synthesis technology at NTT
Media Intelligence Laboratories and presented examples of its application. Thanks to these efforts,
speech-synthesis technology has reached a certain
level from the viewpoint of generating synthesized
speech in the voice of a desired speaker.
Nevertheless, if we were to compare the output ofcurrent speech-synthesis technology with humanspeech, we would find that there are still significant
differences. For example, when reading the news or ascript in the case of a television announcer or voiceactor, the goal would be to understand, for example,
the intent included in the text and to then express thattext in a manner of speaking that includes emotion.
However, current speech-synthesis technology isincapable of interpreting intent and can only generate
synthesized speech with the same tone. As expectations are growing for means of supporting and substi-

Yusuke Ijima

Distinguished Research Engineer, Cognitive
Information Processing Laboratory, NTT MediaIntelligence Laboratories.
He received a B.E. in electric and electronics
engineering from National Institution for Academic Degrees and University Evaluation from
Yatsushiro National College of Technology,
Kumamoto, in 2007, and an M.E. and Ph.D. in
information processing from Tokyo Institute ofTechnology in 2009 and 2015. He joined NTT
Cyber Space Laboratories (now NTT MediaIntelligence Laboratories) in 2009, where heengaged in the research and development ofspeech synthesis. His research interests include
speech synthesis, speech recognition, and speech
analysis. He received the Awaya Prize Young
Researcher Award of the Acoustical Society ofJapan (ASJ) in 2018. He is a member of ASJ andthe International Speech Communication Association (ISCA).

Nozomi Kobayashi

Senior Research Engineer, Cognitive Information Processing Laboratory, NTT Media Intelligence Laboratories.
She received an M.E. and Dr.Eng. in information science from Nara Institute of Science andTechnology in 2004 and 2007. She joined NTT
Cyber Space Laboratories (now NTT MediaIntelligence Laboratories) in 2007. Her current
research interests include natural-language processing, especially information extraction. She isa member of the Information Processing Societyof Japan (IPSJ) and the Association for Natural
Language Processing (NLP).

NTT Technical Review

Vol. 18 No. 12 Dec. 2020

tuting human activities, the further spread of speechsynthesis technology in society will require speechsynthesis that can achieve a level of expression
equivalent to or better than that of humans. Goingforward, we aim to expand the range of speech-synthesis applications by pursuing technologies that canenable expression based on context, intent, and emotion and expression that take into account the attributes and receptiveness of the listener.
References
[1]

[2]
[3]

[4]
[5]
[6]
[7]

K. Mano, H. Mizuno, H. Nakajima, N. Miyazaki, and A. Yoshida,“Cralinet—Text-to-Speech System Providing Natural Voice Response
to Customers,” NTT Technical Review, Vol. 5, No. 1, pp. 28–33, 2007.
h t t p s : / / n t t - r ev i ew. j p / a r c h ive / n t t t e c h n i c a l . p h p ? c o n t e n t s =ntr200701028.pdf
N. Hojo, Y. Ijima, and H. Mizuno, “DNN-based Speech Synthesis
Using Speaker Codes,” IEICE Trans. Inf. and Syst., Vol. E101-D, No.
2, pp. 462–472, 2018.
H. Kanagawa and Y. Ijima, “Multi-speaker Modeling for DNN-based
Speech Synthesis Incorporating Generative Adversarial Networks,”
Proc. of the 10th ISCA Speech Synthesis Workshop, pp. 40–44,
Vienna, Austria, Sept. 2019.
FutureVoice Crayon (in Japanese), https://www.futurevoice.jp/
TV Asahi’s “AI×CG Announcer Hanasato Yuina, https://news.
tv-asahi.co.jp/news_international/articles/000175834.html
docomo AI Agent API (in Japanese), https://docs.sebastien.ai/
Disaster Mitigation Communication System (in Japanese), https://www.nttdata.com/jp/ja/lineup/disaster_mitigation_c/

Hiroko Yabushita

Research Engineer, Cognitive Information
Processing Laboratory, NTT Media Intelligence
Laboratories.
She received a B.S. and M.S. in information
science from Ochanomizu University, Tokyo, in2008 and 2010. She joined NTT Cyber Space
Laboratories (now NTT Media Intelligence
Laboratories) in 2010. She engaged in the
research of image recognition and received theIEEE Japan Council Women in Engineering Best
Paper Award in 2013. Her current interest is in
the digitization of human capabilities. She is amember of the Institute of Electronics, Information and Communication Engineers (IEICE).

Takashi Nakamura

Senior Research Engineer, Cognitive Information Processing Laboratory, NTT Media Intelligence Laboratories.
He received an M.E. in information and communication engineering from the University ofElectro-Communications, Tokyo, in 2005 andjoined NTT Cyber Space Laboratories (now NTT
Media Intelligence Laboratories) the same year.
His interests include the practical development
and business development of speech synthesis.
He is a member ASJ.

52

Feature Articles: Media-processing Technologies for Artificial
Intelligence to Support and Substitute Human Activities

Speech Recognition Technologies for
Creating Knowledge Resources from
Communications
Yuichi Nakazawa, Takeshi Mori, Yoshikazu Yamaguchi,
Yusuke Shinohara, and Noboru Miyazaki
Abstract

Speech recognition technologies have been used in an increasing number of situations to support orsubstitute work traditionally done by people such as call analysis in contact centers and support for creating minutes of council meetings. We are promoting research and development of speech recognition
technologies because we believe that speech significantly supports human activities, especially in business. In this article, we describe the historical development of speech recognition technologies cultivated by NTT laboratories and the future contributions and roles of such technologies in human andcorporate activities. It also introduces the use of non-linguistic information read from voice, such as
emotion, gender, and age, which has been attracting attention.
Keywords: speech recognition, digital transformation, human-to-human communications

1. Development of speech
recognition technologies“Hey, Siri,” “Ok, Google.” These are the first words
you speak to your voice assistant, and no doubt mostpeople have used them. Speech recognition technologies have spread rapidly with the advent of voiceassistants that control devices and provide information to people when speaking into a smartphone or
artificial intelligence (AI) speaker. Various speech
recognition technologies that enable such dialogue
between people and computers have been put into
practical use such as in automatic voice response
devices (interactive voice response: IVR) in the
1980s and car navigation in the 1990s. However, withthe recent introduction of deep-learning technologies,
speech recognition accuracy has improved significantly, and the use of voice assistants and speechtranslation combined with machine translation due toincreasing demand for it in the globalized world, hasbegun.
Nevertheless, speech is an important means of comNTT Technical Review

Vol. 18 No. 12 Dec. 2020

munication between people. Although the abovementioned voice assistants work with relatively shortutterances, commercialization of speech recognition
for long utterances (long sentences, conversations)has also been studied. Since 2000, speech recognition
technologies have been used in situations in which
manuscripts were at hand and targeted speech wascomparatively clear, such as captioning for news programs and support for council-minutes creation.
However, the application of speech recognition technology for speech occurring in natural communications between people, such as analysis of operatorcustomer conversations at contact centers and realtime conversational support, has recently been
advancing. Thus, speech recognition technologies
have been developed with improved recognition
accuracy and a wider variety of targeted speech(Fig. 1).

53

Feature Articles

Speech recognition
technologies

Targeted voice

Related services

Speech recognition formobile and smart devices
Human-toCall voicehumanconversational
voice
Conference
voice

Command voice

Person-to-person
conversation

Transcription systemfor councils using
speech recognition

Presentation (monologue)

Free speech
between machinesand humans

Spoken dialogue agent
Closed-caption-creation
system for broadcasters

Announcer reading

Read speechContact-center voicemining system

Telephone-voice-answering
services
Single words

Wristwatch-type PHS

Word

Financial CTI system

2013 ~
2010

2008
2003

2003

2002

1998

1982

CTI: computer telephony integration
PHS: personal handy-phone system

Fig. 1. NTT speech recognition technology initiatives.

2. Role of speech recognition technologies insupporting digital transformation in businessWe believe that by further expanding the range ofspeech targeted with speech recognition technologies, such technologies will be able to play a role indriving the transformation of corporate activities.
Using information technology (IT), including AI
technologies, to transform business processes isknown as digital transformation (DX). The significance of DX is gaining attention regardless of industry or business type. However, advancing DX requires
effort such as streamlining and automating businessprocesses using IT and creating new value throughseamless collaboration between business and IT.
Speech recognition technologies have demonstrated
their effectiveness in streamlining and automating
business processes.
We use a large amount of speech when communicating with others. Although communications tools
using text, such as social networking services, email,and chat, have advanced, real-time voice communications face-to-face or via telephone are probably more
often chosen to confirm or convey complex content orfor decision making that requires the consensus of
NTT Technical Review

Vol. 18 No. 12 Dec. 2020

many people.
Compared to text communications, voice communications are advantageous because they are in realtime and convey unspoken information expressed
through the nuances of voice. However, if recordings
or notes are not made, information communicated byvoice is volatile, i.e., as soon as it has been uttered it
disappears. Although an enormous amount of voicecommunications occurs on a daily basis in corporate
activities, currently only a limited amount of voicecommunications is targeted for data analysis such as
calls between operators and customers at contact centers. The data in most voice communications are not
used.
However, a large amount of voice communications
that occurs face-to-face or via telephone between
sales representative and customers contain information that could be useful from a variety of perspectives such as marketing or compliance management.
Information from meetings and communications
among employees (e.g., short consultations), is also
useful for improving business activities. Examples ofsuch information are seeds of new business ideas,
business improvement tips, or employees’ mental
health conditions. Preserving such information by54

Feature Articles

turning it into text using speech recognition technologies to create knowledge resources for various processes, thus leading to business improvements, has
the potential to contribute to the rationalization andautomation of business processes. In the next section,
we introduce example applications of speech recognition technologies to streamline business processes.
3. Use cases of speech recognition technologies
Compared to current speech recognition technology, research and development (R&D) in speech recognition technologies is now addressing broken utterances, which adds another layer of technical difficulty, enabling progress in business DX in an increasing number of situations. We introduce example use
cases that are becoming more widespread due toimprovements in speech recognition accuracy regarding broken utterances.
3.1 Conference speech recognition
Even though minutes are often taken in businessmeetings, many people who take minutes often feelthat rather than quick notes, it requires much morework than predicted to actually record meetings.
Attempting to faithfully take notes in a meetingmeans the minutes taker will not be able to properly
participate in the meeting or its discussions or will
worry whether the minutes will be accurately recorded sometime after the meeting. Also, recording all ofa meeting and creating minutes while listening later
takes more time than the meeting itself, so it might bea good to have an additional minutes taker participate
in the meeting. Many people wish for such a device
that can automatically and quickly create minutes.
Current speech recognition is not sufficiently accurate and is limited to searching speech tied to time
information in the hope that important words can berecognized. However, in addition to supporting simple minutes creation, it is hoped that by enabling
speech recognition for broken utterances, AI will
become proficient in roles usually played by humans(facilitators), such as automatic creation of minutes in
conjunction with summarization technology, linkage
with issue-tracking systems by automatic extractionof action items, and facilitation of meetings.
3.2 Remote work support
Where remote operations and responses are expected to grow such as in healthcare and education, inconveniences that occur due to interactions not being
face-to-face will have to be eliminated. In healthcare,
NTT Technical Review

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for example, although it is technically possible tooperate remote devices with buttons and levers aswell as recording conversations, unobtrusive AI support technologies will be required so that the doctor,
who only obtains a lower-than-normal amount of
information about the patient through a screen, canfocus on treatment. Such technologies could be used
to pass a thermometer in response to “let’s take yourtemperature” or automatically control the lighting
inside the patient’s mouth in response to “please open
your mouth.” Linking with dialect-conversion technology also holds promise for smooth communications in remote healthcare in rural areas where strong
dialects are spoken.
In one-to-many classes, which are fundamental in
education, student levels of understanding are understood through calling on all students for their
responses. In online classes, however, it is clear thatcross-talk occurring in such situations makes it difficult to establish voice communications. Unobtrusive
AI that will prevent device operations from interfering with students’ concentration, such as comprehending students’ speech with real-time speech-totext recognition and executing raised hand commands
for student responses of “yes,” will likely become as
indispensable.
3.3 Contact centers
In contact centers, speech recognition technologies
are being used for operator speech. If speech recognition results of customer speech can also be obtainedwith sufficient accuracy, speech recognition technologies could contribute to making business support
more efficient, reducing operator tasks, increasing thenumber of calls that can be handled, and improving
customer satisfaction to meet future increasing
demand on contact centers as various services go
online.
4. Use of non-linguistic information
As well as lingual information (text), non-linguistic
information (gender, age, etc.) and para-linguistic
information (emotions, intent, attitude) are contained
in information conveyed through voice communications. Hence, there is demand to use such information
to advance speech services in actual business.
We developed RexSenseTM, a software engine thatcan extract non-linguistic and para-linguistic information in speech, by studying technologies for recognizing and using non-linguistic and para-linguistic
information as well as taking initiatives to recognize
55

Feature Articles

Spoken dialogue
recommendations

Information presentation

He sounds sad...
I’ll recommend
somewhere to cheer
him upMorning...

Good morning
A movie that will cheer
you up is now showing!

Where is thenearest restroom?

Emotionrecognition

VoC analysis

That’s not
the answer!

Sounds like a female...
I’ll provide guidance
to the women’s
restroom first

The women’s
restroom is on the northside of the women’s
clothing department.
The men’s
restroom is...

Clothes

Speaker attributes
Question detection

Enhanced IVR
Is there an appropriate
response when theoperator angers
an adult user?

Recorded data

Urgency level
High
Urgent mattershandled byoperators

Speaker attributes
Question detection
I’m in trouble!!
Please call me back!!

Emotion recognition

Urgency level
estimation

Fig. 2. RexSenseTM application examples.

text information from speech with high accuracy.
This software engine enables recognition and estimation of (1) speaker attributes (adult male, adultfemale, child), (2) emotions (joy, anger, sadness,
calmness), (3) questions and non-questions, and (4)urgency, with high accuracy from voice data. We also
developed the RexSense system to enable web application programming interface (Web API) services
integrating this software engine with speech recognition for uses such as contact center advancement.
RexSense also makes it possible to provide advanced
services with robots by giving appropriate responses
and recommendations according to human emotions
or to provide advanced digital signage that presents
more appropriate content (guidance, advertisements,etc.) based on non-linguistic information such as
speaker attributes determined from voice. We expect
the implementation of advanced voice of customer(VoC) analysis in contact centers, more sophisticatedautomated response services in IVR, and provision of
more advanced audio conferencing solutions using
non-linguistic and para-linguistic information(Fig. 2).
We also developed a customer-satisfaction-estimation technology for analyzing customer-voice characteristics and various conversation characteristics fromthe voices of operators in contact centers and customNTT Technical Review

Vol. 18 No. 12 Dec. 2020

ers to extract customer satisfaction (satisfaction anddissatisfaction) and introduced it to the contact centerAI solution ForeSight Voice MiningTM; commencing
services in April 2019. We are developing a responselikability estimation technology for evaluating thelikability of operator responses and considering putting it into service. These technologies hold promise
for applications, such as call analysis (searching forgood operator-response examples, analysis of customer satisfaction, etc.), operator support, operator
and contact center evaluation, and operator education.
5. Future outlook
By expanding application areas from business scenarios and targeting all types of voice communications, the speech recognition technologies introducedin this article are crucial for achieving human Digital
Twin Computing (DTC) [1], one element of the Innovative Optical and Wireless Network (IOWN) promoted by the NTT Group. In the cyber-physicalinteraction layer in the DTC architecture (Fig. 3), it
will be necessary to collect the data required to generate digital twins by sensing real-space things (objects)
and humans, and speech recognition technologies
will play an important role in sensing human56Feature Articles

Simulations on Earthand outer-spacescalesDiscovery andresolution of latenturban issues

New economic
activity through thecreation of sister cities

Prediction andsuppression of
disease spread

Multifaceted personal
decision-making

Application layer
Applications executed using the digital-world-presentation layer

Urban
spacesTraffic
environments

HumangroupsDigital-world-presentation layer
Digital twin usage framework

Digital-twin layer
Generating and maintaining digital twins of humans and things

Cyber/physical-interaction layer
Interaction between real space and cyberspace

HumansThingsReal space

Fig. 3. Digital Twin Computing architecture.

thoughts.
Societies will become more convenient, richer, andsafer as human DTC and DX of corporate activities
progress. We will contribute to the creation of such asociety through R&D of speech recognition technologies for human-to-human communications.

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Reference[1]

I. Toshima, S. Kobashikawa, H. Noto, T. Kurahashi, K. Hirota, and S.
Ozawa, “Challenges Facing Human Digital Twin Computing and Its
Future Prospects,” NTT Technical Review, Vol. 18, No. 9, pp.19–24,
Sept. 2020.
h t t p s : / / n t t - r ev i ew. j p / a r c h ive / n t t t e c h n i c a l . p h p ? c o n t e n t s =ntr202009fa2.html

57

Feature Articles

Yuichi Nakazawa

Senior Research Engineer, Cognitive Information Processing Laboratory, NTT Media Intelligence Laboratories.
He received a B.E. and M.E. from Keio University, Kanagawa, in 2001 and 2003. He joined
NTT Cyber Space Laboratories (now NTT
Media Intelligence Laboratories) in 2003 and has
been involved in R&D of automatic speech recognition technologies. He is a member of Acoustical Society of Japan (ASJ).

Takeshi Mori

Senior Research Engineer, Cognitive Information Processing Laboratory, NTT Media Intelligence Laboratories.
He received a B.E. and M.E. from Tokyo Institute of Technology in 1994 and 1996 and a D.E.
from University of Tsukuba, Ibaraki, in 2007.
Since joining NTT in 1996, he has been engaged
in research on speech and audio processing algorithms. He is a member of the Institute of Electrical and Electronics Engineers (IEEE), ASJ, andthe Institute of Electronics, Information andCommunication Engineers (IEICE).

Yoshikazu Yamaguchi

Senior Research Engineer, Cognitive Information Processing Laboratory, NTT Media Intelligence Laboratories.
He received a B.E. and M.E. in electrical engineering from Osaka Prefecture University in
1993 and 1995. He joined NTT Human Interface
Laboratories (now NTT Media Intelligence
Laboratories) in 1995 and has been involved in
R&D of automatic speech-recognition technologies.

NTT Technical Review

Vol. 18 No. 12 Dec. 2020

Yusuke Shinohara

Senior Research Engineer, Cognitive Information Processing Laboratory, NTT Media Intelligence Laboratories.
He received a B.S. and M.S. in mechanoinformatics from the University of Tokyo in 2002 and2004. From 2004 to 2017, he was a research scientist at Toshiba Corporate Research and Development Center, Kanagawa. Since joining NTT in
2017, he has been involved in R&D of speech
recognition, in particular, acoustic modeling withvarious types of deep neural networks. His
research interests include speech recognition,
computer vision, and machine training. He
received the IEICE Pattern Recognition andMedia Understanding (PRMU) Young Researcher Award in 2004 and the ASJ Awaya Prize Young
Researcher Award in 2013. He has served as acommittee member (2013–2015, 2017-present)
and secretary (2015–2017) for the Information
Processing Society of Japan Special Interest
Group (IPSJ SIG) on Spoken Language Processing. He is a member of IEEE, IEICE, ASJ, and
IPSJ.

Noboru Miyazaki

Senior Research Engineer, Supervisor, Cognitive Information Processing Laboratory, NTT
Media Intelligence Laboratories.
He received a B.E. and M.E. from Tokyo Institute of Technology in 1995 and 1997. Sincejoining NTT in 1997, he has been engaged inresearch on spoken-dialogue systems, speechsynthesis, and speech-recognition technologies.
He received the IEICE Inose Award in 2001. He
is a member of ASJ, IEICE, and the JapaneseSociety for Artificial Intelligence (JSAI).

58

Feature Articles: Media-processing Technologies for Artificial
Intelligence to Support and Substitute Human Activities

Knowledge and Language-processing
Technology that Supports andSubstitutes Customer-contact WorkKyosuke Nishida, Kuniko Saito, Tetsuo Amakasu,
Kazuyuki Iso, and Shuichi Nishioka
Abstract

At NTT Media Intelligence Laboratories, using natural-language-processing technology cultivatedover many years as one of our core competences, we are researching and developing knowledge andlanguage-processing technology that contributes to improving the productivity of contact centers andoffices. In this article, a language model, document-summarization technology, and interview-support
technology are introduced.
Keywords: AI, document summarization, response analysis

1. Introduction
At NTT Media Intelligence Laboratories, we haveresearched and developed knowledge and languageprocessing technology for contact centers for analyzing documents (such as work manuals and frequently
asked questions) and presenting appropriate documents to agents handling customers. Not only incontact centers but also in offices, it is necessary tofurther improve productivity of agents and employees. In response to this necessity, we are developing
technology for understanding and generating largescale documents and various responses. We first
explain a language model for handling documents
and document-summarization technology using thislanguage model then describe interview-support
technology for analyzing responses between a customer and agent.
2. Development of natural-language
understanding by using a language
model (BERT)
It has been considered difficult for artificial intelligence (AI) to understand human language. However,
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Vol. 18 No. 12 Dec. 2020

with the advent of Bidirectional Encoder Representations from Transformers (BERT) [1], announced by
Google in October 2018, the research and development (R&D) of natural-language understanding has
undergone a large paradigm shift. For example,
regarding the task called machine reading [2], which
requires reading comprehension to understand thecontent of text and answer questions, it has beenreported that AI using BERT greatly exceeded the
response score of a human. Performance of naturallanguage-processing tasks other than machine reading has also improved significantly, and language
models are attracting attention as a basic technology
for giving AI the ability of language comprehension.
A language model estimates the plausibility of sentences (Fig. 1). For example, many people may feelthat “cake” is more natural than “egg” as the word
“X” in the sentence “Today is my birthday, so I ate[X].” Also, it seems natural that the two sentences“It’s a fine day today.” and “It’s a good day to do thelaundry.” appear consecutively. BERT learns inadvance such fill-in-the-blank problems (word prediction) and relationships between two consecutive
sentences (prediction of the next sentence) on thebasis of a large set of texts such as all Wikipedia
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Feature Articles

Collection of a large amountof text from sources
such as Wikipedia(without a teacher)

Prior learning

Fine-tuning(1) Fill-in-the-blank problem  Prediction of words“Today is my birthday,
so I ate [X].”

Languagemodel (BERT)

What is X?
Excellent: “cake”
Neutral: “an egg”

Task-dependent
dataset(supervised)Task-dependency model
Classification: text classification,
dialog acts, etc.
Extraction: proper-expression extraction,
extraction reading comprehension, etc.

Task example: extraction of answer phrase to question(2) Judgment of relationship between two sentences Prediction of the next sentence“It’s a fine day today.”“It’s a good day to do
the laundry.”

RelatedQuestion:“What does insurance A cover?”Answer:… “For 2000 yen per month, A covers injury.” …

Task adaptation as judgment of relationship between
a question and answer and fill-in-the-blank answers

Fig. 1. Language model BERT.

sentences. By learning (fine-tuning) with various
task-dependent datasets, the language model (BERT)
obtained in this manner can be applied to various
tasks (such as classifying texts by genre and extracting phrases that will answer questions) and achieve
high performance even when a large amount of learning data is not available for the applied task.
BERT has had a great impact on natural-language
processing, and language models are still being
researched and constructed all over the world. At
NTT Media Intelligence Laboratories, while collecting a large amount of Japanese text data and creating
a Japanese-language BERT, we are researching technologies that use language models for tasks such assummarizing documents [3, 4], retrieving documents[5], and answering questions [6, 7]. In each case, highperformance is achieved by not only simply applying
BERT but also using the knowledge we have gained
through our previous research on natural-language
processing and deep learning. Moreover, by investigating the characteristics and internal operation of
BERT [8], we are researching with the aim of constructing our own language model that remedies theshortcomings of BERT.
3. Document-summarization technology forsummarizing documents by specifying length
Document-summarization technology is introduced as a representative example of a technology
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that uses the language model described above. Document summarization has been grappled with for many
years. For customer-contact work such as at contact
centers, if AI returns a long sentence as a result of
searching previous calls and answering a customer’squestion, it will be difficult for the customer to readsuch a long sentence; accordingly, it is desirable toappropriately adjust the length of the sentence.
Given the above-mentioned issue, NTT MediaIntelligence Laboratories developed a documentsummarization technology that can control sentence
length by using a neural network [3]. This technology
consists of a combination of an extractive model(which identifies important points in a sentence) andabstractive model (which generates a summary sentence from the original sentence). The extractivemodel learns on the basis of the language model. By
controlling the number of important words output by
the extractive model according to the specified sentence length and by generating a summary sentence
based on both the important words and the original
sentence, it was possible to establish a technologythat can control sentence length and summarize withhigh accuracy.
This document-summarization technology is usedas the core engine of the COTOHATM API summarization function developed by NTT Communications[9]. COTOHA Summarize offers a service for outputting a summary text when a document is input andprovides our customers a free tool for generating
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Feature Articles

summary texts of sites browsed with a web browser.
We plan to further develop this technology for the
NTT Group.
We will continue to advance R&D to improve thissummarization technology, which includes not only
document summarization but also summarizationthat targets dialogue and summarization that allows
the viewpoint and keywords of the summarization to
be specified externally. To further strengthen thecompetitiveness of our language model, we plan tocontinue technological development while incorporating the results of our latest research on language
processing. Such work will include scaling up themodel, constructing a generative language model for
generating more-natural sentences, and establishingsummarization technology based on that model [4].
4. Technology for using knowledge obtained
from contact-center calls
4.1 Challenges in supporting customer contacts
NTT Media Intelligence Laboratories developed an
automatic knowledge assistance system [10]. This isa technology that automatically retrieves and presents
documents according to the content of a conversation
with a customer. By supporting the agent with knowledge and responding promptly to customers withappropriate information, it became possible toimprove relationships with customers.
However, contact centers are required to play a newrole as the business style changes due to the digitaltransformation (DX) of offices and the spread of thenovel coronavirus. One of these roles is as a sales
method called inside sales. Inside sales is explained
as follows. In contrast to the conventional sales
method, namely, a full-time salesperson conducts
face-to-face sales to understand customer needs andconclude business negotiations, inside sales is basedon (i) maintaining continuous communication withthe other party while gathering information (such as
understanding needs) that triggers business negotiations via telephone or web conference and (ii) dispatching a salesperson when the possibility of receiving an order or contract increases. The number ofcontact centers that handle customers by using insidesales is increasing. The challenges facing contact
centers playing this role are as follows.
• Further improvement of agent or productivity:
When creating a report after finishing a call, it isnecessary to support the extraction of important
information in the response from business negotiations in which the flow of conversation is
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complicated and the topics are diverse.
• Improvement of productivity of sales-information analysis: From the standpoint of the agents’supervisor, it is necessary to understand andanalyze the likelihood of making contracts, thetendency of customer needs, and other conversational tendencies included in business negotiations between each agent and customers. Planning and agent operations are then improved on
the basis of the analyzed information. It is thus
necessary to support such analysis and improvement work.
4.2 Interview-support technology
To address the above-mentioned challenges, wedeveloped interview-support technology. This technology consists of two elements (Fig. 2). The first
specifies a series of responses to the customer foreach section of the topic. The second extracts important utterances such as questions, answers, and explanations from the sections specified by the former.
A characteristic of a business conversation thatdraws out issues and requests concerning a customer
is that the topic continuously changes according to
the answers given by the customer. Since conventional technology cannot cope with such a dynamic
situation, we developed a means of firmly determining the point at which the topic changes. This is thefirst feature of the interview-support technology. Sections of the same topic are specified by machinelearning using characteristic expressions at the pointof change of the topic appearing in a conversation,and the type of topic is acquired by machine learning
using characteristic expressions in the topic. The second feature of this technology is that the utterances ofquestions and answers of agents and customers foreach topic are extracted by machine learning using
characteristic expressions included in importantutterances such as questions and answers.
By using the text of a call that is divided into sections for each topic by using the interview-support
technology, after the call, the agent can quickly identify the points at which he/she heard about the customer’s needs and budget and create a report based onthat information. Moreover, by collecting such information from many calls, it becomes possible to support the analysis of sales information. We aim to
apply this technology to responses via voice but also
via online chat (text messages), which has been
increasing in popularity.

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Feature Articles

Reception(text-based voice is input)
1. Extract sections
for each topic
and specify the
topic

Agent

Customer“How about the presentation system you
asked about the other day?”

2. Extract questions,answers, andexplanationutterances foreach section of
the topic“How about the presentation system you
asked about the other day?”Question

Section 1[Topic]Presentation
system“Well, I haven’t been able to study itlately because of the coronavirus
countermeasures.”“The coronavirus is a problem.”

Topic: Presentation system
Question

AnswerAnswer“Well, I haven’t been able to study itlately because of the coronavirus
countermeasures.”

Topic: Remote work

“It certainly is.”Question
Question“If so, is your remote work increasing?”“I can work remotely, but meetings are
difficult. There is a limit to the number ofpeople who can attend a conference call.”“How about introducing a web conference?
It can handle a large number of people.”

Section 2[Topic]Remotework“If so, is your remote work increasing?”

The important pieces
of information areused for a report
created from theutterances extracted
for each topic.
They are also used
for sales analysis byconsolidating them.

AnswerAnswer“I can work remotely, but meetings are
difficult. There is a limit to the number ofpeople who can attend a conference call.”Explanation…

…

Explanation“How about introducing a web conference?
It can handle a large number of people.”…

Fig. 2. Interview-support technology.

5. Concluding remarks
We introduced our language model, documentsummarization technology, and interview-support
technology. Going forward, we plan to work on two
technologies for understanding and generating largescale documents and various responses: (i) a technology that can read various document layouts andsearch for necessary information at high speed andhigh accuracy and (ii) a technology that supports strategic conversations by grasping the details of a conversation and extracting needs that customers may
have difficulty noticing themselves.
References
[1]

[2]

J. Devlin, M. Chang, K. Lee, and K. Toutanova, “BERT: Pre-trainingof Deep Bidirectional Transformers for Language Understanding,”
Proc. of NAACL-HLT 2019, Vol. 1, pp. 4171–4186, Minneapolis,
Minnesota, USA, June 2019.
K. Nishida, I. Saito, A. Otsuka, K. Nishida, N. Nomoto, and H. Asano,“Toward Natural Language Understanding by Machine Reading
Comprehension,” NTT Technical Review, Vol. 17, No. 9, pp. 9–14,
Sept. 2019.
https://www.ntt-review.jp/archive/ntttechnical.php?contents=

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Vol. 18 No. 12 Dec. 2020

ntr201909fa3.html
I. Saito, K. Nishida, K. Nishida, A. Otsuka, H. Asano, J. Tomita, H.
Shindo, and Y. Matsumoto, “Length-controllable Abstractive Summarization by Guiding with Summary Prototype,” arXiv, 2001.07331,
Jan. 2020.
[4] I. Saito, K. Nishida, K. Nishida, and J. Tomita, “Abstractive Summarization with Combination of Pre-trained Sequence-to-Sequence andSaliency Models,” arXiv, 2003.13028, Mar. 2020.
[5] T. Hasegawa, K. Nishida, S. Kaku, and J. Tomita, “Sparse Contextual
Sentence Representation for Fast Information Retrieval,” Proc. of the34th Japanese Society of Artificial Intelligence (JSAI 2020), Mar.
2020 (in Japanese).
[6] K. Nishida, K. Nishida, I. Saito, H. Asano, and J. Tomita, “Unsupervised Domain Adaptation of Language Models for Reading Comprehension,” Proc. of the 12th Conference on Language Resources and
Evaluation (LREC 2020), pp. 5392–5399, May 2020.
[7] K. Nishida, K. Nishida, I. Saito, H. Asano, and J. Tomita, “Explainable Machine Reading Comprehension,” Proc. of the 26th Annual
Meeting of the Association for Natural Language Processing (NLP
2020), pp. 1–19, Mar. 2020 (in Japanese).
[8] Y. Ohsugi, I. Saito, K. Nishida, H. Asano, and J. Tomita, “How Do
Masked Language Models Perform When the Input Sequence Length
Changes?”, Proc. of JSAI 2020, Mar. 2020.
[9] COTOHATM API, https://api.ce-cotoha.com/contents/about.html[10] T. Hasegawa, Y. Sekiguchi, S. Yamada, and M. Tamoto, “Automatic
Knowledge Assistance System Supporting Operator Responses,”
NTT Technical Review, Vol. 17, No. 9, pp. 15–18, 2019.
https://www.ntt-review.jp/archive/ntttechnical.php?contents=
ntr201909fa4.html[3]62

Feature Articles

Kyosuke Nishida

Distinguished Researcher, Knowledge Media
Project, NTT Media Intelligence Laboratories.
He received a B.E., MIS, and Ph.D. in information science and technology from Hokkaido
University in 2004, 2006, and 2008 and joined
NTT in 2009. He was a chief engineer at NTT
Resonant from 2013 to 2015. He received the
Yamashita SIG Research Award from the Information Processing Society of Japan (IPSJ) in
2015 and the Kambayashi Young ResearcherAward from the Database Society of Japan(DBSJ) in 2017. His current interests include
natural-language processing and AI. He is amember of the Association for Computing
Machinery (ACM), IPSJ, the Association for
Natural Language Processing (NLP), the Japanese Society for Artificial Intelligence (JSAI),
the Institute of Electronics, Information andCommunication Engineers (IEICE), and DBSJ.

Kuniko Saito

Senior Research Engineer, Supervisor, NTT
Media Intelligence Laboratories.
She received a B.E. and M.E. in chemistryfrom the University of Tokyo in 1996 and 1998.
She joined NTT Information and Communication Systems Laboratories in 1998. Her research
focuses on natural-language processing and AI.
She is a member of NLP and IPSJ.

Kazuyuki Iso

Senior Research Engineer, Social Knowledge
Processing Laboratory, NTT Media Intelligence
Laboratories.
He received a B.E. from Gunma National College of Technology in 1998, and an M.S. and
Ph.D. in knowledge science from Japan
Advanced Institute of Science and Technology,
Ishikawa, in 2000 and 2020. He joined NTT
Cyber Space Laboratories in 2000. He is currently a senior research engineer at NTT Media
intelligence Laboratories. His research interests
include communication systems and computersupported cooperative work. He is a member of
the Institute of Electrical and Electronics Engineers (IEEE), the Virtual Reality Society ofJapan (VRSJ), IPSJ, and IEICE.

Shuichi Nishioka

Senior Research Engineer, Supervisor, Social
Knowledge Processing Laboratory, NTT MediaIntelligence Laboratories.
He received a B.E. and Ph.D. in engineeringfrom Yokohama National University, Kanagawa,in 1995 and 2005. He joined NTT in 1995. His
current interests include data engineering and AI.
He is a member of IPSJ and DBSJ.

Tetsuo Amakasu

Senior Research Engineer, Supervisor, Social
Knowledge Processing Laboratory, NTT MediaIntelligence Laboratories.
He received a B.E. and M.E. from Tohoku
University, Miyagi in 1997 and 1999. Since joining NTT in 1999, he has been researching spoken
dialogue systems, spoken language processing,and speech analytics. He joined NTT Communications in 2008 and was involved in the development of IP phone services on smartphones. He isa member of the Acoustical Society of Japan and
JSAI.

NTT Technical Review

Vol. 18 No. 12 Dec. 202063

Feature Articles: Media-processing Technologies for Artificial
Intelligence to Support and Substitute Human Activities

Spatial-information Processing
Technology for Establishinga 4D Digital Platform
Yasuhiro Yao, Kana Kurata, Naoki Ito, Shingo Ando,
Jun Shimamura, Mayuko Watanabe, Ryuichi Tanida,and Hideaki Kimata
Abstract

The four-dimensional (4D) digital platform integrates various sensing data about humans, things, andenvironments in real time into high-precision spatial information, enabling fusion with various industries’ platforms and future predictions. The platform achieves these by matching and integrating 4D
information such as latitude, longitude, altitude, and time with high accuracy on an advanced geospatialinformation database. For maintaining an advanced geospatial-information database with high accuracy
and abundant semantic information, this article introduces two spatial-information processing technologies: (i) real-world digitalization to detect features from images and sparse/low-density 3D point clouds
and (ii) 4D-point-cloud coding that efficiently stores and uses 3D data including time variations.
Keywords: 4D digital platform, real-world digitalization, 4D-point-cloud coding

1. What is a 4D digital platform?
The four-dimensional (4D) digital platform collects
various sensing data concerning people, things, andenvironments in real time, and matches and integrates
4D information such as latitude, longitude, altitude,
and time with high accuracy. In other words, it is aplatform that enables data fusion with various industrial platforms as well as future prediction (Fig. 1).
By combining the 4D digital platform and various
data collected from Internet of Things, the platformmakes it possible to determine the exact position ofvarious mobile objects in geographic space, and various future predictions based on that data will become
possible. We thus believe that it can be used in various
applications such as smoothing road traffic, optimizing the use of urban assets, and maintaining social
infrastructures.
Regarding the elemental technologies that make up
the 4D digital platform, namely, spatial-information
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Vol. 18 No. 12 Dec. 2020

processing technologies necessary for constructing
an advanced geospatial-information database withhigh accuracy and abundant semantic information,
we are promoting the research and development of (i)real-world digitalization technologies for detecting
features from images and sparse and low-density 3D
point clouds and (ii) 4D-point-cloud coding technology for efficiently storing and using 3D data including time changes. We introduce both technologies
and discuss the status of our efforts concerning them.
2. Real-world digitalization technologies
To construct an advanced geospatial-information
database, it is necessary to maintain highly accurate
3D spatial information centered on roads; however,satisfying this requires enormous cost and effort.
Specifically, a costly dedicated vehicle equipped witha sensing device called laser-imaging detection andranging (LiDAR) and a manual process for generating
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Feature Articles

Cross-industry platform for precise sensing-data integration and future prediction

For various industries
High-speed processing of large-volume data
and future prediction with various simulations
4D digital platform

Semi-static

Semi-static

Real time

Advanced Geospatial Information Database(1) Development of Advanced Geospatial Information
Database as a control point for 4D digital platform
Existing map data
Rich semantic
information

2D map(2) Real-time sensing-data collection withhighly accurate position and time information

High-precision 3D
spatial information
MMS(mobile mapping system)

Sensing data
Mobility
sensing

Environmental
sensing

Fig. 1. Conceptual diagram of 4D digital platform.

maps are required.
To efficiently collect high-precision 3D spatial
information, we are researching and developing realworld digitalization technologies that automaticallyand highly accurately detect various natural and artificial objects near roads from a combination of sparseand low-density 3D point clouds (measured with lowcost LiDAR) and images taken with a camera (Fig. 2).
Our real-world digitalization technologies include:(i) 3D-data high-resolution enhancement technology
for generating high-resolution 3D data from sparse
3D point clouds based on images and videos; (ii)
3D-scene understanding and state-estimation technology for recognizing entire scenes (including complex shapes) and understanding states; and (iii)
3D-moving-object detection technology for using thesensing data obtained from moving-body sensors (inaddition to 3D point clouds and images) to determine
the position and orientation of vehicles and people.
We also discuss our latest research achievements concerning our 3D-data high-resolution enhancement
and 3D-scene understanding and state-estimation
technologies.

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2.1 3 D-data high-resolution enhancement technology
The 3D-data high-resolution enhancement technology increases the resolution of 3D data, namely, a 3D
point cloud with texture, from a combination ofsparse and low-density 3D point clouds (measured by
low-cost LiDAR) and images taken with a camera.
Three-dimensional measurement with low-cost
LiDAR provides sparse measurement results, andalthough 3D measurement is possible regardless of
distance, the measurement results include noise. On
the contrary, images taken with a camera containdense data; however, stereoscopic 3D measurement
using multiple images is not highly accurate for distant objects. We believe it is possible to generate 3D
data with the same measurement accuracy as LiDAR
and the same density as images taken with a camera
while removing noise by processing the informationfrom both LiDAR and camera data in an integrated
manner.
We are working in stepwise on the research anddevelopment of the 3D-data high-resolution enhancement technology. Under the assumption that data aremeasured while a vehicle with in-vehicle sensors is
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Feature Articles

3D-moving-object detection
In addition to 3D data and images,
sensing data obtained from movingobject sensors are mutually used todetermine the position and orientation ofmoving objects such as cars and people.

Sparse 3D data from the depth sensor(with texture)

3D data (with texture)

3D data (with texture)

Degradation
X
Wall
Grass
High-resolution enhancement 3D data(with texture)

Tree

3D-scene understanding and state
estimation
From images and 3D data obtained
from the 3D point cloud, the entire
scene (including complex shapes) isanalyzed, and the state is determined.

Columnarobject3D-data high-resolution enhancement
Further increasing high resolution ofsparse 3D-point cloud measured using
low-cost LiDAR, etc. by using camera
images and videos as clues

Grass

Car

GroundScene-analysis result

Detected moving object

Fig. 2. Real-world digitalization technologies.

moving, the amount of information that we will integrate increases as follows for improving accuracy: (i)one image and one frame of LiDAR measurement
data, (ii) multiple images and one frame of LiDAR
measurement data, and (iii) multiple images and multiple frames of LiDAR measurement data that arecontinuous in a time series. Note that “one frame” of
LiDAR measurement means data for one measurement over 360 degrees. Although it depends on the
LiDAR equipment, LiDAR measures 360 degrees ofthe surroundings while rotating about 10 times per
second.
We now introduce the 3D-data high-resolution
enhancement technology, which derives high-density
3D data in real time without supervision from a single
image and a sparse 3D point cloud measured by
LiDAR.
First, a 3D point cloud measured by LiDAR is projected onto an image to generate an image that holds
depth information called a depth map. The depth map
created in this manner is referred to as a sparse depth
map with many pixels that do not have depth values(Fig. 3).
This sparse depth map is processed using the input
image as a clue to generate a dense depth map inwhich all pixels have a depth value. Such a method
can be called depth completion. Even though depth
completion had been developed, the conventional
method generates a dense depth map for pixels thatNTT Technical Review

Vol. 18 No. 12 Dec. 2020

do not have depth by smoothly connecting theobserved depth values [1]. This conventional depth
completion is effective for complementing continuous observations between sparse observations; however, it smoothly interpolates the depth between different objects (Fig. 3).
To solve this problem, we previously proposed amethod for smoothly connecting the observed depth
values while adding a constraint that the change in
depth becomes discontinuous when it straddles anobject [2]. Compared with the conventional depth
completion, our method not only improves accuracy
but also obtains natural results when the depth map isvisualized as 3D data (Fig. 3). In the future, we willfurther improve accuracy by increasing the amount of
information to be integrated in the manner describedabove.
2.2 3 D-scene understanding and state-estimation
technology
The 3D-scene understanding and state-estimation
technology is used for analyzing an entire scene(including complex shapes) and understanding its
state. The aim with this technology is to automatically identify object areas such as buildings and roads
from 3D data obtained from LiDAR and cameras andestimating states such as position and orientation.
The construction of an advanced geospatial information database faces two technical challenges: (i)66

Feature Articles

Input

Result obtained from theconventional method

Results obtained fromour method(Upper row: depth map; middle row: 3D data representation of “upper row”; lower row: enlarged view of “middle row”)

Fig. 3. 3D-data high-resolution enhancement technology using one image and one frame of LiDAR measurement.

being able to identify various natural and artificial
objects near roads and (ii) keeping high classification
accuracy while using data splitting and sampling toefficiently process large-scale (large-area and highdensity) 3D data. To address the first challenge, wehave been researching deep-learning-based pointcloud classification methods for making it easier to
identify various objects. To address the second challenge, the problem that the identification accuracy
decreases due to data splitting and sampling in deeplearning based methods needs to be solved.
We are working on solving this tradeoff between
efficiency and classification accuracy. We experimentally identified that random sampling, often used to
improve processing efficiency, deteriorates classification accuracy. Therefore, we are developing a deeplearning-based object classification model with sampling method that takes into account local shapearound each 3D point of point clouds.
Specifically, high classification accuracy is achievedby executing the deep-learning-based classification
process with a sampling method that preferentiallyleaves the points that have high distinguishability
with respect to other points and do not change due torotation or translation of the object instead of random
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Vol. 18 No. 12 Dec. 2020

sampling [3]. This research is just beginning; accordingly, we would like to improve the performance ofobject identification through technical improvement
and application in real environments.
3. 4D-point-cloud coding technology
In the real world, each person has a different purpose, and we use objects that have substance and takeactions that suit our purposes. In this world, thingschange with the passage of time. There are various
scales for purposes, objects, and units concerning thepeople involved, and the scales differ according to theaction. We expect that the ability to acquire and reuse
the states of things in the real world, which have different spatial and temporal scales, can provide adiversity of value to people. Aiming to use point
clouds for various purposes, we are researching anddeveloping 4D-point-cloud coding technology.
The method called LASzip has been applied forcompressing a 3D point cloud. And the ISO/IEC*1
international standardization is currently promoting*1 ISO/IEC: The International Organization for Standardization(ISO)/International Electrotechnical Commission (IEC)67

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Point-cloud data (saved data)with time-series information
Latest pointcloud data
Collected with
LiDAR

Various changes are saved in aform that can be detected andreproduced.
Among the stored data, only the changed
portion is updated to the latest data. These
data are hierarchically compressed andencoded in a form that enables them to berestored as had been in the past.

Fig. 4. Overview of 4D-point-cloud coding technology.

international standardization of a point-cloud coding
method under the name MPEG*2 geometry-based
point cloud compression (G-PCC). Neither method
has a mechanism for preserving temporal changes, sothey are insufficient for our purpose. We are researching and developing a representation and compression-coding method for point-cloud data that is basedon our knowledge of video coding that expresseschanges over time as differences in the latest pointcloud data.
The 4D-point-cloud coding technology is illustrated in Fig. 4. Since some of the spatial information canbe obtained when acquiring point-cloud data, theentire space to be expressed is divided into a grid.
When the grid is made, it has a structure of multiple
sizes (spatial layers) in the form of recursive inclusion, just like a matryoshka doll. The grid of eachminimum unit can possess point-cloud data, and thegrid of the point-cloud data is expressed by a grid of
the intermediate hierarchy. As a result, point-clouddata in the entire space can be compactly represented
by a hierarchical grid. Also, if it is desirable to replace
NTT Technical Review

Vol. 18 No. 12 Dec. 2020

some data with the latest point cloud data, pointcloud data partially included in the grid is replaced.
While the point-cloud data are updated and the entire
space is re-encoded, some of the past data are compressed and encoded as a difference in point-clouddata. As a result, the latest data can be decoded andexpressed at any time, and the functionality that partially traces back to the past can be achieved. The
functionality of compression coding as a difference inpoint-cloud data can be applied to quickly detect anobject that was not there in the past. It is assumed that
MPEG G-PCC will be used for compression encoding of point-cloud coordinate data.
We believe that the following use cases can beapplied by compressing, encoding, and storing apoint cloud by using this method. By acquiring point
clouds from vehicles traveling on the same roads in acity every day, it becomes possible to obtain information*2 MPEG: The Moving Picture Experts Group (MPEG) is a working group of ISO/IEC that develops standards for coded representation of digital audio and video and related data.

68

Feature Articles

in real time about things that do not usually exist.
Moreover, when a change is found, it becomes possible to reproduce the state before the change
occurred retroactively in the past and simulate a secular change.
To apply these use cases, 4D-point-cloud coding
technology, which can be used to store the point
cloud (including temporal changes), will be indispensable. We will continue to promote research anddevelopment of 4D-point-cloud coding to make thereal world more convenient.

4D-point-cloud coding that efficiently stores and uses
3D data (including temporal changes).
We will continue to study the means of establishingeach of these technologies and evaluate their performances with actual data through demonstration
experiments, and in doing so, we will contribute toestablishing a 4D digital platform.
References[1]

4. Future developments[2]

For constructing an advanced geospatial information database with high accuracy and abundant
semantic information, the following technologies arebeing developed: (i) real-world digitalization for
detecting features with high accuracy from images
and sparse and low-density 3D point clouds and (ii)

NTT Technical Review

Vol. 18 No. 12 Dec. 2020

[3]

D. Ferstl, C. Reinbacher, R. Ranftl, M. Ruether, and H. Bischof,“Image Guided Depth Upsampling Using Anisotropic Total Generalized Variation,” ICCV 2013, pp. 993–1000, Sydney, Australia, Dec.
2013.
Y. Yao, M. Roxas, R. Ishikawa, S. Ando, J. Shimamura, and T. Oishi,“Discontinuous and Smooth Depth Completion with Binary Anisotropic Diffusion Tensor,” IEEE Robotics and Automation Letters, Vol. 5,
No. 4, pp. 5128–5135, Oct. 2020.
K. Kurata, Y. Yao, S. Ando, and J. Shimamura, “Point Cloud Classification by Using Sampling Method for Complex Shapes,” IPSJ SIG
Technical Reports Computer Vision and Image Media, 2020-CVIM220, pp. 1–6, Jan. 2020.

69

Feature Articles

Yasuhiro Yao

Research Engineer, Universe Data Handling
Laboratory, NTT Media Intelligence Laboratories.
He received a B.S. in pharmaceutical sciencesfrom the University of Tokyo in 2007 and an
M.E. in quantum engineering and system sciences from the University of Tokyo in 2010. He
joined NTT in 2010, where he has been engaged
in research and practical application development in computer vision and pattern recognition.

Kana Kurata

Staff, Universe Data Handling Laboratory,
NTT Media Intelligence Laboratories.
She received a B.S. in earth and planetary sciences from Nagoya University in 2016 and an
M.E. in environmental studies from Nagoya
University in 2018. She joined NTT in 2018,
where she has been engaged in research in computer vision and pattern recognition.

Naoki Ito

Senior Research Engineer, Universe Data Handling Laboratory, NTT Media Intelligence Laboratories.
He received an M.E. from Toyohashi University of Technology, Aichi, in 2001. He joined
NTT the same year and engaged in research on
character recognition. He moved to NTT EASTin 2004 and engaged in the development of security systems. He then moved to NTT CyberSpace Laboratories (now NTT Media Intelligence Laboratories) in 2008. He has been working on the development of real-world digitalization technologies.

Shingo Ando

Senior Research Engineer, Supervisor, Visual
Media Project, NTT Media Intelligence Laboratories.
He received a B.E. in electrical engineeringfrom Keio University, Kanagawa, in 1998 and a
Ph.D. in engineering from Keio University,
Kanagawa, in 2003. He joined NTT in 2003,
where he has been engaged in research and practical application development in the image processing, pattern recognition, and digital watermarks. He is a member of the Institute of Electronics, Information and Communication Engineers (IEICE) and the Institute of Image Information and Television Engineers.

NTT Technical Review

Vol. 18 No. 12 Dec. 2020

Jun Shimamura

Senior Research Engineer, Supervisor, Scene
Analysis Technology Group Leader, NTT MediaIntelligence Laboratories.
He received a B.E. in engineering science from
Osaka University in 1998 and an M.E. and Ph.D.
from Nara Institute of Science and Technology in2000 and 2006. He joined NTT Cyber Space
Laboratories (now NTT Media Intelligence
Laboratories) in 2000. His research interests
include computer vision and mixed reality. He isa member of IEICE and the Institute of Image
Information and Television Engineers.

Mayuko Watanabe

Research Engineer, Signal Modeling Technology Group, NTT Media Intelligence Laboratories.
She received a B.S. and M.S. in mathematics
from Tohoku University, Miyagi, in 2007 and2009. She joined NTT in 2009, where she has
been engaged in research and practical application development in video coding.

Ryuichi Tanida

Research Engineer, Visual Media CodingGroup, Visual Media Project, NTT Media Intelligence Laboratories.
He received a B.E. and M.E. in electrical engineering from Kyoto University in 2001 and 2003.
He joined NTT to study video coding to develop
a real-time H.264 software encoder application.
He moved to NTT Advanced Technology Corporation in 2009 to manage various software development projects related to video compression,
distribution, and TV phones. He has been at NTT
Media Intelligence Laboratories since 2012 as a
research engineer in the visual media coding
group. His current research focuses on the development of HEVC (High Efficiency Video Coding) software encoders. He is a member of
IEICE.

Hideaki Kimata

Senior Research Engineer, Supervisor, Signal
Modeling Technology Group Leader, NTT
Media Intelligence Laboratories.
He received a B.E. and M.E. in applied physics, and a Ph.D. in electrical engineering from
Nagoya University in 1993, 1995, and 2006. He
joined NTT in 1995 and has been involved in the
research and development of video coding, realistic communication, computer vision, and video
recognition based on machine learning (deeplearning). He is a chair of the IEICE TechnicalCommittee on Image Engineering.

70

Feature Articles: NTT DOCOMO 5G Initiatives for Solving
Social Problems and Achieving Social Transformation

History of 5G Initiatives
Yoshihisa Kishiyama and Satoshi Suyama
Abstract

In March 2020, NTT DOCOMO began providing commercial fifth-generation mobile communications systems (5G) services, which we have been studying since about 2010. Since then, we havesteadily released products supporting 5G, including six smartphone models and one data communications device (a Wi-Fi router), and have introduced seven services utilizing the high speed and high capacity of 5G, such as “Shintaikan Live CONNECT,” which enables multi-angle (multi-viewpoint) and virtual-reality live viewing.
This article describes the history of NTT DOCOMO’s development work and co-creation with partnercompanies and organizations in actualizing 5G, from when we started studying 5G through to provisionof these services.
Keywords: 5G concepts, 5G trials, 5G use cases

1. Introduction
In 2010, when NTT DOCOMO began its Long
Term Evolution (LTE) services, we started investigating fifth-generation mobile communications systems(5G) with the goal of implementing them within ten
years. We studied the basic concepts and radio accesstechnologies for 5G, prototyped 5G simulators, andin 2014, we published a 5G white paper [1]. Thatsame year, we also began 5G trials in collaboration
with individual hardware vendors from around theworld, and evaluation of 5G frequencies and radio
access technologies. We also expanded collaborationin 5G trials with various partner enterprises aroundthe world and developed many use cases for featuresof 5G through joint trials with these partners. In February 2018, we started the NTT DOCOMO 5G Open
Partner Program to promote co-creation of solutions
with partner organizations. This article reviews these
initiatives at NTT DOCOMO, from proposing basic
5G concepts to co-creation of solutions through trial
activities.
2. Preparing for publication of the 5G
white paper (2010 to 2014)
In about 2010, looking forward to 2020, mobile
communication traffic was expected to increase rapNTT Technical Review

Vol. 18 No. 12 Dec. 2020

idly, by a factor of 1000 in ten years, so to meet suchrequirements, we proposed a “cube” concept(improve spectral efficiency*1 × expand frequency
bandwidth × increase network density) as a direction
for technical advancement [2]. In around 2011, we
proposed specific technical candidates for implementing this cube concept, such as a radio accesstechnology combining the existing low-frequency
bands with high-frequency bands of 6 GHz or greater[3]. Then, around 2012, requirements that are now
features of 5G in addition to high speed and capacity
came to light, including low latency and many-terminal connectivity, and the current definition of 5G
combining extensions to LTE and a new radio accesstechnology (New RAT)*2 was proposed [4]. By about
2013, the technical concepts described in the 5G
white paper published in 2014 were largely complete,
including services and applications anticipated forthe 5G era [5]. In this way, the basic concepts andradio access technologies for 5G advanced, building
consensus with major companies around the world,
and also including proposals from other companies
that shared requirements and other concepts, such as
exploiting higher frequency bands [6, 7].
*1 Spectral efficiency: The number of data bits that can be transmitted per unit time and unit frequency.
*2 New RAT: A new radio interface standard that is not backward
compatible with 4G LTE. Also referred to as 5G New Radio.

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2013

2014

2015

3GPP Cell model

Shinjuku model

Stadium model

2016
Rural model(YRP version)

Rural model(Ise-Shima version)<Demo content>
5G application in high-density environments:
Achieve over 100 Mbit/s per user through 5G
Enable simultaneous download of 4K
resolution video by multiple users<Demo content>
Effects of introducing 5G and 5G+:
Phantom cell structure with macrocell (3sectors/macrocell, 2 GHz band, 20 MHz
bandwidth, 2×4 MIMO) and small cells (12small cells/sector, 5 GHz band, 200 MHz
bandwidth, 128×4 massive MIMO).
System throughput will be increased by
400x for 5G, over 1000x for 5G+.
Real-time transmission and smoothplayback of 4K high-definition video

Fig. 1. History of 5G Simulator.

Initially, NTT DOCOMO used the name “futureradio access” externally, but the name “5G” was firstused in October 2013 at CEATEC. At this first exhibit of 5G, a demonstration was given using a simulator
to visualize the basic 5G concepts and radio accesstechnologies, which received the Minister for Internal
Affairs and Communications CEATEC Award 2013[8]. As shown in Fig. 1, development of the 5G simulator continued after that, with updates such as thestadium model. Around the time when the “5G” wasfirst used, the focus for the 5G radio access technology was on using high-frequency bands efficiently,
with massive multiple-input multiple-output (Massive MIMO) *3, in 5G research projects such as
Mobile and wireless communications Enablers for
the Twenty-twenty Information Society (METIS)*4 inEurope. NTT DOCOMO participated in METIS,
leading proposals for technologies such as Phantom
cells*5 (C/U (control and user plane) separation) [9]and non-orthogonal multiple access (NOMA)*6 [10].
These radio technologies and simulator prototyping
were also published in the “DOCOMO 5G White
Paper,” as the latest work at the time [1].

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3. 5G trials and development of use cases(2014 to 2020)
3.1 N
 TT DOCOMO initiatives with partner
enterprises
To verify 5G frequencies and key radio access technologies, NTT DOCOMO conducted 5G trials collaborating individually with major global vendors. In
May 2014, we announced joint testing with six companies [11], and in July 2015, we expanded this to 13
companies [12]. Through joint testing with each ofthese companies, we verified 5G radio access technologies such as Massive MIMO in frequency bands*3 Massive MIMO: Large-scale MIMO using a very large numberof antenna elements. Massive MIMO is expected to be useful for
5G because antenna elements can be miniaturized when using
high-frequency bands, and many elements can be placed within
the same area.
*4 METIS: A European Union’s research project laying the foundation of 5G wireless technology. Ran from November 2012 toApril 2015. Participants included communication vendors, mobile carriers, and universities. A successor project, METIS-II ran
from July 2015 to June 2017.
*5 Phantom cell: The name of the advanced small-cell system proposed by NTT DOCOMO, with the basic concept of C/U plane
separation. Reflected under Dual Connectivity in 5G standardization.
*6 NOMA: A method for improving efficiency of multiple access bypermitting resources to overlap among users when allocating resources such as time, frequencies, or codes to multiple users.

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NTT DOCOMO R&D Center

Base station
antenna
Massive-MIMO antenna

Mobilestation #2
Mobilestation #1

Two-user total
throughput

Fig. 2. 20 Gbit/s field trial.

Table 1. Major results in 5G radio technology trials (press releases).
Press release date(year/month/day)

Test results

Trial partner

2015/3/3

Successful reception of data at over 4.5 Gbit/s using the 15 GHz band

Ericsson

2015/3/3

Successful reception of data at over 2 Gbit/s using the 70 GHz bandNokia

2015/11/26

Successful communication tests at over 2 Gbit/s in real environments with commercial
equipment using the 70 GHz bandNokia

2015/11/26

Successful data communication receiving over 2.5 Gbit/s in a high-speed mobile environment
traveling at approximately 60 km/h using the 28 GHz band

Samsung

2016/2/22

Successful multi-user 5G communication trial achieving capacity over 20 Gbit/s in an outdoor
environment using the 15 GHz band

Ericsson

2016/5/24

Successful real-time 5G transmission of 8K video using the 70 GHz bandNokia

2016/11/16

Successful 5G wireless data transmission at 2.5 Gbit/s while moving at high-speeds of 150
km/h using the 28 GHz band

Samsung

2016/11/16

High-speed, high-capacity communication achieved between 23 terminals distributed in an
outdoor environment, totaling 11.29 Gbit/s, using the 4.5 GHz band

Huawei2017/11/2

Successful field trial of 5G ultra-reliable, low-latency communication (URLLC) using the 4.5
GHz bandHuawei2017/11/2

Successful trial increasing spectral efficiency using the world’s first smartphone-sized NOMA
chip set

MediaTek

2017/11/6

Successful 5G trials for implementing connected cars in the Odaiba area using the 28 GHz
band

Toyota, Ericsson, Intel

2018/4/23

Successful 5G wireless communication trials in an ultra-high-speed environment of 300 km/h
using the 28 GHz bandNEC

2018/5/23

Successful 5G wireless communication with multiple mobile terminals, reducing interference
using cooperative beamforming among 4.5 GHz base stations

NEC2018/7/25

Successful 5G communication for connected cars using “vehicle glass mounted antenna” andusing the 28 GHz band

AGC, Ericsson

2018/11/22

Successful field trial of 27 Gbit/s communication using the 28 GHz band

2019/5/29

Successful communication using a glass antenna for 5G terminals using the 28 GHz band

up to the 70 GHz band, and in February 2016, we
successfully conducted the world’s first multi-user
5G communication tests, achieving total throughput
exceeding 20 Gbit/s in an outdoor environment
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Vol. 18 No. 12 Dec. 2020

Mitsubishi Electric
AGC, Ericsson(Fig. 2). Results of other major 5G radio technology
trials published in press releases are shown in
Table 1. By March 2018, we had given 170 conference presentations based on these 5G trials [13].
73Feature Articles

Starting around 2016, we have developed various
use cases utilizing features of 5G in collaboration
with partner enterprises in various industries [14]. To
date, we have conducted 430 service trials (as of June2020), such as building 5G trial sites as 5G test environments in the Tokyo Rinkai Fukutoshin district(Odaiba/Aomi areas) and the Tokyo Skytree Townneighborhood.
3.2 MIC 5G field trial initiatives (2017 to 2019)
Over three years starting in FY2017, the Ministryof Internal Affairs and Communications (MIC) has
conducted the 5G Field Trials, toward creation of newmarkets and new services and applications, with participants from various application fields [15]. The
content of 5G field trials conducted by NTT DOCOMO throughout Japan with collaboration partners
over the three years is shown in Table 2. Trial group
GI conducted field trials in low-speed mobile environments, while trial group GII conducted field trials
in high-speed mobile environments (60 km/h andgreater). In 5G field trials in FY2017, GI conducted
trials of 10 Gbit/s ultrahigh-speed communication indensely populated areas and service and application
trials using 4.5-GHz and 28-GHz bands in the application fields of entertainment, smart city, and medicine [16]. NTT DOCOMO also participated in
2-Gbit/s high-speed communication trials whilemoving at high speed (GII), which were conducted
mainly by NTT Communications, with entertainment
field trials communicating using the 28-GHz band
while traveling at the high speed of 90 km/h [16].
In 5G field trials in FY2018, we conducted trials at
ultrahigh-speeds (GI) averaging 4 to 8 Gbit/s in an
outdoor environment to verify both maximum andaverage 5G performance, and in office/workplaceenvironments in addition to the three application
fields tested in FY2017 [17]. We also conducted trialsof high-speed communication averaging 1 Gbit/s forfast-moving objects traveling at 60 to 120 km/h (GII),and in the field of transportation in addition to entertainment [17].
In January 2019, MIC held the 5G Utilization Ideas
Contest, with the objective of uncovering unique
ideas that can resolve various issues that arise in outlying areas [18]. 5G field trials in FY2019 emphasized user models that use 5G to solve regional issues
or contribute to regional revitalization, encompassing
results from the idea contest described above, andfrom earlier technology trials. This involved trials
with various new collaboration partners in various
application fields [19].
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Vol. 18 No. 12 Dec. 2020

4. Creating solutions with partners
To expand initiatives for creating new user scenarios with a wide range of partners, NTT DOCOMO
started the DOCOMO 5G Open Partner Program in
February 2018 (Fig. 3). This program provided information regarding 5G technologies and specifications
to partner enterprises and organizations, and 5G Partner Workshops as a place for them to exchange ideas
with each other. As of the end of June 2020, there
were 3440 partners participating.
NTT DOCOMO also operates the DOCOMO 5G
Open Lab as a permanent 5G technology test bed,
providing partners with test equipment free of charge,such as 5G test base stations and video transmittersconnected to mobile test stations. By participating inthis program, partner enterprises and organizations
can build and test services using 5G early, before
commercial services begin, improving the quality of
their own services and creating new services using
the features of 5G, which are high speed, high capacity, low latency, and multi-terminal connectivity. Asof the end of June 2020, DOCOMO 5G Open Lab had
11 locations in Japan and internationally. DOCOMO
5G Open Lab is also providing the DOCOMO Open
Innovation Cloud trial environment connected directly to cloud computing infrastructure and is conducting technology verification.
NTT DOCOMO also began the 5G pre-commercial
service in September 2019. It uses the same network
equipment and frequency bands as 5G commercial
services, and represents the start of services substantially the same as connecting to a 5G commercial
service, enabling business creation to begin in earnest, and allowing users to experience an environment equivalent to a 5G commercial service. The 5G
pre-commercial service is for the over-3000 partners
participating in the DOCOMO 5G Open Partner Program, and by lending out terminals that support the
5G pre-commercial service together with provisionof 5G Open Lab, over 200 of field trials have been
conducted throughout Japan, toward creating industries using 5G and solving social issues. From these,seven services and 22 solutions produced throughco-creation with all of our partners, such as Shintaikan Live CONNECT, were announced at the start of
5G services, focused on solving social issues such asadvancing industry and reform work practices. In the
future, we intend to provide new kinds of value
related to work practices, utilizing the 5G features ofhigh speed, high capacity, and low latency, and focusing on areas such as remote work support and74

Feature Articles

Table 2. Location and details of 5G field trials conducted, 2017 to 2019.
Trial
group

Application field

Use case(1) Live music VR experience using 5G(2) MR communication using 5G(3) 8K video multichannel MMT transmission using 5G(4) 4K low-power digital signage using 5G(5) High-definition video transmission in a shopping-mallenvironment

Location

Fiscal year2017 2018 2019

Sumida Ward, Tokyo
Sumida Ward, Tokyo
Sumida Ward, Tokyo
Sumida Ward, Tokyo
Sumida Ward, Tokyo

Yokohama City,
Kanagawa Prefecture(7) Ultra-high-definition video transmission to steam locomotive
Nikko City,“SL Taiju” using 5G
Tochigi Prefecture
Kyoto City,(8) Tourism promotion using 5G and 8K video
Kyoto PrefectureEntertainment
Kamakura City,(9) Live video distribution for wheelchair basketball using 5G
Kanagawa Prefecture
Katsuyama City,(10) Remote museum visits using 5G and VR technology
Fukui Prefecture
Aizuwakamatsu City,(11) Live viewing of tourism events using 5G
Fukushima Prefecture
Kawasaki City,(12) Remote live support for sporting events using 5G
Kanagawa Prefecture(13) Promoting tourism in Okinawa Prefecture with 5G and Body Nago City Region,
Sharing technology
Okinawa Prefecture
Sendai City,
GI(14) Real-time cloud editing/relay solution using 5G
Miyagi Prefecture(1) Facilities monitoring using 5G
Koto Ward, Tokyo(2) Wide area surveillance using elevated cameras and 5G
Sumida Ward, Tokyo
Kamiyama Town,
Smart city(3) Car security using 5G
Tokushima Prefecture
Aizuwakamatsu City,(4) Rural monitoring services using 5G
Fukushima Prefecture
Wakayama City,
Wakayama Prefecture(1) Remote medical examination and treatment using 5G
Hidakagawa Town,
Wakayama Prefecture
Medicine
Maebashi City,(2) Advanced urgent conveyance solutions using 5G
Gunma Prefecture
Hiroshima City,(3) Monitoring and behavior understanding using highly
accurate face recognition authentication, sensors, and 5G
Hiroshima Prefecture
Kamiyama Town,(1) Mobile satellite offices using 5G
Tokushima Prefecture
Workplace
Imabari City,(2) Ensuring work safety for highly skilled workers using 5G
Ehime Prefecture
Nakatsugawa City,(1) Transmission of traditional arts using 5G (remote education)
Gifu Prefecture
Smart life
Nakatsugawa City,(2) Lifestyle support through visualization of sound using 5G
Gifu Prefecture
Tochigi, Kanuma Cities,(1) Transmission of high-definition video to high-speed moving
Tochigi Prefecture;objects using 5G
Kasukabe City,Entertainment
Saitama Prefecture
Nagano City,(2) Guidance at a golf course using 5G
Nagano Prefecture
Takatsuki City,(1) Monitoring for safe operation of high-speed trains using 5G
GIIOsaka Prefecture
Oita City Region,
Transportation(2) Driving assistance during heavy fog using 5G
Oita Prefecture
Osaka City,(3) Support ensuring subway safety using 5G
Osaka Prefecture(1) Protection against snow damage using 5G (improving
Eiheiji Town,
Smart city
efficiency of snow removal)
Fukui Prefecture
MMT: MPEG Media Transport
MR: mixed reality
VR: virtual reality(6) Live viewing of a sports event using 5G

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Feature Articles

Initiative to create new businesses with a wide
range of business partners using 5G
Finance

Services

Transportation

Media

Participatingcompanies

3440 *

Manufacturing

Use DOCOMO 5G toexpand co-creation,

Retailcreate new markets,
solve societal issues

Construction
Education
Entertainment

Basic
trials2010

5G trial sites…

20172018 to 20192020
*As of June-end, 2020
©2020 NTT DOCOMO, INC. All Rights Reserved.

Fig. 3. NTT DOCOMO 5G Open Partner Program.

high-definition video transmission.
5. Conclusion
In this article, we described the path leading to thestart of 5G services by NTT DOCOMO. In the future,
NTT DOCOMO will continue to study technologies
and conduct research and development, with the goalof further advancing 5G.
This article is the reproduction of the special article
published in NTT DOCOMO Technical Journal (Vol.
22, No. 2, Oct. 2020).
References
[1]
[2]
[3]
[4]
[5]
[6]
[7]
[8]

NTT DOCOMO, “DOCOMO 5G White Paper,” Sept. 2014.
https://www.nttdocomo.co.jp/english/corporate/technology/
whitepaper_5g/index.html
Y. Kishiyama, “LTE Enhancements and Future Radio Access,” APCC
2010 Seminar on Future Wireless Technologies, Auckland, New Zealand, Nov. 2010.
Y. Kishiyama, “LTE Enhancements and Future Radio Access towards
Next Decade,” 1st NGMN Innovation Day, Stockholm, Sweden, Sept.
2011.
Y. Kishiyama, “Future Radio Access Challenges,” WWRF WirelessWorld 2020 Workshop, Berlin, Germany, Oct. 2012.
NTT DOCOMO, “Future Radio Access for 5G,” ARIB 2020 and
Beyond Workshop, Tokyo, Japan, Nov. 2013.
Ericsson, “White Paper - 5G Radio Access,” June 2013.
Nokia Solutions and Networks, “White Paper – Looking ahead to
5G,” Dec. 2013.
NTT DOCOMO Press Release: “‘Next-generation Mobile Communications System (5G)’ Receives Minister for Internal Affairs and Communications Award at the CEATEC AWARDS 2013,” Oct. 2013 (inJapanese).

NTT Technical Review

Vol. 18 No. 12 Dec. 2020

[9]

H. Ishii, Y. Kishiyama, and H. Takahashi, “A Novel Architecture for
LTE-B: C-plane/U-plane Split and Phantom Cell Concept,” IEEE
Globecom, Anaheim, CA, USA, Dec. 2012.
[10] Y. Saito, Y. Kishiyama, A. Benjebbour, T. Nakamura, A. Li, and K.
Higuchi, “Non-orthogonal Multiple Access (NOMA) for Cellular
Future Radio Access,” IEEE VTC-Spring, Dresden, Germany, June
2013.
[11] NTT DOCOMO Press Release: “DOCOMO to Conduct 5G Experimental Trials with World-leading Mobile Technology Vendors,” May2014.
https://www.nttdocomo.co.jp/english/info/media_center/
pr/2014/0508_00.html[12] NTT DOCOMO Press Release: “DOCOMO to Collaborate on 5G
with Five Additional Vendors—Expands 5G collaborations to 13
world-leading vendors—,” July 2015.
https://www.nttdocomo.co.jp/english/info/media_center/
pr/2015/0722_00.html[13] NTT DOCOMO, “Overview of 5G Trials in Collaboration withWorld-leading Vendors” (in Japanese),https://www.nttdocomo.co.jp/corporate/technology/rd/tech/5g/5g_
trial/[14] T. Nakamura, “Views of the Future Pioneered by 5G—A World Converging the Strengths of Partners—,” NTT DOCOMO Technical
Journal 25th Anniversary, pp. 53–59, Dec. 2018.
[15] MIC Press Release: “5G Integration Trials Begin,” May 2017 (inJapanese).
https://www.soumu.go.jp/menu_news/s-news/01kiban14_02000297.
html[16] NTT DOCOMO Press Release: “Results of FY2017 Ministry of Internal Affairs and Communication ‘5G Comprehensive Demonstration
Test’,” Mar. 2018 (in Japanese).
https://www.nttdocomo.co.jp/binary/pdf/corporate/technology/rd/
topics/2017/topics_180326_03.pdf[17] NTT DOCOMO Press Release: “FY2018 MIC ‘5G Integration Testing’ Results: Verification of Various 5G Use Cases Contributing toRegional Revitalization and Japan’s First Application of 5G for Emergency Medical Treatment,” Mar. 2019 (in Japanese).
https://www.nttdocomo.co.jp/binary/pdf/info/news_release/
topics_190319_01.pdf[18] MIC Press Release materials: “Holding the ‘5G Utilization Idea Contest’,” Dec. 2018 (in Japanese).

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https://www.soumu.go.jp/menu_news/s-news/01kiban14_02000362.
html[19] NTT DOCOMO Press Release: “FY2019 MIC ‘5G Integration Testing’ Results: Verification of 12 5G Use Cases Contributing to Regional Revitalization,” Mar. 2020 (in Japanese).
https://www.nttdocomo.co.jp/binary/pdf/info/news_release/
topics_200316_00.pdf

Yoshihisa Kishiyama

Manager, 6G Laboratories, NTT DOCOMO,
INC.
He received a B.E., M.E., and Ph.D. from Hokkaido University in 1998, 2000, and 2010. Sincejoining NTT DOCOMO in 2000, he has been
involved in the research and development of 4G
and 5G radio access technologies, including
concept development, standardization, and trials.
He is currently working on “5G evolution & 6G”
and is the main writer of “DOCOMO 6G White
Paper.” He has more than 400 granted patents inthe area of mobile communications. In 2012, hereceived the ITU Association of Japan Award forhis global contributions to LTE.

NTT Technical Review

Vol. 18 No. 12 Dec. 2020

Trademark notes

All company names or names of products, software, and services appearing
in this article are trademarks or registered trademarks of their respective
owners.

Satoshi Suyama

Manager, 6G Laboratories, NTT DOCOMO,
INC.
He received a B.E., M.E., and Dr. Eng. from
Tokyo Institute of Technology in 1999, 2001, and2010. From 2001 to 2013, he was an AssistantProfessor at Tokyo Institute of Technology. Sincejoining NTT DOCOMO in 2013, he has been
involved in research and development of 5G/6G
radio access technologies. He received the Best
Paper Prize from the European Wireless Technology Conference (EuWiT) in 2009, the Best Paper
Award from the International Symposium on
Personal, Indoor and Mobile Radio Communications (PIMRC) in 2016, and the Paper Award
from the Institute of Electronics, Information andCommunication Engineers (IEICE) in 2012.

77

Feature Articles: NTT DOCOMO 5G Initiatives for Solving
Social Problems and Achieving Social Transformation

NTT DOCOMO Contributions to
5G Standardization
Satoshi Nagata, Atsushi Minokuchi, Anil Umesh,
Yuta Oguma, and Shinji Takeda
Abstract

At the 3rd Generation Partnership Project (3GPP), the initial version of the fifth-generation mobile
communications systems (5G) standard was completed in June 2018, and at the Open Radio AccessNetwork (O-RAN) Alliance, specifications for achieving a 5G open radio access network were releasedin March 2019. NTT DOCOMO is an active contributor to standardization efforts at 3GPP and O-RAN
and the commercial 5G service that it launched in March 2020 conforms to 3GPP and O-RAN specifications. This article provides an overview of NTT DOCOMO’s contributions to the formulation of these
specifications.
Keywords: 3GPP, 5G standard, O-RAN

1. Introduction
At the 3rd Generation Partnership Project (3GPP)
Technical Specification Group (TSG) #84 plenary
meeting held in June 2018, 3GPP Release 15 specifications were declared frozen marking the completionof the initial fifth-generation mobile communications
systems (5G) standard. The 5G standard is a new
radio access specification called New Radio (NR). It
features a new core network*1 called the 5G core network (5GC) and includes functions for linking withthe existing Long Term Evolution (LTE)/Evolved
Packet Core (EPC)*2 system.
At 3GPP, 5G standardization envisioned the following three usage scenarios based on the ITURM.2083 vision recommendation issued by the International Telecommunication Union - Radio Communication Sector (ITU-R) in September 2015: (i)enhanced mobile broadband (eMBB), (ii) massive
machine type communications (mMTC), and (iii)
ultra-reliable and low-latency communications(URLLC). Studies for Release 14 commenced in
March 2016 with a study item (SI) established onrequirements and elemental technologies. This wasfollowed by studies for Release 15 from March 2017with a work item (WI) established on detailed speciNTT Technical Review

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fications.
NTT DOCOMO proactively contributed to the formulation of 5G specifications at 3GPP over the twoyear, three-month period from the beginning of
Release 14 to the completion of Release 15. This
contribution included technical proposals, compilation of specifications, and Chairman and Vice Chairman roles at meetings. During this time, the numberof contributions to 5G specifications submitted by
NTT DOCOMO came to approximately 3700 items,
which ranked ninth among worldwide companies andfirst as a mobile operator [1]. NTT DOCOMO wasalso evaluated as first among mobile operators interms of the number of 5G candidate essential-patentapplications [2].
In parallel with these 3GPP standardization activities, NTT DOCOMO established the Open Radio
Access Network (O-RAN) Alliance with AT&T,
China Mobile, Deutsche Telekom, and Orange inFebruary 2018. Aiming to construct a highly extendible,*1 Core network: A network consisting of packet transfer equipment, subscriber information management equipment, etc. Mobile terminals communicate with the core network via the radio
access network.
*2 EPC: A core network that can accommodate diverse radio accesssystems including LTE.

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open, and intelligent next-generation radio accessnetwork*3 as typified by 5G, the O-RAN Alliance has
been working to (i) promote open interfaces withhigh interoperability, (ii) apply virtualization andwhite box*4 schemes to radio network equipment,and (iii) leverage artificial intelligence (AI) and big
data.
As a result of these efforts, 3GPP Release 15 specifications were completed in June 2018 and O-RAN
fronthaul*5 specifications were completed in March
2019 thereby opening the door to development for 5G
commercial services.
This article describes the activities involved in the for­mulation of 5G specifications and NTT DOCOMO’s
contributions to this effort.
2. Activities toward early formulation of 5G
specifications at 3GPP and contributions
In the formulation of 5G specifications at 3GPP,
studies on NR, the new radio access system, proceeded with the aim of standardizing specifications
targeting use cases for achieving parts of eMBB and
URLLC in Release 15 and standardizing remaining
specifications including URLLC and mMTC in thenext stage consisting of Release 16 and beyond. Here,it was decided to formulate specifications in a systematic manner through stepwise standardization of
5G that encompasses a wide array of requirements
and use cases. For example, standardization activities
would first tackle specifications for the case of combining and operating 4G base stations and the 4G core
network with 5G base stations in a scheme called“non-standalone” operation and then take up all
specifications in the next stage including “standalone” operation that enables operation with only 5Gbase stations and the 5G core network.
NTT DOCOMO made major contributions not only
to promoting 5G standardization overall but also toearly formulation of 5G specifications. Specifically,
while guaranteeing the standardization of non-standalone operation and standalone operation in the same
release (Release 15) by June 2018 as originallyscheduled, NTT DOCOMO and 47 other companies
jointly submitted to 3GPP a document stating thatspecifications for non-standalone operation would becompleted by December 2017 thereby contributing toearly formulation. This early completion of specifications helped to accelerate the worldwide deploymentof commercial 5G.

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3. Activities and contributions in 3GPP TSG
Service and System Aspects/Core Network
and Terminals
At 3GPP, TSGs carry out technical studies under
the Project Coordination Group that oversees all projects. These TSGs consist of three groups: TSG Service and System Aspects (SA), TSG Core Network
and Terminals (CT), and TSG Radio Access Network(RAN). The first of these, TSG SA, studies use casesand derives system requirements, decides on architecture and derives requirements for individual functions, and decides on the flow of information between
functional units and the operation of individual functions. It also prescribes specifications for each of theabove items. TSG CT, meanwhile, prescribes protocols between terminals and the core network andbetween functional units within the core network plus
detailed specifications for an external application
programming interface (API) for third-party users
and for a universal subscriber identity module(USIM)*6. Among its work in prescribing 5G specifications, NTT DOCOMO has held Vice Chairman
posts in TSG CT and SA Working Group 3 (SAWG3) and has contributed greatly to establishingoverall policies on prescribing specifications, drawing up schedules, and promoting technical studies.
In prescribing 5G specifications, TSG SA/CT
expanded functions for accommodating NR as a nonstandalone system in EPC and studied 5GC as newcore-network specifications. Thinking that a suitableamount of time would be needed to expand the 5G
coverage area, NTT DOCOMO emphasized thelaunching of 5G services in a non-standalone system
within SI studies for Release 14. However, at the
beginning of these studies, it was the opinion of both
vendors and mobile communications operators thatstudies on a new core network should be unified, so
no approval for our proposal could be obtained. However, thanks to repeated explanations and progress in
TSG RAN technical studies (described below),
approval was gradually obtained, and later, priority*3 Radio access network: The network situated between the core
network and mobile terminals consisting of base stations andother equipment for controlling the radio layer.
*4 White box: Hardware whose internal configuration and processing are open in contrast to black box hardware.
*5 Fronthaul: The circuit between radio equipment and the baseband processor of base-station equipment achieved by opticalfiber, etc.
*6 USIM: An integrated circuit card used to store information for
identifying a subscriber having a contract with a mobile phonecompany.

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was placed on drafting specifications for a non-standalone system as 5G at the RAN/SA #72 plenary meeting. Despite this drawn-out chain of events, many
operators around the world have since adopted thisnon-standalone system as the main network configuration in the initial period of 5G deployment.
In addition, specifications for a new core-network
concept were established as 5GC for application to
the 5G service rollout period.
NTT DOCOMO contributed to advancing specifications for both accommodating NR in EPC (nonstandalone system) and for 5GC.
3.1 SA-WG1
SA-WG1, whose responsibilities are to study use
cases and derive system requirements, studied only
5GC without relation to the non-standalone system.
Here, NTT DOCOMO hypothesized that customer
needs and desires would be varied with a long-tail*7
distribution that would require not a uniform network
but rather a divided network (the network slice*8 concept), which it promoted. NTT DOCOMO also took
up issues fundamental to operators and took the lead,in particular, in drawing up specifications for access
control.
3.2 SA-WG2
SA-WG2 is responsible for determining architecture, deriving requirements for each functional unit,
and deciding the flow of information between functional units and the operation of individual functions.
This WG studied both the non-standalone system and
5GC.
Some European communications operators treatedthe non-standalone system as part of the flow towardhigher LTE radio speeds. With this being the case,policies were established on operating this system
without any changes to the core network so that many
operators could deploy it and on inserting functions
enabling advanced control for operators willing to
make changes to the core network. Additionally, forindividual technical items, policies were establishedon studies regarding encryption and serving gateway(S-GW) *9 selection in SA-WG3 and CT-WG
described below.
In 5GC, several companies declared their candidacy for the position of Rapporteur thereby delaying thedrafting of the work item description (WID), but NTT
DOCOMO took on the role of Editor at the request of
WG members and got the WID completed. After that,NTT DOCOMO also contributed to studies on network slicing and EPC-5GC interoperability.
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3.3 SA-WG3
SA-WG3 has the responsibility of making comprehensive studies on security issues from use cases toprotocol. In this WG, studies were made on both of
the systems described above. NTT DOCOMO served
as Vice Chairman and led technical studies. In the
non-standalone system, it was decided that security
applied to the secondary radio access technology(Secondary RAT)*10 radio interval would operate byreading LTE security capabilities into NR security
capabilities without having to make changes to themobility management entity (MME) *11 . NTT
DOCOMO led the drafting of specifications for thisscheme. In 5GC studies, NTT DOCOMO served as
Rapporteur of main specifications documents for
5GC overall and led improvements to encryption andauthentication in the radio portion.
3.4 CT-WG1
CT-WG1 is responsible for specifying protocol indetail between terminals and the core network and of
studying certain basic functions in terms of architecture. In relation to 5GC, NTT DOCOMO promoted
the drafting of specifications for the Earthquake andTsunami Warning System, Steering of Roaming, andaccess control.
3.5 CT-WG4
CT-WG4 is responsible for specifying protocol indetail between functional units within the core network. In the non-standalone system, NTT DOCOMO
promoted the drafting of specifications for a system
to select an appropriate S-GW for accommodatinghigh traffic.
4. Activities and contributions in 3GPPTSG RANTSG RAN is responsible for the formulation of all
specifications describing the radio access network.
*7 Long tail: Here, a state in which a distribution appears to have along tail when plotting the number of customers desiring eachkind of service in descending order. In other words, a state having not a small demand for each of a variety of services.
*8 Network slice: One format for achieving next-generation networks in the 5G era. Architecture that optimally divides the core
network in units of services corresponding to use cases, businessmodels, etc.
*9 S-GW: The area packet gateway accommodating the 3GPP access system.
*10 Secondary RAT: Radio access technologies such as NR, LTE,
3G, GSM, and Wi-Fi used by secondary nodes.
*11 MME: A logical node that accommodates a base station (eNodeB)
and provides mobility management and other functions.

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NTT DOCOMO led technical discussions in the WGs
under TSG RAN and contributed greatly to the formulation of 5G RAN specifications by serving as
TSG RAN Vice Chairman.
4.1 RAN-WG1
RAN-WG1, which carries out studies in relation to
the radio interface on the physical layer, studied, inparticular, radio access systems and multiple transmit/receive antenna technologies. For example, in
NR, it defined orthogonal frequency division multiplexing (OFDM)*12 based on multiple subcarrier*13
intervals with the aim of supporting a variety of use
cases and a wide frequency band from low-frequency
bands used in existing cellular systems to high-frequency bands including the millimeter-wave frequency band.
Here, NTT DOCOMO made a number of technicalproposals for band-expansion technology, antenna
technology, and initial-stage access technology andmade significant contributions to completing specifications by coordinating progress management andconcerned individuals as Rapporteur for formulating
5G specifications, serving as RAN-WG1 Chairman,
and taking leadership roles in various elemental technologies.
4.2 RAN-WG2
RAN-WG2, which has the responsibility of studying radio interface architecture and protocol, drafteda specification to enable NR communications to be
carried on top of existing LTE communications
through Dual Connectivity*14 between LTE and NR.
This specification is based on the specification for
LTE Dual Connectivity that was promoted by NTT
DOCOMO as Rapporteur and standardized in
Release 12. It enables high speeds and low latency by
NR while making use of the existing LTE network byadopting a split bearer*15 [3] that terminates at a secondary node (SN)*16 proposed by NTT DOCOMO
for 5G. NTT DOCOMO made significant contributions to completing specifications in RAN-WG2 toachieve diverse use cases and meet requirements for
5G. For example, given that functions and capabilities
reported by a terminal to a base station and param­
eters set in a terminal by the base station can resultin an enormous amount of data, NTT DOCOMO
led studies on Radio Resource Control messages fortransferring such data.
4.3 RAN-WG3
RAN-WG3 is responsible for studying RAN archiNTT Technical Review

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tecture and interfaces. This WG extended the X2interface between eNB base stations and enabledeNB–gNB linkage to achieve Dual Connectivity
between LTE and NR. Additionally, to enhance theflexibility of RAN rollout with improved performance and cost efficiency in mind, RAN-WG3
specified a F1 interface to enable a base station to be
split into a central unit (CU), which terminates the
Packet Data Convergence Protocol sublayer andabove, and distributed unit, which terminates theRadio Link Control sublayer and below. It also specified an E1 interface to enable CU to be split into a
CU-control plane, which terminates the control
plane*17, and CU-user data plane, which terminates
the user plane*18 [3].
Here, to enhance multi-vendor interoperability inthese interfaces, NTT DOCOMO submitted manytechnical contributions by leveraging its experiencein achieving multi-vendor capabilities in the commercial LTE network. It also contributed by serving
as Moderator to encourage discussions in this area
and as Editor of specifications.
4.4 RAN-WG4
RAN-WG4 bears the responsibility of formulating
specifications for base-station and terminal radio frequency (RF) performance and radio resource management. In addition to frequency bands below 6
GHz as used in LTE/LTE-Advanced, this WG studied
the use of sub-millimeter wave*19 and millimeterwave*20 frequency bands introduced for NR (defined*12 OFDM: A multi-carrier modulation format where information
signals are modulated with orthogonal subcarriers. A type of digital modulation scheme where information is split across multiple
orthogonal carriers and transmitted in parallel. It can transmit
data with high spectral efficiency.
*13 Subcarrier: Individual carrier for transmitting signals in multicarrier transmission such as OFDM.
*14 Dual Connectivity: A technology that achieves wider bandwidths
by connecting two base stations in a master/secondary relationship and performing transmission and reception using multiple
component carriers supported by those base stations.
*15 Split bearer: In Dual Connectivity, a bearer that is transmitted
and received via both master node (MN) and secondary node(SN) base stations.
*16 SN: A base station that provides a UE in Dual Connectivity withradio resources in addition to those provided by the MN. In LTENR Dual Connectivity, the SN is an NR base station (gNB) if the
MN is an LTE base station (eNB) and an LTE base station (eNB)
if the MN is an NR base station (gNB).
*17 Control plane: Control processes to transfer data, such as routecontrol of data in use for communications.
*18 User plane: The part of the signal sent and received in communication, which contains the data sent and received by the user.
*19 Sub-millimeter wave: Radio signals of frequencies in the millimeter wave range, from approximately 10 to 30 GHz.

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Z

Z
Target equipment

Radiated beam

X

X

Y

Y
TRP provision(specified over an entire
spherical surface)
TRP definition

EIRP/EIS provision (specified
in the beam direction to include
antenna performance)
EIRP/EIS definition

Fig. 1. Definitions used in the RF specification of NR base stations and terminals.

in Release 15 as Frequency Range 2 (FR2): 24,250–
52,600 MHz frequency band). Serving a four-year
term as Vice Chairman of RAN-WG4 from 2015,
NTT DOCOMO actively submitted technical proposals and promoted discussions toward the formulationof frequency bands taking the 5G frequency allocation plan in Japan into account and toward the drafting of RF performance specifications for base stations, terminals, etc. The legal system surrounding
5G in Japan advanced in step with these specifications.
The use of FR2 heralds the coming of wide-bandcommunications, but it is not without its problems
from the viewpoint of configuring a RF section. Specifically, it means an increase in power loss and signal
propagation loss in the RF section due to high frequencies and a contraction of area coverage broughton by a drop in power density due to wideband communications. To solve these problems, coverage must
be ensured by achieving high antenna gain through anantenna array formation. However, it is difficult toimplement a radio-signal transceiver and antennas at
high density in a small space, so for FR2, a conventional RF configuration with connectors for measurement use cannot be applied. For this reason, an over
the air (OTA)*21 provision was introduced in the FR2
RF specification to enable testing of the RF configuration without connectors.
As shown in Fig. 1, the OTA provision defines total
radiated power (TRP) emitted from the equipment
targeted for measurement as well as equivalent isotropic radiated power (EIRP)*22 and equivalent isotropic
sensitivity (EIS)*23 in the beam direction that includes
antenna characteristics.
Focusing on EIRP maximum transmission power in
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FR2 for a terminal, the adopted provision uses acumulative distribution of each EIRP value obtained
when manipulating the beam direction over an entire
spherical surface centered about the terminal (Fig. 2).
The purpose of introducing this provision was to statistically guarantee that the beam is correctly oriented
in the intended direction (the direction of the basestation performing communications) and within thenecessary range. The value that must be minimallysatisfied by at least one of the measured EIRP values
is defined as the Min peak value and the value at X%of the cumulative distribution, that is, the value forwhich the (100–X)% area in spherical surface spacemust be guaranteed, is defined as spherical coverage.
Thinking that spherical coverage would be essential
to ensuring FR2 area performance as an operator,
NTT DOCOMO expanded discussions while gaining
the approval of various operators that spherical coverage would preferentially become a high-performanceprovision among various types of provisions withtechnical feasibility considered.

*20 Millimeter wave: Radio signals of frequencies in the range from30 GHz to 300 GHz.
*21 OTA: A method for measuring radio characteristics transmitted
or received from a base station or terminal, by positioning it opposite to a measurement antenna. Configurations have been defined for NR base stations and terminals that have no antennaconnectors, so this method has been established for regulatingsuch devices.
*22 EIRP: The transmission power at the reference point in radio radiation space.
*23 EIS: The received power at the radiated requirement reference
point in radio reception space.

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Feature ArticleszMeasurement point
EIRP (, )

Statistically guarantees that the beam is correctly oriented in
the intended direction (direction of the communicating basestation) and within the necessary rangey

Cumulative distribution (%)



x

100
90
80
70
60
50
40
30
20
10
Spherical 20%

Min peak Max

EIRP (dBm)

Fig. 2. OTA EIRP evaluation technique using a cumulative distribution.

5. Activities and contributions in O-RAN
NTT DOCOMO joined up with AT&T, China
Mobile, Deutsche Telekom, and Orange in February
2018 to launch the O-RAN Alliance with the aim of
constructing a highly extendible, open, and intelligent RAN for the 5G era. Today, many operators andvendors are participating as members (about 200
subscribed members as of June 26, 2020) and a wide
range of studies are being conducted in WGs in suchareas as open interfaces to enable interoperability in
RAN, virtualization, use of AI and big data, white
box hardware, and open source software. Many
specifications have already been released. As a resultof these activities, the O-RAN Alliance has been
attracting attention and generating many expectations.
Among these activities, many operators and vendors have announced their intention to adopt O-RAN
open fronthaul specifications released in March 2019
to facilitate multi-vendor interoperability in the fronthaul interface between the digital processing section(centrally located) and radio section (remotely located) of a base station that up to now has been vendorspecific on a global basis [4]. This declaration is
expected to have a major impact on the industry andis attracting much attention as a result.
In actuality, NTT DOCOMO had already achieved
an original multi-vendor RAN since the time of the
LTE generation using a common fronthaul interface
in cooperation with partner vendors. Making use of
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the know-how gained there, NTT DOCOMO served
as SI Rapporteur for base-station functional split carried out for 3GPP Release 14/15 from March 2016 to
December 2017. NTT DOCOMO also participated in
the xRAN Forum from February 2018 prior to its
integration with the O-RAN Alliance and promoted
the drafting of fronthaul specifications there in cooperation with approving operators and vendors. Now,
as well, NTT DOCOMO is actively promoting fronthaul standardization such as by serving as a co-chairof WG4 that manages open fronthaul specifications.
In addition, NTT DOCOMO has achieved multivendor interoperability using O-RAN open fronthaul
specifications in a commercial 5G network as theworld’s first, and going forward, it seeks to achieve a
global ecosystem of multi-vendor RAN in 5G. Multivendor interoperability in RAN expands the portfolioof base-station solutions that can be used and enables
quick, flexible, and cost-efficient network construction. These features will become all the more important in 5G having many and varied requirements.
At the O-RAN Alliance, NTT DOCOMO has also
served as a co-chair of WG5 that studies the conversion of 3GPP interfaces such as X2 and F1 to open
interfaces and contributed to the release of profile*24
specifications to ensure multi-vendor interoperability
in those interfaces. From here on, NTT DOCOMO*24 Profile: Refers to the method of using inter-equipment messages
as defined by a standardization agreement to ensure interoperability between equipment according to the application.

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also plans to promote other O-RAN Alliance studies
in such areas as virtualization and the use of AI and
big data.
6. Conclusion
This article described activities and NTT DOCOMO
contributions at 3GPP and the ORAN Alliancetoward 5G standardization.
Today, at 3GPP, specifications for Release 16
toward advanced 5G have been completed and technical studies for Release 17 that aim to specify more
usage scenarios are progressing. Meanwhile, at the
O-RAN Alliance, in addition to further studies onopen interfaces, technical studies on the virtualization of the radio network and use of big data are moving forward.
At NTT DOCOMO, we view 5G as the technicalfoundation for wireless communications over the
next 20 years. To achieve further advances, we willcontinue making proactive contributions to standardization activities at 3GPP and the O-RAN Alliance.

published in NTT DOCOMO Technical Journal (Vol.
22, No. 2, Oct. 2020).
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[2]

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[4]

Cyber Creative Institute Co. Ltd., “Application Trend of ETSI Standard Essential Patent (5G-SEP) Candidates Contributing to Realization of 5G and Proposal Trend of Contributions for Standards,” 2019
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Candidates and 5G-SEP Declarations Contributing to Realization of
5G and Proposal Trend of Contributions for Standards,” 2020 (inJapanese).
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Higher Layer in 5G,” NTT DOCOMO Technical Journal, Vol. 20, No.
3, pp. 62–78, Jan. 2019.
https://www.nttdocomo.co.jp/english/binary/pdf/corporate/technology/rd/technical_journal/bn/vol20_3/vol20_3_008en.pdf
NTT DOCOMO Press Release: “Mobile Industry Leaders to Develop
O-RAN Fronthaul-compliant Products and Drive Multi-vendor Radio
Access Networks,” Feb. 2019.
https://www.nttdocomo.co.jp/english/info/media_center/
pr/2019/0222_00.html

Trademark notes

All company names or names of products, software, and services appearing
in this article are trademarks or registered trademarks of their respective
owners.

This article is the reproduction of the special article

NTT Technical Review

Vol. 18 No. 12 Dec. 202084

Feature Articles

Satoshi Nagata

Yuta Oguma

Manager, 6G Laboratories, NTT DOCOMO,
INC.
He received a B.E. and M.E. from Tokyo Institute of Technology in 2001 and 2003. In 2003, hejoined NTT DOCOMO, where he engaged inresearch and development (R&D) of wirelessaccess technologies for LTE, LTE-Advanced,
and 5G. He is currently a manager working for
5G and 3GPP standardization. He has been contributing to 3GPP over 10 years. He served as avice chairman of 3GPP TSG-RAN WG1 from
November 2011 to August 2013, and a chairman
from August 2013 to August 2017. He has been avice chairman of 3GPP TSG-RAN since March2017.

Research Engineer, Communication Device
Development Department, NTT DOCOMO,
INC.
He received a B.E. in electrical and electronicengineering in 2015 and an M.E. in informatics
in 2017 from Kyoto University. Since joining
NTT DOCOMO in 2017, he has been involved in
R&D activities related to radio frequency wireless technologies for LTE, LTE-Advanced, and
5G, and contributed to the 3GPP TSG-RAN
WG4 as a delegate from NTT DOCOMO.

Atsushi Minokuchi

Manager, Communication Device Development Department, NTT DOCOMO, INC.
He received a B.E. and M.E. from Kyoto University in 1999 and 2001. Since he joined NTT
DOCOMO in 2001, he has been involved in the
research activities of wireless technology related
to Wi-Fi, WiMAX and Celullar. He has also been
involved in the development of commercial services of LTE and VoLTE since 2008. He has been
managing the development of 5G technologies incommunication devices since July 2020. Prior tothe current assignment, he managed the standardization activities in the R&D strategy department.

Shinji Takeda

Assistant Manager, Core Network Development Department, NTT DOCOMO, INC.
He received a B.E. and M.E. from The University of Tokyo in 1992 and 1996. Since joining
NTT DOCOMO in 1996, he has been involved in
R&D of 2G to 5G core network. He contributed
to ITU-T SSG, SG19, and SG13 from April 2001
to March 2010, and since 2011 he has been
involved in 3GPP SA and CT. From March 2015
to March 2019, he served as a vice chairman of
3GPP TSG-CT.

Anil Umesh

Manager, Radio Access Network Development
Department, NTT DOCOMO, INC.
He received a B.S. in engineering from Case
Western Reserve University, USA, in 2002. He
joined NTT DOCOMO in 2002 and has since
been involved in R&D activities related to wireless access technologies for HSPA, LTE, LTEAdvanced, and 5G. He has been the O-RAN
Alliance Working Group 4 co-chair since thestart of O-RAN Alliance in 2018 and has chaired
open fronthaul topics.

NTT Technical Review

Vol. 18 No. 12 Dec. 202085

Feature Articles: NTT DOCOMO 5G Initiatives for Solving
Social Problems and Achieving Social Transformation

5G Network
Yuta Sagae, Shinsuke Sawamukai, Yusuke Ohwatari,
Kohei Kiyoshima, Keiichi Kanbara, and Jo Takahashi
Abstract

NTT DOCOMO launched its fifth-generation mobile communications system (5G) commercial service in March 2020 achieving early provision and stable quality by making maximum use of functions
and coverage provided by LTE (Long Term Evolution). This article describes the development of radio
base-station equipment and core network equipment for providing the 5G commercial service.
Keywords: 5G/NR, sub-6/mmW, non-standalone

1. Introduction
Featuring high speed and large capacity, low latency, and massive connectivity, the fifth-generation
mobile communications system (5G) is generatinghigh expectations. In addition to providing richer
content as in video services, it is anticipated that 5G
will help find solutions to social problems that haveso far been difficult to resolve and create new industries at the same time.
After launching its 5G pre-commercial service in
September 2019, NTT DOCOMO launched its 5G
commercial service in March 2020. As of July 6,
2020, the number of 5G subscribers had alreadyexceeded 170,000. The demand for 5G is expected toincrease from here on, so the plan is to enhance theterminal lineup and expand 5G coverage in stages.
In this article, we describe the development of radio
base-station equipment and core network equipment
to provide the 5G commercial service.
2. Overview of 5G frequency bands
In 5G, there is a need for a network that can accommodate a variety of use cases and usage scenarios.
From a technical perspective, this means adopting
new radio technology, that is, 5G New Radio (NR),and supplementing existing frequency bands withthose of even higher frequencies [1].
Three frequency bands have been allocated inJapan for 5G: the 3.7 GHz band (3.6–4.1 GHz), 4.5
NTT Technical Review

Vol. 18 No. 12 Dec. 2020

GHz band (4.5–4.6 GHz), and the 28 GHz band
(27.0–29.5 GHz, among which the 28.2–29.1 GHz
interval is unallocated) (Table 1). The first two frequency bands are called the “sub-6” bands and thethird one is called the millimeter wave (mmW) band.
The sub-6 bands each feature a 100 MHz bandwidth/operator within Japan’s 5G frequencies. Compared with Long Term Evolution (LTE), they enablewideband usage and can achieve the same coverageas the LTE 3.5 GHz band. The mmW band, meanwhile, is significantly different from the frequencies
that have come to be used by LTE, and expectations
are high for the spot-like rollout of services based ontransmission speeds of several Gbit/s through ultrawideband allocation. In general, however, the higher
is the frequency the more difficult is wave propagation, so there will be a need to construct a heterogeneous network that combines the mmW band withexisting LTE frequencies and the sub-6 bands. In
addition, antenna downsizing is simple in the case ofhigh-frequency bands, so the rollout of networks
using massive multiple-input multiple-output (Massive MIMO) as advanced MIMO technology is
anticipated.
It must also be noted that existing systems are operating within Japan’s 5G frequency bands, so coexistence and segregation are important. In particular,
since satellite system operators are also using the 3.7
GHz band, this band will be rolled out for 5G while
making inter-system interference adjustments withthose satellite systems. As a consequence, the 4.5
86Feature Articles

Table 1. Features of Japan’s 5G frequencies.
3.7 GHz band

4.5 GHz band

28 GHz band

Allocatedfrequencies

3.6–4.1 GHz(500 MHz bandwidth)

4.5–4.6 GHz(100 MHz bandwidth)27.0–28.2 GHz
29.1–29.5 GHz(1.6 GHz bandwidth)

Allocatedbandwidth

100 MHz bandwidth/operator

100 MHz bandwidth/operator

400 MHz bandwidth/operator

Use of
Massive MIMO

Coverage extension by
beamforming

Used in MIMO multiplexing

Use with
other systems

Satellite systems

Airplane radar altimeter

Satellite systems

Overseas
trendsChina, Korea, Europe,
United States

Planned for future use by China

United States, Korea

LTEAdvanced

3.7 GHz band

4.5 GHz bandMay use original TDD Config
since there is no operation atneighboring frequencies

Must operate with same TDD Config
to avoid mutual interference
Fixed satellite communications (downlink)

3400 MHz

3600 MHz

Airplane radar
altimeter
4200 MHz

4400 MHz

Fig. 1. Frequency allocation in 3.7 GHz band and relationship with LTE 3.5 GHz band.

GHz band has been attracting attention as a frequency
band that could play an effective role in achieving an
early 5G rollout (Fig. 1). Additionally, looking atoverseas trends, the use of the 3.7 GHz band and 28
GHz band has already begun in various countries, andfrom the perspective of future terminal support,
roaming support, etc., these bands should be effective
in rolling out high-speed and large-capacity services
reflecting the unique features of 5G. The plan is to
execute a commercial rollout that will make maximum use of the strengths of each of these frequency
bands based on the features of those frequencies,
coexistence with existing operators, and global
trends.
As a result of approval received from the Ministryof Internal Affairs and Communications (MIC) forbase-station installation plans [2], NTT DOCOMO
has been allocated the frequency intervals 3.6–3.7
GHz and 4.5–4.6 GHz for a total of 200 MHz in thesub-6 bands and 27.4–27.8 GHz for a total of 400
MHz in the mmW band. In this way, NTT DOCOMO
can provide a downlink peak rate of 3.4 Gbit/s in thesub-6 bands and of 4.1 Gbit/s in the mmW band in
NTT Technical Review

Vol. 18 No. 12 Dec. 2020combination with the existing LTE system (Table 2).
The sub-6 bands and 28 GHz band have been allocated as time division duplex (TDD)*1 bands, butfrom the viewpoint of spectrum efficiency, the same
downlink/uplink resource allocation (TDD Config)*2
must be used between neighboring cellular operators.
In Japan, the TDD Config shown in Fig. 2 based on
the TDD Config agreed upon at the 3rd Generation
Partnership Project (3GPP) is being used for 5G
operation taking data traffic and 5G service forecasts
into account. In particular, to mitigate interference
between the 3.7 GHz band and the neighboring LTE
3.5 GHz band, this TDD Config has been designed touse the same timing for downlink/uplink resources(Fig. 2). Additionally, in the 4.5 GHz band acquired
solely by NTT DOCOMO, the plan is to establish an
original TDD Config tailored to use cases with
*1 TDD: A bidirectional transmit/receive system. This systemachieves bidirectional communication by allocating different
time slots to uplink and downlink transmissions on the same frequency.
*2 TDD Config: Parameters determining how to allot uplink anddownlink slots. Prescribed by 3GPP specifications.

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Feature Articles

Table 2. Technical features.
Sub-6 bands

mmW band

Method of using Massive MIMOMIMO multiplexing

Coverage extension by beamforming

Coverage approach

Same coverage as LTE 3.5 GHz band

High throughput by wideband use
Spot-like rollout

Peak rate (3GPP standard
values including LTE)

Downlink 3.4 Gbit/s, uplink 182 Mbit/s

Downlink 4.1 Gbit/s, uplink 480 Mbit/s

Number of MIMO layers

Downlink 4×4, uplink SISO

Downlink/uplink 2×2 MIMO

Modulation scheme

Downlink 256QAM, uplink 64QAM

Downlink/uplink 64QAM

QAM: quadrature amplitude modulation
SISO: single input single output(Sub-6 NR)
DL 4

D D D D D D D S U U D D D D D D D S U U
D(3.5G LTE)
D

D

U
D

S

U

U

D
D

D

S

D

(28G NR)

D

D

UL 1
D

S

U

U

*Designed to have same resource timing to
mitigate interference with 3.5G LTE

D

Downlink resource

U

Uplink resource

S

Downlink/uplink switching resource

Fig. 2. TDD Config used in Japan’s 5G frequencies.

spectrum efficiency in mind.
3. 5G commercial development
3.1 5G base-station equipment development
The 5G network configuration is shown in Fig. 3.
With a view to 5G service development, NTT
DOCOMO developed a central unit (CU) that consolidates the base band (BB) signal processing section supporting 5G, extended existing BB processing
equipment known as high-density base-station digitalprocessing equipment (BDE), and developed a 5G
radio unit (RU) having signal transmit/receive functions. Furthermore, to have a single CU accommodate many RUs, NTT DOCOMO developed a 5G
version of the fronthaul multiplexer (FHM) [4]deployed in LTE. Each of these three types of equipment is described below.
3.1.1 CU
(1) Development concept
With the aim of achieving a smooth rollout of 5G
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Vol. 18 No. 12 Dec. 2020

services, NTT DOCOMO developed a CU thatenables area construction without having to replace
existing equipment while minimizing the construction period and facility investment. This was accomplished by making maximum use of the existinghigh-density BDE that performs BB signal processing, replacing some of the cards of the high-density
BDE, and upgrading the software to support 5G.
(2) CU basic specifications
An external view of this CU is shown in Photo 1.
This equipment has the features described below.
As described above, this equipment enables
5G-supporting functions by replacing some of thecards of the existing high-density BDE. In addition,
future software upgrades will load both software supporting conventional 3G/LTE/LTE-Advanced andsoftware supporting 5G. This will enable the construction of a network supporting three generations ofmobile communications from 3G to 5G with a single
CU.
The existing LTE-Advanced system employs
88Feature Articles

Suburbs

Macrocell<5G radio section>
RRU<BB signal processing section>
5G CU

City center, urban districts, etc.

LTE BDE

Small cell

O-RAN X2 interference
O-RAN fronthaul
5G FHM<5G radio section>
SRU
5G area
LTE/LTE-Advanced area

Fig. 3. 5G network configuration.

Photo 1. CU external view.

advanced centralized radio access network (C-RAN)*3
architecture [4] proposed by NTT DOCOMO. Thisarchitecture is also supported in 5G with the connection between CU and RUs made via the fronthaul.
Standardization of this fronthaul was promoted at theOpen Radio Access Network (O-RAN) Alliancejointly established in February 2018 by five operators
including NTT DOCOMO. Since the launch of 5G
services, the fronthaul in the NTT DOCOMO network was made to conform to these O-RAN fronthaul
NTT Technical Review

Vol. 18 No. 12 Dec. 2020

specifications that enable interoperability between
different vendors, and any CU and RU that conformto these specifications can be interconnected regardless of vendor. The specifications for interconnecting
base-station equipment also conform to these O-RAN*3 Advanced C-RAN: A new C-RAN architecture proposed by NTT
DOCOMO. Being controlled by the same base station, a radio
access network makes a linkage between a macrocell (which covers a wide area) and a small cell (which covers a local area) byapplying carrier aggregation.

89

Feature Articles(a) Antenna-integrated SRU(b) Antenna-separated SRU

Photo 2. SRU external views.

specifications, which means that a multi-vendor connection can be made between a CU supporting 5G
and a high-density BDE supporting LTE-Advanced[5]. This enables NTT DOCOMO to deploy a CU
regardless of the vendor of the existing high-density
BDE and to quickly and flexibly roll out service areas
where needed while making best use of existing
assets. In addition, six or more fronthaul connections
can be made per CU and the destination RU of eachfronthaul connection can be selected. Since 5G supports wideband transmission beyond that of LTEAdvanced, the fronthaul transmission rate has been
extended from the existing peak rate of 9.8 Gbit/s toa peak rate of 25 Gbit/s while achieving a CU/RU
optical distance equivalent to that of the existinghigh-density BDE.
3.1.2 RU
(1) Development concept
To facilitate flexible area construction right fromthe launch of 5G services, NTT DOCOMO developed the low-power small radio unit (SRU) as the RU
for small cells and developed, in particular, separate
SRUs for each of the 3.7 GHz, 4.5 GHz, and 28 GHz
frequency bands provided at the launch of the 5G precommercial service in September 2019. Furthermore,with an eye to early expansion of the 5G service area,
NTT DOCOMO developed the regular power radio
unit (RRU) as the RU for macrocells to enable theefficient creation of service areas in suburbs and elseNTT Technical Review

Vol. 18 No. 12 Dec. 2020

where.
A key 5G function is beamforming*4 that aims to
reduce interference with other cells and thereby
improve the user’s quality of experience. To support
this function, NTT DOCOMO developed a unit that
integrates the antenna and 5G radio section (antennaintegrated RU). It also developed a unit that separates
the antenna and 5G radio section (antenna-separated
RU) to enable an RU to be placed alongside existing
3G/LTE/LTE-Advanced radio equipment (RE) andfacilitate flexible installation even for locations withlimited space or other constraints.
(2) SRU basic specifications
As described above, NTT DOCOMO developed the
SRU to enable flexible construction of 5G service
areas. It developed, in particular, antenna-integrated
SRUs to support each of the 3.7 GHz, 4.5 GHz, and28 GHz frequency bands provided at the launch of the
5G pre-commercial service and antenna-separated
SRUs to support each of the 3.7 GHz and 4.5 GHz
frequency bands (Photo 2). These two types of SRUs
have the following features.
The antenna-integrated RU is equipped with an*4 Beamforming: A technology that gives directionality to a transmitted signal, increasing or decreasing the signal power in a particular direction. Analog beamforming works by controlling thephase in multiple antenna elements (RF devices) to create directionality, while digital beamforming controls phase in the baseband module.

90

Feature Articles

Photo 3. RRU external view.

antenna panel to implement the beamforming function. In the 3.7 GHz and 4.5 GHz bands, specifications call for a maximum of 8 beams, and in the 28
GHz band, for a maximum of 64 beams. An area may
be formed with the number of transmit/receive beams
tailored to the TDD Config used by NTT DOCOMO.
In addition, the number of transmit/receive branches
is 4 for the 3.7 GHz and 4.5 GHz bands and 2 for the
28 GHz band, and MIMO transmission/reception canbe performed with a maximum of 4 layers for theformer bands and a maximum of 2 layers for the latterband.
The antenna-separated SRU is configured with only
the radio as in conventional RE to save space andfacilitate installation. With this type of SRU, theantenna may be installed at a different location.
Moreover, compared to the antenna-integrated SRU
operating in the same frequency band, the antennaseparated SRU reduces equipment volume to 6.5 L or
less. The antenna-separated SRU does not support the
beamforming function, but features four transmit/
receive branches the same as the antenna-integrated
SRU for the same frequency band.
(3) RRU basic specifications
The RRU was developed in conjunction with the
5G service rollout as high-power equipment compared with the SRU with a view to early expansion of
the 5G service area (Photo 3). This type of equipment
has the following features.
Compared with existing remote radio equipment
for macrocells, the volume of RRU equipment tends
to be larger to support 5G broadband, but in view ofthe latest electronic device trends, NTT DOCOMONTT Technical Review

Vol. 18 No. 12 Dec. 2020

took the lead in developing and deploying an antenna-separated RRU that could save space and reduceweight. Maximum transmission power is 36.3 W/100
MHz/branch*5 taking the radius of a macrocell area
into account. The RRU features four transmit/receive
branches and achieves the same number of MIMO
transmission/reception layers as the antenna-separated SRU.
NTT DOCOMO also plans to deploy an antennaintegrated RRU at a later date. The plan here is toconstruct 5G service areas in a flexible manner making best use of each of these models while taking
installation location and other factors into account.
3.1.3 5G FHM
The 5G FHM is equipment having a multiplexing
function for splitting and combining a maximum of
12 radio signals on the fronthaul. It was developed inconjunction with the 5G service rollout the same as
RRU (Photo 4).
If no 5G FHM is being used, each RU is accommodated as one cell, but when using a 5G FHM, amaximum of 12 RUs can be accommodated as one
cell in a CU. At the launch of 5G services, this meantthat more RUs could be accommodated in a single
CU when forming a service area in a location having
low required radio capacity (Fig. 4). Additionally,
since all RUs transmit and receive radio signals of thesame cell, the 5G FHM can inhibit inter-RU interference and the occurrence of handover *6 control*5 Branch: In this article, an antenna and RF transceiver.
*6 Handover: A technology for switching base stations without interrupting communications when a terminal with a call in progress straddles two base stations while moving.

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Feature Articles

Photo 4. 5G-FHM external view.

Without 5G FHMWith 5G FHM

Accommodates
a maximum of 12
RUs as one cell

Accommodates
each RU as
one cell

5G FHMCU

CU

5G FHM

5G FHM

CU

Fig. 4. Concept of 5G FHM implementation.

between RUs as in the conventional FHM. Furthermore, the 5G FHM supports all of the 5G frequency
bands, that is, the 3.7 GHz, 4.5 GHz, and 28 GHz
bands, which means that service areas can be constructed in a flexible manner applying each of thesefrequency bands as needed.
3.2 5G radio access network support
This section presents an overview of the radio
access network (RAN) configuration for achieving
5G services and LTE-NR Dual Connectivity, beam
management technology, and NR high-speed support
as RAN technologies.
3.2.1 RAN configuration
At the launch of its 5G commercial service, NTT
DOCOMO provided services through non-standalone operation, a key feature of NR. Here, “nonstandalone” means an operation format that provides
services while using LTE/LTE-Advanced areas as ananchor*7 instead of providing 5G areas by NR alone.
As shown in Fig. 5, evolved NodeB (eNB)*8 in NR
non-standalone operation connects to a gNB*9 basestation that provides NR using an X2*10 interface. In
addition, eNB and gNB connect to the Evolved
Packet Core (EPC)*11 using S1 interfaces. The use of
LTE as an anchor in this way through non-standalone
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Vol. 18 No. 12 Dec. 2020

operation made for early commercialization of 5G byenabling the use of existing network infrastructure
through shared use of LTE equipment while maintaining the same level of quality with respect to connectivity that had so far been provided. In addition,
the connection between eNB and gNB conforms to
the O-RAN X2 specifications spearheaded by NTT
DOCOMO thereby enabling interoperability between
different LTE and NR operators. In short, non-standalone operation made it possible to roll out 5G areas
in a speedy and flexible manner against LTE areasthat had already been established.
In contrast, NR standalone operation, whose provision is scheduled for the future, will enable the provision of services through only gNB base stations. In
this format, RAN will connect to the new core network, namely, the 5G core network (5GC), and gNB
base stations will connect to each other using an Xn*7 Anchor: A logical node point acting as a switching point for control signals and user bearers.
*8 eNB: A radio base station for LTE radio access.
*9 gNB: A radio base station for NR radio access.
*10 X2: A reference point between eNBs, defined by 3GPP.
*11 EPC: The core network on 3GPP mobile communication networks, mainly accommodating Evolved Universal Terrestrial
Radio Access (E-UTRA).

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Feature Articles

Internet

Internet

EPC

eNB
(LTE)

5GC

S1

S1
X2

NG
gNB
(NR)

gNB
(NR)

NR
area

NR
area

NR area

LTE area
Non-standalone operation

Standalone operation

Fig. 5. Network configuration in 5G.

interface and a gNB will connect to 5GC using an NG
interface.
3.2.2 LTE-NR Dual Connectivity
In the non-standalone configuration describedabove that combines LTE/LTE-Advanced and NR toprovide services, architecture that simultaneouslytransmits radio signals to the user equipment (UE) by
both LTE and NR is called LTE-NR Dual Connectivity. In this architecture, the simultaneous transmission of both LTE and NR types of radio resource is
specified as a split bearer*12, which achieves simultaneous transmission of a maximum of five LTE carrier*13 signals and NR and enables a speedy and flexible rollout of 5G commercial services.
In LTE-NR Dual Connectivity operation in whichan LTE base station acts as master node (MN), master
cell group (MCG) split bearer would have to be performed on the LTE-base-station side as the bandwidth on the NR side becomes larger. It would therefore be necessary to augment the equipment on the
LTE-base-station side whose capacity is limited
compared with NR equipment thereby incurring
equipment development and operation costs. Consequently, with the aim of minimizing the upgrading of
LTE-base-station equipment and avoiding throughput
limitations due to the capacity of that equipment, asecondary node (SN)-terminated split bearer and an
SN-terminated MCG bearer were specified so that abranch point for user data in LTE-NR Dual Connectivity could be set up as NR equipment acting as a SN.
The secondary cell group (SCG) split bearer is amethod in which user data is transferred on both the
MN carrier and SN carrier so that data can be transNTT Technical Review

Vol. 18 No. 12 Dec. 2020

mitted to the user simultaneously from the SN and
MN. This method achieves higher transmission
speeds. In addition, the SN-terminated MCG bearer
is a method that enables data to be transferred fromthe SN even if outside the NR area, which makes formore stable communications.
3.2.3 Beam management technology
New beam management technology has been
adopted in the NR system. As described above, theantenna-integrated RU has a configuration enabling
the forming of multiple beams (Fig. 6).
Specifically, in Frequency Range 1 (FR1)*14, thistechnology uses digital beamforming that forms
beams by applying phase rotation to the BB signal,
and in Frequency Range 2 (FR2)*15, it uses analog
beamforming that forms beams by controlling thephase in the radio frequency (RF) signal. The technology includes processing for selecting an optimal
beam when beginning an NR connection and processing for switching beams due to a change in radio
quality caused by UE movement or other factors.
Using an optimal transmit/receive beam according to
the position of the communicating UE ensures user
coverage even in a high frequency band. At the sametime, it can be expected that orienting the beam
*12 Split bearer: In Dual Connectivity, a bearer that is transmitted
and received via both the master and secondary base stations.
*13 Carrier: A radio signal (carrier wave) that is modulated to transmit information.
*14 FR1: A frequency band specified in 3GPP ranging from 450 to6000 MHz.
*15 FR2: A frequency band specified in 3GPP ranging from 24,250
to 52,600 MHz.

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Feature Articles• Beamforming• Digital beamforming• Analog beamforming
H polarization

PRB

I
F
F
T

TXRU
TXRU

TXRU

V polarization
TXRU

IFFT: inverse fast Fourier transform
PRB: physical resource block
TXRU: transceiver units

Fig. 6. Types of beamforming technologies.

toward the position of the UE will have the effect of
inhibiting radio-wave radiation in unnecessary directions and suppressing inter-cell interference.
3.2.4 NR high-speed support
In 5G, even higher communications speeds can beachieved since Dual Connectivity allows NR to be
simultaneously used with LTE/LTE-Advanced. Currently, in LTE/LTE-Advanced, NTT DOCOMO provides services with a downlink peak rate of 1.7 Gbit/sby combining five instances of a component carrier
through carrier aggregation*16. However, in NTT
DOCOMO 5G, Dual Connectivity of five LTE/LTEAdvanced component carriers and NR was achieved
from the launch of 5G services enabling a downlink
peak rate of 3.4 Gbit/s when using either the 3.7 GHz
or 4.5 GHz NR band and a downlink peak rate of 4.1
Gbit/s when using the 28 GHz NR band.
Furthermore, in uplink communications in the 28
GHz band, in addition to the simultaneous use of two
component carriers for a total bandwidth of 200 MHz
through carrier aggregation, a total data rate of 480
Mbit/s has been achieved through the implementationof 2 × 2 MIMO.
The goal going forward is to increase data rates
even further through the use of even wider frequency
bands. In downlink communications, for example,
this will be achieved through carrier aggregation
between the 3.7 GHz and 4.5 GHz bands, and in
uplink communications, through the carrier aggregation of four component carriers for a total bandwidthof 400 MHz in the 28 GHz band.

tional standards organization. Figure 7 shows thosearchitectures that have been specified at 3GPP.
Option 1 is the architecture under which LTE has
been provided. Options 2–5 and 7 are architectures
for providing 5G, and each operator may decide
which option(s) to adopt. As described above, NTT
DOCOMO decided to launch 5G by adopting Option
3 architecture that connects to NR and provides 5G
by extending EPC that had been in commercial
operation for LTE. This approach made it possible toachieve early provision of the 5G commercial service
while ensuring a stable level of quality provided by
LTE/LTE-Advanced that was already in place. Many
operators around the world adopted Option 3 at thetime of their 5G launches.
NTT DOCOMO will continue its studies toward
future means of migration including the introductionof 5GC, the new core network.
3.3.1 Option 3x architecture
As described above, user data is transferred by a
SCG split bearer process to keep equipment development and operation costs down. In other words, control signals are exchanged between EPC and eNB
while user data is exchanged between EPC and gNB.
This architecture has been specified in the standard as
Option 3x. The main feature of Option 3x architecture is that it enables NR to be accommodated byextending the S1 interface with eNB and the nonaccess stratum (NAS) interface with UE, which eases
the impact on core network equipment and achieves
both stable quality and early deployment.

3.3 Development of core network equipment
Multiple migration architectures toward the provision of 5G have been proposed at 3GPP, an interna-*16 Carrier aggregation: A technology for increasing bandwidth bysimultaneously transmitting/receiving signals over multiple component carriers.

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Feature Articles

Option 1

Option 2

Option 3

EPC

5GC

EPC

eNB

gNB

Option 4

Option 5

Option 7

5GC

5GC

5GC

eNB

gNB

eNB

gNB

eNB

eNB

C-planegNB

U-planeFig. 7. Migration architectures toward 5G.

3.3.2 High-throughput support
At present, the 5G commercial service provides a
maximum downlink throughput of 4.1 Gbit/s. NTT
DOCOMO intends to continue its development
efforts toward even higher data rates and larger
capacities. However, EPC consists of various types ofequipment of diverse levels of performance, and there
are some types of equipment that cannot easily provide the throughput required by 5G. With this inmind, and considering that a serving gateway/packet
data network gateway (S/P-GW) for performing datatransfer processing within EPC needs to be selected,
NTT DOCOMO has developed a mechanism forselecting an S/P-GW that can provide 5G throughput.
In more detail, the existing method for selecting an
S/P-GW via a mobility management entity is to use
tracking area and access point name as keys to querythe domain name system (DNS) and to then select an
S/P-GW from the record included in the response. To
this method, NTT DOCOMO added a value (+nc-nr)
indicating 5G capability to the service parameters(network capability) included in the DNS response.
For the 5G user, using this value as a basis for selecting an S/P-GW that can provide 5G throughput
achieves high-speed communications.
3.3.3 Low-latency network
NTT DOCOMO began the provision of its docomo
NTT Technical Review

Vol. 18 No. 12 Dec. 2020

Open Innovation Cloud simultaneously with thelaunch of its 5G commercial service. The docomo
Open Innovation Cloud provides a form of multiaccess edge computing. It enables end-to-end communications latency to be shortened by deploying
computing resources normally included in the core
network at locations near user terminals. It also provides NTT DOCOMO’s “Cloud DirectTM” service
that shortens network transmission latency by optimizing the transmission path between the connected
terminal and the cloud platform.
4. Conclusion
This article described the development of radio
base-station equipment and core network equipment
at NTT DOCOMO to provide a 5G commercial service. Going forward, NTT DOCOMO will pursue
co-creation opportunities through 5G with a varietyof partners to contribute to the creation of a prosperous society while continuing to develop groundbreaking and advanced technologies.
This article is the reproduction of the special article
published in NTT DOCOMO Technical Journal (Vol.
22, No. 2, Oct. 2020).

95

Feature Articles

References
[1]

[2]

[3]

MIC, “Information and Communications Council, Information andCommunication Technology Sub-committee, New Generation
Mobile Communication Systems Committee Report (July 31, 2018),”
July 2018 (in Japanese).
https://www.soumu.go.jp/main_content/000567504.pdf
MIC Press Release: “Approval to Applications for Authorization of
Establishment Plans on Specified Base Stations for Spreading 5G
Mobile Communications Systems,” Apr. 2019.
h t t p s : / / w w w. s o u m u . g o . j p / m a i n _ s o s i k i / j o h o _ t s u s i n / e n g /pressrelease/2019/4/10_01.html
M. Fujii, S. Suwa, R. Toba, and T. Toeda, “Base-station Equipmentwith the Aim of Introducing 3.5-GHz band TD-LTE,” NTT DOCOMO Technical Journal, Vol. 18, No. 2, pp. 8–13, Oct. 2016.
https://www.nttdocomo.co.jp/english/binary/pdf/corporate/

Yuta Sagae

[4]

[5]

technology/rd/technical_journal/bn/vol18_2/vol18_2_003en.pdf
T. Yoshihara, T. Toeda, M. Fujii, S. Suwa, and T. Yamada, “Radio
Equipment and Antennas for Advanced C-RAN Architecture,” NTT
DOCOMO Technical Journal, Vol. 17, No. 2, pp. 19–24, Oct. 2015.
https://www.nttdocomo.co.jp/english/binary/pdf/corporate/technology/rd/technical_journal/bn/vol17_2/vol17_2_004en.pdf
NTT DOCOMO Press Release: “DOCOMO to Commence Deployment of World’s First 4G/5G Multi-vendor Radio Access Network
Conforming to O-RAN Specifications,” Sept. 2019.
https://www.nttdocomo.co.jp/english/info/media_center/
pr/2019/0918_00.html

Trademark notes

All company names or names of products, software, and services appearing
in this article are trademarks or registered trademarks of their respective
owners.

Kohei Kiyoshima

Manager, Corporate Strategy & Planning
Department, NTT DOCOMO, INC.
He received a B.E. and M.E. in Tokyo Instituteof Technology in 2003 and 2005. He joined NTT
DOCOMO in 2005. He was in charge of 5G
network system strategy and 5G base stationequipment development.

Manager, Radio Access Network Development
Department, NTT DOCOMO, INC.
He received a B.E. in electronics engineeringand an M.E. in information science and technology in engineering from Kyoto University in2006 and 2008. He joined NTT DOCOMO in
2008. He is currently in charge of 5G base-station equipment development.

Shinsuke Sawamukai

Keiichi Kanbara

Director, Radio Network Control Group, NTT
DOCOMO, INC.
He joined NTT DOCOMO in 2002 and hassince been involved in research and development(R&D) activities related to the development of
the base station equipment.

Manager, Solution Integration Department,
DOCOMO CS, INC.
He received a B.E. in electronics engineeringand an M.E. in information science and technology from Hokkaido University in 2006 and 2008.
He joined NTT DOCOMO in 2008. He is currently in charge of providing solutions for business utilizing 5G and IoT.

Yusuke Ohwatari

Jo Takahashi

Manager, Radio Access Network Development
Department, NTT DOCOMO, INC.
He received a B.E. in electronics engineering,an M.E. in information science and technology,
and a Ph.D. in engineering from Hokkaido University in 2005, 2007, and 2013. He joined NTT
DOCOMO in 2007. He is currently in charge of
5G base-station equipment development. His
research interest includes MIMO systems formobile communication systems.

NTT Technical Review

Vol. 18 No. 12 Dec. 2020

Chief Engineer, Packet Network Division,
DOCOMO Technology, Inc.
He received a B.A. from Cornell University,
USA, in 2004. Since joining DOCOMO Technology in 2005, he has been involved in R&D
activities related to radio access network technologies for LTE/LTE-Advanced. After joining
the current division in 2018, he has contributed to
R&D for LTE/5G core network technologies.

96Regular Articles

Soft Error Countermeasure for
10G-EPON ONU
Ryota Kita, Kenta Ito, Haruka Nagoshi, Shun Morishima,
Satoshi Shimazu, and Tomoaki Yoshida
Abstract

We developed a soft error countermeasure for the 10 Gigabit Ethernet passive optical network(10G-EPON) optical network unit (ONU). This countermeasure allows an ONU to detect a soft error andreboot by autonomously turning its power off/on. This feature reduces the rate of inquiries because ofsoft error failure in 10G-EPON ONUs. It also reduces the number of operations required to respond to
failures in telecommunication services.
Keywords: ONU, soft error, reboot

1. Overview of the 10G-EPON system
The passive optical network (PON) system is afiber-to-the-home optical access system that uses anoptical splitter to branch optical signals from a single
optical fiber shared by multiple users. The 10 Gigabit-Ethernet PON (10G-EPON) system is a PON
system with a maximum transmission rate of 10
Gbit/s. This system consists of an optical line terminal (OLT) installed at the central office, optical network units (ONUs) installed in each user’s home, an
optical splitter, and optical fiber network connecting
them (Figs. 1 and 2).
2. Soft error in the ONU
A soft error is an event in which a bit in memory is
inverted by electrical noise. Operation failures causedby soft errors can be recovered by resetting the electronic device or overwriting the data [1]. Soft errors
are primarily caused by cosmic rays (high energy
protons). Cosmic rays collide with atomic nuclei(nitrogen and oxygen) to generate neutron rays,
which then collide with silicon atomic nuclei in semiconductor devices, generating electrical noise(Fig. 3).
If ONU operation fails due to soft error, network
service may be disconnected. An example of a soft
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error failure is that due to electrical noise generated ina semiconductor device, where bit inversion occurs in
the main signal path control function written in thevolatile memory (random access memory: RAM) of
the PON-Media Access Control (MAC) large-scale
integrated circuit. The main signal is thus disrupted,
resulting in communication interruption. In this case,by rebooting the ONU (turning the power off/on), theinformation or program in RAM is refreshed from
nonvolatile memory (read only memory: ROM),
which recovers communication. When such a soft
error failure occurs, it is necessary to restart the ONU
by turning it off and on at the user’s home.
Soft errors occur very infrequently in semiconductor devices, making it difficult to isolate and identify
faults. For ONUs that are distributed and deployed inlarge numbers, it is important to improve the efficiency of responding to soft error failure.
Further advances in semiconductor miniaturization
are inevitable given the demand for 10 Gbit/s-classhigh-speed communication. As these semiconductor
devices hold less charge in a memory cell, they are
more susceptible to neutron rays. Therefore, it isassumed that the occurrence rate of soft errors andfrequency of troubleshooting will increase.
Error correction for soft errors includes (1) autonomous correction by using hardware, (2) autonomouscorrection by using a device-control program, and (3)97Regular Articles

10G-EPON OLT
10-Gbit/s
service

10G-EPONONU
ONU
10G-EPON
Optical splitter

10G-EPON ONU

10G-EPON ONU

Fig. 1. 10G-EPON system.

Space

ProtonSun

Atmosphere
Neutron

Soft
error

NeutronElectronic
deviceSemiconductor devices

Fig. 2. 10G-EPON ONU.

Fig. 3. Mechanism of soft error occurrence.

correction by manual intervention [2]. (1) Autonomous correction by using hardware uses components
with functions such as error check code (ECC) correction. (2) Autonomous correction by using adevice-control program includes methods such assystem reset and device reset. The system can be reset
without any special control, just like a power off/on
restart. Device reset resets just the target device (components), which shortens the correction time, but
requires coordination with peripheral circuits forstate matching, resulting in complex control. (3)Manual correction can be done by remote control
reset by the maintainer or by instructing the user toturn the power off/on. Either approach takes a longtime to correct the problem, and it is necessary for thetelecommunication company to respond to the problem.
For a 10G-EPON ONU, we developed a function(autonomous reset function) that corresponds to thesystem reset for (2); the ONU detects a soft error andrestarts itself by turning its power off and on autonomously. Compared with (1) and device reset for (2),this function is economical, easy to implement, andreduces the amount of manual operation for error correction.

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Regular Articles

10G-EPON ONU
Detect errors in the set-value
storage area for priority control
and transfer processing

Detect errors in the RAM
CPU
PON-IF

UNI

PHY

PON-MAC
processing unit

Encryption
processing
unit

Detect errors in theframe-buffer area

Detect errors in theframe-buffer area

Detect errors in theframe-buffer area

FEC
unit

OpticalmoduleDetect errors in theframe-buffer area

Fig. 4. 10G-EPON ONU functional block and soft error detection.

3. Soft error detection and autonomous
reset-target classification
The functional block of an ONU is shown in Fig. 4.
An uplink signal input from a user network interface(UNI) connected to user equipment, etc. is received
by the physical layer (PHY), priority control andtransfer processing are executed by the PON-MAC
processing unit, and if enabled in the encryption unit
and forward error correction (FEC) unit, the signal is
encrypted and an error correction code is assigned toeach block and transmitted from the optical module
to the OLT. In addition, the downlink signal input
from the OLT PON interface (IF) is received by the
optical module, corrected by the FEC unit, decodedby the encryption processing unit, subjected to priority control and transfer processing by the PON-MAC
processing unit, and transmitted from the PHY to theuser equipment.
Soft error is first detected in the frame-buffer areaof the RAM of each functional block of the main
signal path through which the uplink and downlink
signals mentioned above pass. Most soft errors
detected are bits in a single frame in the frame-bufferarea, which are discarded after detection and havelittle effect on communication. However, in rarecases, similar soft errors are continuously detected in
the frame-buffer areas of the PHY, PON-MAC processing unit, encryption unit, and FEC unit. In thiscase, since the soft errors are continuous and extend
over multiple frames, it is assumed that there is aproblem with the continuity of the main signal in the
ONU. Therefore, it is determined that a soft error
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event occurred, and the autonomous reset function istriggered.
Our countermeasure also detects errors in the RAMof the central processing unit (CPU) and the set-value
storage area for priority control and transfer processing of the RAM of the PON-MAC processing unit. If
a soft error is detected in the RAM of the CPU, it isassumed that CPU processing is abnormal. If a soft
error is detected in the transfer-control-setting tableof the PON-MAC processing unit, it is also assumed
that there is a problem with the main signal transfer
to the OLT. Therefore, both are judged to be soft
errors, which triggers the autonomous reset function.
4. Autonomous reset function as a soft
error countermeasure
Figure 5 shows the transition flow of the autonomous reset function. A semiconductor device affectedby a neutron beam may suffer a physical defect aswell as soft error. A physical defect is an event inwhich the structure of the semiconductor device
degrades and malfunctions due to the effect of neutron beams; such failures (physical defect failure)cannot be recovered by restarting the semiconductor
device or overwriting data. This suggests that theautonomous reset function may repeatedly turn the
power supply off and on.
Our solution is a mechanism that memorizes thenumber of resets and, when the number of resets
exceeds a threshold within a certain period, a physicaldefect failure is determined and the ONU communication function is terminated along with autonomous
99Regular Articles

No conditions/
Reset counter i := 0

Detect errors in the frame-buffer areaor
Detect errors in the RAM
or
Detect errors in the set-value storage
area for priority control andtransfer processing/
No processing

Reset counter
confirmation

SurveillanceStarting

Certain period has passed/i := 0i > Threshold/No processing

Failed statei  Threshold/Autonomous reset(power supply off and on),i := i + 1(At transition flow)
Conditions/processing content

Fig. 5. Transition flow of the autonomous reset function.

reset. Specifically, reset counter i is provided torecord the number of resets in the ONU. After the
ONU is started, reset counter i is initialized to 0, andeach autonomous reset increments the counter value
by 1. When autonomous reset occurs repeatedly, resetcounter i increases, and if the counter value exceeds
the specified threshold, the device transitions to afailed state. This prevents endless repetition of autonomous reset and enables soft error failure to be distinguished from physical-defect failure.
5. The results of our soft error countermeasurewith neutron irradiation test
Neutron irradiation tests are generally conducted toreproduce soft errors. To measure the effectiveness ofour soft error countermeasure, we subjected
10G-EPON ONUs running the autonomous resetfunction to the neutron irradiation test [3] provided
by NTT Advanced Technology based on the Telecommunication Technology Committee (TTC) standard JT-K 130 [4].
In the neutron irradiation test, the target (beryllium)
is irradiated with protons accelerated using a cyclotron proton accelerator to generate neutron beams,
and several 10G-EPON ONUs are irradiated simultaneously. This test uses a very high neutron intensity
corresponding to about 100 million times the natural
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exposure rate (Fig. 6).
A main signal was connected to the 10G-EPON
ONUs using an OLT and traffic-generator analyzer.
To assess the possibility of log-file destruction due tothe effect of neutron beams during the test, we collected the logs inside the 10G-EPON ONUs in realtime and measured all autonomous resets. Throughthis test, the 10G-EPON ONUs were exposed to neutron beams equivalent to about 110,000 years per unit
on average. Due to the irradiation, soft error was
detected 1150 times per unit on average; among
them, errors in the frame-buffer area, not the subjectof autonomous reset, were confirmed 1104 times perunit, and fault recovery by autonomous reset was
confirmed 46 times per unit.
Because the autonomous reset function worked as
expected, it was confirmed that when the ONU exhibits a soft error fault, it can recover autonomously;
thus, this is effective as a soft error countermeasure.
If we assume that 10 million units will be deployed,
we can expect to reduce the number of failures per
year by about 4200.
6. Future plans
We developed a countermeasure against soft errors,
which are expected to become more frequent with the
adoption of 10G-EPON ONUs. This countermeasure100Regular Articles

Control/measurement terminal

OLT

Trafficgenerator
analyzer

Shielding wall

Target(beryllium)
NeutronOptical splitter

ProtonCyclotron
10G-EPON ONU

Fig. 6. Neutron irradiation test.

will contribute to the reduction in ONU failures andin manual operations required by the user or carrier
personnel. In the future, we will examine the application of this countermeasure to other types of ONUs.

References
[1]
[2]
[3]
[4]

NTT Technical Review

Vol. 18 No. 12 Dec. 2020

TTC Standard JT-K 124: “Overview of particle radiation effects oftelecommunication systems,” 2018.
TTC Standard JT-K 131: “Design methodologies for telecommunication
systems applying soft error measures,” 2019.
NTT Advanced Technology, Soft Error Test Service, https://www.
ntt-at.com/product/softerror/
TTC Standard JT-K 130: “Neutron irradiation test methods fortelecommunication equipment,” 2019.

101

Regular Articles

Ryota Kita

Engineer, Optical Access Systems Project,
NTT Access Network Service Systems Laboratories.
He received a B.E. from Ehime University in2010 and joined NTT WEST the same year. He
has been with NTT Access Network Service
Systems Laboratories since 2019 and has been
engaged in the research and development of
Gigabit Ethernet (GE)-PON and 10G-EPON
systems.

Kenta Ito

Engineer, Optical Access Systems Project,
NTT Access Network Service Systems Laboratories.
He received a B.E. in engineering from
Yamagata University in 2014, and an M.S. in
information science from Tohoku University,
Miyagi, in 2016. In 2016, he joined NTT EAST,
where he engaged in the maintenance operationof the Next Generation Network. In 2018, hejoined NTT, where he has been engaged in the
research and development of optical access systems. His current research interests include GEPON and 10G-EPON systems.

Haruka Nagoshi

Engineer, Optical Access Systems Project,
NTT Access Network Service Systems Laboratories.
She received a B.E. from University of Teacher
Education Fukuoka in 2010, and an M.S. from
Yamaguchi University Graduate School of Science and Engineering in 2012. She joined NTT
WEST in 2015. She has been with NTT AccessNetwork Service Systems Laboratories since
2018 and has been engaged in the research anddevelopment of GE-PON and 10G-EPON systems. She is a member of the Institute of Electronics, Information and Communication Engineers (IEICE) of Japan.

NTT Technical Review

Vol. 18 No. 12 Dec. 2020

Shun Morishima

Manager, Network Department, NTT WEST.
He received a B.E. from Kobe University in2009 and joined NTT WEST the same year.
From 2017 to 2019, he was with NTT AccessNetwork Service Systems Laboratories, where
he engaged in the development of 10G-EPON
systems.

Satoshi Shimazu

Senior Research Engineer, Optical Access
Systems Project, NTT Access Network Service
Systems Laboratories.
He received a B.E. and M.E. from Tohoku
University, Miyagi, in 2006 and 2008. He joined
NTT Access Network Service Systems Laboratories in 2008, where he has been engaged in the
research and development of GE-PON and
10G-EPON systems.

Tomoaki Yoshida

General Manager, Optical Access Systems
Project, NTT Access Network Service Systems
Laboratories.
He received a B.E., M.E., and Ph.D. in communication engineering from Osaka University
in 1996, 1998, and 2007. In 1998, he joined NTT
Multimedia Systems Development Center, where
he engaged in the development of asynchronoustransfer mode optical access systems. In 1999, hemoved to NTT Access Network Service Systems
Laboratories. Since 2000, he has been engaged inresearch on next-generation optical access networks and systems. From 2013 to 2015, he wasinvolved in the research of wavelength divisionmultiplexing/time division multiplexing-PON
and worked on the standardization of opticalaccess systems such as NG-PON2 (next-generation passive optical network stage 2). He is amember of IEEE and IEICE.

102

Global Standardization Activities

Trends in Standardization of
Mapping and Multiplexing
Technologies for Optical Transport
Networks
Kengo Shintaku, Shoichiro Kuwahara,
and Shoukei Kobayashi
Abstract

This article introduces recent standardization activities in the International Telecommunication
Union - Telecommunication Standardization Sector (ITU-T) related to optical transport networks,
including fifth-generation mobile transport networks.
Keywords: OTN, OTN security, 5G mobile transport

1. Introduction

2. OTN multiplexing and mapping structure

The International Telecommunication Union Telecommunication Standardization Sector (ITU-T)
Study Group 15 Question 11 (Q11/15) have addressed
the standardization of framing and multiplexing technologies for optical transport networks (OTNs) usedin the backbone networks of telecommunication carriers. Of particular note, the OTN specified in ITU-T
Recommendation G.709 has been widely applied to
optical transmission systems as key technology toachieve long-distance and large-capacity transmission as well as cost reductions. Therefore, NTT has
been extensively contributing to OTN standardization
activities.
Section 2 outlines the OTN, specifically, the basic
and latest framing/multiplexing technologies. Section 3 explains Flexible OTN (FlexO), an enhanced
interface that provides flexibility and scalability.
Security has also become important in transmission
networks, and Section 4 describes OTN and FlexO
encryption technologies. Section 5 explains fifthgeneration (5G) mobile transport networks. Finally,
Section 6 introduces future activities.

An OTN has two functions; (1) packaging various
client signals into OTN frames and (2) multiplexing
OTN frames into a higher-speed OTN frame (seeFig. 1). As shown in Fig. 2(a), an optical channeltransport unit-k (OTUk) frame (k = 1,2,3,4) uses the
fixed frame of 4 × 4080 bytes and contains threeareas; the payload area accommodates the client signal, overhead (OH) area carries maintenance andcontrol information, and forward error correction(FEC) area transfers redundant data to correct the bit
errors that may occur during transmission. The OH
area in Fig. 2(a) consists of three regions, optical
payload unit (OPU) OH, optical data unit (ODU) OH,
and optical transport unit (OTU) OH. The OPU OH
includes information that supports the adaptation ofclient signals, ODU OH includes information formaintenance and operational functions to supportODU connections, and OTU OH includes information for maintenance and operational functions to
support OTU connections.
The OTN was originally standardized in 2001 withthree bitrate classes, OTU1 (2.67 Gbit/s), OTU2 (10.7
Gbit/s), and OTU3 (43.0 Gbit/s) to accommodate

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103

Global Standardization Activities

e.g., 1000 km
Optical network
Router/switch
Optical fiber
Equipment forleased line service

Optical transmission system

Client signal

OTN frame
Ethernet

OH

OH

Higher-speed OTN frame

EthernetOTN frame
SDH

Optical fiber

OH

OH

Ethernet

OH

SDH

Optical (WDM)
signalSDH
Optical fiber(1) Client signal
adoption(2) Multiplexing(3) Modulation

Optical transmission system
WDM: wavelength division multiplexing

Fig. 1. OTN.

synchronous digital hierarchy (SDH), which was thedominant client signal at that time. Next, OTU4 (112
Gbit/s) was specified in 2010 to transport 100 Gigabit
Ethernet (100GbE) [1]. Subsequently, with the development of higher-speed client signals, such as the
200GbE and 400GbE specified in the Institute ofElectrical and Electronics Engineers (IEEE) 802.3,
Q11/15 began discussing an OTN for rates beyond
100 Gbit/s (Beyond 100G; B100G). The B100G OTN
takes into account flexibility and extensibility, which
are considered important features in addition to transmission capacity. Therefore, the optical channeltransport unit-Cn (OTUCn) frame (The index Cn isused for n × 100G (C = 100G); n is a positive integer)
was specified to have an n multiplexing structure thatconcatenates OTUC frames that have 100G transmission capacity, as shown in Fig. 2(b). The OTUCn
frame also supports multi-carrier transmission byusing multiple optical signals (wavelengths) in parallel, and the optical interface can be configured flexibly by multiplexing OTUC frames according to thetransmission speed per optical signal. The FEC area,
which is one of the characteristic functions of the
OTUk frame, is separate from the OTUCn frame andis specified not in G.709 but in G.709.1, G.709.2, andG.709.3. This is because it is necessary to specify the
optimum FEC coding scheme and area according to
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the physical transmission speed of the optical signal.
The next section describes how to transmit OTUCn
frames in combination with FEC.
3. OTN enhancements
Standardization activities have recently progressed
towards aggregating multiple physical interfaces toconfigure large-capacity links. A well-known example of aggregated virtual links is the Link Aggregation Group (LAG) of IEEE802.3ad, which bonds
Ethernet physical interfaces. For example, a
400-Gbit/s aggregated link can be configured bybonding four 100GbE interfaces with this LAG.
However, the bandwidth of a data flow is limited tothat of the physical interface since each single data
flow is transported via only one of the physical interfaces. In this 400-Gbit/s-link LAG, a single flow cannot exceed 100 Gbit/s. This limitation between thedata flow and physical interface can induce traffic
imbalances on some physical links. Therefore, itremains difficult to efficiently use the large-capacity
link configured using the LAG. To overcome thischallenge, the Optical Internetworking Forum (OIF)
standardized Flexible Ethernet (FlexE) in 2016.
FlexE configures large-capacity links by bonding
multiple Ethernet physical interfaces similar to the104Global Standardization Activities

1

FAS

7

17

3824
Payload area

n

OH
FAS

1
4

OTUC frame

FAS

Payload area

OH

Multiplexing
Frame alignment signal
1
7
17
1 FAS
4

OH

3824
Payload area

7×n

4080

1

FEC area

9×n

3808×nFAS

Payload areaOH4(a) OTUk(b) OTUCn

Fig. 2. OTU frame structure.

Logical channel for 10GbE

Large-capacity linkcombining multiple physical interfaces
FlexE equipment

1

FlexE process

10GbE Client

25GbE Client

100GbE

2

100GbE

3

100GbE

4

100GbE

Logical channel for 25GbE(Logical channel can be mapped
across multiple physical interfaces)

1
10GbE Client2

3

FlexE process

FlexE equipment

25GbE Client4: 64b/66b block

Fig. 3. FlexE.

LAG, but it can freely provide logical channels acrossmultiple physical interfaces. The logical channel acts
as an Ethernet link equivalent to a real physical interface, as shown in Fig. 3.
In Q11/15, FlexO, which bonds multiple physicalinterfaces, was specified in Recommendation
G.709.1 in 2017. FlexO transfers OTUCn, making
the optical transmission flexible and scalable fromboth logical and physical aspects. Figure 4 shows anexample of G.709.1; one OTUCn frame is mapped
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into n × FlexO frames, with each FlexO frame containing one OTUC frame. The n × FlexO frames are
combined, interleaved, and mapped into FlexO-x<fec>-m (m = [n/x], where [ ] is the ceiling function).
For example, an OTUC4 frame can be transported
using two 200G FlexO frames (m = 2, x = 2) or a single
400G FlexO frame (m = 1, x = 4). Since it is assumed
that FlexO will be applied to communication between
adjacent network equipment, G.709.1 adopted KP4
FEC (RS10(544,514)) as FlexO FEC (-<fec>-),105

Global Standardization Activities

4 OTUC instances (n = 4)
FAS

FAS

OTUCn

PayloadOH

1

AM

n × FlexO

OH

AM

OH

FAS

Payload

OH

AM

OH

FAS

OH

OH

AM

PayloadOH

Payload

Payload

Payload

Payload

Payload128
5140 bits

AM

FlexO-x-<fec>-m(m = [n/x])

AM

OH

OH

FEC

PayloadInterleaving 2 FlexO frames (x = 2)

Payload

FEC

Interleaving 2 FlexO frames (x = 2)

2 interleaved FlexO frames (m = 2)
AM: alignment marker

Fig. 4. FlexO frame example.

which is the same as that of 200G and 400G Ethernet,
which have transmission distances of up to 10 km.
NTT has been actively involved in and contributing
to FEC discussions, believing that it is necessary to
specify an FEC for metro and long-distance transmission to apply OTUCn/FlexO to practical OTNs. We
took a leadership role in standardizing the best FEC
possible. For example, we summarized various opinions from many network service providers and vendors and submitted many contribution documents to
Q11/15 and Question 6 (Q6/15). In addition to standardization activities, NTT also has conducted anddemonstrated inter-operability verification trials on a
candidate FEC between optical devices developed bydifferent vendors. After more than five years of these
activities, Staircase FEC was successfully standardized in 2018 as 100G FlexO FEC (Recommendation
G.709.3). At the same time, Staircase FEC was also
adopted as the FEC for long-reach OTU4 application
in Recommendation G.709.2. NTT has also played amajor role in open FEC (OFEC) standardization for
450-km 200G/400G FlexO applications (G.709.3 in2020). OFEC is also adopted by the Open ROADM
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Vol. 18 No. 12 Dec. 2020

(reconfigurable optical add/drop multiplexer) MultiSource Agreement (MSA).
4. OTN secure transmission
Due to the recent worsening of security risks,
Q11/15 began discussing OTN cryptographic standardization, which covers two functions: encryption
to prevent wiretapping and authentication to verify if
the received frames were tampered with. Prior to thestandardization study, there was a discussion about
the order in which to proceed with the standardization. Since FlexO OH can carry more cryptographic
control information than OxU (OPU/ODU/OTU) OH
and it seemed easy to study, Q11/15 decided to startwith FlexO encryption. OxU encryption will follow
after FlexO.
Figure 5 illustrates FlexO frame encryption. The
encryption area of FlexO consists of a payload region
and a portion of the OH region. With regard to theencryption algorithm, it has been agreed to adopt acurrent algorithm (e.g., Advanced Encryption Standard (AES)*) rather than develop new ones. Studies
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Global Standardization Activities

Study point 2:
Message format of the cipher
control information

AM

FlexO OH
(Plain text)

AM

FlexO Payload(Plain text)

Ciphercontrol

Study point 1:
Target area for encryption
and authentication

FlexO OH FlexO OH(Plain text) (Cipher text)

FlexO Payload(Cipher text)

CipherFlexO frame

Cipher FlexO frameFig. 5. Example of FlexO frame encryption.

Demand for highly isolated
network slicing (hard slice)
Low latency service(e.g., self-driving
cars)

Virtual network (slice) #A

Virtual server

Virtual network (slice) #B

Virtual server

Virtual network (slice) #C

Virtual server

High-capacity broadband(e.g., 4K/8K
video streaming)
Massive device
connections(e.g., IoT)

Configures virtual networks (slices)
depending on service requirements
Network equipment/Opticaltransmission system
Transport network
Physical infrastructure and network

Physical server

Fig. 6. Characteristics of 5G networks.

and discussion are still on-going, but agreement in thenear future seems likely.
5. Slicing techniques for 5G mobiletransport network
One of the characteristic requirements for 5G
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mobile networks is network slicing (Fig. 6 [2]). Network slicing involves managing physical equipment(physical resources), such as servers and routers, as
resources that can be partitioned virtually (virtual*

AES: An encryption algorithm standardized as FIPS197 by the
National Institute of Standards and Technology, which is a standardization body of the United States.

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Global Standardization Activities

links, virtual network functions, etc.).
In Q11/15, these network-slicing requirements are
specified in G.8300; it summarizes the requirementsfor 5G mobile transport networks and a new multiplexing technology, tentatively called Metro Transport Network (MTN). MTN requires strongly isolated network slicing (hard slicing) to prevent interference among slices that access the common physicalnetwork.
Two candidate techniques have been proposed toachieve hard slicing; one is an OTN-based techniqueand the other is a FlexE-based technique. In the former, the payload area of high-speed OTN frames isdivided into small areas called time slots, and lowspeed frames are allocated to them so that hard slicing
can be achieved by treating the OTN time slots as
virtual resources. In the latter, the logical channel isused to implement hard slicing. These two techniques
are specified in Supplemental G.sup.67.

Kengo Shintaku

Engineer, NTT Network Innovation Laboratories.
Since joining NTT Network Innovation Laboratories in 2013, he has been researching OTNs.
He has been participating in ITU-T SG15 activities addressing OTN technologies since 2019.

Shoichiro Kuwahara

6. Future activities
OTN technologies, which play important roles in
the information society, will become even more
important. NTT will continue contributing to standardization activities in ITU-T while staying informed
about technological trends in optical transmission
and advancing discussions in other standardization
bodies and MSAs.
References
[1]

[2]

T. Ohara and O. Ishida, “Standardization Activities for the Optical
Transport Network,” NTT Technical Review, Vol. 7, No. 3, 2009.
https://www.ntt-review.jp/archive/ntttechnical.php?contents=
ntr200903gls.html
S. Yasukawa, H. Sato, T. Hirota, T. Tojo, K. Endo, Y. Kasahara, and H.
Suzuki, “Research toward Realizing a Future Network Architecture,”
NTT Technical Review, Vol. 16, No. 6, 2018.
https://www.ntt-review.jp/archive/ntttechnical.php?contents=
ntr201806fa5.html

Shoukei Kobayashi

Assistant Manager, NTT Electronics Corporation.
He received a B.E. and M.E. in applied physics
from Nagoya University in 1992 and 1994. In
1994, he joined NTT, where he was engaged inresearch on optical and wireless access network
systems, high-speed Ethernet transport, terabitclass local area networks, and virtualized network architecture, and in standardization activity
in IEEE 802.3. In 2009, he joined NTT EAST asa technical manager. In 2014, he again joined
NTT and kicked off research in autonomous
operation and maintenance systems. He is currently engaged in standardization activities in
ITU-T SG15, Open ROADM MSA, and OpenZR+ MSA.

Senior Research Engineer, NTT Network Innovation Laboratories.
He received an M.S. in electrical engineeringfrom Shizuoka University in 1995. After hejoined NTT Optical Network System Laboratories the same year, he engaged in the research anddevelopment of adaptive chromatic dispersion
equalization and standardization activities in
ITU-T SG15 Q6. He is currently with NTT
Device Innovation Center.

NTT Technical Review

Vol. 18 No. 12 Dec. 2020108

Practical Field Information about
Telecommunication Technologies
Practical Field Information about Telecommunication Technologies

Manual for Assisting with
Troubleshooting in PHS Digital
Cordless Telephone Systems
Technical Assistance and Support Center, NTT EAST
Abstract

This article introduces a manual for assisting with troubleshooting in digital cordless telephone systems. The Technical Assistance and Support Center, NTT EAST, published this manual to help service
personnel in the field. This is the sixty-first article in a series on telecommunication technologies.
Keywords: digital cordless telephone, PHS, DCL, DECT

1. Introduction
Digital cordless telephones based on the personal
handy-phone system (PHS) (hereafter, called theDCL) enable voice communications between a master unit called a cell station (CS) and a slave unit
called a personal station (PS) via digital wirelesscommunications. The DCL is widely used by business customers and consumers in Japan. On the other
hand, the digital enhanced cordless telecommunication system (DECT), which uses the same frequency
band as the DCL (i.e., 1.9-GHz band), has come into
wide use as digital cordless telephones and intercoms
for consumers. This indicates that these two wirelesssystems, the DCL and DECT, co-exist in the same
frequency band, and the number of technical consultations concerning problem cases has been increasing.
The Technical Assistance and Support Center(TASC) of NTT EAST has published a manual toprovide efficient and simple troubleshooting methods
for digital cordless telephones for maintenance personnel on site. This manual presents simple techniques for evaluating wireless communication conditions without the need for dedicated measurement
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instruments, such as a spectrum analyzer or wirelessprotocol analyzer. It also provides a simple procedureon how to determine the cause of a problem.
2. Features of DCL and DECTThe DCL [1] and DECT [2], which both use the
1.9-GHz band, have the following features (their frequency allocations are illustrated in Fig. 1 [3]).
• DCL: When a call channel is used, the carriersensing function automatically selects an unoccupied channel. It automatically switches channels when interferences occur during a call.
• DECT: Before the revision of the Radio Law in
October 2017, it had five channels (from ch 1 toch 5), and the use of three channels (from ch 2 toch 4) was prohibited when a DCL radio wave is
detected for the purpose of protecting the DCL
control channels (ch 12 and ch 18). After therevision, the total number of channels wasincreased to six. Currently, the use of ch 1, ch 2,
ch 5, and ch 6 is allowed and the use of ch 3 andch 4 is conditionally allowed depending on theirtransmission power.

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Practical Field Information about Telecommunication Technologies

DECT call/control channel

DCL call channel

ch 251 ch 1

ch 11

ch 19

DCL control channel
ch 37(a) DCL
ch 1

ch 2

ch 3

ch 4

ch 5

ch 6

ch 1

ch 2

ch 3

ch 4

ch 5

ch 6

(b) DECT(c) Interferences between
DCL and DECT
1.89

Frequency (GHz)

1.91

Fig. 1. Frequency allocation of the DCL and DECT.

Table 1. List of estimated causes and countermeasures obtained from DCL problems cases.
Problem

Estimated cause
Handover*• Call is breaking up
Insufficient received• No soundsignal strength• Intermittent sound, etc. Interference with the
DECT

Countermeasure
Placement redesign of CS• Placement redesign of CS• Installation of additional CS• Turning off DECT terminal• Unification of wireless system (DCL or DECT)*Even in the case of normal handover operation, the call is interrupted for about 1 s.

3. DCL problem-isolation process flow
Table 1 summarizes the problems, estimatedcauses, and their countermeasures concerning the
DCL categorized using the data gathered from cases
we have dealt with thus far. The problems concerning
the DCL claimed by customers are roughly divided
into (i) “call is breaking up,” (ii) “no sound,” and (iii)“intermittent sound.” There are three factors that leadto such problems: (a) handover, (b) insufficient
received signal strength, and (c) interference withother wireless systems. The following are examples
of such problems:
Let us consider a case in which multiple CSs are
installed in an area and a customer holds a PS that isregistered with one of those CSs. The PS monitors
radio waves from the registered CS and receives radio
waves from other unregistered CSs. It switches its
registration to another CS when the received signal
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strength of the radio waves becomes weaker due tothe customer moving around. This operation is called
handover and a time lag of about one second occurs
by switching CSs, which is a normal operation of
wireless communication. However, some customers
claim that a problem occurred and say, “the call wasinterrupted” or “no sound.” In another case in whichsome CSs are closely located and their reach overlaps, a PS could attempt to keep receiving radio
waves from the registered CS even when the received
signal strength decreases. In this case, the handover
does not work well. This may increase errors and leadto deterioration in call quality.
To solve these problems, the terminal-maintenancemode of the PS enables easy checking of radio communication on site. This mode enables checking of
the received signal strength (level), identifier (ID) of
the registered CS (BS-ID), and frame error rate(FER). Various causes of a problem can be isolated by110Practical Field Information about Telecommunication Technologies

Set the DCL to terminal-maintenance mode
Judgment point

Conduct an extension-call test whilemoving near the reported area

Estimated cause
Countermeasures

Problem reproduction
Change of CS
installation position

Yes

Handover toappropriate CS

HandoverNo

No

Received signal strengthis 30 dBµV or less

Yes

Insufficient received
signal strength

Yes
Normaloperation

Placement redesign of CS
Installation of additional CS

No

Increase in FER

Yes

Possibility of interference by
DECT radio waves
Interference with the DECT
Search, placement
optimization, andelimination of DECT

Problem resolved
No

Possibility of rare problem  Consult TASC

Fig. 2. Flow chart of problem isolation in the DCL.

comparing these values before and after the problem
occurred.
A troubleshooting flow chart is shown in Fig. 2.
Before troubleshooting, the area where the problem
occurred is estimated through consultations with thecustomers, and reproduction of the claimed problem
is attempted. After the problem is observed, the PS is
set to the terminal-maintenance mode, and threeparameters are evaluated. First, the ID of the currently registered CS (displayed on the PS) is checked,
and the PS is moved to where the problem occurred.
The ID of the registered CS before handover is compared with that after the handover. If the CS selection
is appropriate, the received signal strength is thenchecked. The DSL determines if there is insufficient
received signal strength and executes handover when
the received signal strength is 30 dBμV or less.
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Around this limit of 30 dBμV, errors sometimes occur
due to quality deterioration of the call. If both CS
selection and received signal strength are appropriate,
the FER is finally evaluated. If the FER increased(approximately 24 or higher), the DECT or otherwireless systems could be interfering with the DCL.
By investigating a case according to the procedurein the manual using the terminal-maintenance mode,the cause of the reported problem could be determined and the primary countermeasure could beapplied on site.
4. Example case of problem
4.1 Details of problem
A customer using a DCL of the αA1 key telephonesystem reported that “calls in the store are breaking
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Practical Field Information about Telecommunication Technologies

Calls of all PSs
are breaking up

About 10 m

Office desk

CS

Kitchenette

DCL
Counter
Office desk
About 15 m

Automatic door

Fig. 3. Configuration of customer’s equipment.

Received signal strength (dBµV)
80
DCL
60

10-dB
difference

40

Radio-interference
frame error up to 19

DECT

Time:
5 ms

DCL

20
1.894

1.900

1.906

1.890

1.900

Frequency (GHz)

Frequency (GHz)(a) Frequency spectrum(b) Spectrogram

1.910

MR: multiplied responsivity

Fig. 4. Results of measuring radio environment.

up.” The customer’s equipment is illustrated in Fig. 3.
A CS is installed near the center of the first floor in
the store.
4.2 Investigation and results
The PSs were set to the terminal-maintenancemode on the first floor of the store, and the investigation was conducted according to the flow chart shownin Fig. 2. In this case, the handover was not executedsince there was only one CS. The investigation was
therefore started by checking the received signal
strength. The strength in the customer’s store was 40
dBμV or higher, so there was no issue with that. Thenthe FER was checked, revealing that the FER
increased in a specific place in the store.
The results of this investigation following the
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manual indicate that radio waves of other wirelesssystems may have been interfering with the DCL
radio waves. However, no equipment using otherwireless systems was found in the store. To evaluate
the electromagnetic environment in more detail, theradio-wave spectrum was measured using a spectrum
analyzer where the problem occurred. The results ofthat measurement are illustrated in Fig. 4. The frequency spectrum and spectrogram (i.e., time changeof the spectrum) when a call was interrupted areshown in Figs. 4(a) and (b), respectively. From Fig.
4(a), we can see the DCL signals exist in the frequency band of the DECT. Similarly, the spectrogram
revealed that the DCL and DECT signals overlap.
These results indicate that the reported problem wasdue to interference between the DCL and DECT112

Practical Field Information about Telecommunication Technologies

radio waves.
4.3 Estimation of cause
As a result of investigating the electromagnetic
environment including the area outside the customer’s store, we found that DECT terminals were being
used in an adjacent store and the radio waves fromthose terminals were interfering with other terminals
in the customer’s store. The DECT can usually prevent interference with DCL radio waves by detecting
the usage status of radio waves with carrier sensing
and switching channels depending on the results.
However, in the case of this customer’s environment,interference prevention would not work well because
the terminals in the two stores were far apart.
4.4 Countermeasure
We confirmed that wireless signals including thosefrom the adjacent store are transmitted by less than 12
slots in the customer’s area and replaced the customer’s PSs, i.e., A1-DCL-PS terminals (DCL), with
A1-DECT-PSSET terminals (DECT), which can beadapted for up to 12 slots (12 terminals) in the same
frequency band.
5. Current status of DCL problems
The number of technical consultations concerning
the DCL using the 1.9-GHz band is gradually increasing; we had 159 cases in 2018. The main reasons of
the consultations are “the call is breaking up,” “cannot make a call,” etc., and analysis of our on-site
investigation revealed that most causes of those problems are either interference with the DECT or handover. Moreover, some customers using the DCL often
report one-second interruption caused by normal
handover operation as a problem. Thus, it is important to explain that handover will occur with someinterruption to customers before they start using the

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DCL.
When a problem occurs, using the terminal-maintenance mode of PSs helps determine the problem
because service personnel can determine the operating status of the PSs and radio-wave environment.
Based on such information, measures that optimize
the wireless environment, such as adjusting the
installation position of CSs, increasing or decreasing
the number of installed CSs, and adjusting their signal power can be implemented.
The “DCL Troubleshooting Manual” is available
for download at CyberTasc (URL: https://www.
cybertasc.com/), a site for disseminating information
to telecommunication construction companies (limited access and written in Japanese).
6. Conclusion
This article introduced the content of the troubleshooting manual concerning the DCL. TASC provides this manual to improve the efficiency of suchtroubleshooting in the field. The Network Interface
Engineering Group in TASC works on solving various problems related to the network protocol, interface, signals, and so on, toward early resolution ofproblems on site. TASC will continue to actively
engage in technical cooperation and development anddisseminate its technologies through activities suchas technology seminars.
References
[1]
[2]
[3]

Association of Radio Industries and Businesses, “Second-generation
Cordless Telephone System (7.1 Ver.),” ARIB STD-28, 2018.
Association of Radio Industries and Businesses, “Radio Equipmentfor Radio Stations of Time-division Multiple Access Wideband Digital Cordless Telephones (2.0 Ver.),” ARIB STD-T101, 2018.
H. Ito, “Actual EMC Problems in Wireless Systems,” Electro Magnetic Compatibility (EMC), Vol. 379, pp. 33–42, Nov. 2019, Kagakujyoho Shuppan Co., Ltd (in Japanese).

113External Awards
ELEX Best Paper Award
Winners: Takuya Hoshi, Norihide Kashio, Yuta Shiratori, Kenji
Kurishima, Minoru Ida, and Hideaki Matsuzaki, NTT Device Technology Laboratories
Date: September 15, 2020
Organization: The Institute of Electronics, Information and Communication Engineers (IEICE) Electronics Society
For “InGaP/GaAsSb/InGaAsSb Double Heterojunction Bipolar
Transistors with 703-GHz fmax and 5.4-V Breakdown Voltage.”
Published as: T. Hoshi, N. Kashio, Y. Shiratori, K. Kurishima, M.
Ida, and H. Matsuzaki, “InGaP/GaAsSb/InGaAsSb Double Heterojunction Bipolar Transistors with 703-GHz fmax and 5.4-V Breakdown Voltage,” IEICE Electronics Express, Vol. 16, No. 3, p.
20181125, 2019.
Communications Society: Distinguished Contributions
Award
Winner: Toshihito Fujiwara, NTT Access Network Service Systems
Laboratories
Date: September 16, 2020
Organization: IEICEElectrical Science and Engineering Award
Winners: Hideki Maeda and Hiroki Kawahara, NTT Network Service Systems Laboratories; Asuka Matsushita, NTT Electronics
Date: September 28, 2020
Organization: The Promotion Foundation for Electrical Science andEngineering
For the development and deployment of Beyond-100G opticaltransport system.
Best Paper Award
Winners: Keisuke Tsunoda, Naoki Arai, and Kazuaki Obana, NTT
Smart Data Science Center
Date: October 7, 2020
Organization: Information Processing Society of Japan (IPSJ)
For “Estimating Number and Dwell Time from CO2 Concentration
Based on Partial Modelling with Variable Time Window.”
Published as: K. Tsunoda, N. Arai, and K. Obana, “Estimating Number and Dwell Time from CO2 Concentration Based on Partial Modelling with Variable Time Window,” Multimedia, Distributed, Cooperative, and Mobile Symposium (DICOMO) 2020, 2F-1, June 2020.

For his contribution to the operation of Technical Committees in
Communications Society.

Papers Published in Technical Journals andConference Proceedings
Relationship between Scenery Structure and Illusion
Strength on the Basis of the Three-dimensional Interpretation of the Café Wall Illusion
K. Maruya and T. Ohtani
Journal of Graphic Science of Japan, Vol. 54, No. 2, pp. 13–18,
September 2020.
When the shadows of a group of linear columns projected on a
stepped three-dimensional (3D) plane are diagonally viewed, theretinal image shows a geometric pattern that is similar to the Caféwall illusion, which is a popular geometric illusion. In this study, weinvestigated the relationship between the geometric structure of a

NTT Technical Review

Vol. 18 No. 12 Dec. 2020

scene, such as the 3D shape, viewing angle, and direction of the lightsource, and the strength of the Café wall illusion perceived from theretinal image of those 3D situations. The difference in the depth atwhich the shadows fall should be approximately 1–2% of the observation distance to result in a strong Café wall illusion. The results of
our investigation indicate that the information of the shadow that falls
on an object is an effective clue for estimating the shape of the object
surface with relatively small depth differences in the case of large
viewing distances.

114