NTT Technical Review

September 2022 Vol. 20 No. 9
View from the Top

Katsuhiko Kawazoe, Senior Executive Vice President, NTT

Front-line Researchers

Makio Kashino, NTT Fellow and Director of Kashino Diverse Brain Research Laboratory, NTT
Communication Science Laboratories

Rising ResearchersKenya Suzuki, Distinguished Researcher, Project Leader, NTT Device Innovation CenterFeature Articles: Access Network Technologies for IOWNAccess Network Technologies to Implement IOWN

R&D Challenges in Solving Social-infrastructure Problems

Wireless Technologies for Future Network Services Tailored to the Requirements of Various
Users and ApplicationsTechnical Approaches for Operations to Accelerate Digital Transformation



50 Years at Tsukuba R&D CenterFeature Articles: Intent-based Application, Cloud-server, and Network Collaboration
Technologies
Intent AI Mediator (Mintent) for High-satisfaction-level ServicesTechnology for Extracting and Converting Various and Ambiguous Intents to Implement
MintentOptimize Cloud-server Resources for Comfortable Web Conferencing

Intent-based Application and Network Cooperative Control Technology for Video-streaming
Services

Regular Articles
Wireless-link-quality Prediction and Device-position Estimation Based on Relationshipbetween Wireless-communication-link and Physical-space Information

Development of Compact/Power-saving Optical Open Line System

Global Standardization Activities


Report on the 34th Asia-Pacific Telecommunity Standardization Program Meeting

External Awards/Papers Published in Technical Journals and Conference Proceedings

View from the Top

Let’s Open the Door beyond Limits
with Hope
Katsuhiko Kawazoe
Senior Executive Vice President, NTT
Abstract

The NTT Group is taking on the challenge of creating a Smart World thatenriches the lives of all people around the world through its efforts in avariety of fields. By implementing the Innovative Optical and Wireless
Network (IOWN), the group aims to solve social issues and create innovative services. We interviewed Katsuhiko Kawazoe, senior executive vicepresident of NTT, who coined the term “IOWN,” about the development
status of IOWN and his approach to his work as a top executive.
Keywords: IOWN, research and development, space datacenter

The target for implementing IOWN is set to beachieved by Expo 2025 Osaka, Kansai, Japan—Congratulations on your new position. Whatchanges have you seen since assuming your current
position?
The change in my position from the head of the
Research and Development Planning Department to
my current position as senior executive vice presidentof NTT has changed the sense of time in which I perceive matters. For example, we must predict and create the future during the research process. If we do
not proceed while constantly thinking about what lies
ahead of current technology, by the time the research
results are published, they may be obsolete. When I
was head of the Research and Development Planning
Department, my main focus was advancing researchand development by looking at the results that would
be achieved in the near future while considering thedirection and importance of the research and technology in question from a medium- to long-term perspective.
On becoming senior executive vice president, I’ve
become responsible for not only research but also
technology in general as chief technology officer. My
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Vol. 20 No. 9 Sept. 2022

prime mission is to solve the social issues we are currently facing as quickly as possible by using ourtechnologies. This mission also includes improving
or developing technologies for securing communications as social infrastructure in times of disaster andother emergencies and technologies for providing
high-quality and stable services. Of course, I’m alsoin charge of research and development, so I’ll promote such activities from a medium- to long-term
perspective. Another important role of mine is tosmoothly introduce the results of advanced researchand development into our business in a manner that
contributes to society; in that sense, I believe theimplementation of IOWN is a symbolic theme.
I also hold the positions of chief information officer(CIO) and chief digital officer (CDO), who must
focus on digital transformation (DX). Akira Shimada,the new president and chief executive officer of NTT,
has stated that he will “create better CX (customer
experience) through better EX (employee experience).” DX is essential for this purpose, so I intend toactively promote DX in cooperation with other CIOs
and CDOs of NTT Group companies.

1View from the Top—What initiatives should we pay attention to? First,
let’s talk about IOWN, the term you coined, which hasentered its third year.
Since the announcement of the concept of IOWN in
2019, various social issues have been highlighted,
such as the importance of achieving carbon neutrality, the recent shortage of semiconductors, and ensuring Japan’s economic security against the backdropof unstable global situations. In light of the need torespond quickly to these issues, we moved the target
set for implementing IOWN forward five years from2030 to 2025. This change was largely due to the
influence of the IOWN Global Forum (IOWN GF)
established in 2020. To tackle these social issues, wemust collaborate with experts and global partners in awide range of research and technical fields. IOWN
GF, launched with the support of Intel and Sony
Group, has grown to include more than 100 participating companies and organizations, including major
technology companies such as Ericsson of Sweden
and NVIDIA of the US. The number of companies
participating in the forum is increasing annually,
demonstrating that IOWN is attracting attention
worldwide. Thanks to the progress of discussions at
IOWN GF and research at NTT, we could move the
IOWN implementation target forward. Consequently,
we plan to present IOWN-related products to theworld at Expo 2025 Osaka, Kansai, Japan, which willdraw global attention.
Various innovations are possible with IOWN, andI’d like to talk about one of them, the Space Integrated
Computing Network. In July 2022, in collaboration
with SKY Perfect JSAT, we established Space
Compass Corporation to construct the first space
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datacenter in human history in a manner that overcomes the current limitations on the use of space. As
I mentioned in my interview in 2019, when I joined
the company in 1987, my research theme was satellite
communications, and NTT was developing and operating communication satellites even then. Later, due
to the widespread use of terrestrial optical fibers andthe enormous cost of developing communication satellites, NTT withdrew from the business. However,recent technological innovations have enabled highspeed communication by using higher frequencies.
Because of the potential of these innovations, we cansay that the possibility of achieving what we had previously abandoned has increased.
As exemplified by the recent disruption to social
life caused by communication failures in Japan,
building a solid infrastructure that can cope withuncertainties is NTT’s social mission. Communication
satellites are playing an increasingly important role inaccomplishing that mission. Against this background,
we plan to establish the first space datacenter toaddress this social mission.
The initiative for building the Space Integrated
Computing Network consists of two projects: (i) ahigh-capacity communication and computing infrastructure in space and (ii) a communication infrastructure for beyond fifth- and sixth-generation
mobile communication systems (beyond 5G/6G).
The former project’s aim is to provide a high-capacity communication and computing infrastructure inspace, i.e. a space datacenter, by sequentially deploying geostationary orbit satellites equipped withadvanced computing capabilities. NTT plans to demonstrate its high-capacity optical transmission technology in space at the aforementioned World Expo2

View from the Top

and intends to expand this service worldwide.
The latter project’s aim is to launch space radio
access network businesses, such as image sensing
through high-altitude platform stations installed in
the stratosphere. By taking up the challenge of building new infrastructures, such as optical wireless communication networks in space and mobile networks inthe stratosphere, we hope to contribute to developing
the global space industry and creating a sustainable
society.
NTT’s strengths are its underlying technology
and accumulated knowledge—I’m fascinated by IOWN and other ambitious projects. They are being developed through the researchand technological capabilities of NTT, correct?
I believe that one way to hone our foresight inresearch and development is to continue our research
activities while thoroughly pursuing basic research.
The current times, issues, and trends tend to drive
people to focus on technologies and research resultsthat can be used immediately; for example, GAFA
(Google, Apple, Facebook, and Amazon) are heavily
investing in artificial intelligence (AI) now. Some saythat companies or researchers and developers withsuch a mindset are using the achievements of others
to develop the technology underpinning AI.
NTT laboratories, however, are involved in basic
research and fundamental technologies. The basic
research on light that supports IOWN has accumulated over more than 60 years since the 1960s. NTT’s
strengths are its foundational technologies and accu-

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mulated knowledge. We can continue our research by
generating research funds from the profits from ourbusinesses and can use these hard-earned profits todemonstrate the necessity and importance of basic
research. Precisely because our researchers have been
fulfilling their responsibility to demonstrate theirideas, we have been able to continue our research
activities to this day.
Throughout my career at NTT, I have served asboth a researcher and developer. Perhaps more thanhalf of my work has been in business-oriented development. When I joined NTT, research and development were likened to the two wheels of a cart, emphasizing the importance of keeping both wheels runningwell. In other words, there were roles for people
specializing in either research or development and forpeople responsible for both, turning both wheels, so
to speak. I was positioned right in the middle ofresearch and development—turning both wheels.
—It is reassuring that you know about both researchand development. Do you have a specific vision for
how research and development should be conducted?
The idea that I’m currently developing is to ask
employees responsible for research and development
to focus on either research or development instead of
the two roles that I have played in the past. I understand that this is a difficult request. Research life has
its ups and downs, and when things aren’t going well,
one may work on development, get an idea from it,then resume research to find a way to survive. By taking on both roles, one will be able to take on a certain
amount of work in a stable manner and probably get

3View from the Topa sense that they are doing their job and serving apurpose. However, if one is asked to focus on researchor development, they must work extremely hardbecause their escape route is cut off.
Doing what I suggest is tough, and not everyone
will be able to overcome this challenge, but I wantthem to understand how much greatness awaits
beyond that pain. As senior executive vice president,
it is my job to create a challenging environment that
fosters a willingness to work hard. I also strive tosecure enough resources for research and development to ensure that our researchers, developers, andemployees can enjoy their work and be happy.
Think of work and private life as a combination
rather than as separate—I understand that you are also active as the head ofvarious organizations. How do you balance work andprivate life?
In addition to being president and chairperson of
IOWN GF, I’m also president of the Institute ofElectronics, Communications and Information
Engineers and, in a different vein, president of theJapan Windsurfing Association.
I’m sometimes asked, “How do you manage yourtime when you are so busy?” I do not try to distinguish between work and private life; rather, I view mywork and private life as a combination.
For example, let me talk about windsurfing. Whenit was decided that a major international sporting
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event would be held in Tokyo, Tsuneo Hasegawa,then president of the Japan Windsurfing Association,
visited me and said, “Mr. Kawazoe, please makewindsurfing a popular sport. Can’t we do that byusing NTT’s technology and ideas?” In response, I
said I wondered if it would be possible to use Kirari!,
NTT’s ultra-realistic viewing technology, to deliver arealistic sensation of watching a competition in aremote venue. We were very excited about the idea. I
was told that Mr. Hasegawa said before his death, “I’dlike to ask Mr. Kawazoe to be the next president.” I
decided to accept the offer because I used to windsurf
in my student days. When I’m practicing windsurfing
while remembering those days, ideas and applications for the next technology or service come to mind,
and the conversations with the other windsurfers
inspire me to apply the ideas that come to mind for
my work.
I’m also involved with cooking, and to improve mycooking skills, I joined the All Japan Food Association.
Chefs and cooks who met me at conferences andother events talk about archiving their cooking andfood through the technology of NTT. They say, “Thefood we create is a once-in-a-lifetime work of art, andwe want to preserve it.” Their words express theirwish to record the entire food-preparation process—from the filleting of a fish to preparing the dish—with
the power of information and communication technology. My visions and ideas began to expand as I
wondered what kind of technology I could use to help
them actualize their passion or what IOWN could do
for them. This situation illustrates exactly what I
4

View from the Topmean by thinking of work and private life as a combination rather than separate, in a way that is fun andmeaningful. Don’t you think that everything is connected and that both work and play stimulate eachother in various ways?
—It is important to have a broad perspective andcomprehensive view of matters. Finally, please say a
few words to researchers and developers.
To researchers, I’d like to see you hone your expertise in either research or development rather thanboth. Aim to be number one in your field of expertiseand accomplish what no one else has done. With thespirit that you are the only one working on thisresearch, you should aim to be the very best. Unlike
researchers, developers are constrained by development costs and time and must consider what they can
do within those constraints. You should challengeyourself while keeping a strong awareness of what
you can do within those constraints of your goals andthe conditions. Whether your focus is research ordevelopment, sometimes you may feel uneasy; nevertheless, at such times, it is essential to set a starting
point based on yourself or to think of yourself as asource of inspiration. If you are a researcher, you’lltake great pleasure in achieving results in the form of
papers or academic awards and being respected bysociety; if you are a developer, you’ll take great pleasure in seeing your developments implemented insociety and in people’s daily lives. I hope that you
will approach your daily work with that joy in mind.

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Although I won’t say it’s my motto, I always keep
the phrase “stress-free” in mind and try not to stressmyself too much. Speaking like this may make meseem emotionally detached, but that’s not the case.
On the contrary, if you force things while trying to
break through a metaphoric brick wall, you will beleft with feelings of pain and even more stress, just as
hitting an actual wall with your hand hurts.
Alternatively, when you take on a big challenge,
rather than thinking of the process as forcing something to break through a wall, imagine a door. Then,
open the door while imagining what could be on theother side. Let’s open this door with hope, even if others say we are at our limits.

Interviewee profile
Katsuhiko Kawazoe joined NTT in 1987. He
became vice president of the Research andDevelopment Planning Department in 2008, headof NTT Service Evolution Laboratories in 2014,
head of NTT Service Innovation Laboratory
Group in 2016, and executive vice president,
head of Research and Development Planning
Department in 2020, and assumed his current
position in June 2022. He has also served as
director of NTT Research, Inc. since April 2019and president and chairperson of the IOWN
Global Forum since January 2020. He received aPh.D. in engineering.

5

Front-line Researchers

Intrinsic Motivation Is the Driving
Force in Developing a New
Methodology
Makio Kashino
NTT Fellow and Director of Kashino
Diverse Brain Research Laboratory,
NTT Communication Science
Laboratories
Abstract
In 2021, the Japanese national team won the gold medal in thewomen’s softball competition at a major international sporting
event held in Tokyo. Behind this triumph was a new attempt toclarify the brain mechanisms that enable top athletes to execute
their superior techniques and improve such techniques throughtraining using the latest information technology. We interviewed
NTT Fellow Makio Kashino, who is exploring the mechanisms of the mind and body by focusing on theimplicit brain function, and asked him about his research activities and his approach as a researcher.
Keywords: implicit brain function, cognitive neuroscience, inverse translational science

Uncover and develop the brain function of which
you are unaware—Can you tell us about the research you are currently working on?
What I am consistently interested in is the implicit
brain function, that is, the brain function that wehumans are not aware of. The various activities ofdaily life, such as understanding situations, making
decisions, performing appropriate actions, communicating with others, and feeling various types of emotions, are all the result of an enormous amount of
information processing in the brain, and we areunaware of most of those processes. In fact, our mind
is supported by our implicit brain function.
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The implicit brain function is also deeply connected to our body. For example, athletes achieve superhuman feats by skillfully moving their bodies instantaneously without having to think about it. When weare moved by music, we also have physical reactions
such as trembling and shedding tears. Physical discomfort leads to mental discomfort. The researchgoal of our project is to explore the mechanism of theimplicit brain function, namely, the interface between
mind and body, and establish a methodology that
leads to a more desirable state of mind and body.
This process inevitably involves the issue of diversity. Since each person’s brain, body, and accumulated experiences are different, there is no single
correct interpretation of the desirable state. Therefore, how can we enable people to make the best use
6Front-line Researchers

Activities of thepersons concernedLaboratory

Identification of
intrinsic problems

Cognitive neuroscience
Information technology
Physical skill proficiency
and dysfunction

Professional baseball teams,
NTT EAST/NTT WEST semiprofessional baseball teams

Japan Softball Association

Reflective marker

Mind-body interaction
Scene camera

Interpersonal interaction
NTT e-Sports
Docomo Team Dandelion Racing

Developmental
disorders

Eye camera

Fast ballSnowboard Big Air

Hearing
loss

Music

Slow ballVerification of effectivenessFig. 1. Exploring the implicit brain function with the approach called inverse translational science.

of their individual characteristics, maximize theirabilities, and achieve a sense of well-being? We hopeto provide the scientific and technical foundation foranswering this question.
—What is your specific approach to research activities?
Over the past few years, I have been emphasizing
the approach called inverse translational science.
Instead of researching in a direction from basic
research to real problems, research should be conducted in the direction from real problems to basic
research. This mindset does not simply mean thatapplied research with a defined purpose and shortrange goal is preferable. As Alan Watts said, “If you
want to study a river, you don’t take out a bucketfulof water and stare at it on the shore.” I have come torealize that the research on cognitive neurosciencethat we, researchers, have been doing is a bit likeexamining water in a bucket. That is, if we conduct
experiments with established methodologies in the
research field, the problem setting itself may be unrealistic and off the mark in terms of actual humanactivities.
With the above-described considerations in mind,
first, I decided to go back to looking at the actual
river. One of the fields characterized by themes suchas implicit brain function and diversity was, in mycase, sports. We are attempting to do basic researchon cognitive neuroscience while keeping in mind the
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direction of solving real problems faced by the people
involved (Fig. 1). We will not only publish the resultsthat we obtain as a paper but also feed them back to
the field to validate them and their effectiveness.
Considering the results, we repeat the feedback loop
so that we can delve deeper into the next problem. It
has been about five years since we implemented thisapproach, and, fortunately, with the cooperation of
top athletes and various others in the field, we are
beginning to produce concrete results.
We are focusing on three main research themes.
The first theme is the state of being proficient in certain skills and conversely, the state of being in aslump. The second theme is the mechanism of mindbody interaction, so-called mentality. In other words,
we want to reveal the mechanism by which an athlete
fails when they become nervous or succeed when
they are in the zone. The third theme is interpersonal
interaction, namely, the mechanism by which thepresence of others, teammates, opponents, or spectators, influences an athlete’s performance. Since wewant to elucidate the implicit brain function, or
unconscious processes, involved in these themes, it ispointless to ask the person in question, and it is essential to have an objective method of measuring thefunction in as natural a situation as possible. Devisingsuch a measurement method is part of the research.

7

Front-line Researchers

Eye-tracking measurement

Spatial memory test

Pitch-quality measurement

Visuomotor tasks
2019 NTT Atsugi R&D Center2017 Gunma

2017 Okinawa

2018 USA2019 Kochi

2020 Guam, USA

Fig. 2. Initiatives with the Japan women’s national softball team.

Contribution to winning a gold medal at the
major international sporting event held in 2021
—Your practical work with the Japanese women’s
softball team, which won the gold medal at the majorinternational sporting event held in Tokyo in 2021,
was a great achievement.
That work has certainly become a symbolic example of our approach. Of course, the outcome of agame is partly a matter of luck, so they could have
won the gold medal even if we had not been involved.
It goes without saying that the team’s staff and players are the ones who made the most of their time andeffort. Thus, I hesitate to say that we contributed towinning the gold medal; however, it is true that wehave had a close relationship with the Japan Softball
Association since we launched the Sports Brain Science Project (currently, Kashino Diverse Brain
Research Laboratory) in 2017. It was decided inAugust 2016 that softball would return to the official
competitions of the international sporting event to beheld in Tokyo. Immediately after that decision, Shinsuke Yabata, deputy training manager (now generaltraining manager) of the Japan Softball Association,
Reika Utsugi, head coach of the national softballteam, and others showed interest in our research. Perhaps the desire to take on new challenges resonatedNTT Technical Review

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on both sides. In October 2017, we signed an agreement to conduct a joint experiment and have beenworking with the national softball team on various
initiatives to put into practice the motto “train yourbrain and win the competition” (Fig. 2).
One example of our joint efforts is a pitchingmachine, which was featured as a “secret weapon” ontelevision and in newspapers after the competition. At
first glance, it does not look much different from apitching machine with a video screen at a batting
cage. However, this apparatus is a pitcher simulator
that reproduces as precisely as possible the pitching
form and quality of pitches thrown by pitchers whoare expected to compete against each other. The
pitching form and pitch quality are based on footage
taken by members of our research team at international matches. Provided with that footage, we used a
proprietary algorithm to analyze the pitch-rotation
information and reproduce it in a programmable
pitching machine. This machine can be considered asolution, from the perspective of cognitive neuroscience, to the problem that Mr. Yabata has been talking
about since the beginning, namely, “The biggest challenge to winning the gold medal is to counter moving
fastballs thrown by US pitchers.” Our experiments
demonstrated that good batters use information about
the pitching form and the trajectory of the ball immediately after it is released by the pitcher to predict the8Front-line Researchers

ball’s arrival point without being aware of doing so.
Even if the batter knows that pitcher A’s riseballchanges by a certain number of centimeters at a ball
speed of over 100 km/h, the batter will not be able tohit the ball. To refine the predictive model in the batter’s brain, the batter must repeatedly watch the videoof the pitch, “experience” the trajectory of the ball,attempt to hit the ball, then learn the correspondence
between these factors. Our pitching machine enabled
batters to carry out this task in the absence of actual
competition due to the COVID-19 pandemic.
Although it is difficult to quantitatively evaluate theeffectiveness of this machine, the fact that the players
were actively using it at such a crucial time—justbefore the competition—seemed to us to be the mostpositive evaluation of all.
—It is great that you have gained the trust of top athletes. Can you tell us again about the academic andsocial significance of this initiative?
I think the primary significance lies in the fact that
we have practiced the approach of conducting
research from real problems to basic research. Thisapproach is easy to say but hard to apply properly.
There is a reason that until now there has been little
interaction between basic research on cognitive neuroscience and an actual practice like sports. Basic
researchers simplify a problem, break it down into its
elements, and attempt to uncover the mechanism
behind the phenomenon causing the problem throughrigorously controlled experiments. From this viewpoint, actual sports is too complicated to handle. The
complexity of the challenge in the laboratory and inan actual sporting environment vastly differs. Take
softball as an example. Pushing a button of an experimental apparatus in response to a simple visual
stimulus is completely different from using one’s
entire body to hit an American pitcher’s moving fastballs. Even if we examine the effects of psychologicalstress, the ethically acceptable level of stress that canbe exerted in a laboratory is incomparable with that
faced by a batter in a gold-medal game. From theperspective of basic researchers, it is impossible towrite a proper paper on a real problem, and from theperspective of sports, scientific findings are impractical.
Therefore, our aim was to bring up real problems
for discussion in the realm of basic research without
sacrificing reality. This would have been difficult ten
years ago. In the late 2010s, wearable-sensor andcomputer-vision technologies advanced dramaticalNTT Technical Review

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ly; thus, it became possible to measure the movements and biological responses of athletes in action ina relatively natural way. We are also actively adopting
these technologies as well as developing new ones.
For example, when we analyzed the relationshipbetween the state of the autonomic nervous systemand performance by measuring the heart rate andbody movements of athletes during actual competition, we found phenomena that could not be observedin a conventional laboratory setting.
Advanced information technology can also be used
to modulate implicit brain functions and improve
unconscious movement. The aforementioned pitcher
simulator is one example of such technology, and weare also developing various training systems using
technologies such as virtual reality, motion visualization, and motion sonification. These systems are usedby top athletes in the field, and we are improving
them while listening to the opinions of these athletes.
Naturally, such initiatives will contribute to not
only improving performance of athletes but alsochanging the world of sports. Sports is said to involvea combination of three fundamentals: mind, skill, andbody. However, although scientific training for bodyhas become widespread, those for mind and skill has
yet to become widespread. It is not uncommon forcoaches to force their athletes to do things on thebasis of their own successful experiences or to label
athletes who do not do well as having no sense or
weak mentality. As the implicit brain function that
supports mind and skill becomes more fully understood, systematic and rational methodologies fordeveloping abilities in accordance with the characteristics of each individual athlete should become mainstream.
We are not only focusing on the world of elitesports. We have targeted athletes because, in a sense,
their implicit brain function appears in the purest
form. We believe that the methodologies and accumulated knowledge developed in this field can beapplied to various types of people such as the young,elderly, and those with disabilities with diverse characteristics and in different situations.
The starting point of research is pursuing what isinteresting and irresistible—What is your current attitude toward research
activities?
I have become aware of the importance of goingback to the basics. On reaching a certain level in your9

Front-line Researchers

career as a researcher, you’ll stop getting so excitedabout your research or the research of others. Youmight feel, “OK, I get the picture…” However, when
I think back on my childhood, I literally forgot to eat
and sleep as I was absorbed in space, castles, music,
electronics, and other topics. My passion had nothing
to do with schoolwork, and I was acknowledged by
no one. In fact, even if I was prohibited from doing
something, I wouldn’t be stopped. I was motivatedpurely by intrinsic feelings such as curiosity and fun.
I suppose that I’m just inclined that way. As it turns
out, that intrinsic feeling is my strongest source ofenergy. I don’t think it’s easy to reach a point ofexpressing one’s originality through extrinsic motivation, such as being told to do certain research or
choosing a certain research theme, because the topic
is currently popular, might produce results, or willallow you to be acknowledged.
Quite often, Nobel-Prize-class research is the resultof new entry of researchers into a field that differsfrom their original specialty. In some respects, since
the new entrants do not know the conventional wisdom in that field, they are able to come up with ideasthat overturn the conventional wisdom. If you arefamiliar with a certain field, you may implicitly set a
problem that seems to be solvable by using an established methodology in that field. Essentially, you
should first decide what you want to know then thinkof a methodology of acquiring that knowledge
accordingly; however, the more you advance along
your research-career path, the more you find that thisprocess is somehow reversed. One of my motivations
behind starting research in sports brain science wassuch reflection. It is not easy to come up with new
methodology of doing things, but trying to do so is
exciting.
However, simply doing what is interesting and what
you like to do is hardly enough for research professionals. To secure research funding and recruit teammembers, you must be able to explain the value of
your research effectively from a variety of perspectives. That sounds like opportunism; however, it is

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also a good opportunity to think about the potential ofyour research from the perspective of others.
—How do you plan to proceed in the future?
I want to hit the jackpot! In academic terms, thatmeans overturning established theories or proposing
new concepts. In social terms, that means changing
people’s lives or shaking up their view of humannature. However, I haven’t accomplished anything onthat level yet. Regardless, I’ve been taking on a newchallenge over the past five years, and I feel that thisendeavor is expected to produce quite interesting
results. We are now entering a phase of more serious
exploration and development based on these challenges. Although my focus is on the academic side, Ialso want to pursue social and business impact at thesame time.
This endeavor, of course, cannot be accomplishedalone. It is essential to collaborate with researchers
from various disciplines as well as athletes, teamstaff, business people, and others in various capacities. Fortunately, we have already established such a
network and are gradually expanding it. I have foundthat like-minded people naturally come together forinteresting projects, even if they have different positions and backgrounds. It is quite common for athletes to be almost exclusively researchers in terms ofmentality.
The laboratory that I lead is unique in that many of
its members are young and have moved from completely different fields. They voluntarily joined thelab moving from within and outside the company
because they each like sports, music, and other things
they couldn’t get enough of. In that sense, their intrinsic motivation was sufficient, and the fact that they
already had a background in another field was also
advantageous. I’d like to provide an environment inwhich they can take on essential themes without fearof taking risks. If each one could create a new methodology for research on humankind, it would be agreat asset to society.

10Front-line Researchers

Interviewee profile
Makio Kashino received a B.A., M.A., andPh.D. in psychophysics from the University ofTokyo in 1987, 1989, and 2000 and joined NTT
in 1989. He is currently a visiting professor at theGraduate School of Education, the University ofTokyo. He has been investigating functional andneural mechanisms of human cognition, especially auditory perception, cross-modal and sensorimotor interaction, and interpersonal communication.

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11

Rising Researchers

Smartphotonics® for Highperformance Optical Functional
Devices That Are Essential for the
Future of IOWN
Kenya Suzuki
Distinguished Researcher, Project
Leader, NTT Device Innovation Center
Abstract
Optical functional devices are becoming increasingly widelyused and important. There is a need, however, to improve not only
their functions and performance, but also development time andmanufacturing throughput in order to meet the sophisticated anddiverse demand specifications. We interviewed Distinguished
Researcher Dr. Kenya Suzuki about smartphotonics® to addressthese problems in the manufacture of optical devices.
Keywords: s martphotonics ® , process feed-forward method,
optical device

Smartphotonics® to manufacture optical
functional devices—Tell us about smartphotonics®.
Optical device technology is important in a wide
range of areas, including optical communications andoptical sensing, such as for the Innovative Optical andWireless Network (IOWN) All-Photonics Network(APN). There are challenges, however, in terms of
performance limitations and manufacturability intheir design and manufacturing processes. Smartphotonics® is defined as a technical approach to optimize
research and development (R&D) and improve manufacturing through automation and statistical prediction to facilitate the fabrication of high-precision
optical functional devices (Fig. 1).
NTT Technical Review

Vol. 20 No. 9 Sept. 2022

Conventionally, errors in the manufacturing processes for optical devices have been manually corrected, but many problems from human errors occur
due to the dependence on individual skills. We started
research on smartphotonics® for the automation andconsistent production of high-performance opticaldevices.
To explain using architecture as an example, inconstructing buildings, tolerance levels are definedfor the accuracy of cutting wood and other processes.
Any deviation within a certain range is acceptable inconstructing a building without any gaps. In general,a building is completed after going through many
processes. If tolerance levels are defined for each of
those processes, there would be a need to ultimately
allow a very large tolerance level. One of the goals ofsmartphotonics ® is to extremely minimize the12Rising Researchers

Wafer process

Implementation process

Precise measurement

Precise processing

Characteristics estimation

Automated optimization

Optimal chip selection
Machine learning/automation

Machine learning/automation

Wafer evaluation

Material convergence

Wafer fabrication
Thin film
deposition

Photolithography

Optical connection ofvarious material chips
InP

PLC
Microscopic
image

Multi chip assemblyFilm thickness
distribution

Fig. 1. Overview of smartphotonics®.

tolerance levels and deviations in each process inorder to improve the performance of the final product. In other words, research on smartphotonics® isaimed at making sure that we can achieve the desired
performance by correcting errors in the previous process at every manufacturing step to eventuallyachieve stable quality. This may be similar in principle to the smart factory. For example, the issue of
automation is a commonly recognized problem, andresearch is being conducted mainly focusing on
achieving smart operations by leveraging the characteristics of optical devices.
The Optical Switch Module Project, where I serveas the project leader, is developing a device called amulticast switch. This is the key device used for routing and switching optical paths to provide an alternative route to ensure communications, for example, inthe event of an earthquake or disaster that causes the
optical fibers to fail. This switch is fabricated byrepurposing the manufacturing technology for the socalled LSIs (large scale integrated circuits), such as
central processing units and memory. My group isdeveloping a multicast switch using silica-based planar lightwave circuit (PLC) that uses the same glassmaterial as optical fibers to create an integrated optical circuit on a wafer. Research on smartphotonics®
was started as research on fundamental technologies
for creating such devices with high performance.
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Vol. 20 No. 9 Sept. 2022

The motivation for this research is to manufacturehigh-performance optical functional devices requiredby the APN. We also want to reduce device reworks
that occur due to various errors in the manufacturingof optical devices. We believe that smartphotonics®
technology will enable the stable and high-volume
manufacture of optical functional devices, even in the
future when demand for such devices grows due todiversification of applications, such as when optical
functional devices come into widespread use in ordinary households.
—How do you specifically manufacture with smartphotonics® technology?
We have proposed a process feed-forward method
as the key to the implementation of smartphotonics®
technology. The process feed-forward method isbased on the idea of creating high-performance optical functional devices by reflecting the error information generated in the manufacturing process into later
processes and sequentially compensating for theerrors in each process. Although optical device
wafers are manufactured through various processes,
they are not optimized at each process as describedabove, but the different processes are coordinated sothat they can produce a good product as a whole. In
addition to wafer processing, we are also considering
13Each process had been optimized independently,
resulting in accumulated errors.

Process feed-forward method compensates for errors byreflecting the history of previous processes in each fabrication step.
Process 1

Process 2

Performance withresidual error

Target performance

Performance

Rising Researchers

Process N

Feed-forward control model

Performance estimation(nominal model)
Feedback parameter
derivation algorithm
Plant (control)

Measurement

Opticalperformance
estimation

Control

Fig. 2. Outline of the process feed-forward method.

its use in developing devices that combine different
materials such as silica-based PLCs and optical semiconductors. While it is normal to yield good performance when you combine products with good performance results, our goal is to create high-performancedevices that maximize performance to achieve thetarget values, even when combining products of moderate performance.
Figure 2 illustrates the process feed-forward method in the manufacturing process of optical functional
devices. In optical integrated circuits using wafers as
substrates, the index of refraction has a large effect on
the optical characteristics of devices. This refractive
index value can vary every time the device is manufactured, which causes a major problem. To address
this problem, first, we precisely measure the refractive index and thickness of glass and then use statistical techniques to estimate the performance as an
optical device. When discrepancy and error are likely
to occur, we change the thickness, etc. of the core partthat propagates light in the next step and optimize theprocess so that the equivalent refractive index is thesame. Unlike in conventional processes where errors
would accumulate and performance would widely
vary, the process feed-forward method enables thestable manufacture of high-performance devices withminimal performance variations through repetition ofthese corrective steps in each process.

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Vol. 20 No. 9 Sept. 2022

Realizing the IOWN concept by creating
high-performance optical functional devices—What are the future prospects for applications ofsmartphotonics®?
We have just started our research on smartphotonics®. To eventually realize the IOWN concept using
this technology, we are aiming to further develop anddeploy various devices for wide use. Currently, we
are mainly applying smartphotonics® to silica-based
PLC technology. We intend to apply the ideas andessence of the technology polished through thisapplication to optical semiconductors and opticaldevices based on other materials in order to ensure
good performance.
Smartphotonics® could be a fundamental technology that underpins research on various optical devices. The field of optical devices has thus far beenhighly specialized for each material and conversations among engineers beyond their expertise havenot always been easy. We want to expand the applications of smartphotonics® across a variety of disciplines and enable sharing of the essence of each areaof expertise. One of our goals is to create an unprecedented integrated device that combines the best ofeach material by deploying smartphotonics® technology in all devices.
The manufacturing of optical devices also has its
own unique properties; some devices are being manufactured using equipment with a slightly older design.
For areas where old and difficult-to-replace equipment is still used, we believe that smartphotonics®
can be applied to the automation technology for thecontrol of those equipment. The digital transformation
14

Rising Researchers

become trends in the real world. This is a message toyoung researchers and students.
Going forward, through R&D, I will work withresearchers, students, and business partners to create
value that is recognized not only in Japan but also
around the world.

market is estimated to reach several hundred billionsof yen by 2030. We believe that some of the technologies we have developed in our research can be
deployed to a wide range of manufacturing areas
where these old equipment remain, such as small
businesses and local factories. They can be used as
background technologies for automation and the shift
to Internet of things.
—Could you give a message to researchers, students,
and business partners?
I currently belong to the NTT Device Innovation
Center, which I think provides one of the best R&D
environments in Japan. In particular, we have accessto a wide range of hardware research facilities. I think
the NTT’s R&D environment, which enables you totest and realize your ideas, is ideal for those who
enjoy doing research and are fond of creating newthings or taking on different challenges.
Although writing papers is also a part of our activities, the NTT Device Innovation Center is an organization aimed at the practical use of devices, so you
are expected to conduct development of devices thatgive users a value and can actually be used. Thisentails constantly having conversation with users andidentifying their issues, as well as creating industry
trends. Because the optical device industry has a
global market, meetings with foreign counterparts
occupies a great part of our daily activities. In thissense, R&D of devices is characterized by a different
kind of rigor from writing papers, but it is interesting
to see the techniques and methods you propose

NTT Technical Review

Vol. 20 No. 9 Sept. 2022

Interviewee profile
Kenya Suzuki received a B.E. and M.E. in electrical engineering and Dr. Eng. in electronics
engineering from the University of Tokyo in
1995, 1997, and 2000. In 2000, he joined NTT.
From September 2004 to September 2005, he wasa visiting scientist with the Research Laboratory
of Electronics, Massachusetts Institute ofTechnology, Cambridge, MA, USA. From 2008
to 2010, he was with NTT Electronics Corporation,
where he was involved in the development andcommercialization of silica-based waveguidedevices. In 2014, he was a guest chair professor at
the Tokyo Institute of Technology. He served as
secretary of the Optoelectronics ResearchTechnical Committee of the Institute ofElectronics, Information and Communication
Engineers (IEICE) of Japan from 2014 to 2016.
He was a member of the subcommittee of Passive
Optical Devices for Switching and Filtering of
the Optical Fiber Communication Conference(OFC) from 2019 to 2021, and served as the chairof the subcommittee in 2022. He is currently adistinguished researcher and project leader at
NTT Device Innovation Center. He is also thedirector of the New Energy and Industrial
Technology Development Organization (NEDO)
project on the photonic and electronic hybrid
switch for future datacenter networking of
Photonics Electronics Technology Research
Association (PETRA). His research interests
include optical functional devices and opticalsignal processing. He was the recipient of theYoung Engineer Award and Electronics Society
Activity Testimonial from IEICE in 2003 and2016. He is a member of the Institute of Electricaland Electronics Engineers (IEEE), Optica,
IEICE, and the Physical Society of Japan (JPS).

15

Feature Articles: Access Network Technologies for
IOWN

Access Network Technologies to
Implement IOWN
Yuji Aoyagi
Abstract

NTT Access Network Service Systems Laboratories is engaged in research and development (R&D)
on access networks that link customers to NTT central offices, supporting the world’s communication
infrastructure technology with the world’s most advanced and cutting-edge R&D. In this article, we
introduce our R&D initiatives for the improvement of network functions, smarter operations, and thecreation of new value through using our assets to implement the Innovative Optical and Wireless Network (IOWN).
Keywords: access network, IOWN, R&D

1. Introduction
NTT Access Network Service Systems Laboratories (AS Lab) was established in July 1972 as theConstruction Technology Development Office. Its
current name was adopted in January 1999 after several reorganizations and the merger with departments
in Makuhari (Chiba prefecture) and Yokosuka(Kanagawa prefecture). In 2014, a department of AS
Lab was set up in Musashino (Tokyo Metropolitan
Capital region), and in July 2021, following the reorganization of NTT laboratories in conjunction withthe establishment of NTT Innovative Optical andWireless Network (IOWN) Integrated Innovation
Center, AS Lab’s development work was transferredto NTT Network Innovation Center. In July 2022, AS
Lab marked 50 years since its establishment.
At the time of its establishment, we worked on the
research and development (R&D) of outdoor communication facilities and the development and popularization of technologies to construct and maintainthem in an efficient, safe, and secure manner. To
economically achieve high-speed data-communication services in the period of popularization andexpansion of Internet connection services, we haveconducted R&D on access networks, including optical access systems, operation systems to support the
spread of optical services for quick installation and
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Vol. 20 No. 9 Sept. 2022

efficient maintenance, wireless systems for the provision of seamless access, and infrastructure facilities
such as underground conduits and tunnels.
Society 5.0 is currently being studied as a sustainable and resilient society that integrates cyberspaceand physical space (cyber-physical system). As ameans to achieve this, the Beyond fifth-generation/
sixth-generation (5G/6G), which is the next-generation mobile communication system, is being studied
for launch around 2030, and the R&D of IOWN isbeing accelerated toward early implementation in
2030. As the need for fixed-line broadband services
continues to be strong due to the increase in remote
work caused by the COVID-19 pandemic, expectations for access networks have become even greater.
We are working toward the implementation of
IOWN on the basis of five access network elemental
technologies, namely, access system technology,
wireless access technology, optical fiber access technology, infrastructure technology, and operation
technology, to achieve its mission of “achieving anatural and smart society by continuing to create andsupport services through research and developmentof leading-edge access network technology.” The
three core R&D policies to implement IOWN are (1)to meet extreme requirements and support diversification of services, (2) dramatically make operations
smarter, and (3) use assets for new business areas
16

Feature Articles

Achieving a natural and smart society throughimplementing IOWN

R&D to meet extreme
requirements and support
diversification of services

R&D to dramatically make
operations smarter

R&D to use assets for
new business areas

Operation technology
Access system technology

Leading-edge elemental
technologies

Optical fiber access technology

Wireless access technology

Infrastructure technology

Diversifying needs and working styles, decreasing working population, achieving carbon neutrality,and disaster and infectious disease countermeasures

Fig. 1. R&D policies at AS Lab.

(c) Space RAN(a) Distributed MIMO/
analog RoF(e) Concatenateddloop topology(b) Real-time control
technology(d) Multi-radio proactive control
technologies (Cradio®)Fig. 2. R&D to meet extreme requirements and support diversification of services.

(Fig. 1).
The following sections introduce the main technologies that promote R&D under the above threepolicies.
2. R&D to meet extreme requirements andsupport diversification of services
To meet the extreme requirements for the telecom
domain and support diversification of services, we
are working to innovate communication and infrastructure technologies that will exceed the limits of
network performance and innovate flexible network
technologies that will meet the needs of users andservices (Fig 2).
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Vol. 20 No. 9 Sept. 2022

2.1 I nnovations in communication and infrastructure technologies to exceed network
performance limits
In the area of innovation in communication andinfrastructure technologies, we are engaged in R&D
on technologies to increase the speed and capacity ofwireless and optical communications, reduce thedelay of access links, and expand coverage for highspeed access at sea, in mountainous areas, and in thesky.
In the IOWN/6G era, millimeter-wave and subterahertz wave bands, which are higher frequencies
than those used in current systems, must also be used
for higher speed and capacity. However, the higher
the frequency band, the smaller the communication17

Feature Articles

area. The distributed multiple-input multiple-output(MIMO) technology achieves stable high-capacity
wireless transmission even in a mobile and shieldedenvironment by arranging a large number of antennas(Fig. 2(a)). The area coordination technology withanalog radio over fiber (RoF) for the use of high-frequency bands and high-density deployment of remoteradio units (RRU) achieves remote beam control bysubcarrier multiplexing to send main signals and control signals at one wavelength and reducing signal
distortion through level adjustment in multiplexing
analog and digital signals. This results in installation
and workability improvements through the miniaturization and low power consumption of RRUs andoperation and optical fiber cost reductions by reducing the required number of wavelengths.
Both the need for remote operations and demandfor performing advanced tasks remotely will increase.
Therefore, in addition to end-to-end low latency andlow jitter, returning immediately to normal conditions in the event of network congestion or edge
overload is required. We are investigating real-time
control technology that executes transmission andedge control to switch to optimal optical paths andedge resources, respectively (Fig. 2(b)). By using
timely information collection and switching control,
it becomes possible to switch immediately even forcommunication quality change, enabling services toremotely control drones and robots without stress.
To expand coverage, we are studying a space radio
access network (RAN) using non-terrestrial network
technologies such as geostationary-orbit satellites,
low Earth-orbit satellites, and high-altitude platform
station (HAPS) (Fig. 2(c)). The provision of a space
RAN is expected to support worldwide mobile communications with ultra-wide coverage and improved
disaster resistance as well as enhanced 5G and 6G.
HAPS platforms can also interconnect to the nearest
terrestrial network gateway and extend the reach ofexisting mobile services directly to end-user devices,
providing service options including rural, emergency,
and maritime connectivity.
Wireless communication systems will be expanded
to use high-frequency radio bands to increase thecapacity of communication, but this presents a serious problem in regard to ensuring coverage becauseradio waves do not extend beyond the line-of-sightrange. Therefore, we are researching and developing
a technology to control the direction of radio-wavereflection using a reconfigurable intelligent surface.
We have confirmed in a demonstration experiment ina world first that the radio-wave-reflection direction
NTT Technical Review

Vol. 20 No. 9 Sept. 2022

from a base station can be dynamically changed inaccordance with the movement of a terminal toalways form a radio communicable area.
2.2 I nnovations in flexible network technologies
to suit users and services
As a wireless technology to develop a network thatmatches user services, we are investigating multiradio proactive control technologies (Cradio®) thatcan continuously provide a natural communication
environment without users having to be aware of how
they are using the wireless network through cooperation with diverse systems and applications (Fig. 2(d)).
Cradio® is broadly composed of the following threetechnology groups: Understanding (wireless sensing/visualization), Prediction (wireless-network-quality
prediction and estimation), and Control (wirelessnetwork dynamic design/control). It provides anoptimal wireless environment for users by creating awireless access network suitable for various application requirements amid ever-changing communication quality in wireless channels by implementing
these three technology groups in a highly advancedmanner, interlocking them, and cooperating withvarious applications.
To develop an inclusive optical fiber network fordiversified services, we are investigating new opticalaccess network designs based on a concatenated loop
topology (Fig. 2(e)). The demand for optical fiber
communication will change, and the business model
will change to B2B2X (business-to-business-to-X).
Therefore, the optical access network that supports
IOWN must respond to the change in the businessmodel. The concatenated loop topology achieves
three goals for the optical access network: reliability(optical path duplexing for supporting ultra-reliable
services), tolerance for demand variation (fiber reassignment for meeting uncertain demands), and pathselectivity (optional cable routes for directly connecting decentralized base stations) and provides the optical fiber communication services that meet the various needs of service providers.
3. R&D to dramatically make
operations smarter
We are conducting R&D on smart engineering andmaintenance as an effort to achieve the ultimate smart
access network by digitizing facilities and operations(Fig. 3).
Smart engineering uses remote-operated opticalfiber switching nodes and optical coupling technology
18Feature Articles

Remote-operated
optical fiber
switchingnode (indoor)

Remote-operated opticalfiber switchingode (ouddoor))
doonode(outdoor)
S
Secondaryloop 1
Corrosion-rate
diagnosis

Primaryloop
S
Secondaryloop 2

nication signal
signallCommunication
Control signal/power-over-fiber

Recognize facilities areas

Base
Base
station(a) Remote-operated optical fiber switching node(b) Pole-gripping
construction technology(c) Image-recognition AI technology

Fig. 3. R&D to dramatically make operations smarter.

(Fig. 3(a)). Optical access networks offering rapid
and flexible services will be required to predict theservice area and communication capacity, which is
challenging. To respond to such demand fluctuations,
we are promoting R&D on node technology that
remotely switches the outside nodes installed at theconnection points between the upper and lower loopsof the concatenated loop topology previously introduced and optical coupling technology that arbitrarily
branches from existing optical fiber cores.
We are also investigating pole-gripping construction technology to reduce construction burdens andsignificantly improve safety for on-site contractors(Fig. 3(b)). We aim to achieve this by replacing the
traditional role of a human with a machine that controls the gripping force and grip structure withoutslippage, damage, or breakage, regardless of polematerials and installation conditions.
In smart maintenance, we are researching on
image-recognition artificial intelligence (AI) technology that can distinguish multiple types of infrastructure facilities from images of roadside facilities takenusing a mobile mapping system (MMS) to detect rustthat has formed on the facilities (roadside facilities
and utility pole-mounted facilities) (Fig. 3(c)). This
makes it possible for separate field inspections currently carried out by each infrastructure manager tobe consolidated into one MMS run. This is expected
to reduce the operational costs of inspections. We areaiming to achieve efficient maintenance of all social
infrastructure by using digital information.

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Vol. 20 No. 9 Sept. 2022

4. R&D to use assets for new business areas
We are engaged in R&D to develop new businessareas that will become new sources of revenue forNTT, using the assets it has cultivated in the fields ofaccess systems, wireless access, optical fiber access,infrastructure, and operations: operation expertise,communication facilities, and communication technologies (Fig. 4).
In operation technologies using operation expertise, we are investigating business design support
technology to improve business processes throughrobotic process automation (RPA) (Fig. 4(a)).
Although digital transformation requires an understanding of the current situation based on a highly
objective business analysis, analyzing complex business processes requires specialized skills and a significant amount of time, making it difficult to achieve.
This technology focuses on the co-occurrence of
operation logs and automatically classifies similar
operations while absorbing fluctuations in operation
procedures. It also extracts frequent operation flows
automatically by using sequence alignment. By analyzing and visualizing operation logs, it simplifies thebusiness-process design without requiring any special skills. Users can also edit and output RPA scenario files for automating operations from visualized
operation flows, resulting in the continuous evolutionof business operations.
In optical fiber environmental monitoring using
communication facilities, a deployed optical fiber
network is used as a sensor to obtain high-valueadded information that can provide various social
19

Feature Articles

Decision Decision

AnalysisWorkprocess

Hydrogen tank
H
(Supplier)Optimization
proposal fromthe system
Records

Collection

V bratVibration
bratio
tio d
da
data
a

mechanism

Rules,
work
process

Hydrogen tank
(Customer)

Sensingequipment

Implementation
Manhole, conduit

Flexible procedures
and cooperation(a) Business design support technology to
improve business process(b) Optical fiber environmental monitoring(c) Hydrogen transportation
using communication facilities

Fig. 4. R&D to use assets for new business areas.

services (Fig. 4(b)). Vibrations in the optical fiber
network are recorded in real time using highly sensitive measurement technology. IOWN’s All-Photonics
Network transmits large amounts of high-precisionvibration data, and the data-centric infrastructure
converts it into environmental information. New business can thus be created with optical fiber networks.
As increasing the use of hydrogen energy is expectedto achieve carbon neutrality, the challenge is to
reduce the transportation cost of hydrogen from the
supply areas to demand areas. We are also studyinghydrogen-transportation technology using existing

communication facilities (Fig. 4(c)).
5. Conclusion
This article presented the direction of R&D on
access networks toward IOWN and the main technologies in R&D on the three core policies. We will
promote R&D of access network technologies andachieve further sophistication of social systems andthe development of new business fields by implementing IOWN in view of the changing circumstances and progress toward a smart world.

Yuji Aoyagi

Vice President, Head of NTT Access Network
Service Systems Laboratories.
He joined NTT in 1992 and has been engaged
in developmental research on optical access network systems. He has been in his current position
since July 2019.

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Vol. 20 No. 9 Sept. 2022

20

Feature Articles: Access Network Technologies for
IOWN

R&D Challenges in Solving Socialinfrastructure Problems
Teruhisa Awata
Abstract

To solve problems surrounding social-infrastructure projects, such as a declining workforce, proliferation of aging facilities, increasing maintenance and renewal costs, and frequent disasters, and to create a “smart world,” NTT Access Network Service Systems Laboratories is engaged in research anddevelopment centered on three pillars: (i) digital transformation of maintenance and operations, (ii)construction of safe and secure social-infrastructure facilities, and (iii) use of assets such as facilities andoperations. In this article, the technologies related to these three pillars are introduced.
Keywords: safe and secure social infrastructure, digital transformation (DX) of maintenance, use ofcommunication infrastructure

1. Initiatives of the Civil Engineering Project

2. Technologies for creating a smart world

As well as facing a declining workforce, a rapid
increase in aging facilities, and rising maintenanceand renewal costs, the social-infrastructure industry
must accommodate growing disaster risks due to climate change and seismic motion. To operate socialinfrastructure facilities under these circumstances, it
is urgent to address the following key issues: maintenance and operation that requires less manpower,
renewal that optimizes the life-cycle cost (LCC),
disaster prevention and mitigation, and reduction inenvironmental impact. The Civil Engineering Projectof NTT Access Network Service Systems Laboratories will contribute to the creation of a “smart world”
through their research and development (R&D) on
the basis of three pillars: (i) digital transformation(DX) of maintenance and operations, (ii) construction of safe and secure social-infrastructure facilities,and (iii) use of assets such as facilities and operations(Fig. 1). In this article, the technologies that the Civil
Engineering Project is developing—while aiming tocreate a smart world—are introduced.

2.1 DX of maintenance and operations
NTT uses a mobile mapping system (MMS) anddrones to photograph outdoor communication facilities and uses the acquired images to check facility
conditions and diagnose facility deterioration. We at
the Civil Engineering Project have been researchingand developing inspection and diagnosis technology
for communication infrastructure and facilities using
image recognition. Specifically, we have developed
technologies using image-feature analysis and deeplearning to automatically detect deterioration of manhole covers (i.e., surface wear and the gap between amanhole cover and the surrounding road) and rust on
communication conduits attached to bridges.
Although these technologies are used for diagnosing
the condition of NTT’s facilities, the image-recognition technology must be further improved.
We are currently researching and developing
image-recognition technology that will contribute to
the collaborative inspection of social infrastructure
along roadsides. The goal with this technology is to(i) identify pixel areas of roadside structures, such as
guardrails, and pole-mounted facilities, such as
cables and metal fixtures, from multiple images ofsuch roadside infrastructure facilities taken using

NTT Technical Review

Vol. 20 No. 9 Sept. 2022

21

Feature Articles

Creation of a smart world

Social contribution
by creating innovation

DX of maintenanceand operations

Creating a safe
and secure society

Construction of safe
and secure socialinfrastructure facilities

Effective use ofsocial capitalUse of assets suchas facilities
and operations

Fig. 1. Three initiatives of the Civil Engineering Project.

Fig. 2. Results of infrastructure-facility recognition and rust detection.

MMS with image-recognition artificial intelligence(AI) and (ii) detect the pixel areas containing rustformed on the facilities (Fig. 2). To achieve this goal,
the image-recognition AI incorporates multiple functions (Fig. 3). One function is facility-recognition AI,
which can identify roadside infrastructure facilities
with high accuracy by sufficiently learning the types
of facilities that appear in various forms and compositions. Another function is rust-detection AI, whichuses ensemble learning* [1] to detect minute amountsof rust in pixel areas on a pole-mounted facility andrust in pixel areas in dark areas of images taken underbacklit conditions. Facility-recognition efficiency
with this technology will be approximately 94% andrust-detection efficiency will be approximately 98%,
which are sufficiently high for practical use.
Implementation of the above image-recognition
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Vol. 20 No. 9 Sept. 2022

technology for collaborative inspections of roadsidesocial infrastructure is expected to promote the centralization of on-site inspection work (which has
conventionally been conducted by separate infrastructure operators) and improve the accuracy offacility diagnosis using AI. We now aim to improvethe efficiency of the maintenance and management ofsocial infrastructure by centrally managing facility
information acquired during such inspections on acommon platform.

*

Ensemble learning: A method of generating a single training
model by fusing multiple models (“learners”).

22Feature Articles

Fig. 3. Features of image-recognition AI.

2.2 C
 onstruction of safe and secure social-infrastructure facilities
2.2.1 Technology for extending service life of
structures
NTT maintains facilities in a variety of environments throughout Japan. We aim to establish technology for operating facilities to the load-bearing-capacity limit while ensuring safety by maintaining quality
and optimizing operational resources, that is, maintaining the safety and reliability of a vast amount offacilities without using a large amount of manpower.
Current inspections of infrastructure facilities andequipment are conducted at regular intervals while
the inspection cycle is being optimized. To further
improve the efficiency of inspection operation, weplan to establish a deterioration-prediction technology that can predict the future condition of facilities on
the basis of deterioration phenomena and individual
environments. This prediction is based on elucidating
three factors: the materials that make up the facility,
mechanism of deterioration in each installation environment, and rate of deterioration. The optimal
inspection period for each facility will be determinedusing this deterioration-prediction technology; inother words, it will become possible to eliminate the
NTT Technical Review

Vol. 20 No. 9 Sept. 2022

need for periodic inspections.
In the current infrastructure-facility repair process,when deterioration is confirmed during inspection,
whether to repair is determined in accordance withthe state of deterioration, any deterioration determined to be in need of repair is then repaired appropriately. To further reduce repair costs, it is necessary
to shift from the current partial repair of deterioration
to reinforcement based on an evaluation of the loadbearing capacity of the deteriorated structure. We
plan to establish a structural-analysis technology for
determining the future load-bearing-capacity limit ofa structure by reflecting the future deterioration stateof each facility through structural analysis and evaluating its load-bearing capacity. This structural-analysis technology will clarify the optimal reinforcementtiming for the structure, enabling the facility to operate safely for a long period while optimizing the LCC(Fig. 4).
2.2.2 D amage prediction for infrastructure facilities due to seismic motion
During the 2011 Great East Japan Earthquake in the
Tohoku region of Japan and other major earthquakes,
communication facilities were severely damaged by23

Feature Articles

Current

FutureSoundness

Soundness
Repair for
deterioration events

Deteriorationprediction technology

Load-bearing
capacity

Structural-analysis
technology

Inspection

Inspection
Inspection
Inspection

Inspection

Inspection

Reflection ofdeteriorationprediction
results

Years

Years

Reinforcement
criteria as astructure
Years

Fig. 4. Optimizing LCC.

seismic motion and tsunamis. The Japanese government has announced assumptions concerning largescale damage by possible earthquakes, such as amassive earthquake along the Nankai Trough or anearthquake directly under the Tokyo metropolitanarea, and these assumptions will further increase thedemand for the safety of facilities against earthquake
damage.
Communication-infrastructure facilities, such asservice tunnels, manholes, and conduits, have been
made highly resistant to earthquakes, including theGreat East Japan Earthquake, thanks to the additionof seismic countermeasures (such as flexible joints)
in the facilities. We have been researching and developing technologies that enable old-specification conduits to be seismically reinforced, without having to
excavate roads and remove the cables, by using thenew pipe-insertion type conduit system.
We are currently researching and developing technology for predicting damage to individual conduits
in the event of an earthquake. These predictions will
enable us to identify conduits that require seismic
reinforcement among the vast number of conduitsthat we maintain. We created a database for comparing a large number of parameters (such as facility
information, topography and ground information,and seismic-motion information) regarding the damage caused by past large-scale earthquakes, identified
the effective parameters for estimating damage
through machine learning and variable-contribution
analysis, and built an easy-to-use prediction model.
The prediction model enables us to identify the locations of damage to individual conduits, which will
enable us to take proactive measures such as planned
countermeasures, prompt emergency inspections,and emergency repairs (Fig. 5).
Through the above efforts, we confirmed that a
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Vol. 20 No. 9 Sept. 2022

prediction model that is machine-learned using information on facilities and the surrounding environment
is an effective tool for predicting disaster damage.
Although the content of the database used formachine learning will vary depending on the type ofdisaster, we believe that the technological approach(i.e., the developed prediction model based onmachine learning) can be applied to various types ofdisaster prediction, and we will attempt to constructdisaster-prediction models for disasters involving
severe wind and flooding. We also believe that themodel and technology can be extended to businesses
operating “lifeline” facilities such as electricity, gas,
and water and sewage systems.
2.3 U
 se of assets such as communication facilities
and operations
Since the start of optical-communication services
to general households in 2001, such services have
rapidly spread, and current access networks are shifting from metal cables to optical-fiber cables. Compared with metal cables, optical-fiber ones are lighterand thinner, so they can be accommodated more
efficiently, thus increase the space available in communication-infrastructure facilities. One example ofeffectively using this space is transportation of hydrogen via communication conduits, and we are verifying technologies related to this application.
As a carbon-free energy that contributes to reducing energy-supply and procurement risks, hydrogencan be produced by electrolysis of water or reformingof biomass by using renewable energy. It is thus
attracting attention as one of the next-generationenergies for building a decarbonized society. Tanker
trucks are currently the main means of transportinghydrogen; however, the challenge is to lower the costof transportation and reduce emissions of carbon
24

Feature Articles

Predicting facility damage

Prediction model
erFacility

Topography

Seismic motion

Damage record

Pipe type
Span length
Elapsed years

Average elevation
Average tilt angle
Liquefaction, etc.

Peak ground• Velocity• Acceleration

Post-disasterinspection data

Riv

Conduit A:

Ra
ilw
ay

DangerConduit B:
CautionRoad

StationConduit C:
Safe

Machine learning

Fig. 5. Technology for predicting facility damage due to seismic motion.

Communication manhole

H2
Communicationbuilding

Communication conduit
Hydrogen pipeline(unaccommodated conduits)

End point<Ex.: Facility
configuration>

H2

Inner
diameter:
80 mm

Outer
diameter:
62 mm

Pipe-in-pipe
system

End point
H2

H2

H2

Starting point(hydrogen production plant,
End point
etc.)(hydrogen station, etc.)
Hydrogen station- Public/commercial FCV fleet- Heat/electricity (fuel cell), etc.

H2
Hydrogen pipeline

Starting point

End point

Conduit

ManholeManhole

FCV: fuel cell vehicle
H2: hydrogen gas

Fig. 6. Transportation of hydrogen via communication facilities.

dioxide. One way to face this challenge is to use pipelines that use existing infrastructure.
Transporting hydrogen by pipeline requires airtighttransportation because the gas needs to be pumped
under high pressure. Therefore, we are considering apipe-in-pipe system in which an airtight pipeline for
NTT Technical Review

Vol. 20 No. 9 Sept. 2022

hydrogen transportation is accommodated in theexisting communication conduit (Fig. 6). This system
requires a flexible structure that enables the hydrogen-transportation pipeline to be laid within existing
communication conduits and manholes. We are also
evaluating safety in terms of hydrogen leakage and its
25

Feature Articles

impact on communication facilities as well as studying countermeasure technologies against such leakage.
3. Concluding remarks
The Civil Engineering Project is committed todeveloping technologies—including the initiatives
described in this report—to create new social value
by leveraging our assets: operational expertise, communication facilities, and communication technolo-

gy. We will further promote the above R&D activities
and contribute to solving issues faced by society as awhole by sharing our technology and expertise withfacility-management companies facing similar challenges.
Reference[1]

H. Kawaguchi and Y. Nakatani, “FAI: False-aware Artificial Intelligence That Validates Confidence of Estimation,” Journal of the Institute of Electronics, Information and Communication Engineers, Vol.
104, No. 2, pp. 149–155, Feb. 2021 (in Japanese).

Teruhisa Awata

Project Manager, Civil Engineering Project,
NTT Access Network Service Systems Laboratories.
He joined NTT in 1996 and has been engaged
in developmental research on communication
infrastructures. He has been in his current position since July 2021.

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Vol. 20 No. 9 Sept. 2022

26

Feature Articles: Access Network Technologies for
IOWN

Wireless Technologies for Future
Network Services Tailored to theRequirements of Various Users andApplications
Yasushi Takatori
Abstract

For the IOWN (Innovative Optical and Wireless Network)/6G (6th-generation mobile communication
system) era, wireless systems and use cases, applications, and terminals will evolve, and users and service requirements will diversify. Therefore, drastic innovation is needed, especially for wireless access,which is sensitive to the environment. This article introduces technologies for expanding the potential ofwireless-access performance, control technologies that make the most of the expanded potential, andwireless-environment-verification technologies that support the development of various services.
Keywords: wireless, Extreme NaaS, Cradio®

1. Introduction
Wireless communication began with its use as acommunication infrastructure that connects distantpoints. With the practical application of satellite communication, it then played a role in delivering communication to all locations. Car phones, mobile
phones, and PHS (personal handy-phone system)
have become more common as access methods thatenable voice communication anytime. From around2000, when data communication and wireless local
area networks (LANs) using mobile phones became
common, Internet connection by wireless accessbecame widespread, speeding up and diversifying
devices such as mobile terminals/smartphones, notebook personal computers, and intelligent machines.
The use cases have greatly expanded due to this synergistic effect. As shown in Fig. 1, wireless access is
now a means for everyone to access the Internet andis an indispensable means of communication in people’s lives.
Wireless access has been developed mainly throughcellular systems that address public wireless access
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and wireless LANs that address private wirelessaccess. A local (private) fifth-generation mobile communication system (5G), which provides wirelessaccess in a local area with a cellular system, has also
appeared, and wireless access is diversifying. For the
6G era, new technologies should open up various use
cases by using public and private wireless access systems to create new value that exceeds that of connecting to the mobile Internet.
The total amount of wireless-access traffic is predicted to triple in 2026 compared with that in 2021
[1]. There will also be a significant change in thecontent of the traffic. From around 2020, variousorganizations began to work on technological development related to 6G cellular and next-generation
wireless LANs (Wi-Fi 6E, Wi-Fi 7). These technologies enable building a new wireless environment thatcan withstand such an increase in traffic. The Innovative Optical and Wireless Network (IOWN) Global
Forum members announced IOWN in May 2019, andinnovations are about to begin in the network. IOWN
will make it possible to connect all people and things
to the network and link everything happening in the27

Feature Articles

Explosive growth in communications trafficwith increased value diversification

Creating value beyond“Current Internet × Mobile”

Value of connecting
everything

Expansion of radio frequencies used anddiversification of radio systems
Now

Wireless technology for enabling
networks that adapt to users andservices“Call” to “Communication”
Expansion of usage
Value of Internet through mobile

Value of maintaining basic communication
2000

2010

2020

2030~

Fig. 1. Transition of value created through wireless communication in society.

Mobile and fixed converged core
Cooperative
Function dedicatedinfrastructure
network
platform

Virtual end points
Wirelesssensing

APN (All-Photonics Network)

Environment tracking
across methods
5G/6G

Private 5G

Wi-Fi

IoT wirelessProviding a network that keeps pacewith the environment:
Maximizing potential

Cradio®

High capacity

Unexplored area

Advanced wireless access:Further expansion of potential

3D automatic
modeling

Radio-strength
estimation

Optimal base-stationplacement design

Radio-space-reproduction technology
High capacity

Unexplored area
Wireless sensing

IoT: Internet of Things
3D: three dimensional

Fig. 2. Extension of the network to the wireless access area to meet extreme requirements.

world, such as human experiences and various physical phenomena, and wireless access will be responsible for creating new value. At NTT laboratories,
network services that maintain end-to-end andextreme service requirements, including wirelessaccess, are called Extreme Network as a Service(Extreme NaaS). We are researching and developing
Extreme NaaS, as shown in Fig. 2.
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To further develop wireless access, technologies
that expand the potential of wireless access and thosethat draw out the expanded potential are required.
Increasing the potential of wireless access requires
two approaches: expanding the available frequencies
and improving frequency-utilization efficiency. To
fully draw out the expanded potential, technology tofollow the changes in the wireless environment and28

Feature Articles

meet the required wireless quality is necessary.
NTT Access Network Service Systems Laboratories conducts research and development on increasing this potential by using two approaches. One
involves developing frequencies by sharing frequencies with different systems [2, 3], and the other
involves creating new high-frequency bands that had
not been used to expand the available frequency
resources. To improve frequency-utilization efficiency, we are focusing on developing spatial areas thatuse new wireless devices.
To follow the changes in the wireless environment,
we are researching and developing Cradio®—a set of
multi-radio proactive control technologies that consists of three advanced and coordinated technology
groups: wireless sensing/visualization, wireless-network-quality prediction/estimation, and wirelessnetwork dynamic design/control.
Toward the development of such new wirelessaccess, we are actively contributing to the development of core technologies, standardization of new
wireless systems, and establishment of laws and regulations that enable the effective use of new frequencies. We are promoting research and development incollaboration with various partner companies, universities, research institutes, and NTT Group operatingcompanies, to create new value.
This article introduces advanced wireless accesstechnologies to expand the potential of wirelessaccess. It also introduces tracking technology forchanges in the wireless environment and radio-spacereproduction technology that supports various wireless-access verifications to expand network services
that meet the extreme requirements of the wirelessdomain.
2. Advanced wireless access technologies toexpand the potential of wireless accessTechnology areas that expand the potential of wireless access include those for increasing capacity,
developing into unexplored regions, and integrating
non-communication areas. Potential expansion playsa central role. Frequency bandwidth × frequencyutilization efficiency expresses the capacity of wireless access. The following explains efforts to expand
the available frequency bandwidth. In the low-frequency band where many systems already exist, an
overlay approach is suitable in which different wireless systems share frequencies. The high-frequency
range of 100 GHz or higher is suitable for developing
new use cases.
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For frequencies below the 1-GHz band, IEEE
(Institute of Electrical and Electronics Engineers)802.11ah (11ah), a new wireless LAN for Internet-ofThings (IoT), will be available in Japan [2]. The 11ahenables a wireless LAN at a frequency of 1 GHz orless with low radio-wave attenuation and low penetration loss, which drastically decreases the coverageholes and transmits high value-added informationsuch as video transmission over a wide range. To
develop wireless LANs for IoT, more stable operation
will be achieved by considering new factors such asbattery operation and optimizing in accordance with
the 920-MHz-band radio-wave law that stipulates
many rules for operation. We are making efforts tomake this possible, as shown in Fig. 3.
We are also working on developing a frequency
band that exceeds 100 GHz. For the 6G era, the use of
frequencies above 100 GHz, which makes it possible
to secure a vast bandwidth, is promising. As shown in
Fig. 4, we were the first to start modeling propagation
characteristics up to 300 GHz in an outdoor urban
environment and contribute to the development of
frequency bands by contributing to the Beyond 5G
Promotion Consortium and the international standardization organization ITU-R (International Telecommunication Union - Radiocommunication Sector).
It is time for significant changes in the radio systems throughout all frequency bands. The potential of
future wireless access will significantly grow by
combining the frequency expansion with frequencyutilization technologies that focus on the spatial axis,
such as distributed multiple-input multiple-output
technology.
3. Tracking technology for changes in thewireless environment
The wireless environment will be highly complex
as various wireless systems have been implemented.
The wireless terminals and use cases will become
more diverse and multi-layered. The selection,
design, and control of optimal wireless accessaccording to user requirements and location is complicated. Considering the changes in the environmentand conditions over time, it is challenging to fully use
the potential of multiple wireless-access-system environments.
NTT laboratories are researching and developing
Cradio ® to solve this complicated problem andachieve a broader range of optimization by linking
with various industries and applications other than
29

Feature Articles

Expected use cases and issues
Use case

Operation image by using our technology

Wide-area video
transmission by 11ah
Stabilization

Conventional area(~100 m)Automatedmanagement

Wide area with growing demand (100 m - 1 km)
Issues

WAN

Area
expansion

Transmission-timerate limitation

Transmission instability

Interferences

Operation complications

Battery operation

Quality degradation

Server(control software)

11ah AP* Control software can also be installed in the cloud.
11ah: IEEE 802.11ah
Wi-Fi for IoT using sub-1 GHz

AP: access point
WAN: wide area network

Fig. 3. Wide-area wireless-LAN-management technology.

Mobile communication-system frequency band

60

6G

10 GHz

100 GHz

Frequency

Path loss (dB)1 GHz

ITU-R M.2412 path loss model at 2-GHz
ITU-R M.2412 path loss model at 26-GHz
ITU-R M.2412 path loss model at 300-GHz70
Untappedfrequency
band

5G

Measurement data at 2-GHz
Measurement data at 26-GHz
Measurement data at 300-GHz

80
90
100

110
300-GHzantenna 120
130

300 GHz10

Tx-Rx distance (m)100Modeling with cross-frequency
measurements
Rx: receiver
Tx: transmitter

Fig. 4. Efforts to develop frequencies above 100 GHz.

wireless [4, 5]. Cradio® is shown in Fig. 5, which, asmentioned above, consists of wireless sensing/visualization technology, wireless-network-quality prediction/estimation technology, and wireless-networkdynamic design/control technology. The wirelessnetwork follows changes in the environment anddemands by repeating understanding, prediction, andcontrol. We aim to continue to meet the requirementsof users and services.
Figure 6 shows an example of achieving the optimization of wireless access in conjunction with the
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Vol. 20 No. 9 Sept. 2022

database information of a distribution warehouse.
Cradio® can optimally design and control the wireless system for the warehouse environment by obtaining the information on the planned change of theenvironment in the warehouse from the database. It
takes the data for multiple functions for radio-environment control, e.g., three-dimensional (3D) modeling, automatic optimal design, wireless-network
redesign, and management. Therefore, if IOWN
makes it possible to link in real time with informationand functions possessed by various industries,
30

Feature Articles

Develop to achieve a network in which all things that werepreviously optimized individually are connected, interlocked,
and harmonized as a whole

Diverse application systems…

Cradio®
Prediction

Constantly changing

Diverse wireless networks

Control

Wireless-networkquality prediction/
estimation
Private/public
Licensed/unlicensed

Wireless-network
dynamic design/
control
Coordination

Diverseenvironments
Wireless sensing/visualization
Understanding
Diverseterminals

Diverse use cases
Achieve a natural communication environment in which one does notneed to be aware of the wireless network

Fig. 5. Cradio®.

Wireless (e.g., shielding) environment varies
depending on cargo variation/layout

Automatic design and control
in response to environmental changes
Cradio®

Operational robot
Surveillance robot

Morning

3D modelBase station

Base station

Automatic optimal
designWireless-network
redesign

Afternoon
Shielding

CAD model

Radio-qualityestimation results

Operational robot

CAD: computer-aided design

Fig. 6. Examples of how Cradio® is applied to logistics solutions.

Cradio® will harmonize the entire wireless network
including applications. We believe this will lead tothe creation of new value.
4. Radio-space-reproduction technology
Through the sophistication mentioned above, we
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aim to expand the potential of wireless access, maximize the expanded potential, and ultimately form an
intelligent wireless space around the wireless terminal in advance of the required wireless environment.
It is an entirely new approach to creating an actual
wireless environment, not optimizing for a given
environment [6]. The critical technology supporting31

Feature Articles

Environmental
information
on real world

Real
world

Virtually reproduce radio spacein another environment

Spatial modeling/simulation

Parameter
optimization

Scenario example:
Congested environment

Verification invarious scenarios

Scenario example:
Vehicle environment

Preliminary verification of possible situations
around the user from various angles

Fig. 7. Radio-space-reproduction technology.

this approach is the radio-space-reproduction technology shown in Fig. 7. This technology reproduces
radio space in another space using the wireless-environment information acquired from real space andhighly accurate modeling/simulation technology. By
accurately producing the modeled wireless environment, we aim to improve the spatial-reproduction
accuracy for providing wireless space tailored tousers and services.

the innovation of wireless access for the IOWN/6G
era.
References[1][2]

5. Conclusion[3]

As an initiative to create new value that exceeds thatof connecting to the Internet using mobile phones,
which has brought about the explosive spread ofwireless access, we introduced wireless technologies
to develop a network that matches users and services.
Wireless access will evolve in various ways, e.g.,
integrating non-communication areas such as wireless sensing and the expansion of coverage areas to
unexplored regions, both of which were omitted inthis article due to space limitation. NTT Access Network Service Systems Laboratories will contribute to

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Vol. 20 No. 9 Sept. 2022

[4]

[5]

[6]

Ericsson, “Ericsson Mobility Report,” Nov. 2021. https://www.
ericsson.com/4ad7e9/assets/local/reports-papers/mobility-report/
documents/2021/ericsson-mobility-report-november-2021.pdf
Press release issued by Ministry of Internal Affairs and Communications of Japan (MIC), “Technical Requirements for Wider Band Useof 920 MHz Low-power Radio Stations,” Mar. 2022 (in Japanese).
https://www.soumu.go.jp/menu_news/s-news/01kiban14_02000540.
html
Press release issued by MIC, “Technical Requirements for Introducing 6-GHz Band Wireless LANs,” Apr. 19, 2022 (in Japanese). https://www.soumu.go.jp/menu_news/s-news/01kiban12_02000142.html
K. Kawamura, T. Moriyama, T. Ogawa, Y. Asai, and Y. Takatori,“Wireless Technologies toward Extreme NaaS—Multi-radio Proactive Control Technologies (Cradio®),” NTT Technical Review, Vol.
19, No. 10, Oct. 2021. https://doi.org/10.53829/ntr202110fa3
M. Sasaki, T. Nakahira, T. Moriyama, T. Ogawa, Y. Asai, and Y.
Takatori, “Multi-radio Proactive Control Technology (Cradio®): A
Natural Communication Environment where Users Do Not Need to
Be Aware of the Wireless Network,” NTT Technical Review, Vol. 19,
No. 8, Aug. 2021. https://ntt-review.jp/archive/ntttechnical.
php?contents=ntr202108ra1.html
Beyond 5G Promotion Consortium, “Beyond 5G White Paper—Message to the 2030s,” Mar. 2022. https://b5g.jp/doc/whitepaper_en_1-0.
pdf

32

Feature Articles

Yasushi Takatori

Senior Distinguished Researcher, Executive
Manager, NTT Access Network Service Systems
Laboratories.
He received a B.E. and M.E. in electrical andcommunication engineering from Tohoku University, Miyagi, in 1993 and 1995. He received aPh.D. in wireless communication engineeringfrom Aalborg University, Denmark, in 2005. He
joined NTT in 1995 and has served as a secretaryof IEEE Japan Council Awards Committee andvice chairman of the Wireless LAN systemdevelopment project in the Association of Radio
Industries and Businesses (ARIB). He was avisiting researcher at the Center for TeleInFrastructure (CTIF), Aalborg University from 2004
to 2005. He served as a co-chair of COEX Adhoc
in IEEE 802.11ac from 2009 to 2010. His current
research interests include future wireless accesssystems for IOWN. He received the Best Paper
Awards from the Institute of Electronics, Information and Communication Engineers (IEICE)
in 2011, 2016, and 2020. He was honored with
the IEICE KIYASU Award in 2016. He received
the Radio Achievement Award from ARIB in
2020 and the IEEE Standards Association’s Outstanding Contribution Appreciation Award forthe development of IEEE 802.11ac-2013 in
2014. He is a senior member of IEICE and amember of IEEE.

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33

Feature Articles: Access Network Technologies for
IOWN

Technical Approaches for Operations
to Accelerate Digital Transformation
Tomoko Shibata
Abstract

NTT Access Network Service Systems Laboratories is undertaking research and development into
operations technology to enable smart operations and explore new business areas through such operations. We believe that collaborative business operations involving multiple players will be both deeper
and more common in the future. Therefore, we are focusing on the promotion and expansion of digitaltransformation involving multiple players. This article introduces our recent efforts in self-evolving
zero-touch operations technology and business design support technology based on operation-processclassification.
Keywords: digital transformation, network operation, business design

1. Characteristics, trends, and desired directionsof access network operations
Access networks are widespread across Japan.
Since they directly interface with service users andmaintenance staff at indoor and outdoor telecommunications facilities, access operations are deeply
linked with these various players’ and users’ businesses. There have been growing expectations for
digital transformation (DX) across various infrastructure companies to address social issues, such as theshrinking working-age population [1] and aging
infrastructure [2]. Demand for new services, such asprivate fifth-generation mobile communication(5G)/6G and mobility as a service (MaaS) using automated driving, is expected to grow. Triggered bymeasures taken against infectious diseases, lifestyles
and work styles have become increasingly diversified. These trends will increase our sharing andmutual use of facilities and services with players
other than telecommunications carriers, as well as
collaboration in their operations. Thus, it is important
to develop a mechanism that breaks through businessboundaries and enhances productivity.
In view of the above, the Access Network Operation Project at NTT Access Network Service Systems
Laboratories is developing smart operations thatNTT Technical Review

Vol. 20 No. 9 Sept. 2022

accompany the progress of the Innovative Optical andWireless Network (IOWN). Specifically, we are aiming to actualize an ecosystem in which various players can easily collaborate with each other using theirrespective business operations, systems, and data,and cause a chain reaction of value creation (DXacceleration) within this ecosystem. The form of collaboration can vary depending on the business structure of each player. The targets for collaboration canalso differ, ranging from information to systems andorganizations. We are conducting research and development (R&D) into operations technologies fromthese various perspectives and working with NTT
Group companies to prepare for their commercial
introduction. This article introduces our latest efforts
to enable collaborative end-to-end operations acrossmultiple network layers and domains. Specifically, it
introduces self-evolving zero-touch operations technology, which features versatile network-information
management with autonomous analysis and decisionon the basis of this management, and business design
support technology based on operation-process classification, which improves work efficiency byenabling business-support and business-execution
organizations in the field to develop a common
understanding.

34

Feature Articles

TM Forum
Need for modification at each DB
change (specialized logic)

SID

Individual
Managed objects and granularity are
implementation
specified at the conceptual level.
Need for interpretation andoriginal implementation of SID

Impactassessment AP(IP/Ethernet)
IP layer
Ethernet layer

Need for change in DB
configuration (dedicated DB)

Transmission layer

Impact assessment
IP layer
Ethernetlayer

Impactassessment
AP

IP characteristics

Impact assessment

Conventional technology

Ethernet characteristics

Impact assessment

Equip- Equip
Equip-Building Equipment ment

Equip-- EquipEquip
Equipment ment

Transmission
layer

EquipEquipment

New network type can be added withoutchanging DB configuration(versatile model/external definition).

AP: application
DB: database
IP: Internet protocol

Small modification when adding
network type (versatile logic)

Transmission characteristics
EquipEquipment[Examples of generic logic for
an impact assessment AP]• Impact assessment searchlogic• Uni-/bi-direction cut judgment
logic

Network-resource management technology (NOIM)

Fig. 1. Network-information management.

2. Self-evolving zero-touch
operations technology
NTT Access Network Service Systems Laboratories is undertaking R&D into making network operations dramatically smarter. We believe that one of thekeys to self-evolving smart operations is end-to-end
operations coordination that manages network
resources across multiple domains and carriers. We
are developing technologies for end-to-end operations coordination from the following perspectives:(1)	Network-information management for handling end-to-end resources across multiple
layers and domains;(2)	Analysis and decision for the provision of network services on the basis of the network
information made available in (1).
Figure 1 shows the network-information management referred to in perspective (1). A network-information database is traditionally built for each service
provider, for each service, and for each network type.
This hinders operations and maintenance across multiple network layers and poses a barrier to enabling
operations coordination. A solution to these problems
is a generic versatile network-information model. For
example, TM Forum has standardized one suchmodel as the Shared Information and Data Model(SID) [3]. However, since the SID specifies managed
objects and granularity only at the requirement orconceptual level, service providers need to interpret
the SID in accordance with the type of networks they
NTT Technical Review

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manage and develop their own implementations.
Since the time and cost service providers can expend
in developing their implementations are usually
restricted, it is difficult for them to take generality andfuture extensibility into account when studying thedetails of the data model. Thus, their implementations
tend to be specific to the network type to be added at
the time. The result is that each time a new network
type to be managed is added, it is necessary to changeboth the network-information database and logic.
NTT Access Network Service Systems Laboratories has developed the Network Operation Injected
Model (NOIM), which is now in commercial use [4].
NOIM is a technology for handling network information using a versatile unified model. NOIM does notstop at the conceptual level but specifies a versatile,
extensible, network-type independent mechanism,
which reduces both the need for modifying databases
and logic and the cost that is incurred each time a new
network type is to be added. NOIM makes it easy to
specify a versatile model that focuses on networktype independent properties, such as point and connectivity, specified in the SID. It uses a mechanism bywhich network-type dependent attributes and interlayer relationships can be defined and held externally,
thus ensuring high extensibility. As a result, even if
network types change or new network types emerge,
there is no need to change the databases, and thechanges needed in processing logic are also greatly
reduced. Network information from multiple domains
can also be handled uniformly at the data-format
35

Feature Articles

Recommendations
for response

Alarm analysis
Instruct fault
identification

Alarm/logs
Workload

Fault-point
estimation
No need for analysis,
localization, or testing

SkillsFault-identification
test

Response
results

Fault-point
estimation system

Rules

Logs of response results

Rule learning
Monitoring system

Testing tools

AlarmsSkillsMonitoring system
AlarmsWorkload

Autonomous
learning andaccumulationof new rules
from faultresponse records

Network

Network

Conventional responses to a fault

Responses to a fault using a fault-point estimation system

Fig. 2. Estimation of fault-point and selection of response actions.

level, enabling interoperability, and making it easy
for service providers and services to make mutual useof each other’s network information and rules.
The analysis and decision referred to in perspective(2) are regularly applied to various tasks for the provision of network services such as facility planning,
responses to service orders, and fault monitoring andhandling. Such analysis and decision is highly dependent on operators’ skills and imposes a considerable
workload. To solve this problem and enable optimal
analysis and decision—which is not possible withhuman operators—there have been initiatives in thefield of network operations to use network-artificial
intelligence (AI) such as autonomous network and AI
operations (AI Ops).
The R&D efforts of NTT Access Network Service
Systems Laboratories are aimed at further expanding
the positive effects of network-AI by combining optimal analysis and decision made possible by networkAI with the versatility, extensibility, and interoperability provided by NOIM. The following technologies for fault-point estimation and fault response are
introduced as examples of our efforts.
When a network failure occurs, it is necessary to
identify its fault point from a large amount of event
information that has complex causal relationships
across multiple layers and select response actions
appropriate to the cause of the fault. Such identification and selection are heavily dependent on the skillsof the operators and impose a considerable workload.
To solve these problems, NTT Access Network Service Systems Laboratories has developed a technology that automates fault-point estimation andresponse-action selection. The technology is now in
NTT Technical Review

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commercial use [5]. Figure 2 illustrates the processof network fault-point estimation and responseaction selection. In this technology, a network-AI
learns the relationships (rules) between fault information and fault points across multiple network layers
from alarms, analysis, and identification results
related to previously occurring faults. When a network failure occurs subsequently, the network-AI
estimates the fault point on the basis of these learned
rules. It also recommends a method for minimizing
the recovery time or recovery actions on the basis of
the actions taken in the past for each type of cause of
fault.
The network-AI requires a large amount of pastdata to learn how to identify faults and deal withthem. Therefore, the network-AI cannot adequately
respond in cases where it was only introduced recently and the volume of accumulated data is still small,or where a particular type of event occurs so rarelythat the network-AI is not likely to be able to learn
about it from the accumulated data. The training stageof the network-AI currently requires the skills andwork of experts for the preparation of appropriate
training data. A solution to these problems is to use
data generated in actual network operations as training data. Therefore, NTT Access Network Service
Systems Laboratories is developing an autonomous
learning technology that automatically extracts thecauses of faults and response actions from logs andtrouble tickets that have been created in actual network operations and feeds them back into the network-AI. We are also aiming to enable training data,rules, and AI models that are generated in a network
environment or derived from each network-AI
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TroubleticketLog, etc.

Mutual use of rules and AI models
Extract information to be
fed back to AI
TroubleCause of
Learning forticketAnalysisa fault
Analysisfault-response
Responses
AI
Autonomous learning
Mutual use of dataLog, etc.

Collect

Fault

Alarm
Log

Target environment

Analysis of differences between
environments

Otherenvironments(mirror environment*,other company’s environment, etc.)* An environment in which data and rules equivalent to those in
the target environment can be artificially generated.

Fig. 3. Data and rule transfers between environments.

applied to the environment to be used in other environments. For this purpose, we are developing aninter-environment transfer technology, which analyzes the differences between different network environments using the interoperability of NOIM, andconverts training data, rules, and AI models for application in other environments [6]. Figure 3 illustrates
the transfer of data, rules, and AI models between
different environments.
3. Business design support technology based on
operation-process classification
Business reforms had been led by business-support
organizations. Based on its bird’s-eye view of a company’s business operations and the insights it had
gained from business trends, a business-support organization makes reform proposals that it thought hadhigh potential for creating substantial improvements
in productivity. However, such reforms sometimes
lost touch with actual business conditions andrequired much time and cost to implement. Due to the
spread of robotics process automation (RPA) andlow-code/no-code automation, DX has been led bybusiness-execution organizations. Such an organization implements its reform proposals that are basedon detailed perspectives and insights closely related
to actual business operations. While such reforms arehighly compatible with business reality and bringabout immediate effects, there are growing concerns
about only achieving the individualized optimizationfrom this approach. We believe that, if we are to further enhance the effectiveness of DX in the coming
years, it is important to ensure paraconsistency
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between the above two approaches on the basis of acommon understanding between business-support
and business-execution organizations. This common
understanding should be founded in turn on a sharedperception of how the actual business is run (businessreality). The operations of a business-execution organization (actual operations) vary depending on the
differences or changes in the business environment,such as business needs and trends relating to othercompanies. Consequently, there is a gap between
actual operations and either designed operations,which provide the basis for the formulation of standard operational flows and operations manuals, orassumed operations, which are derived from theobservation of a selective sample of operations andinterviews with business-execution staff. This gap
makes it difficult to develop a common understandingof the reality of actual operations.
At NTT Access Network Service Systems Laboratories, we are developing a technology intended todeepen common understanding between businesssupport and business-execution organizations byenabling accurate and easy understanding of actual
operations on the basis of objective data. Since thesuccess of business reform is highly dependent on an
accurate understanding of actual operations, wedeveloped a business design support technology thatis based on operation-process classification. It focuses on actual operations on terminals, which areamong the operational tasks that a business-support
organization has difficulty in understanding accurately [7]. This technology offers a comprehensive
mechanism for obtaining operation logs, which provide powerful objective data about operations on
37

Feature Articles

x a b y c w d

a b c d a p q b c r d

x a z b c w d x y z

a b c d a p q b c r dp q rFluctuations in operation (e.g., interruption by another task)

Visualization of operation
logs before classificationb

Break point in
operation
Automatic visualization of operation
logs after dividing and classifying themx a z b c w d x y z

y

c
d

a

y
q

p
z

p q r

x

a

x

x a b y c w d

b

c

w
z

d

Work type 3
Work type 1

r

p
w

q

r

Work type 2

Fig. 4. Classification of operation logs into work types.

terminals and, on the basis of them, for visualizing,editing operational flows, and generating an operation automation scenario for RPA. The technology
provides the following features, which are keys tounderstanding the gap between designed/assumed
operations and the actual situation.
(1) Classification of operation logs into work types
When operations are designed or assumed, the process of service order reception, for example, is broken down into several work types, such as orderinformation registration and operational-task reservation, and it is assumed that several service orders arehandled one by one and these work types are executed
sequentially. However, in actual operations, it is notuncommon that different work types for several service orders are executed in parallel. Individual operation logs are also obtained without differentiation by
operation type. Therefore, it is difficult to identify thegap between designed/assumed operations and theactual situation simply by arranging operation logs inchronological order. Our technology automaticallydetects the points in a series of operation logs where
the operation type changes and visualizes the operation logs after dividing and classifying them. This
makes it possible to understand actual operations by
operation type [8]. Figure 4 shows the classificationof operation logs into work types.
(2) Extraction of frequent operation patterns
Among the operations that occur in the course ofwork, designed/assumed operations only cover typical operations, such as entering a value in an input
field or pressing a button. In contrast, fully logged
operations are more comprehensive and include those
operations that are normally ignored in designing orassuming operations but are necessary to the process
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Vol. 20 No. 9 Sept. 2022

of entering a value in an input field such as making
keystrokes to change selections in a list box or scrolling a screen. This means that there are various operations that are not included in designed/assumed
operations, and these vary depending on the task
being performed at the time. Even if a series of consecutive operations that occur frequently are extracted from the operation logs, it is not possible to compare them with the operation patterns that occur frequently in designed/assumed operations. Our technology is used to examine not only the consecutive
operations in the operation logs but also the relationships between distant operations in the logs and automatically extract the most frequently occurring relationships, enabling actual operations to be comparedwith frequently occurring operation patterns indesign/assumed operations. Figure 5 shows theextraction of frequently occurring operation patterns.
To enable the design of highly productive and feasible operations and prompt incorporation of these
into actual operations that can be implemented continuously, we will refine and expand the technologies
mentioned above to promote and deepen common
understanding and develop a mechanism that facilitates the use of not only RPA but also various information and communication technologies in business
operations.
4. Outlook
We are aiming to develop a mechanism that makes
it possible to increase productivity across diverse
players and explore new business areas through collaboration of these players in a manner that takes
advantage of their respective assets. For this purpose,38

Feature Articles

a b c d ea x b z c d e
a b c d f

a
a x
a
a x y

Extract patterns

a x y b c dc d ebb z c d e
c d
f
b
c d
b

Extract consecutive frequent seriesa b c d ea x b z c d e
a b c d fxaa x y b c daInput departure stationeb

Input arrival stationfcSelect datedPress search button

y
c

b

d

z

It is easy to compare actual operations withdesigned/assumed operations.

It is difficult to compare actual operations
with designed/assumed operations.

Fig. 5. Extraction of frequently occurring operation patterns.

we will develop technologies that facilitate collaboration among players and achieve self-evolution ofanalysis and decision. Through this R&D, we will
strive to deepen collaboration with our partners andproduce results in a timely manner.

[4]

[5]

References
[1]

[2]

[3]

National Institute of Population and Social Security Research, “Population Projections for Japan (2017): 2016 to 2065,” Population
Research Series, No. 336, 2017.
https://www.ipss.go.jp/pp-zenkoku/e/zenkoku_e2017/pp29_
summary.pdf
Ministry of Land, Infrastructure, Transport and Tourism, “Estimationof Maintenance and Repair Costs for Social Infrastructure,” 2018 (inJapanese). https://www.mlit.go.jp/sogoseisaku/maintenance/_pdf/
research01_02_pdf02.pdf
Information Framework (SID), https://www.tmforum.org/oda/
information-systems/information-framework-sid/

[6]
[7]

[8]

M. Sato, S. Nishikawa, K. Murase, and T. Kenichi, “Technology for
Understanding Service Impact Using Network Resource Management
Technology that Is Independent of Network Type,” NTT Technical
Review, Vol. 18, No. 11, pp. 48–52, 2020.
https://www.ntt-review.jp/archive/ntttechnical.php?contents=
ntr202011ra2.html
N. Murata, F. Asai, T. Yakawa, S. Suzuki, H. Oishi, and A. Inoue,“Failure Point Estimation Using Rule-based Learning,” NTT Technical Review, Vol. 17, No. 7, pp. 14–16, 2019.
https://www.ntt-review.jp/archive/ntttechnical.php?contents=
ntr201907fa2.html
D. Li, K. Akashi, H. Nozue, and K. Tayama, “A Mirror Environment
to Produce Artificial Intelligence Training Data,” IEEE Access, Vol.
10, pp. 24578–24586, Feb. 2022.
T. Wakasugi, F. Yokose, R. Uchida, K. Tsuchikawa, H. Oishi, S. Yagi,
and Y. Urabe, “Business Design Support Technology to Promote
Digital Transformation,” NTT Technical Review, Vol. 20, No. 8, pp.
64–71, Aug. 2022. https://doi.org/10.53829/ntr202208ra1
Y. Urabe, S. Yagi, K. Tsuchikawa, and H. Oishi, “Task Clustering
Method Using User Interaction Logs to Plan RPA Introduction,” Proc.
of the 19th International Conference on Business Process Management (BPM 2021), pp. 273–288, Rome, Italy, Sept. 2021.

Tomoko Shibata

Senior Research Engineer, Supervisor, AccessNetwork Operation Project, NTT Access Network Service Systems Laboratories.
She received a B.S. in chemistry from Hokkaido University in 1995 and joined NTT AccessNetwork Service Systems Laboratories the same
year. She has been engaged in R&D of datadriven technologies for demand prediction orbusiness-process analysis for providing telecommunication business service, passive optical network (PON)-based optical access systems suchas 10 Gigabit Ethernet PON, and network management systems. She is now in charge of R&D
management strategies of access network operation. She is a member of the Institute of Electronics, Information and Communication Engineers(IEICE).

NTT Technical Review

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39

Feature Articles: Access Network Technologies for
IOWN

50 Years at Tsukuba R&D Center
Hiroshi Takahashi, Hideo Kawata, Ryo Koyama,
Ryo Tanaka, Shinya Otsuki, Kazuto Goto,and Masashi Tadokoro
Abstract

NTT Access Network Service Systems Laboratories was established in Tsukuba City on July 20, 1972
as Tsukuba Telecommunication Construction Engineering Development Center. Since then, it has beenrenamed several times during the era of the Nippon Telegraph and Telephone Public Corporation and the
Nippon Telegraph and Telephone Corporation (NTT). In July 2022, the Laboratories’ Tsukuba Researchand Development (R&D) Center celebrated its 50th anniversary. This article introduces the 50-year history of the Tsukuba R&D Center, focusing on the large-scale experimental facilities that supported R&D
activities in Tsukuba.
Keywords: access network, infrastructure, optical fiber

1. Introduction
Tsukuba Telecommunication Construction Engineering Development Center of the Nippon Telegraph and Telephone Public Corporation, the predecessor of NTT Access Network Service Systems
Laboratories, was established in July 1972 as a member of the 43 research and educational institutions
planned for the Tsukuba Science City, a national
project promoted by Cabinet approval in September
1963 [1]. As shown in Fig. 1, Tsukuba Telecommunication Construction Engineering Development Center was reorganized into Tsukuba Engineering Development Center (1985), Tsukuba Field Engineering
Development Center (1987), NTT Telecommunication Field Systems Research and Development(R&D) Center (1991), and NTT Access Network
Systems Laboratories (1994) and took its current
name of NTT Access Network Service Systems
Laboratories in January 1999. The Tsukuba R&D
Center was established as the organization’s experimental facilities in 1972, and the Makuhari Building
and Yokosuka R&D Center were added to the organization in 1997, and the Musashino R&D Center wasadded in 2014. In July 2022, NTT Access Network
Service Systems Laboratories and its Tsukuba R&D
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Center celebrated their 50th anniversary.
When it was first established as Tsukuba Telecommunication Construction Engineering Development
Center, R&D activities on metal access technology
and infrastructure technology were conducted in Tsukuba to improve the efficiency, safety, and work environment of outdoor construction work due to theincrease in telephone service demand at that time.
NTT Access Network Service Systems Laboratories
is currently conducting R&D in the five fields of
optical-fiber access technology, infrastructure technology, access system technology, wireless accesstechnology, and operation technology. At the Tsukuba R&D Center, our R&D has been focused on communication lines and civil engineering facilities since
the establishment of Tsukuba Telecommunication
Construction Engineering Development Center.
The Tsukuba R&D Center was designed to conductvarious experiments under conditions equivalent tothose in the field to create communication facilities
that can work in various environments. Therefore, it
has large-scale and unique experimental equipment
and facilities for communication lines and civil engineering facilities. This article describes the largescale experimental equipment and facilities that support the R&D in Tsukuba.
40

Feature Articles

History
1972

1980

1990

2000

Tsukuba Telecommunication Construction
Engineering Development Center20102020 2022

NTT Access Network Service Systems Laboratories
NTT Access Network Systems Laboratories
NTT Telecommunication Field Systems R&D Center
Tsukuba Field Engineering Development Center
Tsukuba Engineering Development Center

Technology and location

1972

1980

1990

2000

2010

2020 2022

Tsukuba R&D Center
Metal access
Infrastructure

Optical fiber access
Ibaraki R&D Center
Optical fiber access
Makuhari Building
Operation
Access system
Yokosuka R&D Center

Operation
Access system

Wireless accessMusashino R&D Center
Operation
Access system

Tsukuba R&D Center in 1972
1972

1980

1990

2000

2010

2020 2022

Fig. 1. History of NTT Access Network Service Systems Laboratories.

2. Large-scale experimental equipment andfacilities at Tsukuba R&D Center
The Tsukuba R&D Center has mainly been carrying out R&D on infrastructure technology, metal
access technology, and optical-fiber access technology. This article focuses on the facilities that wereconstructed during the Tsukuba Telecommunication
Construction Engineering Development Center era
but have now completed their intended purposes.
Figure 2 shows the facilities of the Tsukuba R&D
Center in 1983 and the current ones on the vast site ofabout 22 hectares. Many of the facilities were
designed to accurately reproduce installation environments such as the high-rise laboratory building (avertical wiring experiment facility and was the highest in the world), buried object experiment facility bysoil type, shield propulsion experimental facility,
underwater experimental equipment, cable weather
conditioning experiment facility, and vibration
fatigue experiment equipment.
Figure 3 shows photographs of the high-rise laboratory building at the time of its completion (1983).
With the increase in high-rise buildings, such as skyscrapers and large bridges, it was constructed for the
purpose of improving vertical cable-laying methods
and elucidating temperature changes in high-rise
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facilities and seismic behavior of high-rise facilities
during earthquakes. Inside the building, there was a
75-m-high, 1.8-m-long, and 2.5-m-wide shaft (A)(from the 3rd story below ground to 12th story above
ground) for cable laying experiments, static load
tests, and vibration tests, and a 60-m-high, 1.0-m-long,and 1.0-m-wide shaft (B) (from the 1st story above
ground to 12th story above ground) for vertical cable
heat cycle tests (–40 to +80°C). It was used to verify
the vertical installation of various cables. Today, vertically optical wiring installations are common in
high-rise buildings, and their reliability is widely
recognized and unshakable. The purpose of thisexperimental facility was accomplished, thus it wasdismantled.
Figure 4 shows the large-scale experimental facilities used for verification of underground facilities andunderwater communication lines and civil engineering facilities. Figure 4(a) shows photographs of theburied object experiment facility by soil type at thetime of completion (1977). This facility was constructed to replicate installation under roads for the
purpose of examining issues such as design, construction, and maintenance, because ground properties are diverse and difficult to predict accurately. The
facility made it possible to alter soil quality, groundwater level, etc., and run a load car reproducing
41

Feature Articles

In 1983

As of 2022

1: The main building
2: Outside plant experiment building
3: Civil engineering experiment building
4: Large structure experiment building
5: Trainee hostel
6: Cable abrasion test equipment
7: High-rise laboratory building
8: Buried object experiment facility by soil type
9: Shield propulsion experimental facility
10: Underwater experimental equipment
11: Cable weather conditioning
experiment facility
12: Vibration fatigue experiment equipment

13: Underground wiring experiment equipment
14: In-house experimental building
15: Conduits experiment facility
16: Transmission line experiment facility
17: Civil engineering comprehensive
experiment facilities
18: Wireless experiment building
19: Optical access experimental building
20: Access building A, B
21: In-house experimental facility
22: Large structure experiment building(Wheel load/manhole equipment)
23: Fiber manufacturing equipment

24: System for analysis using
wall and pole keeping equipment
25: Vibration fatigue experimentequipment
26: Access network museum
27: Outdoor experiment area
Blue: Equipment that is no longer used
Red: Equipment newly installed
or renewed in recent years
Bold: Facilities introducedin this article

Fig. 2. Large-scale experimental equipment and facilities at Tsukuba R&D Center.

(a) Exterior view at completion (1983)(b) Inside shaft A

Fig. 3. High-rise laboratory building.

automobile loads over it. It was useful in various
verification tests, such as the investigation of theeffect of burying a full-scale manhole, and the investigation of the movement of optical fiber cable in the
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pipeline by burying a pipeline in the soil layer close
to the actual site and driving a load car over it. Underground facilities for communication services havebeen installed all over Japan, thus this facility
42

Feature Articles

Front

Loaded vehicle
traveling on soil

Entire facility(a) Buried object experiment facility by soil type

Experimental pit

Entire facility(b) Shield propulsion experimental facility

Full of water

Drained(c) Underwater experimental equipment

Fig. 4. Large-scale experimental facilities used for verification of underground and underwater facilities.

completed its intended purpose.
Figure 4(b) shows the test pit and panoramic viewof the shield propulsion experimental facility. To
apply the shield-tunneling method developed for road
construction without using open pit methods, thisfacility was constructed to verify the adaptability andsafety of the method, considering various construction conditions expected at different sites. By putting
a sample of the soil layer under test into the pressure
soil tank and using a water-pressure generator toapply arbitrary water pressures to the layer, predetermined ground conditions could be created. Therefore,a small model shield machine installed in the experimental pit was propelled into the soil layer, andexperiments on excavation, soil removal, etc. wereconducted. As the shield-tunneling method for various types of ground has become widely used, and theconstruction of new roads has been reduced, theshield propulsion experimental facility was dismantled upon completion of its purpose.
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Figure 4(c) shows an exterior view of the underwater experimental equipment at the time of completion(1974). It was constructed to test the laying of submarine cables and water-resistance tests of undergroundstructures. It consisted of a water tank 50 m long, 6 mwide, and 5 m deep, underwater observation tunnel,
moving bridge, bridge crane, and measurement room.
The mobile bridge was self-propelled with a runningspeed of 10–50 m/min and used as a simulated submarine-cable laying device for conducting simulation
experiments on submarine cables. Test methods forsubmarine cables and underground facilities were
established, therefore this equipment has been
removed.
Figure 5 shows the large-scale experimental facilities used for the verification of aerial communication
cables and civil engineering facilities in the open.
Figure 5(a) shows the cable weather conditioning
experiment facility at the time of completion (1973).
Since most communication lines and civil engineering
43

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Entire facility

Pipeline

InsideTelephonepole(a) Cable weather conditioning experiment facility

Loading sample
pulling device
Sample fixing device

Measuring room

Vibration device

Wire tension adjusting
Sample pulling device
Entire facility

Inside(b) Vibration fatigue experiment equipment

Fig. 5. L arge-scale experimental facilities used for verification of aerial communication cables and civil engineering
facilities in the open.

facilities are installed outdoors, this facility was constructed to reproduce actual cable installation environments and conduct weatherability tests and temperature-characteristic tests. Having a totally stainless steel roof and being 150 m long, 2.45 m wide,and 2.25 m high, it made it possible to reproduce in atest room the state of installation in a conduit, pipeline, and on a telephone pole. At the time of construction, the temperature could be changed from –20 to+60°C, but now it can be changed from –30 to +70°C.
In optical cables, small bends in the cable affect various transmission characteristics, so we will continue
to use the facility through various tests for the practical application of new fiber and cable structures.
Figure 5(b) shows the vibration fatigue experimentequipment when it was completed (1977). It was conNTT Technical Review

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structed for the purpose of clarifying the response
characteristics, such as the increase in optical loss
and fracture of cables, passive optical components,
closures, and hardware, used in overhead lines by theimposition of vibration to replicate the effect of windon aerial cables, etc. By providing feedback to thedesign, development, and verification of new cables
and components, we have been able to enable safe
long-term use of overhead equipment and facilities.
This equipment can forcibly excite cables and suspended wires with actually installed aerial cables. At
the time of completion, the maximum installation
length was 35 m, but the maximum installation length
has been extended to 65 m, and it will continue to be
used for various experiments.
As of 2022, we also have large-scale experimental
44

Feature Articles

facilities intended to support the Innovative Opticaland Wireless Network (IOWN) and smart facilities,
as shown in red in Fig. 2.
3. Summary
This article introduced the R&D history of the Tsukuba R&D Center over the last 50 years, focusing on
the large experimental equipment and facilities created to support the R&D of communication lines andcivil engineering facilities. We will continue to con-

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duct R&D activities toward the development of smart
facilities that support IOWN, with the aim of achieving technologies that can be used to support both themost advanced in the world and easy-to-use in thefield.
Reference[1]

Website of Ministry of Land, Infrastructure, Transport and Tourism,
Tsukuba Science City, https://www.mlit.go.jp/crd/daisei/tsukuba/
english/index.html

45

Feature Articles

Hiroshi Takahashi

Senior Research Engineer, Access Network
Management Project, NTT Access Network Service Systems Laboratories.
He received a B.E. in electrical engineeringfrom Tokyo University of Science in 2008 and an
M.E. in electrical engineering and information
systems from the University of Tokyo in 2010.
He joined NTT Access Network Service Systems
Laboratories in 2010, where he has been engaged
in research on advanced optical measurements
and optical fiber network evaluation. He is currently a senior research engineer in a researchgroup developing advanced optical measurements for optical fiber network management andoptical fiber sensing. He is a member of theInstitute of Electronics, Information and Communication Engineers (IEICE) of Japan.

Hideo Kawata

Senior Research Engineer, Optical Access
Systems Project, NTT Access Network Service
Systems Laboratories.
He received a B.S. and M.S. from the Collegeof Science and Technology, Nihon University,
Tokyo, in 1996 and 1998, and Ph.D. in engineering from Utsunomiya University, Tochigi, in
2009. In 1998, he joined NTT Access Network
Service Systems Laboratories, where he wasengaged in research on wavelength-divisionmultiplexed access systems. He is currently
engaged in research on Time-Sensitive Networking.

Ryo Koyama

Senior Research Engineer, Access Network
Media Project, NTT Access Network Service
Systems Laboratories.
He received a B.S. and M.S. in precision engineering from the University of Tokyo in 2001 and2003. In 2003, he joined NTT Access Network
Service Systems Laboratories, where he has been
engaged in research on optical fiber wiring andsplicing techniques for optical access networks.
He is an assistant secretary of the International
Electrotechnical Commission (IEC) Subcommittee 86B and member of IEICE.

Ryo Tanaka

Senior Research Engineer, Civil Engineering
Project, NTT Access Network Service Systems
Laboratories.
He received a B.E. from the Department ofGeophysics, Tohoku University, Miyagi, in 2001
and M.E. from the Department of Earth andPlanetary Science, the University of Tokyo in
2005. He joined NTT WEST in 2005 and joined
NTT Network Innovation Laboratories in 2021.
He is presently developing technology forextending service life of structures at NTT
Access Network Service Systems Laboratories.

NTT Technical Review

Vol. 20 No. 9 Sept. 2022

Shinya Otsuki

Senior Research Engineer, Wireless 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 1993, 1995, and 1997. He joined NTT in 1997.
From 1997 to 2008, he studied wireless accesssystems, wireless local area network systems,
and wireless systems for Internet services intrains. From 2008 to 2011, he was involved ininternational standardization efforts in evolved
packet core and services using Internet Protocol
multimedia subsystems at NTT Service Integration Laboratories. He has been with NTT AccessNetwork Service Systems Laboratories since2011 and has been contributing to the activities
of Working Parties 5A and 5C in Study Group 5of the International Telecommunication Union(ITU) Radiocommunication Sector. He received
the Young Engineer Award from IEICE in 2004
and the ITU-AJ International Activity Encouragement Award and the ITU-AJ Accomplishment Award from the ITU Association of Japan
in 2014 and 2022, respectively. He is a memberof the Institute of Electrical and Electronics
Engineers (IEEE) and IEICE.

Kazuto Goto

Senior Research Engineer, Wireless Entrance
Systems Project, NTT Access Network Service
Systems Laboratories.
He received a B.E. and M.E. in knowledge
engineering and computer sciences from Doshisha University, Kyoto, in 2006 and 2008 andPh.D. from the Graduate School of Science andEngineering, Doshisha University, in 2020. He
joined NTT Network Innovation Laboratories in2008. He is currently developing wireless communication using high-frequency bands at NTT
Access Network Service Systems Laboratories.
He is a member of IEICE.

Masashi Tadokoro

Senior Research Engineer, Access Network
Operation Project, NTT Access Network Service
Systems Laboratories.
He received an M.E. in nuclear engineeringfrom the University of Tokyo in 2004 and joined
NTT the same year. He has 13-year experience in
R&D activity of optical access networks including radio-over-fiber technology and Ethernetbased passive optical network technology. His
current research involves operation support
technology based on software for access networks.

46

Feature Articles: Intent-based Application, Cloudserver, and Network Collaboration Technologies

Intent AI Mediator (Mintent) for
High-satisfaction-level Services
Kazuhisa Yamagishi, Masahiro Kobayashi,
Shingo Horiuchi, and Kenichi Tayama
Abstract

This article introduces the intent AI (artificial intelligence) mediator called Mintent that coordinatesand cooperatively controls networks, cloud servers, and applications on the basis of the respective
requirements (intents) of service providers and users. This technology can be used to provide serviceson the basis of such intents.
Keywords: intent, Mintent, cooperative control

1. Introduction
Advances in communication network, cloud-server, and application technologies have made it possible
to provide a higher satisfaction level of communication services. However, the resources of networks,
cloud servers, and user terminals are limited, so theincrease in the number of service users can cause
network congestion and server resource constraints.
When these situations occur, the throughput degrades
and latency increases, and users cannot receive data
properly at their terminals. As a result, users cannot
experience a high satisfaction level of service. To
avoid these quality degradations, service providers
are deploying network, cloud-server, and application
control technologies to overcome temporary resource
constraints. However, improvements in these singledomain control techniques are limited. Therefore, it isimportant to coordinate and cooperatively control
network, cloud-server, and application information.
The requirements (i.e., intents) that service providers and users demand of services generally differ foreach service type. For example, a video-streaming
service provider monitors the number of video users
and the viewing time to encourage continuous use of
the service. Thus, the service provider has an intent,i.e., a demand to increase the number of viewings orlengthen the viewing time. Similarly, the service provider of a connected vehicle has an intent to deliver
NTT Technical Review

Vol. 20 No. 9 Sept. 2022

video to the monitoring center without interruption tomonitor what is happening inside and outside thevehicle. However, since it is not possible to provide
services with an excessive amount of network, cloudserver, and application resources, these intents areconverted into resource information for networks,
cloud servers, and applications, which are coordinated to provide appropriate quality of service (QoS).
Therefore, networks, cloud servers, and applications
need to be controlled in a coordinated manner to provide appropriate QoS.
The intent artificial intelligence (AI) mediatorcalled Mintent is introduced as a technology thatcoordinates and cooperatively controls networks,
cloud servers, and applications on the basis of intentsof service providers and users, as shown in Fig. 1.
2. Recent activities regarding intent-related
technology in standardization
The Industry Specification Group on Experiential
Networked Intelligence (ENI) of the European Telecommunications Standards Institute is developing amethod for specifying the goals and requirements ofoperations that operators want to achieve as intents,
which are described in restricted natural language.
ENI 008 Intent Aware Network Automaticity releasedin March 2021 defines the architecture and functional
blocks required to process an intent and discusses the47

Feature Articles

Multi-domain cooperation control
Multi-orchestrator
Orchestrator
Controller

Controller
Wireless
network

Application

Orchestrator
Orchestrator

Controller

Controller

Wired network

Cloud server

Application control

Network control

Application requirements

Network requirements

Provider B
Provider A

Cloud server control
Cloud-server requirements

Intent-based requirements
IntentIntentAbstract demand

Verbalized demandSpecific demand

Constraints

Users

Operators

Providers

Social demandFig. 1. Concept of intent AI mediator (Mintent).

lifecycle management of intent operations and elements such as external interface requirements. Proofsof concept applying intent management methods tothe cloud and wireless networks are also being actively carried out.
TM Forum, an industry association for communication service providers and their suppliers, advocates a
closed loop architecture to achieve autonomous network operations by taking into account the business,
service, and network-resource layers, and the interworking of loops between each of these layers isdefined as shown in Fig. 2. In this architecture, theautonomous network is achieved using the intents
defined by the users and operators as the goal. Thisarchitecture has been discussed to study how to minimize the total operation cost.
3. Mintent
To provide services that satisfy intents of service
providers and users, it is not enough just to use
respective control technologies in the network, cloudserver, and application domains. Therefore, Mintent
was developed as a technology for sharing and coordinating information obtained from each domain toprovide a higher satisfaction level of services by taking into account the intents of service providers andusers (Fig. 1). An overview of the elemental technoloNTT Technical Review

Vol. 20 No. 9 Sept. 2022

gies of Mintent is described below.
3.1 Application control technologies
Application control technologies to obtain video
data with different video coding bitrates (i.e., adaptive bitrate streaming) or control video coding bitrates(i.e., Google Congestion Control) on the basis of
network throughput, packet loss, and/or fluctuationsin the amount of received data have become common
for video streaming and web conferencing. These
technologies provide a service without stalling by
requesting video data with a lower coding bitrate
when QoS (i.e., throughput, packet loss, and delay)degrades. However, such technologies cannot control
on the basis of the service provider’s or user’s intent
because the control is based only on QoS.
For example, service providers have an intent toavoid increasing content-delivery-network and network costs by providing services with excessive quality if they can maintain a certain level of suitable
quality. Similarly, users have an intent to avoid excessive data consumption by receiving high-quality
video. To satisfy these intents, we proposed technologies that do not transmit excessive data while maintaining a suitable quality level [1, 2]. These technologies consist of a quality-estimation model that estimates quality of experience (QoE) and request video
data so that the QoE satisfies the suitable quality48

Feature Articles

Excerpt from IG1230 “Autonomous Networks Technical Architecture” v1.0.0 in TM Forum
AD: autonomous domain
API: application programming interface
BSS: business support system

Fig. 2. Autonomous network architecture using intent in TM Forum.

level. This prevents excessive quality and excessive
data transmission, thus satisfying the intents of service providers and users.
3.2 Cloud-server control technology
As the environment surrounding the service changes, to maintain optimal resource allocation whilesatisfying QoE requirements, server resources need
to be designed on the basis of various situations andrequirements. With the diversification of services andincreasing complexity of resource control, however,resource design and control based on the experienceof service engineers or system performance-based
methods are no longer adequate. Such a conventional
approach has issues, such as service engineers are
required to have much experience and high skill levels, the difficulty in providing services in accordancewith user quality requirements and usage conditions,and the need to allocate extra resources to satisfy
quality requirements.
To address these issues, we proposed a cloud-service-resource optimal-control technology that proactively and automatically calculates the amount of
resources [3]. This technology handles user-experiNTT Technical Review

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ence quality requirements and service-provider performance requirements as intents, then the optimal
cloud resource that satisfies the intent is calculated on
the basis of the system environment, usage status, andplanned usage information. The optimal cloud
resource that satisfies the intent can be proactively
controlled using an AI model that can predict performance on the basis of the system environment, usage
status, and planned usage information.
3.3 Network control technology
Network control technology optimizes communication paths to maximize the efficiency of network
equipment utilization while satisfying QoS requirements on the basis of the intents of users and network
operators.
With the advancements in virtualization technologies such as software-defined networking and network functions virtualization, networks can be
dynamically and flexibly controlled by software
operations. As the services and terminals usedbecome more diverse regarding user intent, QoS
expected for the network is also becoming morediverse. In addition, network operators have an intent
49Feature Articles

to use equipment efficiently and reduce equipment
expansion.
To satisfy the intents of users and network operators, we proposed an optimal-route-allocation method to accommodate as many users as possible whilesatisfying QoS requirements calculated on the basisof users’ QoE requirements by using network virtualization technology [4]. Since the optimum route allocation at a certain point in time is not always the
optimum allocation in the future, this method
improves the utilization efficiency of equipment by
optimizing the resource allocation in consideration of
future demand.
4. Multi-domain cooperative control technology
Multi-domain cooperative control coordinates thecontrol for each domain (e.g., application, network,
cloud server, and resource of other operators) to optimize the entire resource across domains while satisfying QoS requirements on the basis of the intent.
In future networks, such as IOWN (Innovative
Optical and Wireless Network) and 6G (6th-generation mobile communication systems), controllers andorchestrators will be deployed in each domain andcontrol the resources of the domain autonomously on
the basis of the elemental technologies mentioned
above. In this environment, individual optimization
within a domain does not necessarily translate into
optimization across domains. Therefore, the end-toend intent may not be satisfied and resource utilization may be inefficient.
For optimal control across domains, we are developing sharing of communication quality among
domains and optimization of control order considering control execution time as mechanisms to coordinate autonomous control by controllers in eachdomain. When communication quality in a certain
domain degrades due to resource congestion or otherreasons, resources will be over-allocated in otherdomains if the same amount of resources as in thenormal case is reserved. Therefore, sharing of communication quality among domains prevents overallocation of resources in other domains by sharing
the communication quality achieved in each domain.
As the types of control in each domain become morediverse, the execution time also varies. For example,
rerouting in a network domain may take less than 1
minute, and cloud-server resource expansion may
take 5–10 minutes. When congestion avoidance control is executed in multiple domains during resource
congestion, the end-to-end communication quality
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Vol. 20 No. 9 Sept. 2022

deteriorates until the control with the longest execution time is completed. Therefore, optimization ofcontrol order considering control execution timeshortens communication-quality deterioration by
optimizing the content and order of control in consideration of the difference in execution time for eachcontrol.
5. Use cases of Mintent
Mintent enables multi-domain cooperative control
such as the cooperation between wireless networks
and applications, wired networks and applications,cloud servers and applications, and wired networks
and cloud servers. The following sections describe
the use cases and benefits of the coordination between
wireless networks and applications and between
wired networks and cloud servers.
5.1 Monitoring of in-vehicle camera videos
Studies for automated driving are rapidly advancing. However, fully automated driving will not be
achieved immediately, and for the time being, driverassistance is the initial target. Specifically, images
captured with an in-vehicle camera are sent to amonitoring center, where an observer monitors theimages to assist the driver. In this case, the service
provider has an intent to enable the non-stop deliveryof surveillance video. Since wireless-network quality
generally fluctuates, a high video-coding rate relative
to the throughput causes packet loss, which distorts
the video and prevents proper video monitoring.
Therefore, current technologies either deliver video
at a fixed coding rate designed to be safe with respect
to wireless-network quality or deliver video at a low
coding rate that does not reliably cause packet loss by
capturing dynamic changes in the past wireless-network quality. Therefore, surveillance video with sufficient video quality cannot be provided to theobserver, and there is a concern that the observer may
fail to detect hazards both inside and outside thevehicle. Therefore, as shown in Fig. 3, Mintent
dynamically controls the encoding rate by sharing thethroughput value predicted as the future wirelessnetwork quality with the encoder that encodes thein-vehicle camera video. This enables video transmission in accordance with the wireless-network
quality and aims to transmit high-quality video inaccordance with the wireless-network quality.
5.2 Robot control in smart factories
Automation of robot control is being studied for50

Feature Articles

CodingbitrateVideo-monitoring center
Decoder

Wireless-network
quality prediction
Routeinformation Throughput

Video transmission
Encoder

Coding bitrate

Throughput

CodingbitrateOptimal codingbitrate selection

Coding-bitrate
controlTime

Fig. 3. Monitoring of in-vehicle camera videos.

Temporary solution: load balancing by route change(Execution time: within 1 minute)

Smart factory

Increased processing delay
due to resource congestion

Robot control
function Drastic solution: resource expansion(Execution time: 5–10 minutes)

Camera-image
analysis function

Fig. 4. Robot control in smart factory.

smart factories. Factory managers have an intent tokeep robots running without stopping. To achieve thisintent, communication-quality degradation that
affects robot control must be minimized. Mintent
coordinates the control of multiple domains and optimizes the control content and sequence by taking into
account the control-execution time to shorten thequality-degradation period.
Consider the environment with a network domain
and cloud-sever domain shown in Fig. 4. The robotcontrol function is deployed on the server close to thefactory, and the camera-image analysis function is
deployed on the server far from the factory. Consider
the case in which processing delays increase due totemporary resource congestion on the server where
the robot-control function is deployed. As a fundaNTT Technical Review

Vol. 20 No. 9 Sept. 2022

mental solution, the cloud-server-domain control
needs to expand the resources of the server. However,since server-resource expansion takes about 5–10
minutes to execute, the processing delay continues toincrease during this period. As a result, robot control
is affected, and the factory manager’s intent is notsatisfied. Mintent can shorten the period of quality
degradation by distributing the load to servers with
sufficient resources by changing routes in network
domains with control times of 1 minute or less as atemporary solution until the control of resource
enhancement is completed.
6. Conclusion
In this article, the need for an intent AI mediator
51

Feature Articles(Mintent) was described. Subsequent articles will
introduce the latest research and development of individual technologies at NTT laboratories.
References[1][2]

T. Kimura, T. Kimura, A. Matsumoto, and K. Yamagishi, “Balancing
Quality of Experience and Traffic Volume in Adaptive Bitrate Streaming,” IEEE Access, Vol. 9, pp. 15530–15547, 2021.
M. Yokota and K. Yamagishi, “Quality-based Video Bitrate Control

Kazuhisa YamagishiGroup Leader, NTT Network Service Systems
Laboratories.
He received a B.E. in electrical engineeringfrom Tokyo University of Science in 2001 and
M.E. and Ph.D. in electronics, information, andcommunication engineering from Waseda University, Tokyo, in 2003 and 2013. Since joining
NTT in 2003, he has been engaged in the development of objective quality-estimation models
for multi-media telecommunications. From 2010
to 2011, he was a visiting researcher at ArizonaState University. He has been contributing toInternational Telecommunication Union - Telecommunication Standardization Sector (ITU-T)
Study Group 12 (SG12) on QoS, QoE, and Performance since 2005. He has been a vice-chair of
SG12 since 2022, vice-chair of Working Party 3
in SG12 since 2021, and a co-rapporteur of Question 13/12 since 2017.

Masahiro Kobayashi

Senior Research Engineer, NTT Network Service Systems Laboratories.
He received a B.S. and M.S. in information
science from Tohoku University, Miyagi, in 2007
and 2009. He joined NTT in 2009 and is currently studying network analysis, design, andcontrol. He is a member of the Institute of Electronics, Information and Communication Engineers (IEICE).

NTT Technical Review

Vol. 20 No. 9 Sept. 2022

[3]

[4]

for WebRTC-based Teleconference Services,” Electronic Imaging
2022, IQSP-333, Jan. 2022.
C. Wu, S. Horiuchi, K. Murase, H. Kikushima, and K. Tayama,“Intent-driven Cloud Resource Design Framework to Meet Cloud
Performance Requirements and Its Application to a Cloud-sensor
System,” J. Cloud Comp., Vol. 10, 30, 2021.
K. Kawakami, M. Kobayashi, K. Yogo, K. Okuda, M. Sekiguchi, T.
Kurahashi, S. Arai, T. Tsuchiya, and N. Shirai, “Network Control
Technology to Realize the NetroSphere Concept,” NTT Technical
Review, Vol. 14, No. 10, Oct. 2016.
https://www.ntt-review.jp/archive/ntttechnical.php?contents=
ntr201610fa4.html

Shingo Horiuchi

Senior Research Engineer, NTT Access Network Service Systems Laboratories.
He received a B.E. and M.E. in engineeringfrom the University of Tokyo in 1999 and 2001.
He joined NTT Access Network Service Systems
Laboratories in 2001. He has been researchingand developing access network operation systems. He has also been involved in standardization efforts for operations support systems in TM
Forum. He is a member of IEICE.

Kenichi Tayama

Group Leader, Senior Research Engineer,
Supervisor, NTT Access Network Service Systems Laboratories.
He received a B.S. and M.S. from The University of Electro-Communications, Tokyo, in 1993
and 1995. Since joining NTT in 1995, he has
been engaged in the research and development ofaccess network operations and the planning anddevelopment of internal information technology
systems and network operations and maintenance.

52

Feature Articles: Intent-based Application, Cloudserver, and Network Collaboration Technologies

Technology for Extracting andConverting Various and Ambiguous
Intents to Implement Mintent
Noritsugu Egi and Shingo Horiuchi
Abstract

In this article, we introduce an intent-extraction technology to extract intents of service providers andusers in a next-generation communication service and a resource-requirement-conversion technology toconvert these intents into resource requirements that can be used for resource control in the intent artificial-intelligence mediator (Mintent) that enables operations by taking into account various intents.
Keywords: intent, intent extraction, resource requirements conversion

1. Introduction
Attention has been focused on intents, which arevarious demands for services, to create beneficial
services for service providers and users. By accurately extracting these demands (intents) of service
providers and users and enabling a cooperative
resource control using network, cloud-server, andapplication information on the basis of these intents,
i.e., the intent artificial-intelligence mediator (Mintent), it is possible not only to provide services in
response to these demands but also to achieve efficiency of operation without decreasing the satisfaction of service providers and users by optimallydesigning and controlling resources on the basis ofthese intents. To implement Mintent, it is important toaccurately understand the intents of service providers
and users for services and convert these intents into
requirements of adjustable indicators for operations.
We introduce intents and related technologies in thefollowing sections.
2. What is intent?
Service providers and users have various demands
for various services over a network. For example,
virtual reality (VR) video-service users want to havean audiovisual experience as if they were in a real
NTT Technical Review

Vol. 20 No. 9 Sept. 2022

space, e-sports-service users want to have smooth
operation without performance being affected, andusers of an automatic operation or working robot
want to achieve necessary tasks such as transportation and object detection more accurately and quickly
than a human. While meeting user’s demands, service
providers want to minimize the amount of data transmission to reduce operating costs or respond quickly
to complaints for maintaining user satisfaction. We
define these demands as intents and aim to enable
service operation on the basis of these intents.
Figure 1 shows the elemental technologies forachieving intent-based operation. Intents are notalways given as specific requirements by service
users or service providers. For example, ambiguous
demands related to functional requirements, such as“enjoying watching sports” or “detecting suspicious
persons,” or ambiguous demands that are not a specific index or required values related to the quality or
performance of certain functions, such as “feeling of
being there” or “achieving high accuracy,” can beassumed.
Because these ambiguous intents in service operation are difficult to use, we need to break them into
specific requirements and define indices for theserequirements (intent definition in Fig. 2). To express
quantitative values of non-functional requirements
related to quality or performance, we are studying the53

Feature Articles

Service providers and users
Operationlog

Biologic
information

Intentextraction

PortalData containing ambiguous quality
or performance requirements
Measurement
data- Video/sound quality- Operability
etc.
Set ofrequirements
for intents

Data containing requests
for each requirement
Language
information

Log information
Semi-structured data

Structured data

- Stress- Fatigue
etc.

Unstructured data

- Performance- Behavioral historyetc.

- X-scale media
transmission functionetc.

- Non-utilization rate
- Power cost
etc.

Specific functional
requirements

Otherrequirements

Specific quality/performance requirements

Resourcerequirementtranslation

Chat

Service requirements
Application
requirements

Cloud-server
requirements

Networkrequirements
Mintent

Fig. 1. Fundamental technologies of intent-based operation.

A set of intents of service providers and users
Excellent
operability
Goodvideo/soundquality

Task-processing
accuracy greaterthan that of humans

Reduction of
fatigue andVR sickness

Games can be played
anywhere, anytime
Identification of
suspicious
persons

Holding events at
many remotelocations

Accelerating
fault detection
and recovery

Reducepower
consumption

Define a set of requirements for various intents
Specific functional requirements

Specific quality/performance requirements
Media
quality
index
(MOS)

Operability
index

Performanceindex

Biologic
index

•••

For some requirements for which a quantitativeindex associated with the requirements is notdefined, we define a new index and develop
evaluation methods for quantifying this index.

Video
distributionfunction

Scale

Image
analysisfunction

Scale

New
function+•••

Other requirements
RPO
RTO

Power
consumption

•••

•••

MOS: mean opinion score
RPO: recovery point objective
RTO: recovery time objective

Fig. 2. Intent definition.

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54

Feature Articles

Customer portal
Quantified top intent

DSL
interpretation
IntentLower intent required for upper intent

Intent handling
IntentTo the controlled object
specific requirements

Intent handling
ActionStep 1

Step 2

DSL: domain specific language

Fig. 3. Intent analysis method in the standardization.

definition of general indices for various requirements
and quantitative values for each index. Thus, we aim
to implement Mintent by extracting a set of specificrequirements of service users and service providers(intent extraction) and converting these requirements
into adjustable indicators for operations in Mintent(resource-requirement conversion). The following
sections introduce intent-extraction technology andresource-requirement-conversion technology.
3. Intent-extraction technology
Intent-extraction technology collects information
related to the intents of service providers and users
and extracts a set of specific requirements from thisinformation. As intent-related information, we use(1) language information obtained through the input
interface for service providers or users and (2) information related to these users that can be obtained inservice.
An example of (1) is the language information
received through a user portal or chat function. By
using language-processing technology for this information, we extract the intents for specific demands orconcerns. For example, when the service provider ofa service that delivers real-time high-quality video ofa professional sports competition gives language
information such as “There is a suspicious person in
the audience” or “A player is injured,” intents such as“detection of suspicious persons from images in corresponding area” or “remote diagnosis for the player”
are extracted, and the required functions are extracted
from these intents. We extract the quality and perforNTT Technical Review

Vol. 20 No. 9 Sept. 2022

mance requirements necessary for each function(e.g., detection accuracy rate is 90% or more). We
also aim to provide a flexible service that can automatically switch to the appropriate service plan froma pre-set one on the basis of the intents of service
providers or users.
An example of (2) is operation-log information. We
extract intents of service users from changes in the
behavior patterns obtained by analyzing the operation-log information. For example, in a service related to remote control, service interruption and timespent on each task are extracted from the operationlog. If it is found that service users tend to stop using
the service when the time required for the last specific task is long, intent related to performance isobtained. Specific quality or performance requirements (e.g., within 10% of the average time required
for the task) is determined on the basis of the correspondence between the time and the user’s behavior.
We aim to extract a set of specific requirements that
service users and service providers have by using
various types of information.
4. Resource-requirement-conversion technology
To use Mintent for resource control, specificrequirements extracted using intent-extraction technology need to be converted into indicators that canbe controlled by the resource controller. In addition tointent extraction, we believe that a two-step function
is necessary on the basis of discussions on standardization (Fig. 3).
Step 1 may include several steps of transformation
55

Feature Articles

Resource
intentPurpose

PurposeBusiness
intentPurposeServiceintentPurposeResource
intentExcerpt from “Autonomous Networks Technical Architecture,” v 1.0.0, IG 1230
NE: network element

Fig. 4. Relationship between intent and autonomous network.

from the business layer to a controlled target in a
specific network domain. As specified in the standard, an architecture in which the management target
and business are separated by several layers is thebasis for developing systems such as a business support system and operation support system. Key performance indicators (KPIs), which represent the performance of the management target, exist for each
operation-management layer and have relationships(structures) across layers. We believe that the technology to extract qualitative intents and achieve Step 1
can be developed not only by conventional machinelearning but also by taking into account the hierarchical KPI structure and implications of KPIs for intents.
The intent discussion in the standardization istreated as the purpose of the closed loop mechanism
in each layer that enables an autonomous network forautomating the operation (Fig. 4). Intent based onunderstanding the abstract requirements of the usersthat we aim to achieve not only automates the operation but also contributes to enhancing customer management by increasing user satisfaction [1].
We are not only targeting services such as CaaS
(Connectivity as a Service) services, e.g., network
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slices of 5G (5th-generation mobile communication
systems), but also aiming to establish technologies toextract user requirements for end-to-end services
including application functions provided on thoseservices.
5. Conclusion
We introduced technologies to be studied to enable
operations corresponding to various intents of service
providers and users. In addition to the development ofthese technologies, we will participate in the proof ofconcept (Catalyst Project) at the TM Forum, an
industry association for communication service providers and their suppliers, to increase awareness ofthese technologies, discover applicable business scenarios, and promote research and development so thatthe technologies can be used for a wide range of business requirements. We will also standardize the intent
model and API (application programming interface)requirements and quantification of intent metrics asneeded.

56

Feature Articles

Reference[1]

TM Forum, “IG1230 Autonomous Networks Technical Architecture
v1.1.0,” https://www.tmforum.org/resources/how-to-guide/ig1230autonomous-networks-technical-architecture-v1-1-0/

Noritsugu Egi

Senior Research Engineer, Communication
Traffic, Quality and Operation Research Project,
NTT Network Service Systems Laboratories.
He received a B.E. and M.E. from Tohoku
University, Miyagi, in 2003 and 2005. In 2005,
he joined NTT laboratories, where he has been
engaged in the quality assessment of speech andaudio. Currently, he is working on the quality
assessment of video and web-browsing services
over IP networks. He received the Young
Researchers’ Award from the Institute of Electronics, Information and Communication Engineers (IEICE) in 2009.

NTT Technical Review

Vol. 20 No. 9 Sept. 2022

Shingo Horiuchi

Senior Research Engineer, NTT Access Network Service Systems Laboratories.
He received a B.E. and M.E. in engineeringfrom the University of Tokyo in 1999 and 2001.
He joined NTT Access Network Service Systems
Laboratories in 2001 and has been researchingand developing access network operation systems. He has also been involved in standardization efforts for operations support systems in TM
Forum. He is a member of IEICE.

57

Feature Articles: Intent-based Application, Cloudserver, and Network Collaboration Technologies

Optimize Cloud-server Resources for
Comfortable Web Conferencing
Hiroaki Kikushima and Chao Wu
Abstract

This article introduces an application example of the cloud-server resource-optimization technology,
which is one of the technologies comprising the intent artificial intelligence (AI) mediator called Mintent, to a web-conferencing service. This technology recommends the minimum amount of resources
necessary to meet the requirements (intents) of each service provider and user, reducing resource costs
while ensuring quality of experience, by using AI that predicts performance in accordance with various
service usage conditions. This helps to meet a variety of requirements, such as improving user engagement with services, reducing service providers’ operating and resource costs, and reducing carbon dioxide emissions through more efficient energy use.
Keywords: intent, resource control, web-conferencing service

1. Introduction
Changes in social conditions, such as the rapid
increase in remote work and the promotion of digitaltransformation, have accelerated the rapid change indemand for services and the diversification of userneeds.
To keep up with changes and the diversity in theseenvironments, various systems are becoming cloudnative, and various services and network functions
are being provided on cloud servers with a shorter
lead time. Therefore, server-resource control is
becoming increasingly important; however, it is difficult to provide services that meet quality of experience (QoE) requirements and usage conditions withresource design based on experience and resource
control based on system performance. Therefore,
NTT Access Network Service Systems Laboratories
developed a novel cloud-server resource-optimization technology that takes into account various user’s
QoE in addition to system-performance requirements.
The cloud-server resource-optimization technology
automatically calculates optimal resources undervarious service-usage conditions while maintaining
QoE, leading to reductions in resource design and
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operation and resource costs.
To show the effectiveness of this technology, thisarticle introduces its application to a web-conferencing service.
2. Cloud-server resource-optimization
technology overview
2.1 Technological superiority
The cloud-server resource-optimization technology
uses an artificial intelligence (AI) model that predicts
various types of performance on the basis of systemload and service-usage conditions and calculates theminimum amount of resources that satisfy therequirements (intents) of service providers and users(Fig. 1).
It has the following advantages over conventional
resource-control technology that focus only on system performance.
By combining multiple AI models that predict various types of performance, this technology can calculate the optimum amount of resources not only forsystem-performance requirements but also for intents
including QoE requirements. The AI models used aretrained to use different log data to predict different
types of performance. Basically, a regression analysis
58

Feature Articles< Preprocessing > AI model learning
Log collection

Log [Performance]
QoE,
System performance

Log [Workload]
Service usage status,
system load status

Collect logs forworkload,amount ofresources,
performancei. Performance prediction error
Learned AI model

Optimal resource
settings to meet
intentIntentii. QoE requirements, system
performance requirements

Workloadiii. Service usage (forecast),
system load status

Amount of resources
Number of CPU cores,
memory, VMs, etc.

Regressionanalysis model

Classificationanalysis model

Select minimal resource patterns thatmeet performance requirements(intent)

In the case of a performanceindicator with severe deviation
or fluctuation, useclassification analysis model

Optimal resource amount
Number of CPU cores,
memory, VMs, etc.

of quality
Improve accuracy of prediction Number
deterioration times
by classifying performance
6 or more
into quality levels
times

Threshold

3–6 times
3 times or less

Optimal resource calculation for satisfying intent (performance prediction + optimal resource calculation)
CPU: central processing unit
VM: virtual machine

Fig. 1. Technical overview.

model is used. However, some QoE indicators are
difficult to predict numerically through regression
analysis. In this case, by using a classification analysis model, the technology is able to predict the performance level with high precision (Fig. 1). These features make it possible not only to avoid quality failures and improve resource efficiency but also to provide services tailored to users’ usage conditions andobjectives.
Various efforts have also been made to train AI
models to more reliably meet QoE requirements. The
loss function is unique, for example, with modifications to lower intent violation risk (Fig. 2). The loss
function is also defined separately not only for the
degree of violation but also for the period and numberof violations, which are important for performance
management in network operation.
By using both the service (application) and system(infrastructure) workloads (iii. in Fig. 1) as input tothe AI, it is possible to calculate resources in accorNTT Technical Review

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dance with the service usage status (including predictions) in addition to the system load status. This
makes it possible to proactively control the service inaccordance with the demand forecast of the service
provider. This advantage is especially effective incases such as a web-conference service, where it is
not easy to scale up the host server (virtual machine
instance) of a conference or change the host server
while providing the service once the meeting started.
By calculating the optimal resource in advance on thebasis of the meeting-reservation information, the system can scale out in advance when the meeting starts.
2.2 Application of cloud-server resource-optimization technology
This technology can be used not only in the
resource-design phase when a service is introduced
but also for dynamic resource control in the serviceoperation phase. This technology can be used not
only for SaaS (Software as a Service) but also for59

Feature Articles

Loss functions(absolute error +additional penalty
for violations)< Preprocessing > AI model learning
Log [Performance]
QoE,
system performance

Degree ofviolation
Penaltyi. Predict error
correction

None

DegreePeriodNumberPrediction
accuracy

0.03

–

–

0.90

–
−55%

0.07

–

0.90

–

−67%

0.32
−45%

0.89

0.21

0.58

0.91

0.06

Maintainaccuracyof about90%

Predicted performance

Penalty

Period ofviolation
Number ofviolations

Percentage of violations

1m

Period ofviolation

Number ofviolations

i=m−1

o

1
m

(|perfi − hi| +   bi  |perfi − hi|)



i=m−1

Degree of violation risk control(|perfi − hi| +   bi  duri)

o

1
m

i=m−1

o

(|perfi − hi| +   bi)

1 i=m−1|perfi − hi|
m o

None

<SaaS (machine learning) service application results>
Loss functions(absolute error +additional penalty
for violations)
Degree ofviolation

Loss function

Penalize the loss function inaccordance with the period,
degree, and number ofviolations

bi =

0,
1,

perfi is no worse than hi
perfi is worse than hi

Not in violation of requirements

Violation of requirements
Actual performance

perfi
m

Actual performance
Number of datahiPredicted performance
WeightFig. 2. Requirement-violation risk-control function.

IaaS (Infrastructure as a Service) infrastructure. In
the future, we plan to expand the scope of application
scenarios to virtual network services.
3. Application of cloud-server
resource-optimization technology forweb-conferencing services
For the application of this technology to web-conferencing services, we devised AI models and developed new functions on the basis of the issues andfeatures of web-conferencing services.
3.1 R
 equirements with web-conferencing services
and their solutions
Web-conferencing services have the following
requirements due to the changes in social conditions
described above.
(1)	A web-conferencing service provider wants tooptimize server resources and cost by allocating servers in accordance with server resource
conditions and the amount of meetings.
(2)	By clarifying that it has the technology for
QoE maintenance, a web-conferencing service provider wants to improve the brandvalue for the service.
We can satisfy these requirements by applying thisNTT Technical Review

Vol. 20 No. 9 Sept. 2022

technology, as shown in Fig. 3.
With the conventional resource operation, it is common to prepare sufficient server resources in advancefor the expected maximum service usage (number ofusers, etc.) on the basis of the experience of engineersand verifications. In this case, when there are only a
few users, unnecessary resources are allocated, andwhen more users access than expected, QoE degradation occurs. It is also difficult to increase the numberof central processing unit (CPU) cores while runninga service such as web conferencing, which can lead toa fatal situation.
By combining this technology with the control
functions of cloud services, it is possible to solve thisproblem by operating services with the minimum
amount of resources when the number of users issmall and scaling out in advance servers as the number of users increases.
3.2 C onstructing a performance-prediction AI
model for web conferencing
QoE indicators that are important for web-conferencing services are generally throughput and jitter,
which are indicators of the cleanliness and stability ofvideo and audio. Because these values fluctuate constantly, they are difficult to predict with high accuracy(Fig. 4(a)). Therefore, we focus on the fact that these60

Feature Articles[Before] Provide sufficient resources for the expected maximum number of users
Virtual server

Virtual server

User

Surge in usage

Based on experience,
provide sufficient
resources for the expected
maximum number of users

Operator
Manually increase resources
when the assumed maximum
number of users is exceeded

Surplus resources[After] Provide optimal resources for the conference workload proactively
Virtual server

Virtual server

Surge in usage

User

Optimal resources
Proactively change resource
amount in accordance withconference workload
prediction

AI

Operator

Fig. 3. Image of problem solving.

values do not deteriorate when the CPU usage isbelow a certain level, and we can derive the optimal
resource amount to maintain QoE by using an AI
model (Fig. 4(b)) that predicts the CPU usage fromthe workload and resource amount.
3.3 Peripheral function
As mentioned above, to provide this technology toa web-conferencing service, it is necessary to havethe function of determining the amount of resources
required and the allocation of the conference among
various server instances before the conference starts.
Therefore, it is necessary to provide a function to
supplement the workload information (the number ofvideo and audio streams) from the meeting-reservation information before an actual meeting occurs(Fig. 5(a)). A function is also required to predict the
QoE indicator of each meeting combination in accordance with the supplemented workload and determine whether it can be accommodated in the existing
instances with predetermined instance type (numberof CPU cores) and execute scale-out in advance(Fig. 5(b)). By operating this resource-calculation
function on the basis of these usage conditions (predictions) periodically (from a few minutes to a few
hours), proactive and automatic resource control canalways be achieved during service operation, and the
maximum effect can be obtained in both maintaining
QoE and improving resource efficiency.
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4. Optimal resource-calculation results
and effects
On the basis of the actual usage trends of commercial web-conferencing services, we generated user
workloads assuming that up to 100 users would use
the service and verified the effectiveness of our technology. First, we used this technology to predict CPU
usage for an instance type with four CPU cores andconfirmed that the result was almost the same as theactual measurement when the same number of workloads were actually generated (Fig. 6(a)). On thebasis of these predictions, the system can change theweb-conferencing server to an instance with two
CPU cores in advance when resources are predicted
to be in surplus and change the web-conferencing
server to an instance with eight CPU cores in advancewhen QoE is predicted to degrade. This enables service provision while maintaining CPU usage below
the threshold and efficient use of resources (Fig. 6(b)).
From these results, we found that by using thistechnology to control resources in accordance withthe expected amount of workload, we could reduce
the amount of server resources by approximately
37% while maintaining QoE compared with whenconstantly operating excess resources with eight CPU
cores.
By greatly improving the efficiency of server
resources in this manner, it is possible to reduce61

Feature Articles

Throughput (Voice)Prediction

Evaluation index

CPU usage

ResultEvaluation index

ResultCoefficient ofdetermination (R2)0.604

Coefficient ofdetermination (R2)

−0.062

Root Mean Squared
Error (RMSE)

511.208

Root Mean Squared
Error (RMSE)

0.197

Jitter (Audio)

Prediction

Prediction

Throughput (Video)

Prediction

Prediction

Jitter (Video)

Evaluation index
Coefficient ofdetermination (R2)
Root Mean Squared
Error (RMSE)

ResultEvaluation index

Result0.960

Coefficient ofdetermination (R2)

0.942

0.004

Root Mean Squared
Error (RMSE)

0.788(a) Throughput/jitter

Evaluation index

ResultCoefficient ofdetermination (R2)0.970

Root Mean Squared
Error (RMSE)

5.039(b) CPU usage

Fig. 4. AI-model evaluation results.

Service usage (Forecast)
Instance type: CPU 4 cores, memory 512 GB

Start

IntentEnd
20 users
12 users

Number of users
per conference

50
9:00

Data
supplement

10:00

11:00

• Throughput

More than xx bit/s• JitterLess than xx ms• CPU usage

Less than xx%

12:00

Instance 2

Room
10 users
15 users
5 users

DeleteInstance 2

Add
Instance 2
Current
time

Workload• Bitrate• Number of users• Number of meetings• Number of sessions

Performance
prediction(dashedline)

Instance 1

Meeting reservation data

CPU
usage

Numberof users

Conference allocation image

System performance prediction• CPU usage
Derivation

Amount of resources• Number of CPU cores• Scale-in/out instruction• Conference allocation plan

• Scale in/out in advance• Conference allocation(load balancing)(a) Data supplement function(b) Conference allocation control function

Fig. 5. Application to a web-conferencing service.

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62

Feature Articles

CPU usage (Actual)

CPU usage (Predicted)

Expect CPU usage to exceed
threshold for 4 cores,
and increase resources
to 8 cores

CPU usage (Threshold)Optimal resource amount

8 cores

Fixed resource amount

Number of users

8 cores

Optimalresource
amountCPU usage(Predicted)

Number of users

4 cores

4 coresNumber of users
If the threshold is notexceeded for 2 cores,
decrease resources to
2 cores

2 cores
CPU usage(Predicted)

CPU usage (Actual)

CPU usage (Actual)

Time (s)Time (s)(a) CPU usage when fixed 4 cores are allocated to the VM(b) CPU usage when cloud-server resourceoptimization technology applied

Fig. 6. Optimal resource calculation results and effects by cloud-server resource-optimization technology.

energy consumption, and by adapting this technology
to various use cases, it will be possible to further contribute to carbon dioxide reduction.
5. Conclusion
This article gave an overview of cloud-server
resource-optimization technology and explained its
effectiveness when applied to a web-conferencing
service.
In the future, we will examine the technology’s
interface with the current cloud-resource manage-Hiroaki Kikushima

Research Engineer, NTT Access Network Service Systems Laboratories.
He received a B.E. and M.E. in electrical engineering from the University of Yamanashi in
1994 and 1996. He joined NTT Software Headquarters in 1996. He also worked at NTT COMWARE’s AI Business Strategy Office, where hecreated various services. Since 2020, he has been
researching and developing access network
operation systems at NTT Access Network Service Systems Laboratories.

NTT Technical Review

Vol. 20 No. 9 Sept. 2022

ment systems for commercial introduction to a webconferencing service, improve its functions such as
improving the speed of conference allocation, combine it with other control technologies that comprise
Mintent, and consider expanding the application area
to other use cases in view of the IOWN (Innovative
Optical and Wireless Network) era. We will also promote the creation of new technologies to extract
quantitative intent from more ambiguous intents ofusers and service providers and convert businessservice-layer intent to resource-layer intent.

Chao Wu

Research Engineer, NTT Access Network Service Systems Laboratories.
She received a B.E. from Zhejiang University
in 2009 and M.E. from Waseda University,
Tokyo, in 2013. In 2014, she joined NTT AccessNetwork Service Systems Laboratories, where
she has been researching and developing intelligent management technologies for telecommunications in cloud and virtualization. She is also
involved in standardization efforts of the European Telecommunications Standard Institute(ETSI) and TM Forum.

63

Feature Articles: Intent-based Application, Cloudserver, and Network Collaboration Technologies

Intent-based Application and
Network Cooperative Control
Technology for Video-streaming
Services
Taichi Kawano and Masahiro Kobayashi
Abstract

We have been conducting research and development to implement the intent AI (artificial intelligence)
mediator called Mintent, a technology concept that enables cooperation among application control, network control, and cloud server control on the basis of user requirements (intent) to increase user satisfaction. To implement Mintent, we propose a technology to enable cooperation between application control
and network control by using intent as a common index for video-streaming services. This article provides an overview of this technology and discusses its effectiveness determined through simulation.
Keywords: intent, video streaming, cooperative control

1. Introduction
With the development of network virtualization
technologies such as software-defined networking
and network functions virtualization, networks can be
dynamically and flexibly controlled. However, services over networks are becoming more diverse, andthe requirements for networks are becoming more
complex, such as ultra-high speed, high capacity,
ultra-low latency, ultra-low power consumption, andlow cost. For users to comfortably use services, it isnecessary to understand the user’s requirements(intent) for various services, extract resource requirements to satisfy the intent, and control the services on
the basis of the established resource requirements. In
user applications, the transmission rate of application
data is traditionally controlled in consideration of
network quality to improve user satisfaction with theservice. The information obtained in the network isused to control the routes that satisfy the given network-quality requirements. However, since application and network controls are independent, they may
not be fully effective in terms of user satisfaction and
NTT Technical Review

Vol. 20 No. 9 Sept. 2022

network-resource utilization efficiency. Therefore, anapproach needs to be taken to increase user satisfaction by making application and network controls
cooperate on the basis of intent.
We are engaged in research and development toimplement the intent AI (artificial intelligence) mediator called Mintent, a technology concept for multilayer cooperative control of applications, networks,
and cloud servers on the basis of intent. As an initialstudy for implementing Mintent, we focused on video-streaming services, which are widely used and use
a large amount of network resources, and propose atechnology to enable cooperation between application and network controls on the basis of intent. This
article provides an overview of this technology andthe results of simulations to evaluate its effectiveness.
2. Issues in application and network controls forvideo-streaming services
This section provides an overview of applicationand network control technologies for video-steaming
services and the issues that arise when these controls
64

Feature Articles

IntentRequested-QoE

Intent-based ratedetermination
algorithm

User device data
Throughput series
Bit rate series
Buffer series

Control parameters
Optimal rate

QoE-estimation
model

Intent(Requested-QoE)

Request optimal rate
Network quality: Good
User device

Intent(Requested-QoE)

Stream at requested rate
High quality
Request low rate

Streaming server

Network quality: Bad
Low quality

Stream at requested rate

Fig. 1. Intent-based rate control technology.

operate independently.

network quality deteriorates.

2.1 R
 ate control technology for streaming video
in applications
Adaptive bitrate (ABR) is one of the application
controls at the user device that controls the rate ofvideo streaming. In ABR, multiple-bitrate videos ofdifferent quality are pre-populated on the videostreaming server, and the application controls the rate
request by switching the bitrate of the video to thestreaming server in accordance with network quality
such as throughput. This enables the application toincrease, for example, user satisfaction by reducing
playback stops. As shown in Fig. 1, we also developed a technology for predicting future quality ofexperience (QoE) using our QoE-estimation technology [1] on the basis of throughput measured at theuser device and application information (video/audio
bitrate time-series information, etc.), and controllingrates to satisfy the QoE set by the user (requestedQoE) in the application [2]. This technology ensureshigher user satisfaction than conventional technologies. However, when network quality deteriorates due
to congestion, application control alone cannot satisfy the requested-QoE because it can only lower thebitrate and reduce video quality. Thus, application
control alone cannot maintain user satisfaction when

2.2 Network control technology
Current network control technology estimates quality of service (QoS) by using traffic forecasts for eachtraffic flow on the basis of communication logs
observed with network devices and controls routes sothat the resource-utilization efficiency is high while
maintaining actual QoS. Traffic flow is communication traffic with the same source Internet protocol (IP)address, destination IP address, port number, andprotocol number. We developed a technology thatgroups flows with similar characteristics into macro
flows by using non-negative tensor factorization thenaccurately predicts future traffic for each macro flowby using seasonal autoregressive integrated moving
average (SARIMA) [3, 4]. Network control using
accurate traffic prediction improves the efficiency of
network-resource utilization. Therefore, the network
control technology controls routes on the basis of theactual QoS estimated from communication logs, but
it is difficult to correctly estimate the QoS required tosatisfy a user’s desired QoE from communication
logs alone. The QoS required to satisfy the user’s
desired QoE is called the requested-QoS. Therefore,
if there is a large discrepancy between the requestedQoS and the actual QoS, the QoE may decrease due

NTT Technical Review

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65

Feature Articles

Application-network-cooperation function
Intent per user (Requested-QoE)
Application information
Rate-control function
Intent-based rate

Per macro-flow required bandwidth

Route-control function
Intent-based routing paths

Network
Streaming server

User device

Fig. 2. Intent-based application and network cooperation function.

to insufficient quality, or network-resource-utilization efficiency may decrease due to excessive quality.
In addition, if the application-rate control technology
described above and network control technology
operate independently in video delivery, the QoE may
not be satisfied. When network quality degrades due
to congestion, the application-rate control technology
delivers low-quality video to resolve the congestion.
In this case, the network control technology determines that congestion is naturally resolved and does
not change the route. As a result, low-quality video
continues to be delivered and the QoE decreases.
Thus, the network control technology alone cannot
satisfy the requested-QoE because requirements of
the requested-QoS cannot be accurately estimatedand the effect of the application-rate control technology cannot be taken into account.
3. Intent-based application and network
cooperative control technology
To solve the problem of application and network
controls operating independently in video-streaming
services, our proposed technology enables cooperation between these controls by using the requestedQoE, one of the intents, as a common index for application and network controls. This section describes
the overall structure and details of the main functionsof the proposed technology.
NTT Technical Review

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3.1 Overall structure
The overall structure of the proposed technology isshown in Fig. 2. The technology consists of a ratecontrol function and a route-control function using
our technology described above, and an applicationnetwork-cooperation function that enables cooperation between these functions. In the rate-control function, the user sets the requested-QoE for each user
device, and the user device notifies the applicationnetwork-cooperation function of the requested-QoE
and the representation information (a list of video andaudio bitrates, video resolution, and frame rate thatcan be selected by the rate control). The applicationnetwork-cooperation function predicts the required
bandwidth as future QoS requirements for eachmacro flow that bundles flows of users with the same
requested-QoE on the basis of the requested-QoE foreach user notified by all user devices. Network control satisfies the requested-QoE by executing optimalrouting for each macro flow to satisfy the predicted
required bandwidth. Therefore, application control
and network control enable intent-based cooperative
control by using the requested-QoE as a common
index.
3.2 Application-network-cooperation function
As shown in Fig. 3, the application-network-cooperation function consists of a user-QoS-extraction
function, active-user-prediction function, and66

Feature Articles

Per user requested-QoE
Application information(Representation
information)

Required bandwidth

User-QoS-extraction function

Service A

Estimation

Service B

Per user-estimatedrequired bandwidth

Macro-flow-QoS-extraction function
Required
bandwidth

Service A
Intent (Requested-QoE)

Macro flow 1

Mbit/sMacro flow 2

Mbit/sMacro flow N

Mbit/sPer macro-flow numberof active users

Number of active users

Active-user-prediction function

Prediction

Per macro-flow
predicted number ofactive users

Time

Fig. 3. Application and network cooperation function.

macro-flow-QoS-extraction function. The user-QoSextraction function calculates the network QoS
requirements on the basis of the throughput required
from the requested-QoE and application information
reported through user devices. The relationshipbetween the requested-QoE and throughput required
to achieve it depends on the representation information designed for each video-streaming service.
Therefore, the relationship between the requestedQoE and required throughput is modeled in consideration of the representation information, and themodel is used to estimate the required throughput
from the requested-QoE and representation information. The active-user-prediction function predicts the
future number of active users for each macro flow. It
derives this number using SARIMA, a time-series
prediction method, from the time-series data of thenumber of active users for each macro flow notified
from user devices to the application-network-cooperation function. The macro-flow-QoS-extraction function calculates the required bandwidth for each macro
flow on the basis of the estimated required throughput per user and the predicted number of active users
per macro flow. These functions enable network routing to be controlled on the basis of network requirements considering the requested-QoE.

NTT Technical Review

Vol. 20 No. 9 Sept. 2022

4. Effectiveness evaluation through simulation
To evaluate the effectiveness of the proposed technology, we compared it with conventional technologies in which application control and network control
operate independently and evaluated its effectivenessin terms of user satisfaction and network-resourceutilization efficiency through simulations. This section provides an overview of the simulations and theresults.
4.1 Simulation overview
The simulation scenario assumes routing control
when one path is congested in a network structure
where network bottlenecks are duplicated, such as apoint of interface between a carrier network and theInternet. Simulations were conducted using ns-3, anopen source network simulator. Figure 4 shows thesimulation environment. The network structure consists of three routers fully mesh-connected in a triangular structure. There is one video server connected
to one router and 90 user terminals connected to eachof the remaining two routers. There are also tworoutes between the video server and user devices.
User devices can set their requested-QoE taking values from 1.0 to 5.0 to 4.5, 4.0, or 3.5, and 30 user
devices with each requested-QoE were connected toeach router. Congestion was generated by flowing
background traffic to the video server from a user
67

Feature Articles

Streaming server
Requested-QoE 4.5 users
Internet

Requested-QoE 4.0 user
RouterRequested-QoE 3.5 users
Background traffic

RouterRouterCarrier network

30 users

30 users

30 users

30 users

30 users 30 users

Fig. 4. Simulation environment.

Percentage of users achieving requested-QoE

100%
22%
80%

60%
100%
40%

78%

20%

0%
Application and network
cooperative control(Proposed technology)

Independent applicationand network control(Conventional technology)

Fig. 5. Results of effectiveness evaluation through simulation.

device. The effectiveness of the proposed and conventional technologies was evaluated by comparing
the results of routing control during congestion interms of achieving the requested-QoE and networkresource-utilization efficiency.
4.2 Effectiveness verification through simulation
Figure 5 shows the percentage of users who
NTT Technical Review

Vol. 20 No. 9 Sept. 2022

achieved the requested-QoE after routing with theproposed and conventional technologies during congestion. About 22% of all users did not achieve therequested-QoE with the conventional technology.
When limited to users with a requested-QoE of 4.5,
about two-thirds did not meet the requested-QoE.
This is because the network control was unable tocorrectly estimate the required bandwidth because it
68

Feature Articles

derived this bandwidth using only the information in
the network and could not take into account therequested-QoE or application control. The proposed
technology satisfied the requested-QoE for all users.
This is because this technology estimates the required
bandwidth to satisfy the QoE with higher accuracy
than the conventional technology by using therequested-QoE obtained from the user-device application. Thus, the proposed technology can achieve
higher user satisfaction than the conventional technology. In addition, if the percentage of users whoachieved the requested-QoE with the conventional
technology by increasing the link bandwidth is equal
to that with the proposed technology, a 16% increasein link bandwidth is required. This indicates that the
proposed technology is superior to the conventional
technology in terms of network-resource-utilization
efficiency.

achieves higher user satisfaction and networkresource-utilization efficiency than the conventional
technology in which application control and network
control operate independently. In the future, we willdevelop a technology that can be applied universallyto various services. In addition to application control
and network control, we also aim to develop a multilayer cooperative technology that includes cloudserver control.
References
[1]
[2]

[3]

5. Conclusion
Toward the implementation of Mintent, we proposed an intent-based application and network control cooperation technology for video-streaming services. We have shown that the proposed technology

Taichi Kawano

Senior Research Engineer, NTT Network Service Systems Laboratories.
He received a B.S. and M.S. in information
science from the University of Tsukuba, Ibaraki,in 2006 and 2008. He joined NTT in 2008 and iscurrently studying operation technology basedon QoE and intent in NTT Network Service
Systems Laboratories. He is a member of theInstitute of Electronics, Information and Communication Engineers (IEICE).

NTT Technical Review

Vol. 20 No. 9 Sept. 2022

[4]

K. Yamagishi and T. Hayashi, “Parametric Quality-estimation Modelfor Adaptive-bitrate Streaming Services,” IEEE Trans. on Multimedia, Vol. 19, No. 7, pp. 1545–1557, Feb. 2017.
T. Kimura, T. Kimura, A. Matsumoto, and J. Okamoto, “BANQUET:
Balancing Quality of Experience and Traffic Volume in Adaptive
Video Streaming,” Proc. of the 15th International Conference on Network and Service Management (CNSM 2019), Halifax, Canada, Oct.
2019.
K. Yamagoe, M. Nishio, M. Kobayashi, S. Harada, and H. Nozue,“Intelligent Zero-touch Operation,” NTT Technical Review, Vol. 18,
No. 6, June 2020.
https://www.ntt-review.jp/archive/ntttechnical.php?contents=
ntr202006fa3.html
M. Kobayashi and S. Harada, “Network Control Technologies Basedon Required QoS,” Proc. of the 2020 IEICE General Conference,
BS-4-11, Mar. 2020 (in Japanese).

Masahiro Kobayashi

Senior Research Engineer, NTT Network Service Systems Laboratories.
He received a B.S. and M.S. in information
science from Tohoku University, Miyagi, in 2007
and 2009. He joined NTT in 2009 and is currently studying network analysis, design, andcontrol in NTT Network Service Systems Laboratories. He is a member of IEICE.

69

Regular Articles

Wireless-link-quality Prediction andDevice-position Estimation Based on
Relationship between Wirelesscommunication-link and Physicalspace Information
Riichi Kudo, Hisashi Nagata, Kahoko Takahashi,
and Tomoaki Ogawa
Abstract

Various wireless terminals are being connected to a network via wireless communication accesses, andthe number of wireless terminals and their traffic are steadily increasing. The information from a huge
number of connected devices is expected to be a foundation of new services. The Cabinet Office of Japanproposed Society 5.0, where people, things, and systems are all connected in cyberspace. The optimal
results obtained through artificial intelligence will bring new value to industry and society. Society 5.0
is achieved by the convergence between cyberspace and physical space. Innovative Optical and Wireless
Network (IOWN) will accelerate the obtaining of physical-space information from a number of various
sensors. This article introduces two technologies that use the relationship between physical-space
information and wireless-communication-link information for new services in Society 5.0; 1) wirelesslink-quality prediction using camera images and 2) device-position estimation using Wi-Fi feedback
signals.
Keywords: link quality prediction, position estimation, machine learning, Wi-Fi

1. Background and overview
Advanced wireless-communication systems, suchas Wi-Fi, Long-Term Evolution (LTE), and the 5thgeneration mobile communication system (5G),
enable a wide variety of devices to connect to a network; thus, the number of connected devices andtheir traffic are exploding. The large number of connected devices will be a foundation of Society 5.0 [1],where a huge amount of physical-space informationis accumulated in cyberspace. Physical-space information will be analyzed through artificial intelligence(AI), and the process is expected to bring new value
to industry and society in ways not previously possiNTT Technical Review

Vol. 20 No. 9 Sept. 2022

ble. Innovative Optical and Wireless Network(IOWN) [2] will accelerate the creation of big data ofphysical-space information and make them accessible from everywhere.
The advancement of AI technologies will also
accelerate the use of physical-space information in
the IOWN/6G era [3]. The accuracy of vision-based
recognition using deep learning has been improving
yearly [4], and the autonomous operation of robots
and vehicles have also significantly advanced [5]. By
using advanced AI technologies, physical-space
information is expected to provide new value forwireless-communication systems.
Wireless communications is also being enhanced70Regular Articles

Physical-space information

Wireless-communication-link information

Link-quality
prediction

Sensing

Fig. 1. Image of Society 5.0 and the relationship between wireless-communication-link and physical-space information.

due to the demand of greater wireless link capacities,
latencies, and reliabilities [6]. Wireless-communication systems, such as LTE, 5G, and Wi-Fi [7, 8], support a high-speed data rate and an increasing numberof user terminals. The radio frequency of such systems is expanding to super high frequency (SHF)(3–30 GHz) and extremely high frequency (EHF) (30
GHz–3 THz) bands to obtain larger capacity. Since
the link quality in the higher radio frequency is more
subject to microwave propagation such as object
shielding [7], the relationship between physicalspace and wireless-communication-link information
is becoming stronger.
NTT Network Innovation Laboratories is studying
the relationship between physical-space and wireless-communication-link information and establishing the core technologies for new services in Society
5.0. In this article, wireless-link-quality prediction
and device-position-estimation technologies are
introduced to show the effectiveness of using the relationship between physical-space and wireless-communication-link information.
2. Physical-space andwireless-communication-link information
Figure 1 shows the relationship between physicalNTT Technical Review

Vol. 20 No. 9 Sept. 2022

space and wireless-communication-link information.
In Society 5.0, a huge number of devices will connect
to a network using wireless-communication links,and cameras/sensors will be located everywhere andbe equipped with mobility devices. Cameras/sensors
obtain images of the physical space, and the information is updated via wireless or wired connections. The
position and status information of wireless devices
are also expected to be collected as physical-space
information. Wireless signals will propagate throughalmost all the physical space, and the channel-state
information of the wireless links and their link qualities are the result of the propagation through thephysical space. Therefore, there are solid relationships between wireless-communication-link andphysical-space information.
NTT Network Innovation Laboratories has focusedon the relationship between wireless-communication-link and physical-space information and developed wireless-link-quality-prediction and deviceposition-estimation technologies. Wireless-linkquality-prediction technology uses camera images
and provides future throughput variation by using thedetected movement of the surrounding objects.
Device-position-estimation technology provides
wireless-device position by monitoring the pilot signals between the wireless device and a single base71Regular Articles

HD camera
Object-detection blockBase station

Laptop PC

Bounding boxes and their classes
Throughput-variation-prediction block

Future throughput variation

User terminal

Fig. 2. T he wireless-link-quality-prediction testbed in the experimental environment. The camera images are used as the
input of the object-detection algorithms, and the throughput-variation-prediction block provides future throughput
variation by using the detected bounding boxes and their classes.

station. Both technologies are based on supervised
learning. We evaluated the performances of thesetechnologies through experiments in actual outdoor
and indoor environments, respectively.
3. Wireless-link-quality prediction using
camera images
Wireless-link quality is strongly affected by theobjects blocking the line of sight between a base station and user terminals as the radio frequency
becomes high such as over the SHF band (> 3 GHz).
Wireless-link-quality prediction for the millimeterwave (60 GHz) was proposed [9], and the authors
used a depth camera to detect signal blocking. We
developed prediction technologies for the SHF bandby using high-definition (HD) cameras and confirmed that the long-term prediction corresponding tosecond-order future was achieved using physicalspace information [10]. It is expected that long-term
prediction will provide a sufficient lead time to counter the negative change in link quality. Our wirelesslink-quality-prediction technology provides future
throughput variation between a fixed base station anduser terminal by detecting the movements of theobjects surrounding the user terminal. We conducted
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Vol. 20 No. 9 Sept. 2022

outdoor experiments to confirm the effectiveness of
this technology.
Figure 2 shows our developed user terminal testbed
and the flow of wireless-link-quality prediction using
camera images. The testbed communicates with thebase station in the 5.6-GHz Wi-Fi channel. Cars,
trucks, buses, and pedestrians passed through thissite, and the testbed monitored the surrounding
objects using two HD cameras (1920 × 1080 pixel).
In wireless-link-quality prediction, the object-detection algorithm of M2Det [11] is used to obtain thebounding boxes of the moving objects from the camera images. It was confirmed that better detection
algorithms provide more accurate wireless-linkquality prediction [10]. Object-detection algorithms
output the bounding boxes and their classes. By using
the bounding boxes, the throughput variation at one
second in the future was predicted. The throughputvariation-prediction block uses random forest regression. Throughput variation is defined as the ratio of
the future throughput to the median of the throughputs in the past 30 seconds. Therefore, throughput
variation becomes 1.0 when there is no such variation.
The measured dataset totaled 3490 seconds ofdata, containing 288 cars, 20 buses/trucks, and 36
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Predicted normalized throughput
Measured normalized throughput

Person-transit
event time

Vehicle-passing
event time

1.50
1.25

Throughput variation

1.00
0.75
0.50
0.25
0.00
1.50
1.25
1.00
0.75
0.50
0.25
0.00
0

25

50

75

Time (sec)Fig. 3. Predicted and measured throughput variation for person-transit and vehicle-passing events.

4. Device-position estimation
The relationship between wireless-communicationlink and physical-space information can be used tofind the physical-space information using the wireless-communication system information. Wirelesssensing technologies have gained much attention [12]for the 6G era. Although the Global Positioning System (GPS) is the most well-known position estimaNTT Technical Review

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0.25

90% of absolute error

people-transit events. The dataset was divided into 10
parts, and 9 were used for training to generate thethroughput-prediction model. Wireless-link-quality
prediction was conducted using the remaining dataset. The estimated and measured throughput variations
are shown in Fig. 3. The vehicle-passing events andperson-transit events denote the time when the vehicles or persons are passing around the user terminal.
We can see that throughput degradation was predicted using camera images. Figure 4 shows the 90th
percentile of the absolute errors of three prediction
algorithms against the lead time. We compared theprediction performance of the wireless-link-qualityprediction technology using bounding boxes, pastthroughputs, and both past throughputs and boundingboxes. We found that the prediction performanceusing only the bounding boxes was the best when thelead time was greater than 1 second. These results
indicate that the contribution of past throughputs
becomes negligible for long-term prediction, andphysical-space information is valid when the leadtime is greater than 1 second.

0.2
0.15
0.1
0.05
0
0.5

Bounding boxes
Bounding boxes and past throughputs
Past throughputs
1

1.5

2

Lead time (s)Fig. 4. 9
 0% of the absolute error of throughput variation
against lead time.

tion technology, it works only for outdoor environments and without tall buildings around the device.
To obtain accurate position information in an indoor
environment, wireless-communication systems are
expected to play an important role [13]. We developed our device-position-estimation technology,
which uses angle of arrival (AoA) estimation anddeep learning using Wi-Fi feedback packets with asingle access point [14].
This technology uses a monitoring terminal that
detects the feedback signal in the Institute of Electrical and Electronics Engineers (IEEE) 802.11 standards, as shown in Fig. 5. The monitoring terminal
obtains the information of the channel state information
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Base station

Wi-Fi CSI feedback
User terminal
User terminal

Monitoring terminal

Base station

Device-position estimation block

Fig. 5. D evice-position estimation using Wi-Fi CSI
feedback.

5m

Monitoring terminal

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Vol. 20 No. 9 Sept. 2022

Fig. 6. T he user terminal and movement in an indoor
experiment.

1
0.75
CDF

(CSI) between a base station and user terminal andthe received signal strength indicator (RSSI) of the
feedback signal, which corresponds to the link
between the monitoring terminal and user terminal.
The monitoring terminal calculates the correlation
matrix R then outputs R as well as the signal-to-noise
ratio (SNR) and RSSI into the position-estimation
block. Since the elements of R contain the information of the phase differences in the arrival waves, theAoA estimation uses R [15]. The position of the user
terminal is predicted using the input from the monitoring terminal and a deep neural network (DNN),
which consists of gated recurrent units and three fully
connected layers.
To confirm the device-position-estimation accuracy, we conducted indoor experiments using mobility
robots. Figure 6 shows the developed user terminal
and the indoor experimental environment. The user
terminal ran in a figure eight, as shown with the blacklines. The base station and monitoring terminal wereat the locations shown in Fig. 6. The base station anduser terminal use the 20-MHz channel in 5.6 GHz ofIEEE 802.11ac. The number of transmit and receive
antennas were 4 and 2, respectively. The CSI feedback signal of the user terminal was obtained every
100 ms, and 80 samples of the feedback signals wereused as input of the DNN-based position estimation.
We trained the position-estimation model using a
training dataset containing 13.9 hours of the position
and CSI feedback signal and evaluated the positionestimation error using a test dataset containing 1.0
hour of the CSI feedback signals.
The cumulative distribution function (CDF) of theposition-estimation errors are shown in Fig. 7. The
position-estimation errors of the DNN with all the
input features from the monitoring terminal, i.e., R,
SNR, and RSSI; DNN with R; DNN with SNR and
RSSI; and random forest regression with all these
input features were compared. The DNN with all the0.5
All (R, SNR, RSSI)R
SNR, RSSI
Random forest with all

0.25
0
0.00

0.50

1.00

1.50

2.00

Estimation error (m)Fig. 7. CDFs of position-estimation errors.

features performed best and the median and 0.9 outage of the prediction errors were only 5.1 and 13.1
cm, respectively. The medians of the DNN with R
and DNN with SNR and RSSI were 8.2 and 17.1 cm,
respectively. The R, which is a parameter for AoA
estimation, highly contributed to the accurate position estimation. We also found that the median estimation errors of the random forest regression with allfeatures was 44.9 cm, which was 8.8 times that of the
DNN with all features.
5. Conclusion and future perspective
We presented wireless-link-quality-prediction anddevice-position-estimation technologies and discussed their effectiveness using a dataset measured in
actual outdoor and indoor environments. Wirelesslink-quality prediction uses the bounding box information detected using camera images and predicts
74

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precise long-term throughput variation. Device-position-estimation technology estimates the user device
position on the basis of the correlation matrix, SNR,
and RSSI obtained from the received Wi-Fi feedback
packets. Both technologies are based on the relationship between wireless-communication-link andphysical-space information. We believe that this relationship is one of the keys to creating new value in
Society 5.0 and will continue to develop the coretechnologies of link-quality prediction and position
estimation.
References
[1]
[2]
[3]
[4]
[5]

[6]

Cabinet Office, Government of Japan, “Society 5.0,” https://www8.
cao.go.jp/cstp/english/society5_0/index.html
Website of NTT R&D, “IOWN,” https://www.rd.ntt/e/iown/
NTT DOCOMO, “DOCOMO 6G White Paper,” https://www.
docomo.ne.jp/english/corporate/technology/whitepaper_6g/
L. Liu, W. Ouyang, X. Wang, P. Fieguth, J. Chen, X. Liu, and M.
Pietikäinen, “Deep Learning for Generic Object Detection: A Survey,”
Int. J. Comput. Vis., Vol. 128, pp. 261–318, Oct. 2019.
S. H. Alsamhi, O. Ma, and M. S. Ansari, “Convergence of Machine
Learning and Robotics Communication in Collaborative Assembly:
Mobility, Connectivity and Future Perspectives,” J. Intell. Robot.
Syst., Vol. 98, pp. 541–566, 2020.
NGMN Alliance, “Perspectives on Vertical Industries and Implicationsfor 5G,” Sept. 2016.

Riichi Kudo

Senior Research Engineer, Supervisor, Proactive Communication Research Group, Frontier
Communication Laboratory, NTT Network
Innovation Laboratories.
He received a B.S. and M.S. in geophysics
from Tohoku University, Miyagi, in 2001 and2003 and Ph.D. in informatics from Kyoto University in 2010. In 2003, he joined NTT Network
Innovation Laboratories. He was a visiting fellow
at the Center for Communications Research(CCR), Bristol University, UK, from 2012 to2013 and worked for NTT DOCOMO from 2015
to 2018. He currently specializes in machinelearning technologies using physical-space andwireless-communication-link information. He isa member of IEEE and the Institute of Electronics, Information and Communication Engineers(IEICE).

Hisashi Nagata

Researcher, Proactive Communication
Research Group, Frontier Communication Laboratory, NTT Network Innovation Laboratories.
He received a B.S. from Tokyo University of
Science in 2012 and M.S. in particle physics
from Osaka University in 2014. He joined NTT
Network Innovation Laboratories in 2014 andworked for NTT WEST from 2017 to 2021. He iscurrently engaged in research on network computing and machine learning. He is a member of
IEICE.

NTT Technical Review

Vol. 20 No. 9 Sept. 2022

[7]

A. Osseiran, J. F. Monserrat, and P. Marsch (eds.), “5G Mobile andWireless Communications Technology,” Cambridge University Press,
2016.
[8] B. Bellalta, “IEEE 802.11ax: High-efficiency WLANs,” IEEE Wirel.
Commun., Vol. 23, No. 1, pp. 38–46, Feb. 2016.
[9] Y. Oguma, R. Arai, T. Nishio, K. Yamamoto, and M. Morikura,“Proactive Base Station Selection Based on Human Blockage
Prediction Using RGB-D Cameras for mmWave Communications,”
Proc. of the IEEE Global Communications Conference(GLOBECOM), San Diego, USA, Dec. 2015.
[10] R. Kudo, K. Takahashi, T. Inoue, and K. Mizuno, “Using Vision-based
Object Detection for Link Quality Prediction in 5.6-GHz Channel,” J.
Wireless Com. Network., 207, Oct. 2020.
[11] Q. Zhao, T. Sheng, Y. Wang, Z. Tang, Y. Chen, L. Cai, and H. Ling,“M2det: A Single-shot Object Detector Based on Multi-level Feature
Pyramid Network,” Proc. of the 33rd AAAI Conference on Artificial
Intelligence, pp. 9259–9266, Honolulu, USA, Jan./Feb. 2019.
[12] C. J. Vaca-Rubio, P. Ramirez-Espinosa, K. Kansanen, Z. Tan, E. deCarvalho, and P. Popovski, “Assessing Wireless Sensing Potential
with Large Intelligent Surfaces,” IEEE Open Journal of theCommunications Society, Vol. 2, pp. 934–947, 2021.
[13] A. Tahat, G. Kaddoum, S. Yousefi, S. Valaee, and F. Gagnon, “A Lookat the Recent Wireless Positioning Techniques with a Focus on
Algorithms for Moving Receivers,” IEEE Access, Vol. 4, pp. 6652–
6680, 2016.
[14] R. Kudo, K Takahashi, T. Murakami, and T Ogawa, “Deep Learning
Based Position Estimation Method Using WLAN CSI Feedback,”
IEICE Tech. Rep., Vol. 121, No. 173, CQ2021-45, pp. 40–45, Sept.
2021.
[15] D. N. Simkins, “Multichannel Angle of Arrival Estimation,” Stanford
University, 1981.

Kahoko Takahashi

Researcher, Proactive Communication
Research Group, Frontier Communication Laboratory, NTT Network Innovation Laboratories.
She received a B.S. and M.S. in nano bioscience from Yokohama City University in 2017 and2019. She joined NTT Network Innovation Laboratories in 2019. Since joining NTT, she has
been engaged in research on wireless-communication systems and machine learning. She is amember of IEICE.

Tomoaki Ogawa

Group Leader, Proactive Communication
Research Group, Frontier Communication Laboratory, NTT Network Innovation Laboratories,
and Wireless Access Systems Project, NTT
Access Network Service Systems Laboratories.
He received a B.A. in environmental information and M.A. in media and governance from
Keio University, Kanagawa, in 1996 and 1998.
He joined NTT Network Innovation Laboratories
in 1998, where he has been engaged in researchand development of indoor location systems. His
recent interest focuses on the development of
new wireless network architecture for 6G.

75

Regular Articles

Development of Compact/Powersaving Optical Open Line System
Sachio Suda, Kenichi Aoyagi, Yasutaka Sugano,
Hiroshi Shibata, Takeshi Seki, Kenta Hirose,and Daisaku Shimazaki
Abstract

NTT Network Innovation Center has developed a compact/power-saving optical open line system thatcan provide optical transmission of 100 Gbit/s per wavelength in an economical, space-saving, andpower-saving manner. The goal with this system is to achieve large-capacity transmission up to edgebuildings in the metro network to deal with the rapid increase in traffic accompanying the introductionof the 5th-generation mobile back haul. This article presents an overview of this system and its features.
Keywords: optical transport, DWDM, compact/power-saving

1. Capacity enhancement in core andmetro networks

2. Technical features of thecompact/power-saving OOLSTo deal with increasing volumes of traffic driven by
the expanded use of smartphones, cloud services,
video delivery services, etc., the optical transport
network of the NTT Group has up to now been usinga wavelength division multiplexing (WDM) system
at 100 Gbit/s per wavelength for the core network. At
the same time, traffic across the entire network up toedge buildings located in rural areas is predicted toincrease with the introduction of the 5th-generation(5G) mobile back haul (MBH), so even metro networks will require greater capacities. However, issues
with the current 100G-WDM system in terms of
installation space and power consumption have hindered its implementation, so we developed a compact/power-saving optical open line system (OOLS)
that greatly reduces the amount of space needed for
installation and the amount of power consumed. Thissystem will enable system configurations that optimize installation space and power consumption inaccordance with traffic volumes driven by diverse
service demands.

The system configuration of the compact/powersaving OOLS is shown in Fig. 1. This is an opticaltransport system based on digital coherent technology and dense WDM, which transmits multiple wavelength signals in a single fiber. It achieves a maximum transmission capacity of 8 Tbit/s by multiplexing 100-Gbit/s-per-wavelength signals over a maximum of 80 wavelengths. The system includes anoptical cross-connect function that enables add, drop,or through settings for any optical path in a maximumof eight directions. It can also connect with an existing 100G-WDM system without converting opticalsignals to electrical signals, which can make effective
use of existing facilities. For this compact/powersaving OOLS, we developed technology for a system
configuration conducive to advanced optical devices
and compact/power-saving characteristics with theabove specifications and succeeded in achieving acompact and power-saving system. The main featuresof this compact/power-saving OOLS are (1) compact/
power-saving transponders, (2) alien-wavelength connection, and (3) compact/power-saving system

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76

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Compact/power-saving OOLS
Terminal PC for
NE-OpS server
monitoring and control(1) Compact/powersaving transponders

Transponder

Client signal
10GbE/
100GbE

Optical cross connect
Max 8 directions

Optical fiber

Transponder

Transponder

Transponder

Transponder

Transponder

Transponder

Compact/powersaving OOLSTransponder

Transponder(2) Alien-wavelength
connection

Existing 100GWDM system

Existing 100GWDM system(3) Compact/powersaving system
configuration

Compact/powersaving OOLS

NE-OpS: network element operation system
PC: personal computer

Fig. 1. Overview of compact/power-saving OOLS.

Conventional optical module

COSA
Analog IC

Optical modulator

Optical receiver• Key optical components integrated on onechip• Non-temperature-control operation, nonhermetic package• Economically efficient by reducing numberof components and downsizing• Discrete component format• Hermetically sealed package

DSP
16 nm: 100–200G

Compact/power-saving
OOLS transponder

Fig. 2. Compact/power-saving transponder.

configuration.
2.1 Compact/power-saving transponders
This compact/power-saving OOLS consists of multiple transponders, each of which converts a client
signal used for connecting this system to a service
node to a wavelength signal or vice versa. Each transponder features a compact and power-saving configuration by using advanced devices such as a coherent optical subassembly (COSA) [1], low-power
digital signal processor (DSP), and compact opticalmodule. A COSA is a silicon photonics device developed by NTT Device Innovation Center integrating
NTT Technical Review

Vol. 20 No. 9 Sept. 2022

an optical modulator, optical transceiver, and analog
integrated circuit (IC) on one chip. Combining the
COSA with a DSP developed by NTT Network Innovation Laboratories and maximizing transmission
characteristics has enabled L-band transmission andthe mounting of these devices on a compact/powersaving OOLS transponder (Fig. 2). In addition, the100GbE optical module used to connect this OOLS toa service node adopts a quad small form-factor pluggable 28 (QSFP28) downsized from the conventional
centum gigabit form-factor pluggable, resulting in acompact form factor approximately one-fourth thecurrent size.
77Regular Articles

Network configuration

Alien-wavelength connection with existing facilities

5G traffic takes on a configuration spanning a core
network and metro network
Development of lambda connection function
integrates 100G network.

Achieving direct optical connection with the 100G-WDM
system requires optimization of various parameters
governing wavelength signals, wavelength multiplexed
signals, and optical supervisory signals.

• Reduces investments cost in area-boundary buildings and decreases
fault rate
• Increases efficiency of maintenance operations through network
integration

Compact/powersaving OOLS
network

100G-WDM
network

Service node

100G-WDM system

100G-WDM
network
Direct opticalconnection

Service node

10G-WDM system

Compact/power-saving OOLS
Optical cross
AMP
connect

Mux/Demux

100G - WDM system
100G/

Optical supervisory signal- Auto power reduction,
alarm transmission, etc.

Transponder
Wavelength signal- Center wavelength, wavelength grid,baud rate, modulation method, link
path through, etc.

AMP

Opticalsupervisory
signal100G-WDM
network
Opto/electronic
conversion

Service node

Opticalsupervisory
signalMetro network

Core network

Service node

Wavelength multiplexed signal- Link budget, OSNR, etc.

Optical crossconnect

Mux/Demux

Transponder

Compact/power-saving OOLSAMP: amplifier
Mux/Demux: multiplexer/demultiplexer
OSNR: optical signal-to-noise ratio

Fig. 3. Network configuration and overview of alien-wavelength connection function.

2.2 Alien-wavelength connection
The 5G MBH takes on a configuration spanning acore network constructed using an existing 100GWDM system and a metro network scheduled to beconstructed using the compact/power-saving OOLS.
For constructing optical paths across different networks in the conventional transmission system, it wasnecessary to execute a one-time electric-optic conversion using a transponder, which was a problem interms of facility costs. We therefore developed analien-wavelength connection function in the compact/power-saving OOLS to enable direct connectionof optical signals without electric-optic conversion
with the existing 100G-WDM system. This scheme
achieves a sustainable system through effective use of
the existing 100G-WDM system while reducing
investment costs and improving maintenance andoperations with network integration. A network configuration using the alien-wavelength connection
with existing facilities and a function overview are
shown in Fig. 3.
A WDM system uses wavelength signals (output
from transponders), wavelength multiplexed signals(that multiplex wavelength signals for long-distancetransmission), and optical supervisory signals (usedfor setting and controlling equipment, transmitting
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alarms, etc.). In the compact/power-saving OOLS,
various parameters for those wavelength signals,
wavelength multiplexed signals, and optical supervisory signals are optimized to achieve the alien-wavelength connection with the existing 100G-WDM
system.
2.3 Compact/power-saving system configuration
The compact/power-saving OOLS reduces the sizeand power consumption of the entire system byimproving the system configuration conducive todownsizing and power saving in addition to applying
advanced devices such as a COSA and DSP. Conventional WDM systems have taken on a unit configuration or package-mounted configuration under theassumption of using with a maximum number of
directions and wavelengths. Applying such a system
to a building using a small number of directions or
wavelengths results in unnecessary (empty) slots,
which is an inefficient implementation. In response tothis problem, we developed a compact unit for thecompact/power-saving OOLS that reduces the number of packages that can be mounted for buildings
having small-scale demands such as a small numberof paths or wavelengths and downsized the package
configuration by consolidating function sections and78

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Power-saving/space-saving effects

Transponder

Power-saving effect: −50%
Space-saving effect: −75%
Power-saving
effectSpace-saving
effectCompact/powersaving OOLS

100G-WDM system−40%
Power-saving
effectSystem

100G-WDM system

Compact/powersaving OOLS
−50%

Mux/Demux
Mux/Demux
Transponder

Space-saving
effectMux/Demux
Mux/Demux

Transponder

Transponder

Common unit

Common unit

Power supply

Power supply

100G-WDM system

Compact/powersaving OOLS

* Compared under conditions of 2 directions and 1.2 Tbit/s transmission

Fig. 4. Power-saving/space-saving effects.

high-density mounting. Regarding reducing package
size, we achieved high-density system mounting
without decreasing maintenance work efficiency for
insertion/removal of optical cable connectors, packages, etc. We also developed a unit for mounting on
general-purpose racks to enable a transition fromlarge specialized racks, as used in the past, to generalpurpose small racks. By applying these compact/
power-saving system configurations to the compact/
power-saving OOLS, we achieved downsizing by
approximately 50% and power savings by approximately 40% over the entire system compared withconventional 100G-WDM systems (Fig. 4).
3. Conclusion and future perspective
The compact/power-saving OOLS has achievedlarge-capacity transmission and compact/power-saving configuration for a more economical transport
network. NTT is now conducting research and development for the All-Photonics Network (APN) [2].
The APN is one of the innovative optical transport
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platforms for IOWN (the Innovative Optical andWireless Network). We are aiming to further speed up
signal per wavelength such as 400-Gbit/s or 1-Tbit/s.
We will also target further high-capacity transmission
by using multi-band transmission [3] to expand thetransmission wavelength range or using space-division multiplexing technology such as multi-core fiber[4]. We will continue to conduct research and development for further improving optical transport systems and for dramatically expanding the transmission
capacity.
References
[1]

[2]

[3]

Y. Nasu and S. Yamanaka, “Ultracompact Silicon Photonics Coherent
Optical Subassembly for Ultrahigh-capacity Optical Communication,”
NTT Technical Review, Vol. 18, No. 10, pp. 16–20, Oct. 2020.
https://www.ntt-review.jp/archive/ntttechnical.php?contents=
ntr202010fa2.html
A. Itoh, “Initiatives Concerning All-Photonics-Network-related
Technologies Based on IOWN,” NTT Technical Review, Vol. 18, No.
5, pp. 11–13, May 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 =ntr202005fa1.html
T. Seki, H. Kawahara, T. Miyamura, H. Maeda, K. Hara, S. Kaneko,

79Regular Articles[4]and J. Kani, “Evolution of Transmission Technology toward theRealization of All-photonics Network,” The Journal of the Institute ofElectronics, Information and Communication Engineers, Vol. 104,
No. 5, pp. 463–470, May 2021 (in Japanese).
T. Sakamoto, T. Mori, T. Matsui, T. Yamamoto, and K. Nakajima,

Sachio Suda

“Research and Development of Next Generation Optical Fiber Using
Multiple Spatial Channels,” NTT Technical Review, Vol. 15, No. 6,
June 2017.
https://www.ntt-review.jp/archive/ntttechnical.php?contents=
ntr201706fa5.html

Takeshi Seki

Senior Research Engineer, Transport Network
Systems Project, NTT Network Innovation
Center.
He received a B.S. and M.S. in electronics andoptical science from Osaka University in 2005
and 2007. In 2007, he joined NTT Network Service Systems Laboratories, where he wasinvolved in developing WDM transmission systems. He is currently developing optical crossconnect systems.

Senior Research Engineer, Core Network
Technology Research Project, NTT Network
Service Systems Laboratories.
He received a B.S. and M.S. in electronics andapplied physics from Tokyo Institute of Technology in 2002 and 2004. In 2004, he joined NTT
Network Service Systems Laboratories, where
he was engaged in research on optical crossconnect systems. He is currently developing
WDM transmission systems. He is a member of
the Institute of Electronics, Information andCommunication Engineers (IEICE).

Kenichi Aoyagi

Kenta Hirose

Senior Research Engineer, Transport Network
Systems Project, NTT Network Innovation
Center.
He received a B.S. in applied physics and M.S.
in materials science from Keio University,
Kanagawa, in 2006 and 2008. In 2008, he joined
NTT Network Service Systems Laboratories,
where he was involved in developing packet
transport systems. He is currently developing
optical cross-connect systems.

Researcher, Core Network Technology
Research Project, NTT Network Service Systems Laboratories, and Transport System Project,
NTT Network Innovation Center.
He received a B.S. in applied physics and M.S.
in materials science from Keio University,
Kanagawa, in 2017 and 2019. He joined NTT
Network Service Systems Laboratories in 2019and has been researching multiband opticaltransmission technologies. He is also developing
optical transmission systems at NTT Network
Innovation Center.

Yasutaka Sugano

Daisaku Shimazaki

Senior Research Engineer, Transport System
Project, NTT Network Innovation Center.
He received a B.E. from Tokyo Denki University in 1995. He joined NTT Communications in
2003, where he was engaged in the developmentof wide area Ethernet system. In 2019, he moved
to NTT Network Service Systems Laboratories
and has been engaged in the development projectfor beyond 100-Gbit/s packet transport systems.

Hiroshi Shibata

Research Engineer, Transport Network Systems Project, NTT Network Innovation Center.
He received a B.S. and M.S. in electrical engineering from Osaka University in 1994 and 1996.
In 1996, he joined NTT Network Service Systems Laboratories, where he was involved insoftware engineering. He is currently developing
an operation system for optical transport systems.

NTT Technical Review

Vol. 20 No. 9 Sept. 2022

Group Leader, Transport System Project, NTT
Network Innovation Center.
He received a B.S. in applied chemistry and
M.S. in materials science from Keio University,
Kanagawa, in 1999 and 2001. He joined NTT
Network Service Systems Laboratories in 2001,
where he researched and developed multi-layer
network virtualization as a research engineer. He
has been in his current position since January
2014 and engaged in the development project for
the Beyond 100G Optical Cross Connect System
at NTT Network Innovation Center. He was therecipient of the TELECOM System Technology
Award from the Telecommunication Advancement Foundation in 2016 and the Maejima
Hisoka Award from the Tsushinbunka Association in 2022. He is a member of IEICE.

80

Global Standardization Activities

Report on the 34th Asia-PacificTelecommunity Standardization
Program Meeting
Noriyuki Araki
Abstract

The 34th Asia-Pacific Telecommunity Standardization Program meeting (ASTAP-34) was held from18 to 22 April 2022. This meeting was held in an online format due to the impact of the spread of novel
coronavirus infections and was attended by approximately 230 participants (registered) from 19 Member
States. This article reports on ASTAP-34 and the Industry Workshop held in conjunction with ASTAP34.
Keywords: APT, ASTAP, standardization

1. Introduction
The Asia-Pacific Telecommunity (APT) is an international organization promoting development in thefield of information and communication technology(ICT) in the Asia-Pacific region and was establishedin 1979 with 38 Member States in the Asia-Pacificregion [1].
The APT Standardization Program (ASTAP) is aconference promoting standardization activities in
the APT region and was established in 1998 [2]. The
main objectives of ASTAP are 1) to build a cooperative and collaborative system for standardization in
the APT region and contribute to international standardization; 2) train standardization practitioners in
the APT region and support skill development in the
ICT field among members of the region, especiallythose from developing countries; and 3) make joint
proposals to international standardization organizations such as the International Telecommunication
Union (ITU) as a regional standardization organization.
The 34th ASTAP meeting (ASTAP-34) was heldfrom 18 to 22 April 2022 with approximately 230
participants from 19 Member States. On the first dayof the meeting, the Industry Workshop on “Blockchain” and Standardization Workshop on “Guidelines
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on setting up a National ICT Standardization
Regime” were held and attended by about 130 participants.
2. Structure of ASTAPThe structure of ASTAP and the positions held arelisted in Table 1. Japan is now able to lead the discussions in each Working Group (WG) and Expert
Group (EG) by serving as the Chair or Vice-Chair ofeach group.
3. Review of working methods
A proposal was made by the Steering Committee,
consisting of the Chair, Vice-Chair, and others of
ASTAP, to review the working methods for changing
the title of the Chair during meetings and in documents from “Chairman” to “Chair,” from the perspective of gender equality. The working methods werethus reviewed and changed from “Chairman” to“Chair.” This was implemented from this meeting
and reflected in the meeting and document amendments.
There was also a request not to use the designations“He,” “She,” etc. However, it is not always possible toknow all the names of the speakers in a meeting, and81

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Table 1. Structure of ASTAP.
Group

ChairsVice-Chairs

ASTAP

Dr. Hyoung Jun Kim(Korea)Hideyuki Iwata(TTC, Japan)
Mr. Xiaoyu You(China)Working Group Policy and Strategic
Co-ordination(WG PSC)

Mrs. Nguyen Thi Khanh Thuan(Vietnam)

Kaoru Kenyoshi(NICT, Japan)
Mr. Wu Tong
(China)Expert Group Bridging the Standardization Gap(EG BSG)Mrs. Nguyen Thi Khanh Thuan(Vietnam)

Mr. Ki-Hun Kim(Korea)Masatoshi Mano
(TTC, Japan)

Expert Group Green ICT and EMF Exposure(EG GICT&EMF)

Dr. Sam Young Chung(Korea)Mr. Min Prasad Aryal(Nepal)Mr. Nur Akbar Said
(Indonesia)
Mr. Uttachai Mannontri(Thailand)Expert Group ITU-T Issues(EG ITU-T)

Kaoru Kenyoshi(NICT, Japan)

Mr. Tran Quoc Binh
(Vietnam)Expert Group Policies, Regulatory and Strategies(EG PRS)[No Chair nominated]

Working Group Network and System(WG NS)

Dr. Joon-Won Lee(Korea)Hiroyo Ogawa(NICT, Japan)

Expert Group Future Network and Next Generation
Networks (EG FN&NGN)

Dr. Joon-Won Lee(Korea)Kazunori Tanigawa(NICT, Japan)

Expert Group Seamless Access Communication Systems(EG SACS)

Hiroyo Ogawa(NICT, Japan)

Expert Group Disaster Risk Management and Relief System(EG DRMRS)Noriyuki Araki(NTT, Japan)

Working Group Service and Application(WG SA)

Miho Naganuma(NEC, Japan)

Expert Group Internet of Things Application/Services(EG IOT)

Toru Yamada(NEC, Japan)

Expert Group Security(EG IS)

Miho Naganuma(NEC, Japan)

Dr. Heuisu Ryu(Korea)Expert Group Multimedia Application(EG MA)

Hideki Yamamoto(OKI, Japan)

Dr. Dong il Seo(Korea)Expert Group Accessibility and Usability(EG AU)Dr. Jee-In Kim(Korea)Ms. Wantanee Phantachat(Thailand)

there are sometimes cases in which the designations“He,” “She,” “him” and “her” are used, so we need tobe more aware of this.
4. Main results
At ASTAP-34, 19 output documents were approved,
including reports of each WG. The plenary of thismeeting approved two revised Terms of Reference(EG Future Network and Next Generation Networks(FN & NGN), EG Internet of Things Application/
Services (IOT)), two new report documents, four liai-

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Dr. Jee-In Kim(Korea)Dr. Seung-yun Lee(Korea)Ms. Li Haihua(China)son documents to other standardization bodies (ITU
Telecommunication Standardization Sector (ITU-T)
Study Group 16, ITU-T Focus Group on Autonomous
Networks (FG-AN) [3], ITU-T Focus Group on AI
for Natural Disaster Management (FG-AI4NDM)[4], APT Wireless Group (AWG) [5]), and one questionnaire; the two new report documents are as follows:•	Final draft of the 2nd Version of APT Report on
Asia-Pacific Regional Activities of Human
Exposure to EMF (electromagnetic fields)•	APT Report on Airport Runway Foreign Object
Debris Detection System using Radio over Fiber

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Table 2. Program of the Industry Workshop in ASTAP-34.
Program of ASTAP Industry and Standardization Workshop
Industry Workshop: Blockchain technology and standardization
10:30 – 12:30
Introductory Remarksby Dr. Hideyuki Iwata, TTC, Japan,
Industry Workshop Corresponding Member
Chair: Mr. Xiaoyu You, CAICT, China• Standardization of ISO TC 307 (Blockchain and DLT) by Mr. Jae Hoon NAH, Principle Engineer, ETRI, Rep. of Korea.
• Experience and Thinking on International Standardization of DLT Enhanced Future Network for Supporting Web3.0 by Ms.
Xiaoou Liu, China Telecom Research Institute, People’s Republic of China.
• Technical and Copyright Issue with NFT and Blockchain by Mr. YongJoon Joe and Mr. DongMyung Shin, Principal ResearchEngineer, LSware Inc., Rep. of Korea.
12:30 – 14:00
Industry Workshop: Blockchain industry and application
Chair: Dr. Seungyun Lee, Director, ETRI, Rep. of Korea• Maximize the Benefits of Blockchain in Many Applications of Many Industries by Dr. Manodha Gamage, Founder/Managing
Director, Intelligent Solutions and Consultancy (Pvt.) Ltd., Sri Lanka.
• Digital Federation on Trade Facilitation by Blockchain by Ms. Chaemee Kim, VP (CDO Chief Digital Officer), KTNET, Rep. of
Korea.
• Chinese Blockchain Industry Overview & Xinghuo Blockchain and Infrastructure Facility by Mr. Xiantang SUN, Director ofInternational department of IIIIoT, CAICT, People’s Republic of China.
• Auditing B2C communication Utilizing Blockchain by Shigeya Suzuki, Specially Appointed Professor, Keio University, Japan.
14:10 – 15:40
Standardization Workshop: Guidelines on setting up National ICT Standardization Regime
Chair: Mrs. Nguyen Khanh Thuan, Ministry of Information and Communications, Vietnam• TTC Activity Report by Dr. Hideyuki Iwata, CEO&S.V.P., TTC, Japan.
• The Introduction of ICT Standardization Regime in Korea by Dr. Kyoungcheol Koo, Vice President, Telecommunications
Technology Association, Rep. of Korea.
• The Operations of MTSFB as the Malaysian ICT Standardization Organization by Ms. Zaleha Abu Bakar, General Manager,
MTSFB, Malaysia.
• Guidelines on Setting up National ICT Standardization Regime by Dr. Kamol Uahchinkul, Researcher, National Electronic andComputer Technology Center, Thailand.
• Introduction on Standardization System in China by Mr. Shizhuo Zhao, Director, International Standardization Department, CCSA,
People’s Republic of China.
• Guidelines on Setting up National ICT Standardization Regime by Mr Satish Jamadagni Vice Chair TSDSI (Reliance Jio).
15:40 – 16:00
Conclusion of Industry Workshop
Dr. Hideyuki Iwata, CEO&S.V.P., TTC, Japan
DLT: distributed ledger technology
NFT: non-fungible token

Technologies
5. Industry Workshop
In the ad hoc discussion on strengthening ASTAP
activities held at ASTAP-33, it was agreed that theworkshop at this ASTAP-34 would be an Industry
Workshop in combination with a Standardization
Workshop with standards developing organizations
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(SDOs) invited to attend. The topic of the Industry
Workshop was “Blockchain,” which was carried overfrom the Correspondence Group planning the program of the previous ASTAP-33 workshop. The topic
of the Standardization Workshop was “Guidelines on
setting up a National ICT Standardization Regime,” aWork Item of the EG Bridging the Standardization
Gap (BSG).
Table 2 shows the program of the workshop. The
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Global Standardization Activities

Industry Workshop consisted of two sessions: Session 1: Blockchain technology and standardizationand Session 2: Blockchain industry and application.
Seven experts from Japan, Korea, China, and Sri
Lanka gave presentations. Keio University gave apresentation from Japan. The Standardization Workshop in Session 3 was attended by SDOs from six
countries: The Telecommunication Technology Committee (TTC) from Japan, the Telecommunications
Technology Association (TTA) from Korea, theChina Communications Standards Association(CCSA), the Malaysian Technical Standards ForumBhd (MTSFB), Telecommunications Standards
Development Society, India (TSDSI), and the National Electronics and Computer Technology Center(NECTEC) from Thailand. During the workshop,
information was shared in response to the following
questions raised by emerging countries that do nothave a national SDO.
• National ICT standardization processes•	National legislation relating to ICT standardization•	Role of governments, SDOs, fora, and standardization bodies•	Industry initiatives•	Recommendations to APT Member States on theestablishment of national standardization sys-tems
• Proposals to ASTAP and APT
The schedule was tight because the workshop washeld online, taking into account time differences andother time constraints, and the aim was to have as
many speakers as possible share information.
6. Future plans
The APT Secretary General reported that the next
meeting (ASTAP-35) would be held on site from
April to June 2023, that Bangkok was currently being
considered as a possible venue, and that invitations
were being sought from other countries and regions.
An Industry Workshop will be held independently in
conjunction with the next meeting, while the Standardization Workshop will be organized by the EG
BSG, with mini-workshops inviting emerging countries.
References
[1]
[2]
[3]
[4]
[5]

APT, https://www.apt.int/
ASTAP, https://www.apt.int/APTASTAPITU-T FG-AN, https://www.itu.int/en/ITU-T/focusgroups/an/Pages/
default.aspx
ITU-T FG-AI4NDM, https://www.itu.int/en/ITU-T/focusgroups/
ai4ndm/Pages/default.aspx
AWG, https://www.apt.int/APTAWG

Noriyuki Araki

Director, Standardization Office, Research andDevelopment Planning Department, NTT.
He received a B.E. and M.E. in electrical andelectronic engineering from Sophia University,
Tokyo, in 1993 and 1995. He joined NTT AccessNetwork Service Systems Laboratories in 1995,
where he researched and developed operationand maintenance systems for optical fiber cable
networks. He has been contributing to standardization efforts in ITU-T Study Group (SG)6 since2006. He was the rapporteur of Question 6 of
ITU-T SG6 from 2006 to 2008 and the rapporteur of Question 17 of ITU-T SG15 from 2008 to
2012. He also served as the chairman of the
ITU-T Focus Group on Disaster Relief Systems
and Network Resilience and Recovery. He wasthe vice-chairman of ITU-T SG15 from 2013 to2022. He also contributes to the activities ofInternational Electrotechnical Commission(IEC) Technical Committee 86 (fiber optic systems). He received the ITU-AJ award from the
ITU Association of Japan in 2017. He is a member of the Institute of Electronics, Information
and Communication Engineers (IEICE).

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84

External Awards
The Commendation for Science and Technology by the
Minister of Education, Culture, Sports, Science and Technology, Award for Science and Technology in Research
Category
Winner: Hiroki Takesue, NTT Basic Research Laboratories
Date: April 8, 2022
Organization: Ministry of Education, Culture, Sports, Science andTechnology
For his achievements in quantum communications such as quantum key distribution and quantum entanglement generation for thetelecommunication band and coherent Ising machines based on optical parametric oscillators.
The Commendation for Science and Technology by the
Minister of Education, Culture, Sports, Science and Technology, the Young Scientists’ Award
Winner: Koji Azuma, NTT Basic Research Laboratories
Date: April 8, 2022
Organization: Ministry of Education, Culture, Sports, Science andTechnology

For his achievements in theory of quantum repeaters necessary forthe development of a quantum internet.
IEICE Communications Society Excellent Paper Award,
Best Paper Award
Winners: Yuya Omori, NTT Computer and Data Science Laboratories/NTT Device Innovation Center; Ken Nakamura, NTT Computerand Data Science Laboratories/NTT Device Innovation Center; Takayuki Onishi, NTT Computer and Data Science Laboratories; Daisuke
Kobayshi, NTT Computer and Data Science Laboratories/NTT
Device Innovation Center; Tatsuya Osawa, NTT Corporate Strategy
Planning Department; Hiroe Iwasaki, Tokyo University of Agriculture and Technology
Date: May 11, 2022
Organization: The Institute of Electronics, Information and Communication Engineers (IEICE)
For “4K 120fps HEVC Encoder with Multi-chip Configuration.”
Published as: Y. Omori, K. Nakamura, T. Onishi, D. Kobayshi, T.
Osawa, and H. Iwasaki, “4K 120fps HEVC Encoder with Multi-chip
Configuration,” IEICE Transactions on Communications, Vol.
E104-B, No. 7, pp. 749–759, 2021.

Papers Published in Technical Journals andConference Proceedings
Dither-free Bias Condition Monitoring Technique Utilizing
Reference Light for Inservice Optical IQ modulator
H. Kawakami, S. Kuwahara, and Y. Kisaka
27th OptoElectronics and Communications Conference (OECC2022), TuP-B-7, Toyama, Japan, July 2022.
We developed a dither-free bias condition monitoring techniquefor IQ modulators utilizing reference light and a low-speed powermonitor. Experimental results for confirming the principle are also
shown.

Divide-and-conquer Verification Method for Noisy Intermediate-scale Quantum Computation
Y. Takeuchi, Y. Takahashi, T. Morimae, and S. Tani
Quantum, Vol. 6, p. 758, July 2022.
Several noisy intermediate-scale quantum computations can be
regarded as logarithmic-depth quantum circuits on a sparse quantum
computing chip, where two-qubit gates can be directly applied on
only some pairs of qubits. In this paper, we propose a method to

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Vol. 20 No. 9 Sept. 2022

efficiently verify such noisy intermediate-scale quantum computation. To this end, we first characterize small-scale quantum operations with respect to the diamond norm. Then by using these characterized quantum operations, we estimate the fidelity ψt ρ̂out ψtbetween an actual n-qubit output state ρ̂out obtained from the noisy
intermediate-scale quantum computation and the ideal output state(i.e., the target state) ψt . Although the direct fidelity estimation
method requires O(2n) copies of ρ̂out on average, our method requires
only O(D3212D) copies even in the worst case, where D is the denseness of ψt . For logarithmic-depth quantum circuits on a sparse chip,
D is at most O(log n), and thus O(D3212D) is a polynomial in n. By
using the IBM Manila 5-qubit chip, we also perform a proof-ofprinciple experiment to observe the practical performance of our
method.

Passive Verification Protocol for Thermal Graph States
K. Akimoto, S. Tsuchiya, R. Yoshii, and Y. Takeuchi
Physical Review A, Vol. 106, 012405, July 2022.

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Papers Published in Technical Journals and Conference Proceedings

Graph states are entangled resource states for universal measurement-based quantum computation. Although matter qubits such as
superconducting circuits and trapped ions are promising candidates
to generate graph states, it is technologically hard to entangle a large
number of them due to several types of noise. Since they must be
sufficiently cooled to maintain their quantum properties, thermal
noise is one of the major ones. In this paper, we show that, for any
temperature T, the fidelity G ρ T G between an ideal graph state G
at zero temperature and a thermal graph state ρ T , which is a graphstate at temperature T, can be efficiently estimated by using only one
measurement setting. A remarkable property of our protocol is that it
is passive, while existing protocols are active, namely, they switch
between at least two measurement settings. Since thermal noise isequivalent to an independent phase-flip error, our estimation protocol
also works for that error. By generalizing our protocol to hypergraphstates, we apply our protocol to the quantum-computational-supremacy demonstration with instantaneous quantum polynomial time circuits. Our results should make the characterization of entangled
matter qubits extremely feasible under thermal noise.

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Vol. 20 No. 9 Sept. 2022

Computational Self-testing for Entangled Magic States
A. Mizutani, Y. Takeuchi, R. Hiromasa, Y. Aikawa, and S. Tani
Physical Review A, Vol. 106, L010601, July 2022.
Can classical systems grasp quantum dynamics executed in an
untrusted quantum device? Metger and Vidick answered this question
affirmatively by proposing a computational self-testing protocol for
Bell states that certifies generation of Bell states and measurementson them. Since their protocol relies on the fact that the target states
are stabilizer states, it is highly nontrivial to reveal whether the otherclass of quantum states, nonstabilizer states, can be self-tested.
Among nonstabilizer states, magic states are indispensable resources
for universal quantum computation. Here, we show that a magic state
for the CCZ gate can be self-tested while that for the T gate cannot.
Our result is applicable to a proof of quantumness, where we canclassically verify whether a quantum device generates a quantum
state having nonzero magic.

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