NTT Technical Review

September 2017 Vol. 15 No. 9

Feature Articles: The Internet of Things and New Value Creation

Research and Development Initiatives on the Internet of Things at NTT



NTT Group Initiatives to Create New Internet of Things Business

Natural Sensing with “hitoe” Functional Material and Initiatives towards Its Applications



Data Exchange Technology Providing Real-time Data Processing and Scalability

Optimal Crowd Navigation via Spatio-temporal Multidimensional Collective Data Analysis

Regular Articles
Orbital Angular Momentum Multiplexing Technology towards the Realization of Tbit/s
Wireless Transmission

Global Standardization Activities


ITU-R Study Group 3 Activities towards Use of High Frequency Bands for 5G and Other
Wireless Communication Systems

Short ReportsSuccessful Field Trial of All-Raman Optical Amplification Technology for Multiband Large
Capacity Transmission of 400-Gbit/s Signals in Installed Fiber—Double the Usable Bandwidthof Installed Fiber

External Awards/Papers Published in Technical Journals and Conference ProceedingsExternal Awards/Papers Published in Technical Journals and Conference Proceedings

Feature Articles: The Internet of Things and New Value
Creation

Research and Development Initiatives
on the Internet of Things at NTT
Ryutaro Kawamura and Shuichi Yoshino
Abstract

The Internet of Things (IoT) holds great promise for creation of new value in society, and this age ofutilizing IoT represents a third era, which could be called the driving era, with great changes in the roleof telecommunication networks and their requirements. This article introduces network and information
processing requirements for realizing this new IoT era and describes a reference architecture for thisfunctionality. It also introduces IoT related initiatives at NTT.
Keywords: Internet of Things, sensing, data exchange, social prediction

1. Introduction
The term Internet of Things (IoT) has been appearing in the newspapers every day recently, and there ismuch anticipation of the value that it will bring society. Telecommunication networks have made great
advances over the years. The first generation had therole of connecting, mainly providing communication
between people, and the second generation had therole of understanding, which refers to gatheringknowledge and information from around the world(Fig. 1).
In contrast, the idea of connecting things to the
network and somehow controlling them has beenaround for a long time; examples include infrastructure monitoring using sensors, and home security
technology. This basic concept is also represented inproposed developments such as ubiquitous computing. However, there are several reasons why IoT has
become the large-scale initiative it is today. One isthat the spread of networks and computing functionality has surpassed a threshold, and the number andtypes of things connected to networks is growing
explosively [1]. Another is that in synergy with therapid collection of knowledge brought by the second
generation, the potential for new value is becoming
apparent.
For example, through the development of lowpower wide-area wireless technology able to send1

and receive information using extremely low energy,
it is becoming possible for objects with minimal
capabilities to connect to networks over long periodsof time. Technologies to collect huge amounts of dataon the cloud, and data analysis technologies such asmachine learning and artificial intelligence (AI) to
analyze it, are also being developed, and every day
the knowledge available on the network is being consolidated further.
IoT is being used to visualize, optimize, and control
the activities of physical things and society in thisway; that is, it is driving them. Therefore, this third
generation could be called the driving era, which
involves great changes in the role of telecommunication networks. Second-generation communication
has required the involvement of people, who also
made all final decisions in most cases. In the IoT era,
however, actions occur without human intervention,
automatically and at very high speeds, and the real,
physical world that we can touch is controlled bycyberspace, which is integrated with the real world.
Thus, it is inevitable that the requirements for telecommunication networks will be different than they
have been until now. The Feature Articles in this issue
introduce key new technologies for realizing this IoTera, along with some of their applications in society.

NTT Technical Review

Feature Articles

Today

3rd Generation
Drive: Driving society

Object-centered networking(IoT)

Pervasive,
ubiquitous

2nd Generation
Understand: Acquiring knowledge/information

1st Generation
Connect: Facilitating communication

Web, search, machine learning

1.0 Telephones

2.0 Social

Fig. 1. Third-generation telecommunications: driving society.

• Data exchange and optimal
deployment of processing software
Data &
softwarelogistics

Security• Data digitization• Connecting anddriving things

Sense,
connect &drive

Analytics&
prediction• Analysis• Prediction

Fig. 2. Four roles and technologies in IoT.

2. The four roles and technologies of IoT
Four essential roles and technologies for the development of IoT are shown in Fig. 2.
2.1 Sense, connect & drive
Sense, connect & drive refers to the processes ofdigitizing the information of physical things into aform that computing devices can handle and connecting them to a network through some type of accessmethod. In other words, it is the interface function
between the real world and cyberspace. The number
and variety of connected things is directly related to
the value produced with IoT, so this role is extremely
important. In order to drive society, communication
Vol. 15 No. 9 Sept. 2017

requirements such as reliability and real-time performance must also be satisfied, more than ever before.
2.2 Data & software logistics
Data & software logistics refers to moving the datafrom connected things to a location suitable for processing and utilizing the data, and to deployment of
the software that will perform this processing andutilization. IoT holds promise for creating new value
by combining diverse data spanning different types ofindustries. However, development has advanced
independently in the past, in different industries anddifferent regions around the world, and this has
resulted in a flood of different data models and protocols in the market and made interoperability among
2

Feature Articles

Library/MiddlewareData&
softwarelogistics

Management

Distributed data processing, large-scale/high-speed data processing, etc.

Software components
Edge computing, distributed processing, etc.
IoT data conversion technologies

Security orchestration

Applications
Data analysis, machine learning, AI, security management,
device control, etc.

Analytics&
prediction

External
systems

Security gateway
IoT gateway
Network (wired/wireless)Sense, connect&
drive

Device security
Devices/Sensors

Fig. 3. Reference architecture.

them very difficult in their current condition.
For example, field networks for production management in factories, networks for building management, and networks for device control in automobiles
each have their own de facto standards and regulations in their respective industries.
One important aspect of this function is to absorb
these differences in a practical way to enable
exchange and interoperability of data. Another function is related to how the physical location where data
are processed is increasingly significant with IoT. A
major recent development is that most data processing applications operate in two locations: the cloud,
where servers are located, and the end device, but IoT
is expected to bring great changes in this state ofaffairs. Due to the local nature of IoT data, the needfor real-time control, and the large amounts of data
produced by the many devices such as high-definition
cameras, it is more appropriate to perform processing
closer to the objects—at the network edge—rather
than at a distant location on the cloud in an increasing
number of cases. Since data processing locations are
dispersing in this way, functions to select appropriate
processing locations and to deploy processing algorithms are becoming increasingly important.

3

2.3 Analytics & prediction
Analytics & prediction refers to the creation ofvalue from data. Big data analysis technologies suchas machine learning and AI, which have grown rapidly in recent years, are used to create value by processing the IoT data being collected and circulated,
understanding conditions, and predicting faults andother events that could happen in the future.
2.4 Security
The last element is security. With IoT driving realsociety, security risks such as cyber-attacks are agreater concern than ever [2]. There is also a greater
range of IoT devices and the types of software usedwith them than with personal computers and smartphones, so safety must be maintained using different
principles than have been used thus far.
To build IoT systems using these four functions, wehave created the reference architecture shown in
Fig. 3. The devices and data in the lower layers aregathered and circulated for higher layers, and applications in the top layer analyze the data and create
value, and the results are fed back to control devices
and to drive society.

NTT Technical Review

Feature Articles

3. IoT initiatives at NTT
We introduce in this issue recent IoT initiatives at
NTT, namely the following.
3.1 P
 romoting partnerships to create new businessNTT’s management strategy is focused on a
B2B2X (business-to-business-to-X) business model,
and business in the IoT domain is a typical example.
In the third generation of telecommunication networks, we are aiming for an approach that expands
collaboration with the owners of objects and information and the various stakeholders that derive value
from them. With this in mind, the NTT Group isbuilding partnerships from the early stages of developing technologies and working to create value
through IoT. In the article “NTT Group Initiatives toCreate New Internet of Things Business” [3] in thisissue, we introduce some of the many initiatives
already started, which are undergoing field trials anddemonstrations or that are in technical development
in collaboration with partners, as well as efforts topromote these initiatives.
3.2 New initiatives in sensing and its applications
One promising IoT initiative is creating value inhealthcare using data from the human body. Devices
such as smart watches are being used as sensors, but
devices that can be used as sensors by simply wearing
them like clothing, without any other special implements, are also coming into use. The NTT Group, in
collaboration with Toray Industries, Inc., has developed a functional material called “hitoe” and is working on sensing applications for industries and uses notpossible earlier. The article “Natural Sensing with‘hitoe’ Functional Material and Initiatives towards Its
Applications” [4] introduces the use of “hitoe” in the
medical and safety management fields and the development of middleware technology that will expand
the range of possible applications.
3.3 Initiatives promoting data exchange in society
As explained earlier, data models and their protocols have been developed within their business and/or
regional domains (referred to as silos), whichbecomes a significant barrier of data exchange among
various domains. However, to further increase thevalue created through IoT, it is essential to combine
and use diverse data, and the promise of achieving
IoT data exchanges in society so that data that span
services can be circulated and utilized, is increasing.
Vol. 15 No. 9 Sept. 2017

Industry 4.0 in Germany is an example of this. The
article, “Data Exchange Technology Providing Realtime Data Processing and Scalability” [5] introduces
NTT Group initiatives to promote this sort of IoT dataexchange in society, including IoT data exchange
technologies that deliver data reliably from devices toapplications, and edge computing technology thatrealizes real-time performance by processing the IoT
data being exchanged at an optimal location in the
network.
3.4 I nitiatives to make predictions in society using
data
The NTT laboratories are advancing research anddevelopment (R&D) on spatio-temporal multidimensional collective data analysis technology able tomodel spatial and temporal relationships among thebroad and diverse data and predict the place and timeframe when phenomena will occur [6]. This is one
technology able to create value from the large amountof diverse data being gathered and stored, from thereal world and cyberspace, using sensing technology
and data exchange.
We have recently developed this technology further
using data on the flow of people and vehicles in urban
areas to predict the immediate congestion risk, andwe are conducting R&D on technology to give optimized guidance to groups in advance to avoid thatcongestion. The article, “Optimal Crowd Navigation
via Spatio-temporal Multidimensional Collective
Data Analysis” [7] introduces R&D initiatives ontechnology combining machine learning and simulation that is derived from optimized group guidance.
4. Future prospects
The number of things connected to networks is
expected to continue to increase, so we hope to support society by further expanding and developing ourrange of IoT applications in order to promote development of a society where there is cooperation
among people and things. The NTT Group will continue to collaborate with our partners in diverse fields
and to promote R&D that creates new value.
References
[1]

[2]

Ministry of Internal Affairs and Communications (MIC), “2016 White
Paper on Information and Communications in Japan,” 2016.
http://www.soumu.go.jp/johotsusintokei/whitepaper/eng/
WP2016/2016-index.html
IoT Acceleration Consortium, MIC, and Ministry of Economy, Tradeand Industry, “IoT Security Guideline,” 2016 (in Japanese).
http://www.soumu.go.jp/main_content/000428393.pdf

4Feature Articles

[3]

[4]

[5]

S. Sugimoto, S. Yoshino, T. Horioka, N. Abe, and D. Endo, “NTTGroup Initiatives to Create New Internet of Things Business,” NTT
Technical Review, Vol. 15, No. 9, 2017.
https://www.ntt-review.jp/archive/ntttechnical.php?contents=
ntr201709fa2.html
T. Kondo, Y. Yamato, M. Nakayama, A. Chiba, K. Sakaguchi, T.
Nishiguchi, T. Masuda, and T. Yoshida, “Natural Sensing with ‘hitoe’
Functional Material and Initiatives towards Its Applications,” NTT
Technical Review, Vol. 15, No. 9, 2017.
https://www.ntt-review.jp/archive/ntttechnical.php?contents=
ntr201709fa3.html
A. Terauchi, K. Ooto, N. Takahashi, K. Harada, and I. Yamasaki,“Data Exchange Technology Providing Real-time Data Processing

Ryutaro Kawamura

Director, General Manager, NTT Network
Innovation Laboratories.
He received a B.S. and M.S. in precision engineering and a Ph.D. in electronics and information engineering from Hokkaido University in
1987, 1989, and 1996. He joined NTT Transport
Systems Laboratories in 1989. From 1998 to1999, he was a visiting researcher at Columbia
University, USA. From 2003 to 2015, he was amember of the Board of Directors of OSGi Alliance. He is involved in research on network reliability techniques, network control and management, high-speed computer networks, active
networks, network middleware, and mobile
cloud/edge computing.

5

[6]

[7]

and Scalability,” NTT Technical Review, Vol. 15, No. 9, 2017.
https://www.ntt-review.jp/archive/ntttechnical.php?contents=
ntr201709fa4.html
F. Naya and H. Sawada, “From Multidimensional Mixture Data
Analysis to Spatio-temporal Multidimensional Collective Data Analysis,” NTT Technical Review, Vol. 14, No. 2, 2016.
https://www.ntt-review.jp/archive/ntttechnical.php?contents=
ntr201602fa2.html
F. Naya, M. Miyamoto, and N. Ueda, “Optimal Crowd Navigation via
Spatio-temporal Multidimensional Collective Data Analysis,” NTT
Technical Review, Vol. 15, No. 9, 2017.
https://www.ntt-review.jp/archive/ntttechnical.php?contents=
ntr201709fa5.html

Shuichi Yoshino

Executive Research Engineer, Senior Manager,
Wireless Systems Innovation Laboratory, NTT
Network Innovation Laboratories.
He received a B.E. and M.E. in mechanicalengineering from Kanazawa University in 1990
and 1992. He joined NTT in 1992 and worked on
the development of a satellite Internet system andwireless networking technologies. He has contributed to the practical application of active
radio frequency identification devices for logistics and wireless access technologies for gas
meter reading systems. He is currently engaged
in R&D of wireless technology for IoT andmobile access.

NTT Technical Review

Feature Articles: The Internet of Things and New Value
Creation

NTT Group Initiatives to Create New
Internet of Things BusinessShinji Sugimoto, Shuichi Yoshino, Tsutomu Horioka,
Naoto Abe, and Daiki Endo
Abstract

The NTT Group is promoting initiatives to solve societal issues and bring lifestyle innovation using abusiness-to-business-to-X (B2B2X) model through collaboration with partner companies in a wide
range of industries such as manufacturing, agriculture, and transportation. We are developing businesses
that use the Internet of Things to provide new added value to end users, represented by the X in B2B2X.
The NTT Group is also supporting promotional activities such as trade shows to distribute information
about new business initiatives.
Keywords: Internet of Things, B2B2X, collaboration

1. NTT Group Internet of Things initiatives
The NTT Group is promoting initiatives to solve
societal issues and bring lifestyle innovation using abusiness-to-business-to-X (B2B2X) model throughcollaboration with partners in a wide range of industries. In order for service providers—the second B—
to be able to provide new added value to the end users(X), innovation in both the NTT Group and its partnercompanies in other business areas is important, andwe are working to expand such business, including in
the field of the Internet of Things (IoT), which ishighly anticipated for its wide-ranging potential(Fig. 1).
Moving from trials and proof of concept to businessis a very important task when creating business using
IoT. Accomplishing this task requires more than simply visualizing the data gathered by sensors and other
IoT devices. Data analysis must be used to solve enduser problems or to create new value, and there aremany possible forms of second B service for eachindustrial field and many different problems andtypes of value for end users. As such, domain knowledge and experience are necessary to use data in thisway. The NTT Group is therefore promoting collaboration with various partner companies having theirown domain knowledge and experience in order to1create new business using IoT in a broad range offields including manufacturing, agriculture, transport, smart cities, and healthcare (Fig. 2).
For example, in manufacturing, IoT could be used
to centrally manage equipment such as machine toolson the production floor (factory), and to operate themmore efficiently and with less waste, thereby increasing productivity. The NTT Group is working in collaboration with FANUC CORPORATION, a global
supplier in factory automation, to gather and analyze
data in factories in real time on the operation ofindustrial robots and large machine tools [1]. This isused to increase productivity by tuning the equipment, detecting any signs of malfunction or faults inthe machinery early, and reducing overall factory
down time. As part of this, the NTT Group is usingedge computing research and development (R&D)
technologies, data analysis, and cloud services toimplement real-time data gathering and analysis.
In agriculture, producers are continuing to age, andworkers are in demand, so automating work andusing smart machinery to save on labor are key
issues, along with optimizing harvesting methods tostabilize and increase the harvest [2].
NTT Group companies are collaborating with
Kubota Corporation, an agricultural machinery manufacturer, in studying ways to combine advanced
NTT Technical Review

Feature Articles

B

B

X
Enterprise

NTT
Group

Factory
Retail store
Consumer

IoT, AI,etc.

Partner…

ServiceproviderMainplayerMember

e
ic e)
rv lu
se va
ew ed
N dd
(a

…

…

IoT: Internet of Things
AI: artificial intelligence

VisitorFig. 1. B2B2X model initiative.

Industry

Manufacturing

Agriculture

Transport

CitiesHealthcareHome

New value

Expanding initiatives to use ICT technologies such as IoT and AI in various industries and create new value

Reducing
down time

Increasing
productivity

Supporting
safe driving;
Relievingcongestion

Safe/Securecommunity
building

Extendinghealth andlife spans

Eliminating
waste energy;
Improving qualityof life

Use of ICT
Big data analysis

AI
IoT

Security

Real-timeprocessing

Fig. 2. IoT initiatives in wide-ranging fields.

information and communication technology (ICT)
services such as wireless technology and weather
information and map information, with advancing
R&D technologies such as artificial intelligence and
IoT, to build optimized systems for gathering data
efficiently in farming environments under various
conditions. The data are then used to create value inthe agricultural field. We are collaborating with partners to advance the agricultural machinery and agriculture support systems provided by our partners.
Vol. 15 No. 9 Sept. 2017

This is expected to achieve high-quality agriculture
through visualization, automation, and improved efficiency of agriculture management [3].
In the transport field, we are working to use vital
data in bus operation systems to support safe operation and improve administrative efficiency [4].
As described above, the NTT Group is undertaking
initiatives to create new value through IoT by collaborating with partners in various fields.

2Feature Articles

Photo 1. NTT Group display at CEATEC JAPAN 2016.

Photo 2. NTT Group display at CeBIT 2017.

2. Promotion through trade shows
To disseminate information on NTT Group IoT
business initiatives, we are promoting them throughexhibits at the NTT R&D Forum and external events
such as trade shows.
The NTT Group appeared at CEATEC JAPAN2016, held October 4–7, 2016 at Makuhari Messe
Convention Center, presenting NTT Group IoT busi3

ness initiatives to the many attendees. Five groupcompanies (NTT EAST, NTT WEST, NTT Communications, NTT DOCOMO, and NTT DATA) exhibited 19 solutions in six fields: manufacturing, logistics/retail, cities, healthcare, agriculture, and home,
and also introduced each solution on the presentation
stage (Photo 1).
We also appeared at CeBIT 2017, held in Hanover,Germany on March 20–24, 2017 (Photo 2). CeBIT is
NTT Technical Review

Feature Articles

the largest information technology trade show in theworld, with approximately 3300 companies and organizations from 70 countries around the world exhibiting, and over 200,000 attendees over the five days. In
total, 118 Japanese companies and organizations
exhibited their products and technologies in the Japan
Pavilion. The NTT Group exhibited videos ofadvanced B2B2X business using ICT, including collaborations using IoT in the fields of manufacturing,
agriculture, and transportation [5].
The NTT booth had over 5000 visitors in the fivedays, including many high-ranking and well-known
people, and politicians such as Japanese Prime Minister Shinzo Abe and German Chancellor Angela
Merkel. It was covered by 26 television and newspaper media companies (20 from outside Japan), resulting in NTT Group initiatives being introduced to theworld.
The IoT business continues to advance daily, andwe will continue to carry out promotional activities
such as these as a valuable opportunity to understandcustomer needs and respond to markets so that we cancreate new business in a timely manner.
3. Future prospects
Going forward, the NTT Group as a whole will collaborate with top-tier partners to develop markets andVol. 15 No. 9 Sept. 2017

create new value using the advanced technologies
from Group R&D efforts. We will also identify technologies that can be applied across different fields
through collaboration in various industrial fields, andwork to expand our business range utilizing IoT.
References
[1]
[2]

[3]
[4]

[5]

Press release issued by FANUC on July 28, 2016.
http://www.fanuc.co.jp/en/profile/pr/newsrelease/notice20160728.
html
Agriculture, Forestry and Fisheries Research Council Secretariat,“Outline of ‘Project on Sophistication of Production Systems of Agriculture, Forestry, and Fishery Products by IT and Robotic Technologies’ and Status of Cooperation among Cabinet Office and Ministries,” Proc. of the Fifth Regional Resource Strategy Council Meeting,Mar. 2014 (in Japanese).
http://www8.cao.go.jp/cstp/tyousakai/juyoukadai/chiiki_sigen/5kai/shigen05_shiryo0308.pdf
Press release issued by NTT on June 7, 2016.
http://www.ntt.co.jp/news2016/1606e/160607a.html
T. Kondo, Y. Yamato, M. Nakayama, A. Chiba, K. Sakaguchi, T.
Nishiguchi, T. Masuda, and T. Yoshida, “Natural Sensing with ‘hitoe’
Functional Material and Initiatives towards Applications,” NTT Technical Review, Vol. 15, No. 9, 2017.
https://www.ntt-review.jp/archive/ntttechnical.php?contents=
ntr201709fa3.html
K. Karasawa, “Report on NTT Group Exhibits at CeBIT 2017—
Showcasing Cutting-edge Use of ICT at the World’s Largest Information Technology Trade Fair,” NTT Technical Review, Vol. 15, No. 7,2017.
https://www.ntt-review.jp/archive/ntttechnical.php?contents=
ntr201707sr1.html

4Feature Articles

Shinji Sugimoto

Senior Manager, R&D Produce Group,
Research and Development Planning Department, NTT.
He joined NTT in 1995 and engaged in wireless networking service development and B2B2X
business creation. He is in charge of businessdevelopment using IoT technologies with NTT
research laboratories and NTT Group companies.

Shuichi Yoshino

Executive Research Engineer, Senior Manager,
Wireless Systems Innovation Laboratory, NTT
Network Innovation Laboratories.
He received a B.E. and M.E. in mechanicalengineering from Kanazawa University in 1990
and 1992. He joined NTT in 1992 and worked on
the development of a satellite Internet system andwireless networking technologies. He has contributed to the practical application of active
radio frequency identification devices for logistics and wireless access technologies for gas
meter reading systems. He is currently engaged
in R&D of wireless technology for IoT andmobile access.

Naoto AbeManager, R&D Produce Group, Research andDevelopment Planning Department, NTT.
He joined NTT in 2006 and worked on service
development and maintenance management. He
is in charge of business creation and service
promotion with NTT research laboratories.

Daiki Endo

Associate Manager, R&D Produce Group,
Research and Development Planning Department, NTT.
He joined NTT in 2005 and worked on constructing the NGN (Next Generation Network)
access network. As a project member, he wasinvolved in developing a network congestion
control system. He is currently in charge of ICT
business development with NTT research laboratories.

Tsutomu Horioka

Senior Manager, R&D Produce Group,
Research and Development Planning Department, NTT.
He joined NTT in 1995 and engaged in R&D
of a secure content distribution system andB2B2X business creation. He is currently incharge of business development with NTT
research laboratories for the manufacturing andautomobile domains.

5

NTT Technical Review

Feature Articles: The Internet of Things and New Value
Creation

Natural Sensing with “hitoe”
Functional Material and Initiatives
towards Its Applications
Toshihiko Kondo, Yoji Yamato, Makoto Nakayama,
Akihiro Chiba, Ken Sakaguchi, Takahiro Nishiguchi,
Tomoaki Masuda, and Tokutaro Yoshida
Abstract

This article introduces initiatives to expand the range of applications for “hitoe” wear, clothing thatcan sense biological data when the user wears it, including services for monitoring rehabilitation patientsand bus drivers. It also describes a data collection technology that accommodates the sensors and middleware for storing and analyzing the data, which is called the Fast and Distributed IoT Processing Platform.
Keywords: “hitoe” functional material, rehabilitation, driver management

1. Introduction
The material known as “hitoe” is a new functional
material arising from a cross-industry collaboration
between NTT and fiber manufacturer Toray Industries, Inc. [1]. It was created using conductive fiber
technology developed by NTT by applying a special
conductive polymer coating to one of Toray’s latest
products, a nanofiber material [2] (Fig. 1). This
resulted in a new material that can be used to detectvital data with high sensitivity, even though it is nonmetallic. When “hitoe” is worn next to the skin, it canmeasure vital data such as heart rate and electrocardiographic waveforms, and the level of relaxation canbe estimated from R-wave intervals.
Furthermore, “hitoe” does not contain metal, so it iseasy on the skin, highly hydrophilic, and durable
against sweat and moisture. Thus, it feels like ordinary clothing and can sense vital data very naturally.
The nanofibers are finer than fibers generally used for
clothing, so they have better adhesion and more contact area with the skin, which enables stable sensingof vital data. The conductive polymer also adheres
well, so it is also very durable when washed.
1

In an effort to expand the range of uses for “hitoe,”
NTT is working to find practical applications for it invarious fields through collaboration between NTT
Group companies and external partners. Beyond simply gathering data, it is also important to study howthat data can be processed, analyzed, and interpreted
to produce high-added-value information.
In this case, we have focused on the medical andsafety management fields with applications that makeuse of the strengths of “hitoe,” namely, (1) the ability
to obtain stable data even when the subject is perspiring and thus achieve comfortable monitoring overlong periods, and (2) enabling more accurate bodily
information to be obtained based on electrocardiographic waveforms.
This article introduces initiatives to develop applications in rehabilitation patient monitoring and a bus
driver management service. We also describe technologies supporting these applications, including
data gathering technology that accommodates thesensors and the Fast and Distributed IoT Processing
Platform.

NTT Technical Review

Feature Articles

Conductive fiber technology

Nanofiber

Fiber
Conductive
polymer

50 µm

Fiber diameter: 700 nm
NTT

Toray

“hitoe” wear

Fig. 1. Cross-industry collaboration with Toray to develop “hitoe.”Table 1. Roles of participating organizations in collaborative experiments.
Role
Fujita Health University Study use of “hitoe” in a rehabilitation hospital environment.
Toray

Study “hitoe” wear for rehabilitation.

NTT

Study systems supporting “hitoe” for rehabilitation that can obtain a stable heart rate and collect/display data used
to estimate activity.

NTT DOCOMO

Study commercialization of the rehabilitation patient monitoring system.

2. Rehabilitation patient monitoring and datacollection technology
The goal in monitoring rehabilitation patients is tomeasure the effects of rehabilitation objectively andcontribute to more suitable interventions and fasterrecovery through 24-hour monitoring of the patient’s
heart rate and activity information. Rehabilitation
costs in Japan are expected to increase as the population continues to age, and many hospitals are demanding improved rehabilitation quality and effectiveness.
On February 7, 2017, NTT, in collaboration with
Fujita Health University, Toray, and NTT DOCOMO,
began testing to demonstrate the effectiveness of arehabilitation patient monitoring system using“hitoe” [3]. Fujita Health University Hospital has thelargest rehabilitation department in Japan. The purpose of this testing is to investigate the effectivenessand potential of “hitoe” for 24-hour monitoring ofpatient heart rate and conditions (activity level, position data) and the potential use of such quantitative
diagnostic data in the field of rehabilitation. The rolesof the university and the participating companies are
Vol. 15 No. 9 Sept. 2017

listed in Table 1.
We first describe here the rehabilitation patientmonitoring system used in these tests. Activity data
output from the “hitoe” transmitter are sent via aBluetooth*1 Low Energy link to a smartphone orother receiver module and collected on a data gathering and management server through the network(Fig. 2). Doctors and therapists can refer to the activity data stored on this server at any time, 24 hours aday, using a viewer connected to the network. The
gathered data could also be used to study rehabilitation programs based on the type or seriousness of theailment.
The “hitoe” transmitter has functions to output theheart rate from the electrocardiographic waveform
obtained continuously from the “hitoe” wear and to
output data such as slope and vibrations from thebuilt-in three-axis accelerometer. The output of astable heart rate and calculation of activity level isenabled through the use of NTT’s noise cancellation
technology. NTT’s acceleration analysis technology*1 Bluetooth is a registered trademark of Bluetooth SIG Inc.

2

Feature Articles

Data
extraction

Data gathering/
management

Rehabilitation program study
Network

Relay devices
Receiver module

Smartphone

Doctor“hitoe” transmitter

Draft rehabilitation program.
Therapist
Monitor conditions during
rehabilitation.
Viewer“hitoe” wear forrehabilitation

Nurse

Capture heart-rate/acceleration data.

Monitor activity in hospitalward.

Rehabilitation room etc.

Hospital room etc.

Fig. 2. Overview of rehabilitation patient monitoring system.

HR: Condition: -Latest status
One-week results

Aggregate HRs:
107,558
No. of steps:
10,499Calories:
879 kcalOne-day
results

Heart rate
Cadence
Resting, walking, runningStanding, sitting, lying down
Exercise intensity

Fig. 3. Screenshot of viewer.

can also be used to estimate the cadence or type of
movement. The use of a smartphone in hospitalrooms and hallways makes it possible to gather data
while patients carry out their normal activities. In the
rehabilitation room, data can be obtained and sent
through a receiver module without a smartphone simply by having patients wear “hitoe” clothing, so rehabilitation activities are not inhibited.
Until now, separate devices to monitor heart rate3

have been necessary in the rehabilitation room and in
hospital rooms, and no device was able to record
heart-rate conditions continuously. With this new
system, the latest data from rehabilitation patients’
activity are always displayed on the viewer (Fig. 3).
Results for aggregate heart rates, number of steps,
and calories burned can be displayed, with units ofdays or weeks selected on the calendar. History data
can be displayed by graphing daily data and selecting
NTT Technical Review

Feature Articles

heart rate, cadence, resting/walking/running categories, standing/sitting/lying-down categories, or exercise intensity.
In these experiments, we can visualize the activity
data in a composite form and study whether it cancontribute to designing rehabilitation programs. Specifically, our scheme consists of the following threesteps.
2.1 S
 tep 1: Preliminary study with healthy subjects
Healthy subjects were asked to exercise while
wearing “hitoe” wear, and heart rate data wererecorded while exhaled air was analyzed and SpO2*2
was estimated. We also measured speed and distance
while the subjects were walking. We then checked
whether the data obtained from “hitoe” reflected theexercise load.
2.2 S tep 2: Study rehabilitation improvement
effects
We had in-patients and out-patients undergoingrehabilitation at the Fujita Health University Hospital
Rehabilitation Center wear “hitoe” wear for rehabilitation. We collected the heart rate and activity data
(data indicating active or resting, standing or lying
down, etc.) while they were in rehabilitation. The collected data were used to check whether the exercise
load of the training was appropriate. Also, the viewer
can be used to check changes in heart rate or activity
during a rehabilitation session or the progress of heart
rate and activity in past rehabilitation sessions, so it
should be useful for doctors in creating highly effective rehabilitation programs. In these experiments, weare studying what effects the use of “hitoe” can haveon creating and conducting rehabilitation programs
and on patient recovery.
2.3 S
 tep 3: Evaluating rehabilitation effects with
24-hour monitoring, including life in the hospital ward
When patients in the rehabilitation department
wear “hitoe” wear, their activity in the hospital 24hours a day can be transmitted. Thus, changes inpatient activity could be used to detect activities witha higher risk of falling, and patients prone to becoming bedridden can be encouraged to exercise. We are
evaluating the connection between using “hitoe” andearly detection of dangerous patient activity, and also
promoting activity within hospital wards.
Through these studies, we are able to check andverify the effectiveness of “hitoe” in the rehabilitation
Vol. 15 No. 9 Sept. 2017

field. We will work to create services utilizing “hitoe”
in this field in the future.
3. Driver management service based on Fastand Distributed IoT Processing Platform
Bus travel is a common means of transportation in
Japan, so there is a great need for safety for both passengers and drivers. We describe here a system wedeveloped to improve the safety of bus travel.
3.1 Driver management service
A driver management service is a safety management service that analyzes drivers’ vital data anddetects transportation risks. A serious bus accident
occurred in Japan in 2016, and since then, the demand
for transportation safety management has increased.
We developed this service for safety management ofbus drivers. An outline of the service is shown in
Fig. 4. This service collects vital data, vehicle data,and environment data on a cloud, then visualizes andanalyzes the data and takes appropriate actions. Vital
data include electrocardiographic waveforms andacceleration of the driver acquired by “hitoe” wear;
vehicle data include abrupt handling actions and tirepressure; and environment data include weather, traffic, and vehicle location. For example, the drivermanagement service first visualizes changes in thefatigue level of drivers, then analyzes the correlationof fatigue level and abrupt handling actions or otherfactors, and finally, it recommends actions such as
suggesting the driver take a break based on fatigue
level changes.
Our driver management service was developed
through collaboration between NTT and SAP SE, theenterprise software company. Heartbeat data acquiredby “hitoe” wear are analyzed on the Fast and Distributed IoT Processing Platform (described below) in
the NTT cloud to estimate the fatigue of the central
nervous system (brain and spinal cord) when thedriver is driving, and the fatigue data are then output.
Coordination between the NTT cloud and SAP
cloud is achieved with a representational state transfer (REST) application programming interface (API),and vital information such as fatigue data are provided to the SAP cloud via this and other APIs. The
vehicle and environment data are collected in the
SAP cloud, and all of the information, including thefatigue data, is visualized using a web graphical user
*2 SpO2: The proportion of hemoglobin in arterial blood that is oxygenated, expressed as a percentage.

4

Feature Articles

Bus operator
Fatigue level

Analyze algorithm
Fast and Distributed
IoT Processing Platform

Transportation
statusAPI

NTT cloudSAP cloudLTE/3G/Internet
Vital data
(driver)“hitoe”
wear

Environment
data

Vehicledata

Smartphone

Truck

Bus

Weather

Traffic
information

Fig. 4. Outline of driver management service.

interface (GUI). Then, appropriate actions are takenusing service coordination technologies, for example,
as in the work described in a published study [4].
Operators in bus companies can confirm the total
information of bus location, driver fatigue, and otherdata by checking the web GUI.
We conducted a field trial of the driver management
service with Keifuku Bus Co., Ltd. in Fukui Prefecture in October and November, 2016. About 15 drivers participated in the field trial. The drivers wore
“hitoe” garments, enabling us to collect stable data.
From the data, we were able to verify the validity offatigue estimation and the feasibility of real-time
fatigue estimation analysis. We also confirmed various business aspects such as the operational feasibility of this service and the effectiveness of the analyzed results. We discussed how to improve bus
operations with Keifuku Bus based on the trial results(e.g., bus schedule changes). NTT Communications
launched a commercial driver management service
after the completion of this trial.
3.2 Fast and Distributed IoT Processing Platform
We explain here the Fast and Distributed IoT Processing Platform on which the driver management
service runs. The Fast and Distributed IoT Processing
5

Platform has been developed by the NTT Software
Innovation Center and is a platform to collect, store,
and analyze Internet of Things (IoT) data. This platform was built based on cloud technologies (e.g., that
described by Yamato et al. [5]) and can use information obtained not only from “hitoe” wear but also
from various sensor devices.
The Fast and Distributed IoT Processing Platform
has a data storage function, a data analysis function,
and a messaging function that connects the otherfunctions. There are other IoT platforms such as
Amazon AWS IoT*3 and Microsoft Azure IoT Suite*4.
These platforms also have similar storage, analysis,
and messaging functions. However, our platform has
a lower price structure thanks to the use of open
source software (OSS). It also has a flexible structure,with functions that can be customized for each project.
An outline of the Fast and Distributed IoT Processing Platform is shown in Fig. 5. Sensors such as“hitoe” wear continuously collect IoT data (alsocalled stream data), and the IoT data are transferred*3 Amazon and AWS are trademarks of Amazon.com, Inc. or its affiliates in the United States and/or other countries.
*4 Microsoft and Azure are registered trademarks of Microsoft Corporation in the United States and/or other countries.

NTT Technical Review

Feature Articles

Analysis algorithm

Fast and Distributed IoT Processing Platform
Analysis function
Stream data processing
Provides various pattern stream data for each analysis request(1) Stream processing
Analyzes stream data sequentially
T5

T4

T3

T2

T1

(2) Micro-batch processing
Analyzes short-term (seconds to hours) data
T11 T10 T9 T8 T7 T6 T5 T4 T3 T2 T1
Messaging
function
Batch processing(3) Batch processing: Analyzes bulk data

T4

T3

T2

T1

Stores raw
data

Storagefunction

Raw data

Analyzed data

Storesanalyzed
data

Streams data
API
Outer system

Fig. 5. Outline of Fast and Distributed IoT Processing Platform.

to a cloud by smartphones or other gateways. The
data are delivered to the storage and analysis functions by the messaging function. The Fast andDistributed IoT Processing Platform uses ApacheKafka*5 for the messaging function, Apache HBase
for the storage function, and Apache Spark for the
analysis function.
In the driver management service, stream data ofheartbeat and other vital information acquired by“hitoe” wear are transferred to the Fast and Distributed IoT Processing Platform by smartphones. Kafka
delivers data to HBase for storing and Spark for analyzing. Spark processes the stream data through themicro-batch processing of Spark Streaming and estimates the fatigue level. Micro-batch processing is akind of processing that analyzes short-term data (seconds to hours) and is an intermediate type of processing between batch processing and stream processing.
Because fatigue is estimated based on fluctuation ofheartbeat R-wave intervals, micro-batch processing issuitable for analyzing heartbeat data for particularperiods. In the driver management service, one minute of heartbeat data is analyzed in a micro-batch.
We are now developing additional functions toVol. 15 No. 9 Sept. 2017select not only micro-batch processing of SparkStreaming but also batch processing and stream processing for the analysis function by agile softwaredevelopment based on previous OSS development
experience. These analysis functions can be configured in various patterns for each project. For the
analysis algorithm that runs on the analysis function,
we can run not only the fatigue estimation algorithm*6 described above but other algorithms as well.
Analyzed data such as fatigue level and raw data are
stored in the storage function. Stored data are utilized
in periodic batch analyzing or outer system coordination via a REST or other API. In the driver management service, our system provides analyzed data to
the SAP cloud at regular intervals and also when theanalyzed data exceed certain threshold values.

*5 Apache, Apache Kafka, Apache HBase, and Apache Spark are either registered trademarks or trademarks of the Apache Software
Foundation in the United States and/or other countries.
*6 The fatigue estimation algorithm for the trial was based on central fatigue estimation technology developed by NTT Service
Evolution Laboratories [6].

6Feature Articles

4. Future prospects
In this article we introduced examples of two“hitoe” applications. In our initiatives towards
rehabilitation patient monitoring, we will continue topromote research and development of our monitoring
system so that it can collect data seamlessly, whether
in the hospital, at home, or away from home, for both
home rehabilitation and home medical care in the
future. We also plan to utilize the Fast and Distributed
IoT Processing Platform in rehabilitation applications.
We also introduced a bus driver management system and the Fast and Distributed IoT Processing
Platform to support it. We will continue to use theFast and Distributed IoT Processing Platform to analyze the data collected using “hitoe” wear and othersensors and will promote other projects using it inbusiness.

7

References
[1]
[2]

[3]
[4]

[5]
[6]

NTT press release issued on January 30, 2014 (in Japanese).
http://www.ntt.co.jp/news2014/1401/140130a.html
S. Tsukada, N. Kasai, R. Kawano, K. Takagahara, K. Fujii, and K.
Sumitomo, “Electrocardiogram Monitoring Simply by Wearing a
Shirt––For Medical, Healthcare, Sports, and Entertainment,” NTT
Technical Review, Vol. 12, No. 4, 2014.
https://www.ntt-review.jp/archive/ntttechnical.php?contents=
ntr201404fa4.html
NTT press release issued on February 6, 2017 (in Japanese).
http://www.ntt.co.jp/news2017/1702/170206a.html
H. Sunaga, M. Takemoto, Y. Yamato, Y. Yokohata, Y. Nakano, and M.
Hamada, “Ubiquitous Life Creation through Service Composition
Technologies,” World Telecommunications Congress 2006
(WTC2006), Budapest, Hungary, May 2006.
Y. Yamato, Y. Nishizawa, S. Nagao, and K. Sato, “Fast and Reliable
Restoration Method of Virtual Resources on OpenStack,” IEEE
Transactions on Cloud Computing, 2015.
A. Chiba, K. Tsunoda, H. Chigira, T. Ura, O. Mizuno, and T. Tanaka,“Estimating Critical Fusion Frequency from Heart Rate Variability,”
EMBC 2015 (37th Annual International Conference of the IEEE
Engineering in Medicine and Biology Society), Milan, Italy, Aug.
2015.

NTT Technical Review

Feature Articles

Toshihiko Kondo

Ken Sakaguchi

Senior Research Engineer, Product Strategy
Planning Project, NTT Device Innovation Center.
He received a B.E., M.E., and Ph.D. in engineering from Osaka University in 1994, 1996,
and 2006. He joined NTT in 1996 and studied
energy systems for telecommunications equipment power supplies. Since 2004, he has been
engaged in the research and development of communications systems and terminals for sensor
network services. He is also involved in promoting value creation of IoT devices and subsystems. He is a member of the Institute of Electronics, Information and Communication Engineers(IEICE).

Manager, Research and Development Planning
Department, NTT.
He joined NTT in 2000 and is engaged in producing medical and health ICT services.

Yoji Yamato

Takahiro Nishiguchi

Senior Research Engineer, Cloud System SE
Project, NTT Software Innovation Center.
He received a B.S. and M.S. in physics and a
Ph.D. in general systems studies from the University of Tokyo in 2000, 2002, and 2009. He
joined NTT in 2002, where he is involved indevelopmental research of a cloud computing
platform, peer-to-peer computing, and a service
delivery platform. He is a senior member of theInstitute of Electrical and Electronics Engineers(IEEE) and IEICE.

Manager, Healthcare and Medical Business,
Smart-life Solutions Department, NTT DOCOMO, INC.
He joined NTT DOCOMO in 1999 and isengaged in producing healthcare and medical
ICT services.

Makoto Nakayama

Tomoaki Masuda

Senior Research Engineer, Cloud System SE
Project, NTT Software Innovation Center.
He received a B.E. and M.E. in electronicengineering from Osaka Prefecture University in1998 and 2000. He is currently involved ininstalling the cloud platform and developing
biophysical analytics software for OpCo.

Manager, IoT Office, Corporate Planning,
NTT Communications Corporation.
He received an M.S. in informatics from Kyoto
University in 2006 and an MBA from Massachusetts Institute of Technology, USA, in 2015. He iscurrently engaged in IoT business and productand strategy development.

Akihiro Chiba

Tokutaro Yoshida

Researcher, Networked Robot & Gadget Project, NTT Service Evolution Laboratories.
He received a B.E. and M.E. in engineeringfrom the University of Electro-Communications,Tokyo, in 2011 and 2013. He joined NTT Service
Evolution Laboratories in 2013. His current
research interests include sensing of biologicalinformation. He received the Society of Instrument and Control Engineers (SICE) Young
Author’s Award at the 2011 SICE Annual Conference. He is a member of SICE and the Information Processing Society of Japan (IPSJ).

Vol. 15 No. 9 Sept. 2017Manager, Big-data Taskforce, Applications andContent, NTT Communications Corporation.
He received an M.E. from Waseda University,
Tokyo, in 2004. He is currently engaged in wearable device business and product development.

8

Feature Articles: The Internet of Things and New Value
Creation

Data Exchange Technology Providing
Real-time Data Processing andScalability
Atsushi Terauchi, Kenichi Ooto, Noriyuki Takahashi,
Kei Harada, and Ikuo Yamasaki
Abstract

The pervasive spread of the Internet of Things, or IoT, in recent years is remarkable, and NTT is working to incorporate IoT in industries showing the greatest promise for creating new value—manufacturing, the auto industry, agriculture, and other sectors. This article provides an overview of NTT’s initiatives in IoT data exchange technology and edge computing technology, and reviews standardization
trends pertaining to these technologies.
Keywords: IoT data exchange, edge computing, container-based virtualization

1. Data exchange technology: opening the way
to real-time data processing andgreater scalability
The pervasive spread of the Internet of Things (IoT)
in recent years has been truly remarkable, and NTT is
working to incorporate IoT in industries showing thegreatest potential for creating new value—manufacturing, the auto industry (automatic driving support),
agriculture, and other sectors. IoT really comes
together in the form of services that connect sensors
that gather data, and applications that leverage thegathered data. This involves a vast number and range
of devices, and an equally diverse variety of applications. The true value that IoT creates is derived fromleveraging new combinations of data gathered by sensors, and today we see growing demand from both the
public and private sectors for an IoT data exchange
society that supports leveraging and delivery of IoT
data across services. This requires IoT data exchange
technology that acquires IoT data from a wide variety
of devices, then precisely scales the data for delivery
to a wide range of applications.
However, the ability of certain manufacturing sectors to exploit the IoT has lagged behind as a result of1

growing network loads, processing speed delays, andthe inability to meet the exacting requirements ofrobotic real-time control, self-driving support invehicles, and other stringent requirements. NTT is
now working to address this challenge by applyingedge computing technology to IoT. Edge computing
optimizes cloud computing systems by locating computer resources at the edges of the network and thus
carrying out data processing near the source of thedata. It is apparent that in order to leverage the limited
computer resources deployed at the edge of the network, technology for dynamically configuring a software component at the edge of the network is also
critically important.
This article provides an overview of NTT’s recent
work in IoT data exchange technology and edge computing technology and also reviews standardization
trends pertaining to these technologies.
2. IoT data exchange technology
In this section, we explain aspects of IoT dataexchange technology, focusing on current trends,
research and development (R&D), and standardization.
NTT Technical Review

Feature Articles

1. Mass data from devices
stored and provided toapplications (apps)

3. Accommodation of various devices,
apps with different communication
methods (main protocols differ:e.g., HTTP REST on app interface side,and MQTT on device interface side)

App

App

App

Data
processing

Control
Temporary
management
storage DB

2. Managed device
control

IoT data exchange
platform

Platform provider
4. Management of essentialconsistency to link devices
and apps of various
development operators

Sensor/device

Development
operators
DB: database
HTTP: Hypertext Transfer Protocol
MQTT: Message Queue Telemetry Transport
REST: Representational State Transfer

Fig. 1. IoT data exchange platform.

2.1 Technology trends and R&D
Until now, most schemes for gathering IoT related
data have been implemented as standalone systems,
so IoT data collected from devices are confined to asingle system based on a vertically integrated IoTarchitecture. However, today we are shifting awayfrom this model with rapidly growing demand for IoT
systems based on a horizontally integrated IoT architecture built around a software infrastructure that is
not dependent on vendor-specific applications anddevices, that provides shared capabilities, and that
supports a range of different IoT services and mutual
distribution and utilization of IoT data. Horizontal
integration has a number of significant advantages
over vertical integration [1, 2]:(1)	Sharply reduced system development costs
made possible by using shared functions andcapabilities on the platform(2)	Support for a far greater range of connections
made possible by abstracting connections
between IoT devices and applications(3)	More efficient utilization of gathered data
among many services
In order to create a robust data exchange society
capable of creating value by incorporating IoT acrossdiverse industrial sectors and sharing IoT data, weVol. 15 No. 9 Sept. 2017

must first build a viable platform—an IoT dataexchange platform—that mediates the exchange of
IoT data between applications and devices, and NTT
is making solid progress in establishing the core IoT
data exchange technologies to achieve this objective.
We have already compiled considerable knowledge
by studying IoT system use cases for particular industries, which has enabled us to define the requirementsof the IoT data exchange platform and come up witha platform design that meets these requirements(Fig. 1). NTT has now moved on to the development
phase and plans to unveil a practical IoT dataexchange platform in the near future.
2.2 Associated standardization trends
In order to actually implement a horizontally integrated IoT data exchange platform, it would be preferable to base the interfaces between the platform andapplications, and between the platform and devices,on an international standard and not simply use an
in-house proprietary solution. One promising standard initiative that would meet these requirements isoneM2MTM*1 [3]. To prevent the proliferation ofexcessive standards, oneM2M was launched in July
*1 oneM2M: oneM2M is a trademark of oneM2M Partner Type 1.

2

Feature Articles

Edge computing

Cloud computing
App
Cloud

App

App

App

Deployed at edge

App

App

Cloud

100 ms +Significant delay,poor real-time
performance

Communication
concentrated
in the cloud

Minimal delay,
excellent real-time
performance

Network
Distributed computing
on network edge

App

App

Run PF

App
App

App

Edge computing PF

Network

Distance
shortened
App

App

App

Severalmilliseconds

App

App

Run PFRun PF

App
App

App

App

PF: platform

Fig. 2. Comparison of cloud computing and edge computing.

2012 as a global organization involving the world’s
preeminent standards development organizations*2that collaborated to develop the requirements andspecifications for machine-to-machine (M2M) andIoT technologies. The ultimate goal of oneM2M is todraft standards for a horizontal platform architecturethat can be shared by many different industries andservices; oneM2M currently has more than 200 participating partners and members, including NTT.
Release 1 of the oneM2M standard was issued in
February 2015, followed by Release 2 in August
2016, and many companies and organizations havealready implemented IoT data exchange platforms incompliance with the standard. One key aspect ofoneM2M is interoperability and compliance withother relevant standards—OMA (Open Mobile Alliance), 3GPP (Third Generation Partnership Project),and so on—and considerable work has been done toverify and test the consistency and interconnectivityof oneM2M with other standards. While collaborating with other organizations on standards initiatives,
NTT is convinced that oneM2M will be criticallyimportant in the deployment of IoT data exchange
and is moving boldly to implement an IoT dataexchange platform based on the oneM2M standard.

3. Edge computing technology
Another important area of focus is edge computing
technology. We describe key aspects of this technology in this section.
3.1 Technology trends and R&D
NTT is breaking fresh R&D ground in pursuit ofedge computing that drastically shrinks communication delay from users by deploying edge servers thatprovide computational resources at the edge of the
network in close proximity to user terminals and IoT
devices [4, 5]. Offloading the terminal-side processing to the edge server opens the way to markedly
enhanced performance: large-capacity application
processing at very high throughput unconstrained bycomputational performance of the terminal. Edge
computing is also well adapted for applications
requiring real-time response or big data processingthat involves frequent communication or large-volume communication with the server.
A side-by-side comparison of cloud computing andedge computing is shown in Fig. 2. Cloud computing
provides access to shared computer processing*2 Japan’s participants in oneM2M are the Telecommunication
Technology Committee and the Association of Radio Industries
and Businesses.

3

NTT Technical Review

Feature Articles

Hypervisor-based virtualization

Container-based virtualization

Virtual machine Virtual machine Virtual machine
Application

Application

Application

Container

Container

Container

Middleware
library

Middleware
library

Middleware
library

Application

Application

Application

Guest OS

Guest OS

Guest OS

Middleware
library

Middleware
library

Middleware
library

Hypervisor

Container engine

Host OS

Host OS

Physical machine

Physical machine

OS: operating system

Fig. 3. Schematic of hypervisor-based virtualization and container-based virtualization.

resources on the Internet, and the typical round-trip
delay time (RTD) between terminals is less than 100
ms domestically, 100 ms between Japan and theUnited States, and 200 ms between Japan and Europe.
In the case of edge computing, multiple edge servers are deployed on the periphery of a carrier’s accessnetwork. The edge server can be regarded as a smallscale cloud datacenter in close proximity to the userthat has arithmetic processing functions and storage
capabilities, and that can run application programs
and store data content.
The user terminal communicates directly with anedge server, and the RTD can be slashed to just a few
milliseconds. Moreover, when an edge server is used
to perform regional processing, the computationaland communication resources used can be localized.
For example, by tallying a large number of sensor
readings on an edge server instead of delivering thereadings one at a time to a central server (in thecloud), and by sending just the key statistical parameters (such as averages and deviations) and noteworthy outliers, the overwhelming bulk of communication can be localized just between the sensors and theedge server, and traffic on the network backbone canbe drastically reduced.
3.2 C ontainer-based virtualization edge server
architecture
Edge servers for IoT might be implemented in a
number of different ways, but at NTT, we assumerather tight constraints on the server’s computer
Vol. 15 No. 9 Sept. 2017

resources (the CPU (central processing unit), memory, and disk I/O (input and output)), so our efforts
have focused on a container-based virtualization edge
server architecture. Container-based virtualization isan operating system-level virtualization method forrunning multiple isolated resource systems called
containers on a computer. Compared to the older
hypervisor-based virtualization method, containerbased virtualization uses far fewer resources for processing and also ensures application portability
between different equipment. A schematic overview
of hypervisor- versus container-based virtualizationis shown in Fig. 3.
Adopting this type of containerized virtualization
while incorporating an IoT data exchange platformand various applications onto an edge server would
enable us to tailor the amount of computer resources
for each process according to priority, which would
significantly upgrade resource utilization efficiency.
A schematic of how this would work is illustrated inFig. 4.
Another well-known platform for container-based
virtualization is the open-source container management software Docker [6]. We are currently evaluating this software for distributing and managing containerized applications. Docker is able to deliver andexercise executive control over applications based oncontainerized virtualization. Therefore, by adapting
Docker to connect edge servers in development andcommercial environments, one can build an environment that seamlessly covers the entire sequence from4

Feature Articles

IoT data
exchange
platform

IoT application#1

IoT application#2

Middleware
library

Middleware
library

Middleware
library

Preset amount of available
computer resources
according to priority ofeach process.

Separately allocatecomputer resources asprocessing is executed.
Container engine
Alter amount of available
computer resources
allocated to each process
as required.

Host OS
Physical machine: Container

Fig. 4. Schematic of edge server using container-based virtualization.

Application test

IoT application
Application registration
Development
environment
IoT application

DockerEdge server for
development

Application delivery
Commercial
environment
IoT application
Commercial
edge server
IoT equipment(Robot, camera, sensor, etc.)

Application developer

Fig. 5. Schematic of development, delivery, and management of IoT application based on Docker.

application development and testing to distribution
and monitoring (Fig. 5). This strategy will contributeto the rapid uptake of IoT by creating an environmentthat promotes rapid development of applications andproliferation of edge servers by application service
providers, equipment vendors, users, and other players.

5

3.3 Associated standardization trends
Edge computing has attracted a great deal of interest as a promising new approach for opening the way
to application areas that are currently not covered byconventional cloud computing, and much standardization work has already been completed. The Industry Specification Group (ISG) of ETSI (European
Telecommunication Standards Institute) has been
especially active since 2014 in pursuing work on
NTT Technical Review

Feature Articles

MEC (which initially stood for Mobile Edge Computing, but was later changed to Multi-access Edge
Computing) [7].
NTT and NTT DOCOMO have been involved inwork on MEC since ISG was first organized. We havecarefully considered technical requirements of edge
servers for a variety of different use cases and havedrawn up function-level architectures and basic API(application programming interface) specifications.
In a related development, the Open Edge Computing Initiative organized by Carnegie Mellon University has set up a city-scale testbed that consists of anumber of edge servers and wireless base stations to
conduct field trials of this architecture [8].
4. Future development
A good portion of the technology described in thisarticle is now being applied as a first use case in
manufacturing and is now being incorporated in the
IoT platform FIELD system*3 [9] for manufacturing,which is under development by FANUC CORPORATION, an industry leader in robotics. Building on
work done so far, we will of course further consider

these technologies as manufacturing use cases, but at
the same time will continue to pursue R&D on IoTdata exchange technology and edge computing technology with the goal of applying this work to othersectors and opening a new era of the IoT dataexchange society.
References
[1]
[2]
[3]
[4]
[5]

[6]
[7]
[8]
[9]

T. Fujita, Y. Goto, and A. Koike, “M2M Architecture Trends andTechnical Issues,” J. IEICE, Vol. 96, No. 5, pp. 305–312, 2013.
N. Shiratori, S. Kitagami, T. Suganuma, K. Sugawara, and S. Shimamoto, “Latest Developments of IoT Architecture,” J. IEICE, Vol. 100,
No. 3, pp. 214–221, 2017.
oneM2M, http://www.onem2m.org/
Press release issued by NTT on January 23, 2014.
http://www.ntt.co.jp/news2014/1401e/140123a.html
N. Takahashi, H. Tanaka, and R. Kawamura, “Analysis of Process
Assignment in Multi-tier Mobile Cloud Computing and Applicationto Edge Accelerated Web Browsing,” IEEE MobileCloud 2015, San
Francisco, CA, USA, Apr. 2015.
Docker, https://www.docker.com/
ETSI Multi-access Edge Computing,
http://www.etsi.org/technologies-clusters/technologies/multi-accessedge-computing
Open Edge Computing Initiative, http://openedgecomputing.org/
Press release issued by FANUC on July 28, 2016.
http://www.fanuc.co.jp/en/profile/pr/newsrelease/notice20160728.
html*3 FIELD system: FANUC Intelligent Edge Link and Drive system

Vol. 15 No. 9 Sept. 20176

Feature Articles

Atsushi Terauchi

Senior Research Engineer, Ubiquitous Service
Systems Laboratory, NTT Network Innovation
Laboratories.
He received a B.E. in control engineering from
Osaka University and a Ph.D. in engineeringfrom Waseda University, Tokyo, in 1991 and2007. He joined NTT in 1991 and moved to NTT
Network Innovation Laboratories in 2015. His
research interests include network virtualizationand future network operations. He is a member of
the Institute of Electronics, Information andCommunication Engineers (IEICE).

Kenichi Ooto

Senior Research Engineer, Cloud Solutions SE
Project, NTT Software Innovation Center.
He received a B.S. and M.S. in industrial andmanagement systems engineering from Waseda
University, Tokyo, in 1990 and 1992. He joined
NTT Switching Systems Laboratories in 1992
and moved to the NTT Software Innovation
Center in 2012. His research interests include
software-defined networking and open source
software. He is a member of IEICE.

Noriyuki Takahashi

Senior Research Engineer, Supervisor, Ubiquitous Service Systems Laboratory, NTT Network
Innovation Laboratories.
He received a B.E. and M.E. in informationengineering from Kyoto University in 1990 and
1992. He joined NTT LSI Laboratories in 1992.
He has been in his current position since 2001.
His research interests include routing mechanisms, adaptive networking systems, and architecture design of future networks. He is a member of IEICE and the Institute of Electrical andElectronics Engineers (IEEE).

7

Kei Harada

Researcher, Ubiquitous Service Systems Laboratory, NTT Network Innovation Laboratories.
She received a B.S. and M.S. in sports sciencefrom Waseda University, Tokyo, in 2011 and2013. She joined Network Innovation Laboratories in 2013. She has been in her current position
since 2015. Her research interests include interworking between IoT/ M2M systems, analysis ofbiometric data, and cognitive neuroscience. She
is a member of the Information Processing Society of Japan, the Japanese Society of Cognitive
Neuroscience, and oneM2M.

Ikuo Yamasaki

Senior Research Engineer, Supervisor, Ubiquitous Service Systems Laboratory, NTT Network
Innovation Laboratories.
He received a B.S. and M.S. in electronic engineering from the University of Tokyo in 1996 and1998. In 1998, he joined NTT and started R&D
activities regarding the Open Services Gateway
Initiative (OSGi) in 2001. In 2006, he was a visiting researcher at IBM Ottawa Software Laboratories, IBM Canada. He developed the HomeICT
service platform that adopts OSGi on home gateways, and engaged in standardization work in
OSGi Alliance from 2007 to 2012. From 2012 to2016, he was with NTT EAST, where he managed the team responsible for the HomeICT service platform and for providing smart home services. Since 2016, he has been leading and managing R&D activities related to IoT dataexchange technologies at NTT laboratories.

NTT Technical Review

Feature Articles: The Internet of Things and New Value
Creation

Optimal Crowd Navigation via
Spatio-temporal Multidimensional
Collective Data Analysis
Futoshi Naya, Masaru Miyamoto, and Naonori Ueda
Abstract
We introduce technology for predicting the risk of congestion in the near future from real-time observational data of people or automobile flows and for automatically deriving an optimal crowd navigation
plan online to avoid that risk. This technology can deal with unforeseen situations by performing simulations based on machine learning. Our aim in establishing this technology is to help construct a safe andsecure and more comfortable social infrastructure.
Keywords: spatio-temporal data analysis, congestion risk prediction, collective navigation

1. Introduction
Thanks to improvements in sensing technology andthe rapid spread of smartphone applications anddevices making up the Internet of Things (IoT), it is
becoming possible to measure diverse types of datasuch as the movement of vehicles and things, humanbehavior, and environmental changes from just about
anywhere. However, it is extremely difficult to extract
and appropriately apply significant and useful information lying latent in combinations of such diverseand massive amounts of data collected and stored in
the above way.
NTT is researching and developing Ambient-AI as
an artificial intelligence (AI) technology targeting
IoT [1]. The idea behind this technology is to obtain
information on everything under the sun (things,
people, the environment) from diverse types of datacollected and stored in real space and cyber space,
perform instantaneous event detection, analysis, andprediction based on that information, and feed theresults back to the real world.
A key technology supporting Ambient-AI is spatiotemporal multidimensional collective data analysis,
which has come to be constructed in order to model
the spatio-temporal relationships among multidimensional data having multiple attributes and to predict1

the place and time of a future event [2, 3]. This technology considers time, space, multidimensionality,
and collectivity as four elements of data to foreseeand gain insight on near-future events. Here, the idea
behind collectivity is to estimate the spatio-temporal
flow of people or vehicles only from aggregate data,
as in the number of people or vehicles per cell, in
which individual persons or cars cannot be identified.
In this article, we introduce our latest research anddevelopment (R&D) efforts in extending this technology. Starting with real-space information on people
or vehicles collected from real-time observations, we
input this information online into a simulation environment in cyber space, model the spatio-temporalfeatures of that information, instantaneously predict
immediate congestion risk, and preemptively andoptimally navigate the crowd to avoid that risk [4].
2. Learning multi-agent simulation
Multi-agent simulation (MAS) is becoming
increasingly popular as a technique for modeling theindividual behavior of autonomously acting entities(= agents) such as people, cars, animals, and insects,
modeling the micro-interaction of those agents withthe surrounding environment, and analyzing and predicting macro-phenomena uncovered from the
NTT Technical Review

Feature Articles(a) Conventional MAS(b) Learning MAS

People flow &navigation
scenario data
for learning

Parameters

Navigation plans

Observed
people flow

Parameter learning

Real world

Navigation
plans

Optimal navigation
estimation

Congestion risk prediction

Simulation environment• Manually sets MAS parameters
and navigation plans• Offline evaluation of pre-definednavigation plans

Learning simulation environment• Automatically learns MAS parameters using
people flow observations• Automatically derives optimal navigation plans
online based on pre-learning of possible
navigation and what-if scenarios

Fig. 1. Differences between conventional and learning MAS.

interaction among multiple agents and their interaction with the environment. In fields commonlyreferred to as complex systems, it may be possible tounderstand and describe individual micro-behavior,
say of the people making up a society, neurons making up the neural network of a brain, and molecules,
atoms, and other elements in the atmosphere giving
rise to various types of weather phenomena.
However, analysis techniques using MAS havecome to be widely researched for analyzing largescale systems in which the overall macro-behavior ofthings such as social activities, brain activities, andweather phenomena cannot be broken down into the
behavior of individual elements. These techniques
have recently found use in sensor networks, smart
grids, and intelligent transport systems and in evacuation guidance simulations for disaster preparedness.
To give a specific example, we consider the navigation of tens of thousands of spectators exiting a stadium. In this case, individual spectators are agents
who leave the stadium from their current locations via
exits and move toward train stations as their destinations. In spectator movement, a commonly usedmodel considers an average walking speed (e.g.,
4 km/h) related to the attributes of individual spectaVol. 15 No. 9 Sept. 2017

tors such as age and gender and the attenuation of thatwalking speed on traversed roads in a manner proportional to congestion conditions (crowd density).
Crowd control manuals [5] state that passing becomesdifficult and walking speed begins to drop at a crowd
density of 1.2 persons/m2 and that movement comesto a halt at 4 persons/m2. Crowd density is also called
service level [6], and the road width, space, and flowrate in pedestrian lanes that maintain a certain service
level satisfying safety standards can be computed.
In conventional analysis methods using MAS, it iscommon to manually set parameters such as walking
speed and navigation plans such as pedestrian paths
and flow rate and to conduct simulations to evaluate
beforehand the effects of those plans. Such methods
have been applied when actually implementing
crowd control (Fig. 1(a)). However, these parameters
and navigation plans are limited to a small number of
combinations decided beforehand and do not necessarily match actual human movements or observed
results based on navigation operations.
Today, however, rapid progress in IoT and sensing
technologies means that local people flow and congestion conditions in the real world can be measured
in real time using a variety of positioning means such2

Feature Articles(a) Observed people flowin the past 15 min(b) Prediction after 5 min(c) Prediction after 20 min

Fig. 2. Example of immediate congestion risk prediction.

as surveillance cameras, GPS (global positioning
system), Wi-Fi*, and beacons. With this in mind, theNTT laboratories are moving forward on the development of a learning MAS system that transfers eventsin real space such as people flow observed in realtime to a simulation environment in cyber space,automatically learns modeled parameters based on
observations, and predicts the spatio-temporal
unfolding of events (Fig. 1(b)).
The aim here is to establish technology based on thelearning MAS to predict immediate congestion risk,
automatically derive an optimal navigation plan
online at any time to avoid that risk, and support
navigation through crowd handlers and other personnel.
3. Prediction of immediate congestion risk andautomatic derivation of an optimalnavigation plan
An example of an immediate prediction of congestion risk is shown in Fig. 2. First, people-flow data are
obtained from real-time observations and input into asimulation environment. The prediction process takessome of that data, say from the immediately preceding 15 minutes, to simulate subsequent spatio-temporal behavior. That is, in combination with the spatiotemporal multidimensional collective data analysis
described above, it predicts with high accuracy spatio-temporal congestion risk into the immediate
future, that is, 5 minutes, 10 minutes, and 20 minutes
later. In an example of the flow of spectators moving
toward a stadium from nearby train stations, the process predicts that congestion will occur 20 minutes
later, particularly near the entrance on the north sideof the stadium (Fig. 2). The next step is to automati3

cally generate potential navigation plans by computer
to eliminate this congestion risk and to search for an
optimal navigation plan (Fig. 3).
In this example, one navigation plan is to temporarily close one of the six stadium entrances and direct
spectators toward the other entrances (Plan B in the
figure). However, a huge number of combinations is
possible when considering which entrances to close
and when and for how long to close each one, orwhether to completely or partially close a particular
entrance. This process therefore efficiently prunes themassive number of candidate navigation plans anddiscards those that would have no effect on navigation, those that cannot actually be put into operation
at the site, and those that may generate confusion, andthen immediately searches for an optimal navigation
plan.
Various criteria can be considered here for optimality such as the shortest time required for all spectators
to get inside the stadium or the minimum time or area
in which spectators are held up due to congestion.
However, it is difficult to directly evaluate which
navigation plan has an optimal criterion value, and it
is unrealistic to perform an exhaustive search of thismassive search space.
To solve this problem, we use a machine learningtechnique called Bayesian optimization that efficiently searches for promising candidates from asmall number of search results to derive an optimalnavigation plan. For example, with the search resultsof Fig. 3, this technique derives a navigation plan thatcloses two of the stadium entrances at the 25-minutepoint (Fig. 4). This plan can be presented to crowd
handlers to help them navigate arriving spectators.
*

Wi-Fi is a registered trademark of Wi-Fi Alliance.

NTT Technical Review

Feature Articles

Fig. 3. Automatic generation of candidate navigation plans and searching for optimal plan.

Fig. 4. Automatically derived optimal plan.

Here, we can expect spectator behavior to generally
change due to such navigation, but not all spectators
will necessarily comply with that navigation. Accordingly, this technique takes into account continuous
observations and predictions of new congestion risks
to repeatedly search for an optimal navigation planand perform actual crowd control in the same way as
above through a feedback loop.
4. Examples of application toentering/exiting stadium
The results of simulations for a scene with 80,000
spectators entering a stadium with and without optimal navigation are shown in Fig. 5 for comparison
purposes. The people flow at the point 1 hour and 20
Vol. 15 No. 9 Sept. 2017

minutes after spectators begin to arrive at the stationand make their way toward the stadium is shown in
Fig. 5(a). For the case without navigation shown at
the left of the figure, the concentration of nearby train
stations on the north side of the stadium results incongestion at the entrances near those stations and theappearance on public roads of queues of spectatorsthat are held up and cannot move forward.
In contrast, for the case with navigation shown inthe center of the figure, crowding at particular
entrances is avoided by blocking access to north-side
entrances at key locations, thereby minimizing thegeneration of queues on public paths. With the navigation plan, all spectators were able to arrive and be
inside the stadium at about the two-hour point, asshown in the center of Fig. 5(b).
4Feature Articles(a) 1 hour and 20 min later(b) 2 hours later

Fig. 5. Simulation results with optimal navigation plans for entering stadium.

However, without navigation (left side of Fig. 5(b)),
time is needed to naturally clear congestion, and it
would be about another hour until all spectators were
inside the stadium. A graph with the vertical axis
representing the number of people waiting and thehorizontal axis representing time is shown at thelower right of Fig. 5(a) and 5(b). It can be seen thatwith navigation, the number of people waiting is kept
low. Therefore, determining how to minimize thenumber of people waiting is an important issue, particularly for events held during the hot summer
months. The proposed technique is expected to beeffective in this regard.
Next, we consider a scene of 80,000 spectators exiting a stadium and making their way toward nearby
stations. We examine, in particular, a scenario in
which unforeseen situations (e.g., accidents) notenvisioned in prior studies in conventional navigation
planning occur in rapid succession at four locations
within 20 minutes after spectators begin to exit
(Fig. 6). It is assumed here that the flow of people
walking toward the stations and the flow of people
coming out of the stations become intertwined, that5

the widths of some roads are reduced (path narrowed)
due, for example, to the arrival of emergency vehicles(locations A, B, and D in the figure), and that a roadis closed (path closed) due to an accident (locationC).
The results of simulations performed to determine
whether the proposed technique can derive an optimal navigation plan even under such unforeseen
conditions is shown in Fig. 7. First, simulation results
are shown in Fig. 7(a) for the point 20 minutes after
people begin exiting the stadium, when accidents atfour locations occur. At this time, the flow of people
is the same with or without navigation, but a red congestion location starts to appear due to the effects ofthe path closure in the upper right portion of the map.
Next, simulation results at 30 minutes are shown in
Fig. 7(b). With navigation, the proposed techniqueclears congestion by directing people to a detour to
avoid the congestion caused by the above path closure. It can be seen from the results for the point 2
hours later in Fig. 7(d) that all spectators have arrived
at their target stations. With no navigation, however,congestion occurs at various locations 1 hour and 20
NTT Technical Review

Feature Articles

Fig. 6. What-if scenarios for exiting stadium.

(a) 20 min later(b) 30 min later(c) 1 hour and 20 min later(d) 2 hours later

Fig. 7. Simulation results with optimal navigation plans for exiting stadium.

minutes later, as shown in Fig. 7(c). Even at 2 hours
later as shown in Fig. 7(d), congestion has yet to becleared, and compared with the results with navigation, many people have to wait for 30 minutes or
longer. In this way, the proposed technology is not
limited to the occurrence of accidents envisioned
beforehand; it can also predict immediate congestion
for the multiple and ongoing occurrence of unforeseen situations and automatically derive an optimalVol. 15 No. 9 Sept. 2017

navigation plan at any time.
5. Future outlook
People-flow simulation introduced in this article
has been based for the most part on pseudo data generated from statistics on past numbers of train station
users and other information. NTT, however, is developing a learning-oriented simulation environment
6Feature Articles

based on actual measurements of people flow at
actual event venues and stadiums. We expect theapplication of this technology to extend beyond people flow to include risk prediction and optimal infrastructure control for flows in various types of social
infrastructures such as traffic flow, logistics, and evencommunications traffic. As we look forward to 2020,
we will continue to measure flow in peripheral areas
including large-scale event venues, train stations, androad networks and will work on making simulations
increasingly detailed. Our aim through these R&D
efforts is to achieve optimal crowd navigation technology that can deal with unforeseen situations.

[2]

[3]

[4]

[5]

References[6][1]

T. Yamada, S. Takahashi, F. Naya, T. Ikebe, and S. Furukawa, “Artifi-

Futoshi Naya

Naonori Ueda

Senior Research Scientist, Supervisor, Innovative Communication Laboratory, NTT Communication Science Laboratories.
He received a B.E. in electrical engineering, an
M.S. in computer science, and a Ph.D. in engineering from Keio University, Kanagawa, in
1992, 1994, and 2010. He joined NTT Communication Science Laboratories in 1994. From2003 to 2009, he was with Intelligent Robotics
and Communication Laboratories, Advanced
Telecommunications Research Institute International (ATR). His research interests include communication robots, sensor networks, pattern recognition, and data mining in cyber physical systems. He is a member of the Institute of Electricaland Electronics Engineers (IEEE), the Society of
Instrument and Control Engineers, and the Institute of Electronics, Information and Communication Engineers (IEICE).

NTT Fellow, Head of Ueda Research Laboratory and Director of Machine Learning and Data
Science Center, NTT Communication Science
Laboratories.
He received a B.S., M.S., and Ph.D. in communication engineering from Osaka University
in 1982, 1984, and 1992. In 1984, he joined NTT
Electrical Communication Laboratories, where
he was engaged in research on image processing,pattern recognition, and computer vision. In
1991, he joined NTT Communication Science
Laboratories. From 1993 to 1994, he was a visiting scholar at Purdue University, Indiana, USA.
He was a director of NTT Communication Science Laboratories (April 2010–March 2013). In
addition to his current positions as head of Ueda
Research Laboratory (NTT Fellow) and directorof the Machine Learning and Data Science Center, he also serves as deputy director of the
RIKEN Center for Advanced Intelligence Project, established in April 2016. He is also a visiting professor at the Graduate School of Informatics, Kyoto University, and at National Institute ofInformatics.
He is a member of the Information Processing
Society of Japan, a fellow of IEICE, and a senior
member of IEEE.

Masaru Miyamoto
Senior Research Engineer, Supervisor, Service
Innovation Laboratory, NTT Service Evolution
Laboratories.
He received a B.E. and M.E. in engineeringfrom Waseda University, Tokyo, in 1995 and1997. He joined NTT Human Interface Laboratories in 1997. From 2009 to 2014, he was withNTT Research and Development Planning
Department. He has been in his current position
since 2014. His interests include user experience,
management of research and development, andbig data.

7

cial Intelligence Research Activities and Directions in the NTT
Group,” NTT Technical Review, Vol. 14, No. 5, 2016.
https://www.ntt-review.jp/archive/ntttechnical.php?contents=
ntr201605fa1.html
F. Naya and H. Sawada, “From Multidimensional Mixture Data
Analysis to Spatio-temporal Multidimensional Collective Data Analysis,” NTT Technical Review, Vol. 14, No. 2, 2016.
https://www.ntt-review.jp/archive/ntttechnical.php?contents=
ntr201602fa2.html
N. Ueda, F. Naya, H. Shimizu, T. Iwata, M. Okawa, and H. Sawada,“Real-time and Proactive Navigation via Spatio-temporal Prediction,”
Proc. of the First International Workshop on Smart Cities: People,
Technology and Data, in conjunction with UbiComp2015, pp. 1559–
1566, Osaka, Japan, Sept. 2015.
N. Ueda, “Proactive People-flow Navigation based on Spatio-temporal Prediction,” Japanese Journal of Applied Statistics, Vol. 45, No. 3,
pp. 89–104, 2016 (invited).
Hyogo Prefectural Police Department, “Crowd Control Manual” (inJapanese),
http://www.police.pref.hyogo.lg.jp/sonota/zattou.htm
J. Fruin, “Pedestrian Planning and Design,” Metropolitan Associationof Urban Designers and Environmental Planners, New York, 1971.

NTT Technical Review

Regular Articles

Orbital Angular Momentum
Multiplexing Technology towards theRealization of Tbit/s WirelessTransmission
Doohwan Lee, Hirofumi Sasaki, Hiroyuki Fukumoto,and Hiroyuki Shiba
Abstract

We explore the potential of orbital angular momentum (OAM) multiplexing towards enabling highspeed wireless transmission. OAM is a physical property of electromagnetic waves that are characterized
by a helical phase front in the propagation direction. This characteristic can be exploited to create
multiple orthogonal channels, and thus, wireless transmission using OAM can enhance the wirelesstransmission rate. We clarify two major issues in OAM multiplexing: beam divergence and modedependent performance degradation. To address these issues, we present a simple but practical receiverantenna design method. Because there are specific location sets with phase differences of 90 or 180
degrees, the method allows each OAM mode to be received at its high signal-to-noise ratio region. We
confirm the feasibility of OAM multiplexing in a proof of concept experiment at 5.2 GHz. Finally, we
briefly introduce the future directions of this work.
Keywords: orbital angular momentum multiplexing, uniform circular array, 5G, millimeter wave

1. Introduction
Significant progress has been made in wirelesscommunication technologies in recent decades. In
particular, the utilization of higher frequency bands(e.g., the millimeter-wave (mm-wave) band), where
several gigahertz of bandwidth is available, hasplayed a key role in achieving such progress. Recent
research efforts have shown the feasibility and possibility of utilizing mm-wave technologies to provide
transmission at up to several tens of gigabits per second [1].
Despite such achievements, providing high-orderdata-rate transmission is expected to be necessary tosatisfy the ever-increasing demand for such transmission in the coming decade. We have also observed
from history that the transmission rate of wireless
1

communication has increased 100 times every 10
years, as shown in Fig. 1. Considering this trend andthe current mature status of mm-wave technologies[1], we consider it is high time to discuss the candidates for wireless transmission technologies to provide up to 1-Tbit/s transmission.
The orbital angular momentum (OAM) multiplexing method is an emerging wireless transmission
technology that exploits a physical property of electromagnetic waves characterized by a helical phasefront in the propagation direction [2]. The propagating beams with a distinct number of phase rotations,that is, OAM modes, are orthogonal to one anotherand create multiple orthogonal channels. From awireless communication perspective, the beauty of
OAM multiplexing is that it generates multiple
orthogonal channels in a line-of-sight channel
NTT Technical Review

Regular Articles

Tbit/swireless
communication

Transmission rates (bit/s)

1T
100G
10G

802.
802. 11ad
11ac
LTE-A
802.

5G

i

i-F

W

1G

802.11a/g
802.
11b HSPA

1M
2000

LTE

2010

a
nic

u

mm

o
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la

llu

Ce

n

tio

11n

100M
10M

Beyond 5G

802.
11ay

2020

100 times by
10 yearsyear

5G: fifth-generation mobile communications network
HSPA: high-speed packet access
LTE: Long-Term Evolution
LTE-A: LTE-Advanced

Fig. 1. Evolution trend of wireless communication technologies and transmission rates.

environment without complex signal processing suchas channel diagonalization.
We investigated the performance of wireless communications using OAM multiplexing and clarified
the most significant challenges that hinder the potential of OAM multiplexing, which are beam divergence and mode dependency of the performance. The
higher the OAM mode, the more severe the beam
divergence becomes. This results in the need forlarger receiver (Rx) antennas or reduced Rx signalto-noise ratio (SNR).
We discuss here how we are addressing these issues
with our practical and novel Rx antenna design andcorresponding signal separation methods [2]. We also
describe how we confirmed the feasibility of OAM
multiplexing in a proof of concept experiment at 5.2
GHz. Finally, we briefly introduce the future directions of this work.
2. Background and challenges
Several challenges arise with OAM multiplexing,
which we describe in this section. First, we review thebackground of this technology.
2.1 History of OAM multiplexing
Research on electromagnetic waves carrying OAM
modes goes back a long way. Some work in this fieldVol. 15 No. 9 Sept. 2017

dates from the early 1900s. In the 1980s, the multiplexing concept using OAM modes was suggested byresearchers studying circular array antennas. Fromthe late 1990s to the 2000s, similar concepts werestudied in various fields, including radio astronomy,
photonics, and free-space wireless communication.
From the early 2010s, interest was drawn again to thewireless communication field, influenced by maturing technologies using mm-wave band communications.
Studies related to OAM multiplexing in the wireless communication field are categorized into those
focusing on antenna design and beam generation[3–5], proof of concept experiments [4, 6, 7], signal
processing methods [8], and system studies such as
those concerning capacity analysis and link budget[9].
Much work has been done on antenna design andbeam generation since the renewal of interest in theearly 2010s. Various antenna designs using helicoidally deformed parabolic antennas [3], spiral phase
plates (SPPs) [4, 6, 7], holographic plates (HPs) [4],and elaborately tuned planar SPPs [5] have beenreported. Despite some reports of successful transmissions, it seems to be difficult to perform multiplexing in a practical manner using such antennas
since each OAM mode needs a differently locatedantenna. However, a uniform circular array (UCA)
2

Regular Articles

Mode 0Mode 1

0°

0°

0°

0°

0°
0°

0°

315°

Mode 20° 270°

90°

225°

135°

0°

180°

270°

45°

0°

90°

180°

180°

90°

0°

270°

Phase of each antenna(a)Mode 0Mode 1

0°

0°

0°
0°
0°

0°
0°

0°

0°

45°

Modes 1, 2

Modes −2, 2Modes −1, 1

Modes 1, 2

Modes −2, 2Mode 2
315°

90°

0°

270°

90°

270° 180°

180°

135°

225°

90°

180°

Modes −1, 1

270°

0°

Phase of each antenna
(b)

(c)

Fig. 2. ( a) Generation of OAM modes with a UCA, (b) separation of OAM modes with a UCA, and (c) intensity distribution(above) and phase distribution (below) of combined OAM modes.

and/or multiple UCAs [2, 8] are considered to be suitable for OAM multiplexing because they can transmit
coaxially aligned multiple streams simultaneously.
The feasibility of OAM multiplexing has been validated in many different experiments [4, 6, 7]. Yan etal. successfully demonstrated 32-Gbit/s OAM multiplexing over a 60-GHz mm-wave band with four
concurrent OAM modes (Modes = –3, –1, 1, and 3)
with 16-quadrature amplitude modulation (QAM)[7]. The transmission distance was 2.5 m, and four
SPPs were used for multiplexing with a 4-to-1 combiner. Mahmouli et al. conducted 4-Gbit/s uncompressed video transmission over a 60-GHz mm-wave
band using HPs and SPPs [4]. However, as of yet, no
Gbit/s-level transmission experiments over 10 m havebeen reported.
2.2 OAM beam generation and separation
To generate the beam carrying the OAM mode n (L= n), antenna elements are connected with phaseshifters that make n × 360 degrees of rotation. Examples of beam generations of OAM modes 0, 1, and 2
using UCAs consisting of eight antenna elements areshown in Fig. 2(a). Note that it is possible to use
either a single UCA or multiple UCAs for multiple
OAM mode generation. In the former case, superposed beams are transmitted by a single UCA. In thelatter case, concentric multiple UCAs are used.
The separation of beams carrying OAM modes can
3

be done in a way similar to that for generation using
antenna elements connected with phase shifters thatmake opposite rotation directions. As long as thenumber of antenna elements is larger than 2n, rotations of n × 360 degrees are orthogonal to one another. Therefore, each OAM mode can be separated frommixed OAM mode signals without aliasing. An
example of each antenna element phase corresponding to the example above is shown in Fig. 2(b). Suchbeam separation can also be done by using a single
UCA or multiple UCAs as in the beam generation.
Note that a divider is fitted between antenna elements
and phase shifters in the former case.
2.3 Properties of OAM beams
Some examples of intensity and phase distributionsof beams carrying multiple OAM modes generated
by a single UCA are shown in Fig. 2(c). At the Rx
antenna, the diffraction pattern generated by a single
transmitting UCA is calculated by the summation of
the electric field generated from each antenna element. Since the diffraction pattern of the UCA can beapproximated as a Bessel beam, the electric field distribution of the beam carrying OAM mode L is often
expressed by the Bessel beam’s equation as below.

NTT Technical Review

Regular ArticlesvL (r,θ,z) =λexp[(2πi/λ)

(

4π
2πrD

Rx antenna

r2 + z2 ]r2 + z2),

λ r2 + z2 ·i−L exp[iLθ]·JL
Intensity profile(1)

where JL(·), λ, and D respectively denote the Lthorder Bessel function of the first kind, the wavelengthof the carrier frequency, and the radius of the transmitting UCA. Equation (1) is represented in cylindrical coordinates, where r and θ are respectively theradius and azimuthal angle at the Rx plane that isvertical to the beam propagation direction, and z is
the distance between the centers of the transmitter(Tx) and Rx UCAs.
Under the free-space propagation or AWGN (additive white Gaussian noise) channel, the intensity andphase of the received signals at a certain location canbe analytically obtained by Eq. (1) with parametersconsisting of the radius (D) of the Tx UCA, wavelength (λ), and the Lth order Bessel function of thefirst kind (JL(·)).
2.4 Challenges
This subsection highlights three major issues thathave to be resolved in order to fully exploit the potential of OAM multiplexing. These issues are as follows.
(1) Beam divergence
Beams carrying OAM modes diverge along with
their propagation as shown in Fig. 3. Other thanOAM mode 0, the locations of the first Rx peak intensities of OAM modes diverge as the propagation distance increases. It may be considered that various
beam-forming technologies can generate a sharp nonOAM carrying beam of which the divergence is not
significant. However, the divergence of OAM carrying beams is determined by the Tx UCA radius andwavelength. It is therefore necessary to increase thephysical size of the Tx UCA or to use a higher frequency in order to reduce the divergence of OAM
carrying beams. Since both the Tx antenna size andfrequency band are usually not tunable design factors, we leave this issue as an open problem whilesuggesting that the Tx antenna size be set as large as
the physical environment allows.
(2) Mode-dependent performance degradation
The beam that carries different OAM modes yields
different locations of the peak intensities. This nonidentical peak Rx intensity results in mode-dependent
performance degradation in accordance with theVol. 15 No. 9 Sept. 2017

Tx antenna

on

cti

ion

e
dir

at

g
pa

o

Pr

Fig. 3. Conceptual drawing of OAM beam divergence.

location of the Rx antenna. In addition, the required
Rx antenna size to capture the peak Rx power
becomes larger as the number of OAM modes
increases. If the Rx antenna size is limited to a certain
size, some OAM modes might not have the peak Rx
power. Correspondingly, the performance degradation for higher OAM modes becomes more severe.
We propose a novel Rx antenna design to resolve
the issues above.
3. Proposed Rx antenna design and OAM
beam separation
In this section, we introduce the proposed antenna
design and discuss the issue of beam separation.
3.1 Rx antenna design
In view of the distinct beam propagation and divergence among OAM modes, it may be a good approach
to put the Rx antenna for each OAM mode at its optimum or near-optimum location. For example, the useof many concentric Rx UCAs that capture OAM
modes at their high Rx SNR region may lessen the
beam divergence problem and mode-dependent performance degradation. In such concentric Rx UCA
cases, the outer UCA is generally used for the reception of a higher OAM mode. Since the UCA antenna
radius increases by the power of two in such cases, aspacious antenna is necessary to capture the higher
OAM mode signals in this approach.
To provide a practical solution that allows higher
OAM mode signals to be received at a high Rx SNR
while maintaining a reasonable antenna size, we present a simple but practical Rx antenna design and4Regular Articles

90 degrees
45 degrees
30 degrees

Fig. 4. Proposed Rx antenna.

corresponding beam separation method. Our idea isbased on the fact that there are specific location setsof which phase differences are 90 or 180 degrees.
Since such specific location sets depend not on the
Euclidean distance but on the angle conditions thatwill be explained later, these specific location sets areinvariant in terms of the distance between Tx and Rx.
This provides the flexibility in the system design.
The concept of the proposed Rx antenna for concurrent transmission of seven OAM modes, including
OAM modes –3, –2, –1, 0, 1, 2, and 3, is illustrated inFig. 4. We use a four-antenna-element set to receive apair of OAM modes of which the absolute values are
identical and the signs differ from each other, forexample, OAM modes 1 and –1. We also use anantenna at the center for OAM mode 0. In Fig. 4, theoutermost, middle, and innermost four-antenna-element sets are respectively for OAM modes 3 and –3,
2 and –2, and 1 and –1. Four antenna elements in each
set are located equidistant from the center and form
an ‘X’ type configuration. The angles of the two
upper antenna elements of each set are respectively
30, 45, and 90 degrees. The angles of the two lower
antenna elements of each set are the same as those of
the upper ones. Note that such angles become narrower as the number of OAM modes increases.
Therefore, in contrast to the UCA case, the necessary
area to capture the higher OAM modes does notincrease by the power of two in its radius.
3.2 OAM beam separation
With the presented Rx antenna configuration, OAM
beams are separated by analog cancellation followedby digital cancellation. We explain the details on the5beam separation, which consist of the following threesteps, referring to Fig. 5.
(1)	Step 1) analog separation of odd and even OAM
modes
Diagonally located antenna elements in each fourantenna-element set are combined by either an equalphase or a reverse-phase combination using 2-to-1
analog combiners. Before these combinations, in thefour-antenna-element sets installed for odd OAM
modes, the two antenna elements at the bottom are
first inserted into the 180-degree phase shifters asshown in Fig. 5. With these equal-phase or reversephase combinations, the combined outputs can only
bear either an odd or even OAM mode.
(2) Step 2) analog extraction of each OAM mode
In this step, we begin by explaining an example ofthe extraction of OAM modes –3 and 3. A conceptual
example to extract OAM mode 3 is shown in Fig. 6.
Two reverse-phase combined outputs from the outermost four-antenna-element set contain only odd
OAM modes (i.e., OAM mode –3, –1, 1, and 3) while
even OAM mode signals are canceled out.
Each of the two combined outputs is again divided
into two signals by a 1-to-2 divider. One output of thetwo dividers is inserted into the 90 and –90 degree
phase shifters. The outputs of these phase shifters arerespectively combined again with the outputs of theother two dividers. These combined signals respectively yield OAM modes –3 and 3 containing someinterference from OAM modes –1 and 1. The interference is resolved by digital processing in step 3.
OAM modes –1 and 1, and –2 and 2 are similarly
extracted using two equal-phase combined outputs
from the middle and innermost four-antenna-element
sets. Extraction of OAM mode 0 is directly obtained
from the antenna element at the center since all OAM
modes disappear other than OAM mode 0 at the center.
(3) Step 3) digital pruning of each OAM mode
Each of the OAM mode signals extracted in theprevious step is further pruned by digital signal processing such as successive interference cancellation
or multiple-input multiple-output equalization. This
step can be skipped when the residual interference
after step 2 is negligible, or it can be intentionallyskipped to reduce the complexity.
4. Evaluation: Proof of concept experiments
We conducted proof of concept experiments to
examine the feasibility of OAM multiplexing. First,
beams carrying OAM modes were generated using an
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Regular Articles

RF comp.

ADC

RF comp.

ADC

RF comp.

ADC

RF comp.

ADC

RF comp.

ADC

90 deg. PS–90 deg. PS

RF comp.

ADC

90 deg. PS

RF comp.

ADC

Divider
90 deg. PS

Divider–90 deg. PS
Divider

180 deg. PS

Divider
180 deg. PS
Divider

180 deg. PS

Demodulator

180 deg. PS

Signal separator

Divider–90 deg. PS

Rx configuration
ADC: analog-to-digital converter
RF comp.: radio frequency components
PS: phase shifterFig. 5. Configuration of Rx device using proposed Rx antenna.

L = –3 (–90 deg.)phase direction

L = –1 (–30 deg.)
L=1
L = –1
L = –3(0 deg.) (0 deg.) (0 deg.)

L = 1 (30 deg.)

L=3(0 deg.)

L = 3 (90 deg.)

(a)

(b)

L=3
(0 deg.)L = –3(180 deg.)

L = –1L=1(120 deg.) (60 deg.)(c)

L=1(0 deg.)L=1(120 deg.)

L = –1(0 deg.)L = –1(60 deg.)

L=3(0 deg.)(d)Fig. 6. C onceptual example of OAM signal separation (OAM mode 3). (a) One output of the reverse-phase combination
using two antenna elements among the outermost four-antenna-element set; (b) output of 90-degree phase shifterusing another output of the reverse-phase combination using remaining antenna elements among the outermost
four-antenna-element set; (c) output of −90-degree phase shifter using another output of the reverse-phasecombination using remaining antenna elements among the outermost four-antenna-element set; and (d) combined
signal of (b) and (c) (OAM mode 3 is extracted).

Vol. 15 No. 9 Sept. 20176

Regular Articles

–30°

0°

–60°

30°
60°

mode 0mode 1–90°(a) Experiment environment

mode 290°(b) Tx antenna and radiation pattern(c) Rx antenna

Fig. 7. Experimental setups.

Mode: 0Mode: 1

Mode: 20–20

1.15 m1.15 mIntensity

Phase

2
2
1.5
–40
1.5
1
(dB)
1
0.5
0.5
180
0
0
–0.5
–0.5
0
–1
–1
–1.5
–1.5
–2
–180
–2
–2 –1.5 –1 –0.5 0 0.5 1 1.5 2–2 –1.5 –1 –0.5 0 0.5 1 1.5 2(deg.)(a) Intensity and phase distributions (OAM modes 0, 1, and 2)(b) QPSK(c) 16 QAM

Fig. 8. Experimental results.

unmodulated signal at 5.2 GHz. Their propagation
was also investigated. Second, wireless communication experiments using quadrature phase shift keying(QPSK) and 16-QAM modulated signals at the same
frequency band were conducted using our proposed
antenna. Our experimental environment, Tx antenna
and its radiation pattern, and the proposed Rx antenna
are shown in Fig. 7.
 AM beam generation and propagation
4.1 O
experiments
Three beams respectively carrying OAM modes 0,
1, and 2 were generated using a single Tx UCA atdifferent times. The results of measuring intensityand phase distributions are shown in Fig. 8(a). The
7

figure shows the average intensity distribution normalized to the peak intensity of OAM mode 0 and thephase distribution in terms of the distance from theRx antenna center. We confirmed from the measurement results that the experimental generated OAM
beam propagation patterns were in good agreementwith theory.
Precisely, an unmodulated signal at 5.2 GHz was
generated and fed into a 1-to-8 divider. The divider’seight outputs were then respectively connected toeight tunable phase shifters. Each tunable phaseshifter was connected to the antenna elements of theTx UCA. We set the phases of the tunable phase shifters in order to independently generate beams carrying
OAM modes. For example, we set the phases fromNTT Technical Review

Regular Articles

0 degrees to 315 degrees by increasing them in
45-degree increments to generate OAM mode 1. This
yielded 360 degrees of phase rotation. To capture theintensity and phase distributions at the Rx plane, weused a horn Rx antenna and a moving positioner.
While transmitting the generated OAM beam, werecorded the intensity and phase distribution at twodimensional grids of the Rx plane.
Our experimental setup in the shielding room isshown in Fig. 7(a), and the Tx UCA (top) and its
radiation pattern (bottom) are in Fig. 7(b). Note that
we conducted these experiments using a horn Rx
antenna and a moving positioner. The distance
between the Tx UCA and the Rx horn antenna wasaround 235 cm (40.7 λ), and the diameter of the Tx
UCA was around 11.54 cm (2 λ). The positioner was
moved over a 21 × 21 square grid. One span of thegrid was 5.77 cm (1 λ), and the measured area at the
Rx plane covered around 115.38 cm × 115.38 cm.
The center of the grid was set to be the peak intensity
spot for OAM mode 0, while it was set to be the nullpoints for OAM modes 1 and 2.
4.2 M
 odulated signal transmission using OAM
beams
Here, we describe the results obtained in a wirelesscommunication experiment using modulated signals.
Except for the Rx antenna, the experimental environment was the same as that used in previous experiments. Instead of using a horn antenna, we used our
proposed antenna shown in Fig. 7(c). The width andheight of the Rx antenna were respectively 29 cm and70 cm. QPSK and 16-QAM modulations were used
for both uncoded and coded (1/2 rate low-density parity check (LDPC)) cases. Orthogonal frequency division multiplexing was carried out with 64 subcarriers
over a 20-MHz signal bandwidth. Of the 64 subcarriers, 16 were used. Due to the practical limitations of
the experimental setup, this experiment was conducted using a single stream while varying the OAM
modes among –2, –1, 0, 1, and 2. The OAM multiplexing was evaluated by combining these single
stream signals by offline processing.
The constellation maps of QPSK and 16-QAM signals obtained from a single stream are shown respectively in Figs. 8(b) and (c). Their error vector magnitude values were respectively 14.18% and 13.5%
with OAM mode 1, and 14.3% and 15.65% withOAM mode 2. Through off-line combining of thereceived signals of three single streams (OAM modes
0, 1, and 2) that were obtained by analog extraction,
we confirmed that a bit error rate less than 0.001 is
Vol. 15 No. 9 Sept. 2017

feasible with LDPC coding (rate 1/2). More enhanced
performance is expected when digital pruning is further applied. These results validate the feasibility ofwireless transmission by OAM multiplexing.
5. Conclusion and future directions
We introduced ways of achieving high order wireless transmission using OAM multiplexing. We also
reviewed the current literature on this topic and identified two major issues. To resolve these issues, we
proposed a simple but novel Rx antenna and corresponding mode separation methods. We validated theeffectiveness of the proposed methods through proof
of concept experiments.
Many issues still remain to be addressed. For
example, although in our work we did not explicitly
utilize the fact that nulls of each OAM mode appear
at different locations, this gives rise to other possibilities to be explored such as OAM specific spatialchannel coding. Considering various aspects, fromthe vision of future wireless technologies to practicalconfigurations, we are making efforts to achieve ourgoals that enable Tbit/s wireless transmission.
References
[1]
[2]

[3]

[4]

[5]
[6]

[7]

[8]

Z. Pi, J. Choi, and R. W. Heath Jr., “Millimeter-wave Gbps Broadband
Evolution towards 5G: Fixed Access and Backhaul,” IEEE Commun.
Mag., Vol. 54, No. 4, pp. 138–144, 2016.
D. Lee, H. Sasaki, H. Fukumoto, K. Hiraga, and T. Nakagawa,“Orbital Angular Momentum (OAM) Multiplexing: An Enabler of a
New Era of Wireless Communications,” IEICE Trans. Commun., Vol.
E100-B, No. 7, pp. 1044–1063, 2017.
E. Mari, F. Spinello, M. Oldoni, R. A. Ravanelli, F. Romanato, and G.
Parisi, “Near-field Experimental Verification of Separation of OAM
Channels,” IEEE Antennas Wireless Propag. Lett., Vol. 14, pp. 556–
558, 2015.
F. E. Mahmouli and S. D. Walker, “4-Gbps Uncompressed Video
Transmission over a 60-GHz Orbital Angular Momentum WirelessChannel,” IEEE Wireless Commun. Lett., Vol. 2, No. 2, pp. 223–226,
2013.
L. Cheng, W. Hong, and Z. Hao, “Generation of Electromagnetic
Waves with Arbitrary Orbital Angular Momentum Modes,” Sci. Rep.,
4, 4814, 2014.
Y. Yan, G. Xie, M. P. J. Lavery, H. Huang, N. Ahmed, C. Bao, Y. Ren,
Y. Cao, L. Li, Z. Zhao, A. F. Molisch, M. Tur, M. J. Padgett, and A. E.
Willner, “High-capacity Millimeter-wave Communications with
Orbital Angular Momentum Multiplexing,” Nat. Commun., Vol. 5, p.
4876, 2014.
Y. Yan, L. Li, Z. Zhao, G. Xie, Z. Wang, Y. Ren, N. Ahmed, S.
Sajuyigbe, S. Talwar, M. Tur, N. Ashrafi, S. Ashrafi, A. F. Molisch,and A. E. Willner, “32-Gbit/s 60-GHz Millimeter-wave WirelessCommunication Using Orbital Angular Momentum and Polarization
Multiplexing,” Proc. of IEEE International Conference on
Communications (ICC), pp. 1–6, Kuala Lumpur, Malaysia, May
2016.
A. Haskou, P. Mary, and M. Hélard, “Error Probability on the Orbital
Angular Momentum Detection,” Proc. of IEEE Annual International
Symposium on Personal, Indoor, and Mobile Radio Communication

8

Regular Articles[9](PIMRC), pp. 302–307, Washington, DC, USA, Sept. 2014.
Z. Zhang, S. Zheng, Y. Chen, X. Jin, H. Chi, and X. Zhang, “The

Doohwan Lee

Senior Research Engineer, Wireless Systems
Innovation Laboratory, NTT Network Innovation
Laboratories.
He received a B.S. (Hons.) and an M.S. inelectrical engineering from Seoul National University, South Korea, in 2004 and 2006, and a
Ph.D. in electrical engineering and information
systems from the University of Tokyo in 2009.
He has been with NTT Network Innovation
Laboratories since 2009. From 2012 to 2014, he
was an assistant professor at the Research Center
for Advanced Science and Technology, the University of Tokyo. Since 2016, he has been a parttime lecturer at Kanagawa University. His
research interests include compressed sensing,
software/cognitive radio, signal processing, andOAM multiplexing. He received the Best Paper
Award and Best Technical Exhibition Award
from the Institute of Electronics, Information andCommunication Engineers (IEICE) SoftwareRadio in 2011, the IEICE Communications Society Excellent Paper Award in 2012, the IEICESRW (Short Range Wireless Communications)
Young Researcher’s Award in 2016, the BestTechnical Exhibition Award from SmartCom
2014, the Best Paper Award from SmartCom
2016, and the Best Paper Award and Special
Technical Award in Smart Radio from IEICE
Smart Radio in 2017.

Capacity Gain of Orbital Angular Momentum Based Multiple-InputMultiple-Output System,” Sci. Rep., 6, 25418, 2016.

Hiroyuki Fukumoto

NTT Network Innovation Laboratories.
He received a B.E. in engineering from Kobe
University, Hyogo, in 2013, and an M.E. in informatics from Kyoto University in 2015. He has
been with NTT Network Innovation Laboratories
since 2015. His research interests include digital
signal processing and information theory in wireless communications. He received the Best Paper
Award from IEICE SmartCom 2016 and the Best
Paper Award and Special Technical Award in
Smart Radio from IEICE Smart Radio in 2017.

Hiroyuki Shiba

Senior Research Engineer, Supervisor, Group
Leader, NTT Network Innovation Laboratories.
He received a B.E. and M.E. from Gunma
University in 1995 and 1997, and a Ph.D. fromWaseda University in 2014. Since joining NTT
Wireless Systems Laboratories in 1997, he has
been engaged in the research of smart radio and
OAM multiplexing.

Hirofumi Sasaki

Researcher, NTT Network Innovation Laboratories.
He received a B.E. and M.E. in engineeringfrom Osaka University in 2011 and 2013. He
joined NTT Network Innovation Laboratories in
2013, where he researched a heterogeneous wireless network system in spectrum sharing scenarios. Since 2015, he has been researching superhigh-speed wireless communication in millimeter-wave regions. He received the Best Paper
Award from IEICE SmartCom 2016 and the Best
Paper Award and Special Technical Award in
Smart Radio from IEICE Smart Radio in 2017.

9

NTT Technical Review

Global Standardization Activities

ITU-R Study Group 3 Activities
towards Use of High Frequency
Bands for 5G and Other WirelessCommunication Systems
Motoharu Sasaki, Minoru Inomata, Wataru Yamada,and Yasushi Takatori
Abstract

Before frequencies are allocated to new wireless communication systems, it is important to investigate
frequency sharing to avoid interference with existing systems. The International Telecommunication
Union Radiocommunication Sector Study Group 3 (ITU-R SG 3) supports activities of other SGs bydeveloping and maintaining recommendations for radiowave propagation characteristics and prediction
models necessary for frequency sharing studies. This article introduces the activities of ITU-R SG 3
focused on the use of high frequency bands in new wireless communication systems such as the fifthgeneration mobile communication system (5G).
Keywords: propagation, 5G, high frequency band

1. Introduction
This section explains the roles of the International
Telecommunication Union Radiocommunication
Sector Study Group 3 (ITU-R SG 3) pertaining to thefifth-generation mobile communication system (5G).
1.1 Structure and roles of ITU-R and ITU-R SG 3
In discussions on frequency allocation to new wireless communication systems, it is important to study
frequency sharing to avoid interference with existing
systems and to achieve co-existence with such systems. The scope of ITU-R SG 3 concerns propagationof radiowaves in ionized and non-ionized media andthe characteristics of radio noise for the purpose of
improving radiocommunication systems, as established in Resolution ITU-R 4-7 [1]. ITU-R SG 3 supports the activities of other SGs by developing andmaintaining recommendations for radiowave propagation characteristics and prediction models neces1sary for frequency sharing studies. The structure of
the ITU-R SGs is shown in Fig. 1. The SGs are organized from SG 1 to SG 7 (except for SG 2, which does
not exist). The Radio Regulations (RR) will berevised at World Radiocommunication Conferences(WRCs) based on the study results of each SG.
SG 3 supports studies underway in other SGs byproviding recommendations for radio propagation
characteristics. That is to say, with the results
obtained by SG 3 in examining radio propagation
characteristics, the other SGs conduct frequency
sharing studies, and their results lead to discussionsin WRCs and revision of RR. Because the results ofrevising the RR affect the usage of worldwide wireless communications and may also affect domestic
laws such as the Radio Act, the examination of radio
propagation characteristics in SG 3 is a significant
activity supporting all SGs.
As shown in Fig. 1, ITU-R SG 3 is composed offour Working Parties (WPs) that address issues
NTT Technical Review

Global Standardization Activities

SG 1: Spectrum management
SG 3: Radiowave propagation

SG 3 supports
activities of other SGs.

RR revised based on recommendations

SG 4: Satellite services

WRC(World Radiocommunication Conference)

Question

SG 5: Terrestrial services
SG 6: Broadcasting services

Recommendation
RR(Radio Regulations)

SG 7: Science services
TG 5/1: Task group of frequency
sharing studies for 5G

WP 3J
Fundamentals

WP 3K
Point-to-area propagation

Reflected in domestic laws such as the Radio Act

WP 3L
Ionospheric propagation

WP 3M
Earth–space propagation

Fig. 1. Structure of ITU-R Study Groups.

concerning fundamental propagation such as rainfall
effects and diffraction theory, ionosphere propagation, noise characteristics, terrestrial propagation, andsatellite propagation. Although SG 3 deals only withradio propagation characteristics, its scope of study isdiverse.
1.2 5G related discussion in ITU-R SG 3
ITU-R SG 3 has addressed numerous issues, but inrecent years, attention has focused primarily on topics related to 5G. In discussions on the frequency
allocation for 5G in WRC-19, the candidate frequency bands for the 5G era are those from 24 GHz to 86
GHz, as set in WRC-15. To study frequency sharing
with existing wireless systems, Task Group 5/1 (TG5/1) was newly established under SG 5, which is atemporary research group whose task is to identify
the frequency bands to be used for 5G [2]. SG 3
received a request to provide propagation models at
the relevant frequency bands that can be used for frequency sharing studies in TG 5/1, and March 31,2017 was set as the deadline according to the activity
plan of TG 5/1. Although SG 3 meetings are generally held once a year, additional meetings werescheduled to be held after the above deadline to accelerate the development of recommendations.

Vol. 15 No. 9 Sept. 2017

2. Development of recommendations forfrequency sharing studies for 5G
At the additional meeting held in March 2017, thenumber of contribution documents from each country, the number of participants, and the number ofconference sessions increased because of the importance of this meeting. With the input of information
to TG 5/1, whose deadline had been set on March 31,
2017, two main topics were discussed: the development of recommendations considered to be significant in the study of frequency sharing between 5G
and existing systems, and the liaison statement to besent to TG 5/1 that summarizes the related recommendations.
In considering the frequency sharing study (interference examination) between 5G and existing systems, the participants discussed recommendations
taking into account the interference with satellite stations and terrestrial stations existing in the distance,and obstruction by buildings (clutter) surrounding 5G
radio stations (base stations and terminal stations).
These recommendations are depicted in Fig. 2.
There are six main recommendations: (i) ITU-R
P.[CLUTTER]: propagation loss (attenuation ofradiowaves) due to clutter, which is obstruction ofradiowaves by the buildings around a radio station;
2

Global Standardization Activities

Stations in space(i) P.[CLUTTER]: Clutter loss
Clutter (obstruction by buildings)(v) P.619: Terrestrial–Space
propagation (No major revision)

Stations on thesurface of the Earth(vi) P.452: Terrestrial–Terrestrial
propagation (No major revision)(ii) P.[BEL]: Building entry loss(iii) P.1411: Short range
outdoor propagation(iv) P.1238: Indoor propagation

Fig. 2. ITU-R Recommendations in the liaison statement to TG 5/1 related to frequency sharing studies for 5G.

(ii) ITU-R P.[BEL]: building entry loss when radiowaves enter a building (equivalent to indoor-to-outdoor path); (iii) ITU-R P.1411: outdoor short range
propagation characteristics (up to 1 km) assuming
both the transmitting and receiving stations are located in nearby buildings; (iv) ITU-R P. 1238: indoor
propagation characteristics assuming the case whenboth the transmitting and receiving stations are present in the same building; (v) ITU-R P.619: propagation characteristics between a terrestrial station and aspace station; and (vi) ITU-R P.452: propagation
models for predicting propagation loss between terrestrial stations.
The recommendations in (ii)–(iv) are thought to be
useful for the situations where both the transmitting
and receiving stations are surrounded by buildings,
defined as clutter in (i). The development of recommendations (i) and (ii) progressed significantly at thismeeting and were approved as new recommendations. Additionally, recommendations (iii) and (iv)were revised to cover as wide a frequency range as
3

possible up to the 86-GHz band, which is the highest
candidate frequency band for the 5G era, and the revisions were approved. There were no major revisionsfor (v) and (vi) from the existing recommendations.
Finally, the liaison statement summarizing the detailsof these recommendations was approved for provision to TG 5/1. In this article, we outline the new
ITU-R Recommendations (i) ITU-R P.[CLUTTER]
and (ii) ITU-R P.[BEL], which were regarded as particularly important in this meeting.
2.1 N
 ew Recommendation ITU-R P.[CLUTTER]
Several ITU-R Recommendations on terrestrial
radio propagation describe prediction methods thatincorporate the type of propagation loss called clutter
loss. Clutter loss is propagation loss caused by reflection or scattering due to terrain profiles including thepresence of buildings around the antennas, as shownin Fig. 3. Clutter loss is a factor in many recommendations that address long range propagation such as
ITU-R P.452. To evaluate the interference between
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Global Standardization Activities

Loss caused by obstacles
around antennas“Site shielding” obstacle

Nominal clutter
height, ha (m)TxRx

Nominal groundheight, hg (m)dL (km)ds

dk

Assumed clutter
distance(s),ds and dk (km)Nominalclutter location

P.0452-03

Path length, d (km)Source: Recommendation ITU-R P.452
Tx: transmitter
Rx: receiverFig. 3. Definition of clutter loss in Recommendation ITU-R P.452.

other systems, it is necessary to consider radio stations in the distance. Therefore, it is important toutilize a prediction method that incorporates clutter
loss in long range propagation. However, since theprediction methods that use clutter loss have beenutilized with relatively low frequency bands such as
the VHF/UHF (very high frequency and ultrahighfrequency) bands, it was unclear whether it could be
expanded to the high frequency band. Therefore, it
was decided to study expansion into a high frequency
band and to summarize the prediction methods using
clutter loss as a new Recommendation ITU-R P.
[CLUTTER].
An example of clutter loss calculation in the newrecommendation is shown in Fig. 4. The graph shows
the median values of clutter loss (Y-axis) at each frequency with respect to the distance between the transmitter and receiver (X-axis); the calculation example
is assumed for clutter loss between terrestrial stations. It is clear that in a short distance, the clutter
losses decrease because the incident angle for the
obstructing buildings becomes small. As the distancebecomes longer, the incident angle can be considered
constant, so the clutter losses are also a fixed value.
Such variation with respect to distance is based on
the measurement results in an urban area, which wereVol. 15 No. 9 Sept. 2017

obtained by NTT Access Network Service Systems
Laboratories (Fig. 5). The X-axis represents the pathloss (propagation loss) values at each frequency, andthe Y-axis is the distance between transceivers. Incidentally, the measurement results of multiple frequencies, including the high frequency bands up to
66.5 GHz and long distances up to 1 km, are a significant contribution and are the only data without
reporting examples from other countries. The measurement results in Fig. 5 are also reflected in Recommendation ITU-R P.1411 (outdoor short range propagation) shown in Fig. 2, and reliable prediction formulas based on the measurements have been adopted
for each recommendation.
2.2 New Recommendation ITU-R P.[BEL]
Building entry loss (BEL) is propagation loss that
occurs when radiowaves enter from outside a building into the building (and also can be applied toinside-to-outside). SG 3 has been studying BEL forquite some time. Although the definition and theexamples of measurement results have been summarized in ITU-R P.2040 (effects of building materials
and structures) and ITU-R P.2346 (measurement dataof BEL), the prediction methods (propagation models) had not yet been defined. Therefore, the urgent
4

Global Standardization Activities

45

Median clutter loss (dB)

40

35

30

25

2 GHz
3 GHz
6 GHz
16 GHz40 GHz67 GHz

20

15

0.2

1

Distance (km)

10

100

Fig. 4. Example of clutter loss calculation result in new Recommendation ITU-R P.[CLUTTER].

180
170
160

α = 4.39
β = −6.27
γ = 2.3
δ = 6.89

Path loss (dB)

150
2.2 GHz
4.7 GHz26.4 GHz
66.5 GHz

140
130
120
110
100
90

300

400

500

600

700 800 900 1000 11001200

Distance (km)Fig. 5. Path loss measurement results from 2.2 GHz to 66.5 GHz in urban environment.

development of such a model was the goal of thismeeting.
As a result, a prediction formula was summarizedthat considers the incident angle in the horizontal andvertical direction with respect to the building and that5assumes interference with radio stations in space.
BEL characteristics are greatly affected by the type ofbuilding material. For example, in modern buildings,
the losses increase when windows have high thermal
insulation properties. Therefore, the type of building
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Global Standardization Activities

60

Median BEL (dB)50

Traditional

Thermally efficient

40
30
20
10
0
0.1

10

1

100

Frequency (GHz)

Fig. 6. BEL calculation result example in new Recommendation ITU-R P.[BEL].

was also taken into consideration in developing a
propagation model.
An example of the BEL calculation results used in
the new Recommendation ITU-R P.[BEL] is shownin Fig. 6. It shows the median values of BEL for two
types of buildings (Y-axis) with respect to the frequency (X-axis). It can be seen that the loss characteristics differ greatly depending on the type of building (traditional or thermally efficient). Also, as thehorizontal axis shows, the BEL values can beobtained for a wide frequency range up to 100 GHz.
As with the case using clutter loss, this prediction
formula was developed based on a large number ofmeasurement results including measurement resultsin multiple frequency bands performed by NTT
Access Network Service Systems Laboratories. The
reliable results based on the measurements wereadopted in the recommendation.

for the 5G era carried out in TG 5/1. The authors contributed documents based on measurement data on awide range of environments with multiple frequencies, including high frequency bands, and contributed
to developing reliable recommendations. In addition,
the authors’ participation in various groups was key
in promoting discussions on these topics. Dr. Wataru
Yamada served as Vice-Chairperson of WP 3K andChairman of SWG (Sub-Working Group) 3K-3, andDr. Motoharu Sasaki served as Chairperson of DG(Drafting Group) 3K3B. To accelerate the development of various radio communication systems,
including 5G, in the future, we intend to actively
engage in the modeling of propagation phenomena
and continue contributing to ITU-R SG 3 activities.
References[1]

3. Future overview[2]

ITU-R SG 3 studies various propagation characteristics, but the recent examinations of propagation
characteristics were done mainly as part of the frequency sharing studies of candidate frequency bands

Vol. 15 No. 9 Sept. 2017

ITU-R, Resolution ITU-R 4-7: “Structure of Radiocommunication
Study Groups,” Nov. 2015.
http://www.itu.int/pub/R-RES-R.4-7-2015
ITU-R, Resolution 238 (WRC-15): “Studies on frequency-related
matters for International Mobile Telecommunications identification
including possible additional allocations to the mobile services on a
primary basis in portion(s) of the frequency range between 24.25 and
86 GHz for the future development of International Mobile Telecommunications for 2020 and beyond,” The World Radiocommunication
Conference (WRC), Nov. 2015.

6

Global Standardization Activities

Motoharu Sasaki

Research Engineer, NTT Access Network Service Systems Laboratories.
He received a B.E. in engineering and an M.E.
and Ph.D. in information science and electricalengineering from Kyushu University, Fukuoka,
in 2007, 2009, and 2015. In 2009, he joined NTT
Access Network Service Systems Laboratories,
where he has been engaged in researching propagation modeling of interference between mobile
terminals for spectrum sharing wireless accesssystems. He received the Young Researcher’sAward and the Best Paper Award from the Institute of Electronics, Information and Communication Engineers (IEICE) in 2013 and 2014,
respectively. He is a member of the Institute ofElectrical and Electronics Engineers (IEEE).

Minoru Inomata

Research Engineer, Wireless Access Systems
Project, NTT Access Network Service Systems
Laboratories.
He received a B.E. and M.E. in electrical engineering from Tokyo University of Science in2009 and 2011. In 2011, he joined NTT Wireless
Systems Innovation Laboratories. Since then, he
has been researching and developing radio propagation for 5G systems and system design for
unlicensed wireless communication systems. He
received the Young Researcher’s Award fromIEICE in 2016. He is a member of IEEE.

7

Wataru Yamada

Senior Research Engineer, Planning Section,
NTT Access Network Service Systems Laboratories.
He received a B.E., M.E., and Ph.D. from Hokkaido University in 2000, 2002, and 2010. Sincejoining NTT in 2002, he has been researchingpropagation characteristics for wideband accesssystems. From 2013 to 2014, he was a visiting
research associate at the Centre for Telecommunications Research at King’s College, London,
UK. He received the Young Researcher’s Award,
a Communications Society Best Paper Award,
and a Best Paper Award from IEICE in 2006,
2011, and 2014, respectively. He is a member ofIEEE.

Yasushi Takatori

Senior Research Engineer, Supervisor, Wireless Access Systems Project, NTT Access Network Service Systems Laboratories.
He received a B.E. in electrical and communication engineering and an M.E. in system information engineering from Tohoku University,
Miyagi, in 1993 and 1995, and a Ph.D. in wireless communication engineering from Aalborg
University, Aalborg, Denmark, in 2005. He
joined NTT in 1995. He is currently engaged in
R&D of a high-efficiency wireless access platform. He served as a co-chair of COEX Ad Hoc
in IEEE 802.11ac from 2009 to 2010. He was avisiting researcher at the Center for TeleInFrastruktur, Aalborg University, Denmark, from2004 and 2005. He received Best Paper Awards
from IEICE in 2011 and 2016. He was honoredwith the IEICE KIYASU Award in 2016. He also
received 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.

NTT Technical Review

Short Reports

Successful Field Trial of All-Raman
Optical Amplification Technology for
Multiband Large Capacity
Transmission of 400-Gbit/s Signals in
Installed Fiber—Double the Usable
Bandwidth of Installed Fiber

1. Introduction
NTT recently announced that in conjunction withNTT WEST, it had successfully conducted a fieldtrial on widely installed optical fiber cable. In thisfield trial, all-Raman optical amplification technology*1 was employed in transmission line repeaters, and
400-Gbit/s signals were transmitted in multiple bands
at the same time. Furthermore, we confirmed throughthis field trial that it is possible to add signals in a new
band without affecting the signals in the existingband.
The results of this field trial suggest the possibilityof achieving ultralarge capacity transmission speedsof up 30 Tbit/s. These speeds, together with the ability to upgrade systems already in use, will be needed
to satisfy the demands imposed by communication
between datacenters. We are continuing to verify theultimate performance limits as well as operational
aspects, with the goal being to contribute to various
fields that require ultralarge capacity transmission.
2. BackgroundOur vision of future communication traffic sees thewidespread distribution of high-resolution images
and video such as 4K/8K and full-scale popularization of machine-to-machine services. The obvious
assumption is that capacity demands will skyrocket,
so NTT has been actively pursuing a wide spectrumof studies into achieving ultralarge capacity transmis1

sion of signals exceeding 100 Gbit/s. Furthermore,with the recent increase in traffic between datacenters, greater capacity must be matched by greater costsavings.
Greater capacity is most directly achieved by using
multiple bands (C band + L band etc.), which expands
the usable bandwidth of the optical fiber. However,
several problems arise if we attempt to transmit multiband signals by wavelength division multiplexing(WDM) over dispersion shifted optical fiber (DSF:
dispersion shifted fiber) cable. The zero dispersion
wavelength of DSF lies in the extended band (Cband), and around this wavelength, the signal isdegraded by nonlinear effects (especially four-wave
mixing*2). The common solution, unequally spacing
the signal wavelengths in the C band, degrades the
wavelength utilization efficiency. This is a barrier tofurther capacity increases.
*1 All-Raman optical amplification technology: Raman amplifiers
are a type of optical amplifier. They are based on a nonlinear effect of fiber called Raman scattering. Pump light that has a shorter wavelength than the wavelength band being used is injected
into the fiber, which makes amplification in an arbitrary band
possible. All-Raman optical amplification is the method of setting Raman amplifiers in all repeaters in the optical transmission
line.
*2 Four-wave mixing: Four-wave mixing is a kind of nonlinear effect that occurs in optical fiber. When two or more different
wavelengths of light transit a fiber, a new light with a wavelength
different from any of the incident lights is generated. WDM optical transmission with equi-spaced wavelength intervals triggers
four-wave mixing where the new light overlaps with the signal
lights, which degrades signal quality.

NTT Technical Review

Short Reports

Building A

Building B

C-band C-band mux
backC/L mux
groundSignalsignals
C-band 400G
Transponder

DSF cable
Pump light

Signal power
Largenonlinear
distortion

L-band L-band mux
backgroundsignals
L-band 400G
Transponder

DSF

Transmitter

Large
noise

ReceiverAmplifier
EDFA
All Raman
Transmission distance

Raman amplifier (C+L band
simultaneous amplification)
Mux: multiplexer
EDFA: erbium-doped optical fiber amplifierFig. 1. Field trial configuration (left) and signal power image plot with all-Raman optical amplification (right).

3. Research results
Our approach involves applying all-Raman opticalamplification technology in both the existing band (Lband) and extended band (C band). This approachallowed us to transmit multiple 400-Gbit/s signals(each consisting of two 16-QAM (quadrature amplitude modulation) modulated signals) over 200 km of
installed DSF cable with a sufficient transmission
margin. The usable bandwidth is doubled by combining the existing band and the extended band (Fig. 1).
Because signal degradation due to four-wave mixing increases with the signal’s optical power, low
optical power levels are essential to suppress nonlinear effects. All-Raman optical amplifiers are superior
to general EDFAs (erbium-doped optical fiber amplifiers), as they allow low signal transmission power tobe used. Our approach also improves frequency utili-Vol. 15 No. 9 Sept. 2017

zation efficiency in the C band by equally spacing the
wavelength intervals.
The use of Raman amplifiers raises safety issues
because high power pump lights are used. Our fieldtrials were designed to fully comply with IEC (International Electrotechnical Commission) standards.
Furthermore, we confirmed that appropriately controlling the Raman amplifiers made it possible to add
C band optical signals without altering the transmission quality of the L band signals. This confirms thatcapacity can be upgraded without disrupting L-bandservices already in use.
For Inquiries
Public Relations, NTT Information Network Laboratory Group
Email: inlg-pr@lab.ntt.co.jp
http://www.ntt.co.jp/news2017/1705e/170530a.html

2External Awards
Research Award of Excellence
Winner: Muneaki Ogawa and Yoshikazu Takahashi, NTT Network
Service Systems Laboratories
Date: March 2, 2017
Organization: The Institute of Electronics, Information and Communication Engineers (IEICE) Information and Communication
Technology for Safe and Secure Life (ICTSSL)For “The Trends of Standardization on Emergency Communications and Future Technical Issues.”
Published as: M. Ogawa and Y. Takahashi, “The Trends of Standardization on Emergency Communications and Future TechnicalIssues,” IEICE Tech. Rep., Vol. 116, No. 92, ICTSSL2016-3, pp.
11–15, June 2016.
Achievement Award
Winner: Shoji Makino, University of Tsukuba; Hiroshi Saruwatari,
The University of Tokyo; Hiroshi Sawada, NTT Communication Science Laboratories
Date: June 1, 2017
Organization: IEICE
For pioneering research on blind source separation of acoustic
signals.
Achievement Award
Winner: Kunio Kashino, Hidehisa Nagano, and Takayuki Kurozumi,
NTT Communication Science Laboratories
Date: June 1, 2017
Organization: IEICE
For research and development of extremely robust and fast media
search technology.
ICSS Research Award
Winner: Hiroki Nakano, Yokohama National University; Fumihiro
Kanei and Mitsuaki Akiyama, NTT Secure Platform Laboratories;
Katsunari Yoshioka; Yokohama National University
Date: June 8, 2017
Organization: IEICE Technical Committee on Information andCommunication System Security (ICSS)For “Towards Finding Code Snippets on a Question and Answer
Website Causing Mobile App Vulnerabilities.”
Published as: H. Nakano, F. Kanei, M. Akiyama, and K. Yoshioka,“Towards Finding Code Snippets on a Question and Answer Website
Causing Mobile App Vulnerabilities,” IEICE Tech. Rep., Vol. 116,
No. 522, ICSS2016-68, pp. 171–176, Mar. 2017.
Best Demo Award
Winner: Naoki Higo and Yoshiko Sueda; NTT Network Technology
Laboratories; Arata Koike, Tokyo Kasei University
Date: June 13, 2017
Organization: IEEE International Symposium on Local and Metropolitan Area Networks (LANMAN) 2017
For “Cog: Overlay Network Functions Assisting Connected IoT
Service Systems.”
Published as: N. Higo, Y. Sueda, and A. Koike, “Cog: Overlay Network Functions Assisting Connected IoT Service Systems,” Proc. of

1

LANMAN 2017, Osaka, Japan, June 2017.
The Meritorious Award on Radio Presented by the Chairman of the Board of ARIB
Winner: Makoto Yaita, NTT Device Technology Laboratories; Yasu­
hiro Nakasha, Fujitsu Laboratories Ltd.; Akifumi Kasamatsu,
National Institute of Information and Communications Technology
Date: June 15, 2017
Organization: Association of Radio Industries and Businesses(ARIB)For research on terahertz wireless communications.
TTC Chairman’s Prize
Winner: Kenichi Hiragi, NTT Network Service Systems Laboratories
Date: June 20, 2017
Organization: The Telecommunication Technology Committee(TTC)
For achievement of standardization progress concerning call control protocol from public switched telephone networks to IP interconnection.
Distinguished Service Award
Winner: Muneaki Ogawa, NTT Network Service Systems Laboratories
Date: June 20, 2017
Organization: TTC
For achievement of standardization progress concerning IP interconnection specifications for emergency notification systems.
Distinguished Service Award
Winner: Shoji Kimura, NTT Network Service Systems Laboratories
Date: June 20, 2017
Organization: TTC
For promoting standardization efforts concerning network operations management.
Excellent Presentation Award
Winner: Kana Eguchi, NTT Service Evolution Laboratories
Date: June 30, 2017
Organization: Information Processing Society of Japan (IPSJ) Multimedia, Distributed, Cooperative, and Mobile (DICOMO) 2017
Symposium
For “Missing R-R Interval Complement Method for Heart Rate
Variability Analysis in Frequency Domain Using Wearable ECG
Devices.”Published as: K. Eguchi, R. Aoki, K. Yoshida, and T. Yamada,“Missing R-R Interval Complement Method for Heart Rate Variability Analysis in Frequency Domain Using Wearable ECG Devices,”
Proc. of DICOMO 2017, 4G-5, pp. 888–897, Sapporo, Hokkaido,
Japan, June 2017 (in Japanese).
Young Researcher Award
Winner: Asuka Nakajima, NTT Secure Platform Laboratories
Date: June 30, 2017

NTT Technical Review

Papers Published in Technical Journals and Conference Proceedings

Organization: IPSJ DICOMO 2017 Symposium
For “Investigation of Method to Assist in Identification of Patched
Part of Vulnerable Software Based on Patch Diffing.”
Published as: A. Nakajima, R. Kimura, Y. Kawakoya, M. Iwamura,and T. Hariu, “Investigation of Method to Assist in Identification ofPatched Part of Vulnerable Software Based on Patch Diffing,” Proc.
of DICOMO 2017, 4H-2, pp. 905–910, Sapporo, Hokkaido, Japan,
June 2017 (in Japanese).
Young Researcher Award
Winner: Satoshi Hasegawa, NTT Secure Platform Laboratories
Date: June 30, 2017
Organization: IPSJ DICOMO 2017 Symposium
For “Implementation and Evaluation of Privacy Preserving Fisher’s Exact Test for GWAS.”
Published as: S. Hasegawa, K. Hamada, K. Chida, K. Misawa, S.
Ogishima, and M. Nagasaki, “Implementation and Evaluation ofPrivacy Preserving Fisher’s Exact Test for GWAS,” Proc. of DICOMO 2017, 2H-3, pp. 430–437, Sapporo, Hokkaido, Japan, June 2017
(in Japanese).
Best Paper Award
Winner: Bo Sun, Waseda University; Xiapu Luo, The Hong Kong
Polytechnic University; Mitsuaki Akiyama and Takuya Watanabe,
NTT Secure Platform Laboratories; Tatsuya Mori, Waseda University
Date: July 6, 2017
Organization: The 2017 International Conference on Applications
and Techniques in Information Security (ATIS)
For “Characterizing Promotional Attacks in Mobile App Store.”
Published as: B. Sun, X. Luo, M. Akiyama, T. Watanabe, and T.
Mori, “Characterizing Promotional Attacks in Mobile App Store,”
Applications and Techniques in Information Security—Proc. of ATIS
2017, Auckland, New Zealand, pp. 113–127, in Communications in
Computer and Information Science Series, Vol. 719, Springer, 2017.

Papers Published in Technical Journals andConference Proceedings
Topological Graph Layouts into a Triangular Prism
M. Miyauchi
Discrete and Computational Geometry and Graphs—18th Japan
Conference, JCDCGG2015, Kyoto, Japan, September 14–16, 2015,
Revised Selected Papers, pp. 241–246, in Lecture Notes in Computer
Science, Vol. 9943, Springer, July 2016.
Prism layouts are special cases of track layouts of graphs. A triangular prism layout for graphs is a graph layout into a triangular prismthat carries the vertices along the three crests between two trianglesof the prism and the edges in the three rectangular surfaces such thatno two edges cross in the interior of the surfaces. Also, a topologicalprism layout for graphs is defined so that edges are allowed to crossthe crests. As for topological prism layouts, it is desirable to havegood bounds on the number of edge-crossings over crests for various
classes of graphs. This paper constructs two-color-edge topologicaltriangular prism layouts for complete bipartite graphs with feweredge-crossings over the crests than previous results.

(d, 3)-track Layouts of Bipartite Graph Subdivisions
M. Miyauchi
Proc. of FIT (Forum on Information Technology) 2016, Part 1, pp.
5–10, Toyama, Japan, September 2016.
A (d, k)-track layout of a graph G consists of a k-track assignmentof G and an edge d-coloring of G with no monochromatic X-crossing.
This paper studies the problem of (d, 3)-track layout of bipartite

Vol. 15 No. 9 Sept. 2017

graph subdivisions. As for track layout, V. Dujmovi´c and D. R. Wood
showed that every graph G with n vertices has a (d, 3)-track subdivision of G with 2 logd qn(G) +1 division vertices per edge, whereqn(G) is the queue number of G. This paper improves their result forthe case of bipartite graphs, and shows that for every integer d ≥ 2,
every bipartite graph Gm,n has a (d, 3)-track subdivision of Gm,n withlogd n −1 division vertices per edge, where m and n are the numbersof vertices of the partite sets of Gm,n with m ≥ n.

Linear Predictive Coding without Yule-Walker Approximation for Transient Signal Analysis – Application to Switching Noise
F. Ishiyama, Y. Okugawa, and K. Takaya
Proc. of CSPA 2017 (13th IEEE International Colloquium on Signal Processing & its Applications), pp. 46–50, Penang, Malaysia,
March 2017.
We are investigating countermeasure techniques against electric
noise on telecommunication and related equipment. There are many
types of electric noise, and we focus on the characterization of transient noise such as the switching noise of switching power supplies.
We are developing a method of transient signal analysis based onlinear predictive coding (LPC) for the purpose. Unfortunately, as
standard LPC contains Yule-Walker (YW) approximation, which
replaces a given time series with a periodic time series for simplification, it is not suitable for transient signal analysis. Therefore, our2Papers Published in Technical Journals and Conference Proceedings

method of transient signal analysis does not contain YW approximation. Instead, we use the local linearization technique, which wasdeveloped in the field of quantum mechanics. Our method makes it
possible to obtain instantaneous frequencies and instantaneous decay
rates simultaneously with high precision from a small number ofsamples. We discuss our LPC method avoiding the use of YW
approximation and using the local linearization technique and its
application to repetitive ringing signals, which mimics the switching
noise of a switching power supply, to show the efficiency of our
method.

3360-degree Screen-free Floating 3D Image in a Crystal Ball
Using a Spatially Imaged Iris and Rotational Multiview DFD
Technologies
T. Kawakami, M. Date, M. Sasai, and H. Takada
Applied Optics, Vol. 56, No. 22, pp. 6156–6167, August 2017.
A rotational multiview depth-fused 3D (DFD) display and 360-deg
displaying optics using a spatially imaged iris method are proposed
to realize a 360-deg 3D image. This method enables displaying clear
floating images in a crystal ball. Its symmetric optics provide clear
and natural 360-deg images with smooth motion parallax in horizontal and vertical directions using the directional selectivity of a spatially imaged iris method and natural 3D images of a rotational multiview DFD display.

NTT Technical Review