NTT Technical Review

March 2017 Vol. 15 No. 3
Feature Articles: Wireless Access Technology to Meet Diverse Needs from IoT/M2M toBroadbandEnhancing Wireless Access Technologies Using Multiple Frequency Bands to Enable theWidest Range of Internet of Things Applications

Standardization Status towards the Introduction of 5G in 2020

New Trends in Wireless LAN and Cooperative Wireless LAN Technology

Development Efforts on Wide Area Wireless Access to Accelerate Its Use for M2M



A Study on Wireless Technologies to Improve Communication Performance by Utilizing
Multi-frequency Bands

Regular Articles


An Interpersonal Sentiment Quantification Method Applied to Work Relationship Prediction

Global Standardization Activities


Report on WTSA-16 (World Telecommunication Standardization Assembly 2016)

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

Feature Articles: Wireless Access Technology to Meet
Diverse Needs from IoT/M2M to Broadband

Enhancing Wireless AccessTechnologies Using Multiple
Frequency Bands to Enable theWidest Range of Internet of Things
Applications
Ryutaro Kawamura, Shuichi Yoshino, Masato Mizoguchi,
and Takehiro Nakamura
Abstract

In the future vision of society, wireless communications will play a more critical role in every aspectsof our lives, because the advent of the Internet of Things (IoT) is expanding the application of wirelesstechnology beyond human usage such as mobile or smartphones to thing usage such as industrial applications. It is therefore essential to develop efficient wireless technologies for the wide variety of frequency bands available—from the efficient use of conventional bands to the use of pioneering EHF(extremely high frequency) bands (millimeter wave band). This article introduces such development
efforts in three areas: 5G (fifth-generation mobile communications systems), wireless LAN (local area
network), and wireless access for IoT.
Keywords: 5G, Wireless LAN, IoT

1. Introduction
The advances made in wireless technologies, facilitated by the widespread adoption of smartphones,
tablets, and personal computers, have created an environment in which cellular phones and wireless local
area network (LAN) services enable us to exchange
and retrieve various kinds of information via networks anytime and anywhere, both indoors and outdoors, even while moving. Mobile services in Japan
have been migrating to the fourth generation, and thenumber of total handsets amounts to more than 160
million [1], far exceeding the population. In addition,
the annual shipment of wireless LAN devices surpassed 50 million in 2016 [2] and is expected toincrease in the coming years. The aggregated traffic
generated by wireless applications is increasing sub1

stantially and is being carried not only by mobile
services provided by operators, but also by wireless
LAN services in what is called off-loading. To
accommodate the traffic increase, new wireless technologies for pioneering frequency bands are necessary in addition to those that can expand the capacityof existing mobile and wireless LAN services.
The Internet of Things (IoT) concept is driving theinterconnection of devices in many places and is creating many opportunities for new wireless services
including those in the areas of factory control, industrial machinery, transportation, agriculture, city planning, healthcare, security, consumer electronics,
education, medical care, and countermeasures against
disasters. The use cases and application environments
for such services are obviously very diverse. For
example, real-time communications are needed for
NTT Technical Review

Feature Articles

Frequency band
300 MHz

3 GHz

30 GHzSuper high frequency(SHF)

Ultra high frequency(UHF)

300 GHz
Extra high frequency(EHF)

Mobile communication(cellular phones etc.)
700 MHz

Unlicensed
low-power
service

3.6 GHz

Unlicensed
low-power
service

Unlicensed
low-power
serviceWirelessLAN

Wireless
LAN

400 MHz920 MHz

2.4 GHz

5 GHz

Shared with television broadcasting /FM broadcasting / GPS /Amateur / BWA system, etc.

60 GHzShared with radars / Amateur /UWB radio system, etc.

Shared with radio astronomy/ Satellite telecommunications /Radars, etc.

*The information here is based on “Principal Uses and Characteristics of Radio Wave” from the Ministry ofInternal Affairs and Communications website.
BWA: broadband wireless access
FM: frequency modulation
GPS: Global Positioning System
UWB: ultra-wideband

Fig. 1. Principal uses of radio waves.

factory control and autonomous driving since theircontrol functions must be implemented within thedetermined duration. Low power communications
are needed in infrastructure monitoring and metering,
since the devices that are scattered over a wide area
need to be active for a number of years without having to frequently maintain them. Wireless technologies must offer a variety of quality and performancelevels in addition to higher capacity.
A wireless communication link is established by
selecting a certain frequency band on which to transmit. The basic characteristics of a radio wave dependon its frequency. Technical standards set by governing organizations determine the allocation of frequency bands by the radio wave characteristics inorder to make the best use of the frequency bands.
The principal uses of the major frequency bands inJapan are illustrated in Fig. 1. In general, lower frequency bands are appropriate for wide area communications, since low-frequency radio waves canpropagate well beyond the line-of-sight area. However, capacity is rather small.
In contrast, high frequency bands are appropriate
for high capacity communications, but high-frequency radio waves have poor propagation performance.
Applications should be assigned frequency bands thatVol. 15 No. 3 Mar. 2017

suit the service characteristics. UHF (ultra high frequency) and SHF (super high frequency) are the frequency bands that are most access-friendly in terms
of propagation and capacity. Mobile services andwireless LAN services are allocated to those bands. It
is expected that new technologies will be necessary to
improve the frequency usage efficiency and toincrease capacity in order to accommodate theincreasing traffic. In addition, emerging IoT services,which have various quality and performance demands,
have created the opportunity to pioneer the extreme
high frequency (EHF) millimeter band.
Technology developments and standardization
activities in a range of frequency bands have been
progressing worldwide, and the NTT Group has been
actively contributing to these efforts in order to meetthe demands of our customers. The activities for fifthgeneration mobile communications systems (5G),
wireless LAN, and wireless access for IoT aredescribed in the following sections.
2. 5G
In Japan, the first-generation mobile service started
in 1979 and used analog technology. The second generation introduced digital technology and expanded
2Feature Articles

eMBB (enhanced mobile broadband)

Gigabytes in a second
3D video, 4K/8K screensWork and play in the cloud

Smart home/building

AR (augmented reality)

Voice
Smart city

Future IMT

Industry automation
Mission critical applications
Self-driving cars

mMTC (massive machine type communications)

URLLC (ultra reliable and low latency communications)*Source: Recommendation ITU-R M.2083-0: “IMT Vision – Framework and Overall
Objectives of the Future Development of IMT for 2020 and Beyond,” 2015.
IMT: International Mobile Telecommunications

Fig. 2. Technical directions identified by the ITU-R recommendation.

mobile services substantially, while the third generation provided mobile data services to allow Internet
access. The fourth generation enabled broadband
data access, making mobile streaming services widely available. Today, research and development (R&D)
activities for the fifth generation are progressing inorder to increase capacity as well as to support
emerging IoT applications.
In September 2015, the ITU (International Telecommunications Union) published a vision of 5G
systems and identified three technical directions for
R&D. These are indicated in Fig. 2, along with theirrelationship with the expected applications; eMBB
(enhanced mobile broadband) supports broadband
data access via larger capacity, mMTC (massive
machine type communications) enables massive
numbers of devices to be connected in a certain area,
and URLLC (ultra reliable and low latency communications) achieves reliable data exchanges withinshort periods. 3GPP (3rd Generation Partnership
Project), the standards organization for 5G services,
has been developing detailed specifications of wireless technologies to suit the three directions. The
article entitled “Standardization Status towards theIntroduction of 5G in 2020” [3] in the Feature Articles in this issue details the latest stage in this study.

3

3. Advanced wireless LAN
Smart devices such as smartphones and tablets areusually equipped with wireless LAN functionality
and are widely used in gathering places such as stadiums, airports, and shopping malls. In such environments, people can efficiently utilize the broadband
wireless LAN services provided in addition to mobile
services. Accordingly, there is a growing need to
install wireless LAN access points in an extremely
dense manner at various gathering places in order toprovide more fast and reliable access even in crowded
situations. With legacy wireless LAN technologies,
service capacity is limited by the interference createdby the high density of access points. The NTT laboratories have been contributing to the new technicalspecifications being developed to overcome this issue
by developing more advanced antenna systems andintelligent control methods for multiple accesspoints. The article in this issue entitled “New Trends
in Wireless LAN and Cooperative Wireless LAN
Technology” [4] details IEEE 802.11ax, the in-progress wireless LAN standard being developed by theInstitute of Electrical and Electronics Engineers(IEEE). IEEE 802.11ax will improve the frequency
usage efficiency of wireless LANs in dense installation environments. The article also introduces thecooperative wireless LAN, which reduces interference
NTT Technical Review

Feature Articles

by intelligent control and the use of distributed antenna systems, leading to improvements in the quality ofwireless LAN services, especially 5G applications.
4. Wireless technology for emerging
IoT applications
The use cases and environments of emerging IoTapplications are rapidly diversifying, so the wirelesstechnologies for them must advance in many directions. For example, it is expected that extremely wide
coverage will be needed to connect the sensing devices of certain IoT services, as many will be placed
where people cannot easily access them even for
infrequent maintenance.
A number of new wireless technologies for the 920MHz band have recently emerged worldwide that
support IoT services needing wider coverage. The
920-MHz band is useful for this purpose, as such
radio waves propagate further than those at 2.4 GHz.
This band has until now been utilized by wireless
LAN and Bluetooth*. The article entitled “Development Efforts on Wide Area Wireless Access to Accelerate Its Use for M2M” [5] details the activities ofNTT laboratories involving wireless access technologies for the 920-MHz band.
IoT devices typically exchange small amounts ofdata. However, the capacity to handle much larger
amounts of data is needed to support intelligent security services. For example, high quality moving
images must be transferred to enable detection of
infrastructure anomalies by using advanced data
analysis schemes based on artificial intelligence algorithms. The greater frequency resources available in
the EHF band are promising for developing new
wireless technologies for such new bandwidthdemanding IoT applications.
Furthermore, it is expected that ultra-reliable andlow latency wireless communications will be necessary to support connections to self-driving cars, as
they must support data interaction for remote control
and for obtaining prompt environmental informationon other cars and roadside infrastructures. The article

entitled “A Study on Wireless Technologies to
Improve Communication Performance by Utilizing
Multi-frequency Bands” [6] examines the activities
of NTT laboratories in wireless access technologies
for pioneering frequency bands to achieve greater
capacity and higher reliability with lower latency.
5. Future developments
Recent advances in information technologies basedon wireless communications have created abilities
that will benefit all industry sectors by enabling them
to apply information technologies to improve their
existing workflows. Accordingly, wireless communication is expected to be a key part of the social infrastructure. NTT will continue its R&D activities on
efficiently matching frequency bands to the new
applications and creating new value in this emerging
IoT-enabled world.
References
[1]
[2]
[3]

[4]

[5]

[6]

*

Vol. 15 No. 3 Mar. 2017

Ministry of Internal Affairs and Communications, “Quarterly Data on
the Number of Telecommunications Service Subscriptions and Market Share (FY2016 Q1 (End of June)),” 2016.
ICT Laboratory, “The Result of the Survey on Wireless LAN Services
in 2015,” 2015.
Y. Kishiyama, S. Nagata, and T. Nakamura, “Standardization Status
towards the Introduction of 5G in 2020,” NTT Technical Review, Vol.
15, No. 3, 2017.
https://www.ntt-review.jp/archive/ntttechnical.php?contents=
ntr201703fa2.html
K. Kawamura, K. Ishihara, J. Iwatani, S. Shinohara, M. Akimoto, Y.
Inoue, T. Ichikawa, and Y. Takatori, “New Trends in Wireless LAN
and Cooperative Wireless LAN Technology,” NTT Technical Review,Vol. 15, No. 3, 2017.
https://www.ntt-review.jp/archive/ntttechnical.php?contents=
ntr201703fa3.html
N. Mochizuki, H. Katsuda, Y. Fujino, K. Akabane, H. Nagaoka, T.
Murao, and S. Yonesaka, “Development Efforts on Wide Area Wireless Access to Accelerate Its Use for M2M,” NTT Technical Review,Vol. 15, No. 3, 2017.
https://www.ntt-review.jp/archive/ntttechnical.php?contents=
ntr201703fa4.html
D. Uchida, T. Fujita, K. Hiraga, T. Shimizu, and K. Akabane, “A Studyon Wireless Technologies to Improve Communication Performanceby Utilizing Multi-frequency Bands,” NTT Technical Review, Vol. 15,
No. 3, 2017.
https://www.ntt-review.jp/archive/ntttechnical.php?contents=
ntr201703fa5.html

Bluetooth is a registered trademark of Bluetooth SIG Inc.

4

Feature Articles

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 engaged in research on network reliability techniques, network control and management, high-speed computer networks, active
networks, network middleware, and mobile
cloud/edge computing.

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 technology. He is currently
engaged in R&D of wireless technology for IoT
and mobile access.

5

Masato Mizoguchi

Senior Research Engineer, Supervisor, ProjectManager of 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 in1989 and 1991. Since joining NTT in 1991, he
has mainly been engaged in R&D of wirelessLANs including the IEEE 802.11a systems. He
received the Best Paper Award in 2000, 2011, and2016, and the Achievement Award in 2006 fromthe Institute of Electronics, Information andCommunication Engineers (IEICE). He is asenior member of IEICE and a member of IEEE.

Takehiro Nakamura

Vice President and General Director of 5G
Laboratory, NTT DOCOMO, INC.
He joined NTT in 1990 and has since been
engaged in R&D of the Wideband Code Division
Multiple Access (W-CDMA), High Speed Packet
Access (HSPA), Long Term Evolution (LTE)/
LTE-Advanced, 5G, and Connected Car technologies. He has been involved in standardization activities for W-CDMA, HSPA, LTE/LTEAdvanced, and 5G at the Association of Radio
Industries and Businesses (ARIB) since 1997. He
is currently the leader of 2020 and Beyond Ad
Hoc in ARIB and Acting Chairman of the Strategy & Planning Committee in the 5G Mobile
Communication Promotion Forum in Japan. He
has been contributing to standardization activities in 3GPP since 1999. He served as Vice Chairman of 3GPP TSG-RAN (Technical Specification Group - Radio Access Network) from March
2005 to March 2009 and as Chairman from April
2009 to March 2013.

NTT Technical Review

Feature Articles: Wireless Access Technology to Meet
Diverse Needs from IoT/M2M to Broadband

Standardization Status towards theIntroduction of 5G in 2020
Yoshihisa Kishiyama, Satoshi Nagata,and Takehiro Nakamura
Abstract

There is growing interest worldwide in the introduction of fifth-generation mobile communications
systems (5G) by 2020 or earlier, and extensive efforts on 5G field experiments are being vigorously carried out in many countries and regions. 3GPP (3rd Generation Partnership Project), the standards organization for mobile communications systems, held the RAN (radio access network) Workshop on 5G in
September 2015. Subsequently, discussions started on the standardization of new radio interfaces for 5G.
This article outlines the approaches and the current status of standardization towards the introduction of
5G in 2020.
Keywords: 5G standardization, New RAT, wireless access1. Introduction: Phased approach of
5G standardization
Wireless communications services in the era offifth-generation mobile communications systems(5G) can be characterized by two major trends. One
is enhanced mobile broadband (eMBB), which provides extreme broadband data transfer, and the otheris Internet of Things (IoT), which connects everything to wireless networks. To achieve the services
depicted in Fig. 1, two approaches for technicaladvancement are being considered: enhanced Long
Term Evolution (eLTE), which denotes the continued
evolution of 4G (LTE, LTE-Advanced, LTEAdvanced Pro), and New Radio Access Technology(New RAT), the introduction of the new 5G radio
interface specification.
The former is an evolutionary approach thatemphasizes backward compatibility with existing 4G
systems. The latter is a clean slate approach to drastically improve performance; it emphasizes ultimate
performance over backward compatibility. It is
expected that 5G will be realized by some combination of eLTE and New RAT [1, 2].
A possible 5G deployment scenario that uses the
NTT DOCOMO assumption about the combination
1of eLTE and New RAT is illustrated in Fig. 2. In theearly stage of 5G deployment in 2020, eLTE and New
RAT will be introduced in areas that require large
capacity such as dense urban centers. Dual connectivity technology [1], which separates control and user
data planes, will be used to enable cooperation
between eLTE and New RAT to support sufficient
coverage and mobility, while achieving high-speed
and large-capacity mobile communications environments. In later stages, 5G will gradually be expandedfrom large cities to suburban and rural areas, under
the assumption that new frequency bands such as millimeter wave bands, which are not used for current
mobile communications, will be deployed as necessary.
To achieve the deployment of New RAT in 2020,
the initial version of the specifications should becompleted by mid-2018 at the latest. The standardization work therefore has to progress in accordancewith the rather tight schedule. However, the International Telecommunications Union - Radiocommunications Sector (ITU-R), the organization that recommends international radio standards, wants the final
interface specifications for supporting 5G (IMT2020) to be completed by the end of 2019, allowing
room for additional improvement of specifications.
NTT Technical Review

Feature Articles

Extended and enrichedwireless services
Video streaming

Everything connectedby wirelessNew types of terminals/HIs

Multiple personal devices

Transportation(car/bus/train)

Consumer electronics

Watch/Jewelry/Clothes

4K/8K

Healthcare

Education

Safety and lifeline system

House

Cloud computing

Sensors

HI: human interface

Fig. 1. Potential services available with 5G.

Suburban/rural areas

Urban areas
Before 2020

LTE

LTE

Frequency
bandwidth

LTE

LTE

Frequency
bandwidth

5G+

5G+

New RATNew RAT

eLTE

eLTE

LTE

LTE

5G

eLTE can be tightlyinterworked with New RAT.
New RAT

2020

LTE

eLTE
5G+

5G+
202x

New RATeLTE

New RATeLTE

Frequency
bandwidth

Fig. 2. 5G deployment scenario.

Accordingly, the consensus is to take the phased
approach in 5G standardization for New RAT while
incorporating the vision of achieving the early introduction of 5G as well as its subsequent evolution. In
the phased approach, the initial specification (Phase1) of New RAT has to be completed within a limited
period of time. It is therefore important to establish
the base design with an emphasis on forward compatibility instead of incorporating many new features.
Specifically, it is realistic to design New RAT with aVol. 15 No. 3 Mar. 2017

limited scope that focuses on the basic performanceof eMBB to support 5G attributes such as high speed,large capacity, and low latency (Fig. 3). To augment
wide area coverage, 4G and eLTE will continue to be
used to support IoT services, which must connect
many M2M (machine-to-machine) devices at low
cost. In later stages, the final New RAT specification(Phase 2 and later) will include many new features
and will be introduced gradually to support new
unknown services in the era of 5G and IoT.
2

Feature Articles

5G

5G+

eMBB use cases

eMBB use cases

eLTE
Massive
MTC“IoT”

New RATeLTE

Critical
MTC

Massive
MTC

Phase 1

New RAT“IoT”

Critical
MTC

Phase 2 and after

MTC: machine type communications

Fig. 3. 5G phased approach from use case perspective.

2. Standardization status of New RAT
Standardization is proceeding on various components and technologies associated with New RAT. We
report here on the latest standardization efforts.
2.1 Frequency bands
For 5G, the use of extremely wide frequency bands
is being considered; they range from legacy low frequency bands to higher frequency bands, including
millimeter wave, up to the maximum of 100 GHz.
However, the frequency bands that will be available
for commercial deployment in 2020, the target ofPhase 1 specification, will be limited. During theRAN Workshop on 5G held by the 3rd Generation
Partnership Project (3GPP) in September 2015, a
discussion was held on whether to support frequency
bands above 6 GHz, considering the strategies ofoperators and the usage of frequency bands in individual countries. While each operator and each country targeted different target frequency bands for 5G,
the Federal Communications Commission (FCC) in
the United States announced the allocation of frequency bands above 24 GHz to 5G [3]. The logic ofsupporting higher frequency bands (up to 50 GHz) in
Phase 1 is gradually being accepted by the market.
Thus in 3GPP, it was agreed that the scope of wirelesstechnology in the Phase 1 specification (Release 15)
should include frequencies both below and above
6 GHz [4].
2.2 Optimization for IoT-related services
Another topic of discussion in 3GPP concerns whatfeatures to include in the Phase 1 specification for
optimization in terms of IoT-related services, for3

example, massive machine type communications(mMTC) and ultra-reliable and low latency communications (URLLC). Many opinions were exchanged
during the 3GPP workshop. For example, some participants thought that Phase 1 should focus on eMBB
and that the specification should consider eMBB,
mMTC, and URLLC equally. Later, a number ofcompanies cooperatively proposed that 3GPP complete the Phase 1 Work Items early, and as a result ofextensive discussions, it was agreed that the Phase 1
specification would include eMBB and part of
URLLC only in order to complete standardization
early. However, the basic study according to the current Study Item includes the features for IoT as wellas eMBB, and work is still in progress. Such features
include channel coding schemes for small packets,and non-orthogonal multiple access schemes.
2.3 Support of standalone operation
The previous section mentioned NTT DOCOMO’s
assumption as to the combination of eLTE and New
RAT for a deployment scenario. The dual connectivity technology enables MeNB (master evolved
NodeB) to be used to provide the control plane function of eLTE and SeNB (secondary evolved NodeB)
to be used only to provide the user plane function of
New RAT. In this arrangement, New RAT can operate
only within eLTE coverage, which is referred to as
non-standalone operation. The benefit of this arrangement is that the specification of New RAT can be
simplified since it does not need to provide the functions needed for standalone operation. Those functions include the support of broadcast information
and an idle mode. A proposal was made for earlycompletion of the Phase 1 Work Items, and based on
NTT Technical Review

Feature Articles

2013

2014

2015

2016

2017

2019

Proposals

Requirements

ITU-R

2018

WRC15

Rel. 13
3GPP

2020

2021

2022

2023

Specifications

WRC19

Rel. 14
Study Item

Phase 1

Phase 2

Rel. 15Work Item

Rel. 16Work Item

Non-standalone

Trials for 5G key technologies
DOCOMO 5G trial site

DOCOMO

5G commercial system development
5G commercial
launchEnhancement to 5G+

WRC: World Radiocommunication Conference

Fig. 4. 5G time plan.

that, an agreement was reached on the overall work
plan to complete the Phase 1 physical layer specification for non-standalone operation by the end of 2017,
three months earlier than the original plan [4].
2.4 Standardization schedule
An illustration of our assumed standardization
schedule is shown in Fig. 4. The overall direction has
been publicized in line with the phased approach
agreed on in the 3GPP workshop. The core part ofPhase 1 specifications will be completed by September 2018, and the core part of Phase 2 specifications,which satisfy the requirements of ITU-R, will becompleted by December 2019 [5]. Furthermore, it
was agreed that the Phase 1 physical layer specifications for non-standalone operation are to be completed by the end of 2017 [4].
3. Current status of New RAT study
The standardization of New RAT has been progressing in 3GPP, and research and development(R&D) of 5G radio technologies is underway as partof many national projects and by operators and equipment vendors worldwide. This section introduces two
wireless technologies for New RAT: basic radio
parameters with frame structures, and multi-antennatransmission technology. Standardization efforts andVol. 15 No. 3 Mar. 2017

trials are also described.
3.1 Radio parameters with frame structures
To support a wide range of frequency bands and use
cases for 5G, it is necessary to support multiple different numerologies, which are defined sets of radio
parameters such as sub-carrier spacing and TTI(Transmission Time Interval). One effective approachis to design variable parameters based on the numerology of legacy LTE [1]. Of particular interest are thehigher frequency bands targeted for 5G, which allow
broader sub-carrier spacing than that possible with
LTE. In the current study underway in 3GPP, theworking assumptions to define scalable numerology
based on LTE are as follows.
•	The base of subcarrier spacing f0 is 15 kHz (thesame as that of LTE)•	Subcarrier spacing is defined as fsc = 2m x f0
(scalable in steps of powers of 2)
To realize the dynamic time division duplex (TDD)
scheme, which can dynamically switch between
uplink and downlink within the same carrier, the selfcontained subframe that suits low latency retransmission control is being studied in order to overcome theissues posed by the quasi-fixed TDD scheme. A number of approaches have been discussed (Fig. 5). These
include permanently allocating the head of a
subframe to the downlink control signal and the tail4

Feature Articles

Subframe

Subframe

Downlink control
Downlink data
Uplink control

Dynamic TDD

Downlink control
Uplink data
Uplink control

TDD: time division duplexing

Fig. 5. Possible radio frame structure.

Beam sweeping andbeam power measurement

CSI estimation for
limited number of beams

Fig. 6. Possible beamforming strategy for massive MIMO.

of the subframe to that of the uplink, and allocating
the middle of the subframe to the data signal of both
downlink and uplink, as well as to other reference
signals.
3.2 Multi-antenna transmission technology
One of the key technologies enabling 5G to achieve
drastic increases in capacity is massive multiple input
multiple output (MIMO). It improves frequency
usage efficiency through spatial multiplexing, and its
beamforming gain helps to compensate for the propagation loss in higher frequency bands. The key issue
in radio interface design is to efficiently support beam
search, beam tracking, and diversity control of spatial
multiplexing by controlling massive numbers ofantenna devices with limited reference signals andlow control overhead. Technical solutions have been
proposed in order to address this issue, including theuse of multiple hierarchical reference signals andbeam control schemes suitable for analog-digital
hybrid beamforming [6, 7].
The 5G experiment [8] conducted by NTT DOCOMO and Ericsson demonstrated highly efficient beam
control by using multiple types of reference signals
such as beam synchronization signals, a mobility
5reference signal (MRS) to measure the power of eachbeam, and a channel state information reference signal (CSI-RS) to measure channel quality for MIMO
multiplexing (Fig. 6). First, a base station transmits
synchronization signals and an MRS while rotating
its beam direction, and a terminal receives those signals to establish synchronization and measures the
MRS received power (MRSRP). The base station
receives feedback from the terminal on MRSRP,
selects the effective beam candidates for the terminal,
and transmits the CSI-RS. The terminal receives the
CSI-RS and derives the channel matrix to select the
optimum combination of beams to maximize its
throughput and to control the rank of MIMO multiplexing. This beam control scheme can be extended
for multi-user MIMO and multi-point transmission.
The 5G field experiment in the 15-GHz band conducted by NTT DOCOMO and Ericsson achieved anaggregated throughput of more than 20 Gbit/s byallocating four beams to two users at the same time in
the frequency band of 800 MHz in an outdoor environment (Fig. 7).

NTT Technical Review

Feature Articles

BS antenna
unit

System throughput (2 UEs)

UE #1
UE #2

BS: base station
UE: user equipment

Fig. 7. Experimental environment in which throughput over 20 Gbit/s was achieved.

4. Conclusion[2]

In this article, the current status of 5G standardization was described together with the latest developments achieved in field trials. All the efforts mentioned herein are expected to provide a new area ofservices such as eMBB to provide high-speed andlarge-capacity broadband access, and IoT to connect
everything to networks. NTT DOCOMO will continue to carry out advanced R&D and to contribute tostandardization activities with the aim of achieving
the initial deployment of 5G services in 2020, and thecontinued evolution of 5G (5G+) in later years.

[3]

References[1]

DOCOMO 5G White Paper, https://www.nttdocomo.co.jp/english/
corporate/technology/whitepaper_5g/

Vol. 15 No. 3 Mar. 2017

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

[8]

Association of Radio Industries and Businesses (ARIB), “ARIB 2020
and Beyond Ad Hoc Group White Paper: Mobile Communications
Systems for 2020 and Beyond,” Oct. 2014.
FCC News, “FCC Takes Steps to Facilitate Mobile Broadband andNext Generation Wireless Technologies in Spectrum above 24 GHz,”
July 2016.
3GPP RP-161253: “NR Schedule and Phases,” June 2016.
3GPP RWS-150073: “Chairman’s Summary Regarding 3GPP TSG
RAN Workshop on 5G,” Sept. 2016.
Y. Kishiyama, S. Nagata, A. Benjebbour, and T. Nakamura, “Hierarchical Multi-beam Massive MIMO for 5G Future Radio Access,”
Proc. of the IEICE General Conference, ABS-1-8, 2014 (in Japanese).
T. Obara, S. Suyama, J. Shen, and Y. Okumura, “Joint Processing of
Analog Fixed Beamforming and CSI-based Precoding for Super HighBit Rate Massive MIMO Transmission Using Higher Frequency
Bands,” IEICE Trans. Commun., Vol. E98-B, No. 8, pp. 1474–1481,
Aug. 2015.
K. Tateishi, D. Kurita, A. Harada, Y. Kishiyama, S. Itoh, H. Murai, S.
Parkvall, J. Furuskog, and P. Nauclér, “5G Experimental Trial
Achieving over 20 Gbps Using Advanced Multi-antenna Solutions,”
VTC2016-Fall (2016 IEEE 84th Vehicular Technology Conference),
Montréal, Canada, Sept. 2016.

6

Feature Articles

Yoshihisa Kishiyama

Senior Research Engineer, 5G Laboratory,
NTT DOCOMO, INC.
He received his B.E., M.E., and Dr.Eng. from
Hokkaido University in 1998, 2000, and 2010.
Since joining NTT DOCOMO in 2000, he has
been involved in R&D of 4G/5G radio accesstechnologies and physical layer standardizationin 3GPP. In 2012, he received the ITU-AJ Award
from the International Telecommunication Union
Association of Japan for his contributions to
LTE.

Satoshi Nagata

Senior Research Engineer, 5G Laboratory,
NTT DOCOMO, INC.
He received a B.E. and M.E. from Tokyo Institute of Technology in 2001 and 2003. He joined
NTT DOCOMO in 2003 and has since been
involved in R&D activities related to wirelessaccess technologies for LTE, LTE-Advanced,
and 5G. He contributed to the 3GPP Technical
Specification Group - Radio Access Network(TSG-RAN) Working Group 1 as a vice chairman from November 2011 to August 2013 andled many Release 11 and Release 12 LTE topics.
He is currently the RAN1 Chair, having been
appointed in August 2013 and has chaired all
Release 12, Release 13, and Release 14 LTE/5G
topics.

7

Takehiro Nakamura

Vice President and General Director of 5G
Laboratory, NTT DOCOMO, INC.
He joined NTT in 1990 and has since been
engaged in R&D of Wideband Code Division
Multiple Access (W-CDMA), High Speed Packet
Access (HSPA), LTE/LTE-Advanced, 5G, andConnected Car technologies. He has been
involved in standardization activities for
W-CDMA, HSPA, LTE/LTE-Advanced, and 5G
at the Association of Radio Industries and Businesses (ARIB) in Japan since 1997. He is currently the leader of 2020 and Beyond Ad Hoc in
ARIB and Acting Chairman of the Strategy &Planning Committee in the 5G Mobile Communication Promotion Forum in Japan. He has been
contributing to standardization activities in 3GPP
since 1999. He served as Vice Chairman of 3GPP
TSG-RAN (Technical Specification Group Radio Access Network) from March 2005 to
March 2009 and as Chairman from April 2009 to
March 2013.

NTT Technical Review

Feature Articles: Wireless Access Technology to Meet
Diverse Needs from IoT/M2M to Broadband

New Trends in Wireless LAN and
Cooperative Wireless LAN
Technology
Kenichi Kawamura, Koichi Ishihara, Junichi Iwatani,
Shoko Shinohara, Mamoru Akimoto, Yasuhiko Inoue,
Takeo Ichikawa, and Yasushi Takatori
Abstract

At NTT Access Network Service Systems Laboratories, we are researching and developing wireless
local area network (LAN) technology, which is becoming more popular in homes, businesses, and public spaces. This article introduces standardization trends in wireless LAN technology, cooperative wireless LAN technology that solves interference issues in high-density wireless LAN environments such as
stadiums, and integration of wireless LAN with cellular networks to realize 5G, the fifth-generation
mobile communications system.
Keywords: wireless LAN, cooperative wireless LAN, 5G

1. Introduction
Wireless local area networks (LANs) can be used ina diverse range of applications including smartphones, game consoles, and drones. NTT has beenstrengthening the infrastructure for wireless LAN as
our third access network platform following the fixedaccess network and mobile access network as part ofour 2012 Medium-term Management Strategy. This
has resulted in an increase in public wireless LAN
area owners including businesses and local governments. As we look ahead to 2020, we can expect tosee further expansion of wireless LANs in stadiums
and other such venues. It is also expected that wireless LANs will be used for the fifth-generation
mobile communications system (5G) that is being
developed to cope with rapidly increasing levels ofmobile traffic.
2. Wireless LAN standardization trends
The latest wireless LAN standard, IEEE* 802.11ac
1(11ac), supports high-speed communications over1 Gbit/s, but in environments such as train stations
and stadiums where there is a high density of wireless
LAN devices, the throughput is liable to be impaired
due to increased interference. To address this issue,
the Task Group ax (TGax) was set up in the IEEE
802.11 Working Group, which is responsible forwireless LAN standardization, with the primary goalof improving throughput in high-density environments. TGax has been working on the standardizationof IEEE 802.11ax (11ax) as a next-generation highefficiency wireless LAN standard.
The main scope of TGax described in the Project
Authorization Request, which is the prospectus of thetask group, specifies that there must be at least one
mode of operation such that the average throughput
per user equipment in a high-density environment is
at least four times higher than that of conventional
equipment. Therefore, if this can be achieved, we can
expect a great improvement in user experience*IEEE: The Institute of Electrical and Electronics Engineers

NTT Technical Review

Feature Articles

compared with 11ac. The following approaches arebeing discussed in order to achieve the substantial
improvements in frequency utilization efficiency thatare needed to satisfy this requirement.
2.1 Multi-user transmission technology
Multi-user transmission is a technique whereby
wireless signals for multiple terminals are transmitted
together. In TGax, two approaches are considered—multi-user multiple input multiple output (MUMIMO) and orthogonal frequency division multiple
access (OFDMA). MU-MIMO is a technique that
improves the transmission efficiency by performing
spatial multiplexed transmission to multiple terminals using multiple antennas. OFDMA is a techniquefor flexibly and efficiently sharing frequency resources among multiple terminals. It has been introduced
in standards such as Long Term Evolution (LTE) andis expected to be used in many wireless LAN devices
due to its ease of implementation.
In 11ac, MU-MIMO is introduced only for the
downlink, while for 11ax, both MU-MIMO andOFDMA will be adopted for the uplink as well as
downlink. With the introduction of multi-user transmission in the uplink, it becomes possible for multiple terminals to transmit simultaneously in the
uplink, which is expected to substantially improve thetransmission efficiency. Due to the implementation of
uplink multi-user transmission, TGax is discussing
details such as the specifications of a new control
frame called a trigger frame for the purpose of aligning the transmit timings between terminals.
2.2 T echnology for improving spatial utilization
efficiency of frequency resources
In a wireless LAN, the channel status is observed
before transmitting packets to avoid collisions
between packets, and if the received signal power isgreater than a pre-defined threshold, the channel isregarded to be busy and the transmitter defers thetransmission. In a conventional wireless LAN, thethreshold value at which the channel is judged to bebusy had been set to an excessively low value. In
high-density wireless LAN environments, throughput
degradation becomes more serious due to theincreased occurrence of busy judgments, which eventually leads to fewer transmission opportunities. For
11ax, we are discussing how to address this issue bychanging the conditions under which a channel isjudged to be busy so that the space utilization efficiency of frequencies can be increased. Specifically,
when the detected signal is a signal from another cell,Vol. 15 No. 3 Mar. 2017transmission is permitted as long as certain conditions are met, thereby facilitating simultaneous transmissions in multiple cells and enabling high-density
frequency re-use (Fig. 1). Discussions are also being
held on determining a method to link the control oftransmission power with the control of threshold values used to judge when a channel is busy.
In addition, there is strong demand for outdoor use,
so in 11ax, discussions are being held on matters suchas finding a signal format that is highly robust against
outdoor radio propagation characteristics and transmission modes that are specialized for long-distancetransmission.
Standardization of IEEE 802.11ax is currently
expected to be completed by the end of 2018. It isvery likely that equipment conforming to the draft
standard of 11ax will start to appear before completion of the standardization. It is assumed that equipment conforming to 11ax will become commonplacearound 2020.
3. Cooperative wireless LAN technology for
ultra-high-density wireless LAN services
In the 5G/11ax era, it is expected that the number ofwireless LAN access points (APs) will further
increase in order to cope with IoT (Internet of Things)
and ultra-broadband services. However, since thefrequency bandwidth used by wireless LANs is limited, in situations where APs are installed at highdensity, the number of cases where the same channel
is shared by multiple APs in the same vicinity is
increasing. Also, with the evolution of wireless LAN
standards, we are seeing the expansion and diversification of radio parameters both in the frequency
domain and in the spatial domain such as the frequency bandwidth, the number of spatial multiplexed
streams, and the transmit power. In this sort of situation, it is difficult to obtain adequate performance byusing autonomous distributed APs. To make efficientuse of limited radio resources in order to accommodate more terminals and traffic, it is effective to perform centralized radio resource management by taking information about the surrounding radio environment into consideration.
Cooperative wireless LAN technology makes it
possible to use radio resources with high efficiency
by means of centralized control of the APs in order toprovide carrier-grade communication quality. A radio
resource management engine (RRME) that controls
the AP performs centralized management of radio
environment information obtained from the AP, and2

Feature Articles

Detection range for signals
from the same cell

Access point 1

2 Access point 2
detects the signal andchecks that the cell
identifier is the sameas Access point 2.

1 Signal transmitted
from Terminal 2-2

Access point 2

3 Access point 2 receives
the signal and defers
transmission.

Terminal 1

Terminal
2-1
Terminal
2-2(a) Example of channel judgment for signals transmitted from the same cell

Detection range for signals
from different cells
Access point 1
1 Signal transmitted

from Access point 1

2 Access point 2
detects the signal andchecks that the cell
identifier is not the sameas Access point 2.

Access point 2

3 Access point 2 determines
the signal is from anothercell and starts transmission if
certain conditions are met.

Terminal 1

Terminal
2-1
Terminal 2-2(b) Example of channel judgment for signals transmitted from different cells

Fig. 1. Technology for improving spatial utilization efficiency of frequency resources.

sets suitable radio parameters for each AP so as toimprove system throughput by avoiding throughput
reductions caused by interference. In crowded places
such as stadiums and train stations, the radio environment information acquired by each AP is collected,
and the optimal radio parameters calculated byresource allocation based on the area throughput optimization policy (RATOP) algorithm [1] in the RRME
3

are set in each AP so as to optimize the frequency andspatial resources. Consequently, the RRME canreduce interference and improve the throughput of
the system (Fig. 2). In addition, it can prioritize theallocation of frequency bandwidth to APs carrying
heavy traffic, enabling systems to be designed flexibly.
Also, by applying cooperative wireless LAN
NTT Technical Review

Feature Articles

Stadium• Interference reduction• Wireless band priority control• System operation management

StationWireless
environment
information
Reduces interference and increases
system throughput by applying optimal
parameter settings
Parameter
control
Mall

RRME

CampusFig. 2. Cooperative wireless LAN technology.

APs using the same frequency channel

Received power

Interference area

High

AP

AP

Communication area

Low

The large interference area places limits on the re-use of frequency channels.
(a) Ordinary APs

Distributed antennas

Received power
High

AP

AP

AP

Low

Interference area can be reduced, allowing the APs to be arranged more densely.
(b) APs with distributed smart antennas

Fig. 3. Application of cooperative wireless LAN technology to distributed smart antennas.

technology to distributed smart antenna systems(D-SAS) where there are many parameters and thearea design is very difficult (Fig. 3), it is possible tofurther increase the density of AP placement by optimizing the parameters of APs with D-SAS, enabling
the implementation of high quality wireless LAN
services with higher throughput.
4. Integration of wireless LAN into
cellular networks
To accommodate increasing levels of mobile traffic, some traffic is being offloaded to wireless LANs.
Since the frequency resources available for cellularcommunications are limited, active steps are being
Vol. 15 No. 3 Mar. 2017

taken to use unlicensed frequencies in the 5-GHzband used by wireless LANs wherever possible. In
5G, a heterogeneous network is envisioned that combines multiple wireless access technologies [2], and it
is expected that wireless LANs will increasingly be
integrated with cellular networks as a type of wirelesscell carrying user data.
To facilitate cooperation between wireless LANs
and cellular networks, the 3GPP (3rd Generation
Partnership Project) is standardizing LWA (LTEWLAN Aggregation) and LWIP (LTE WLAN Radio
Level Integration with IPsec Tunnel) [3] in order toimplement integration at the radio access network
level, allowing for seamless connections between
wireless LANs and cellular networks. Additionally,4

Feature Articles

Unlicensed band platform

Database

Control engine

AP control

Terminal
control

5G wireless cell
Macrocellbase station

Wireless environment
information transmission
Low SHF bandsmall cellbase station

High SHF bandsmall cellbase station

Millimeter wave
wireless LAN AP

Wireless LAN AP

Improved unlicensed
band interference

Optimal wireless accessnetwork selection
5G wireless terminal

This technical report includes a part of results of “The research and development project for realization of
the fifth-generation mobile communications system” commissioned by The Ministry of Internal Affairs andCommunications, Japan.

Fig. 4. Unlicensed band platform technology.

traffic integration using multipath TCP (Transmission Control Protocol) [4] has been implemented as amethod for the integrated use of wireless LANs andcellular networks without adding functionality to thecellular networks. The choice between a wireless
LAN and a cellular network is set by ANDSF (AccessNetwork Discovery and Selection Function) in 3GPP
[5], which controls the selection of access networks
from a cellular network. The integration of wirelessLANs into cellular networks in 5G is probably basedon these techniques but requires technology that can
improve the user’s quality of experience (QoE) insituations where wireless cells with diverse characteristics are arranged more densely.
Unlicensed band platform technology (Fig. 4) creates a database of information such as wireless environment information for unlicensed bands and cellular network quality information, and performs optimal control from the network side of the terminal
attributes and AP settings according to a particularalgorithm based on this information. Wirelessresources can be used more efficiently by controlling
the CSMA/CA (Carrier Sense Multiple Access/Collision Avoidance) wireless LAN access method via
cellular networks. For example, to alleviate the hidden node and exposed node problems that typically5occur in unlicensed band wireless systems, the platform collects information about transmission delays
and the reception rate of wireless LAN beacon signals from terminals and APs. The control engine
analyzes the relationships of hidden nodes andexposed nodes and improves the communication
quality by optimizing the AP channel arrangement
and the APs to which terminals are allocated in orderto cancel out these hidden and exposed nodes [6].
With this technology, the aim is to achieve at least
double the system throughput of current networks bysystematically optimizing the diversity of wirelessaccess technologies that constitute 5G (including
wireless LAN) in order to improve the user’s QoE.
5. Future prospects
NTT Access Network Service Systems Laboratories intends to continue developing wireless LANs as
part of the social infrastructure by working on thestandardization of high-efficiency wireless LANs,
using cooperative wireless LAN technology toincrease the system throughput, and carrying out
research and development on the application of wireless LANs to 5G.

NTT Technical Review

Feature Articles

References
[1]

[2]
[3]

B. A. H. S. Abeysekara, M. Matsui, Y. Asai, and M. Mizoguchi, “Network Controlled Frequency Channel and Bandwidth Allocation
Scheme for IEEE 802.11a/n/ac Wireless LANs: RATOP,” Proc. ofIEEE PIMRC 2014 (the IEEE 25th Annual International Symposium
on Personal, Indoor, and Mobile Radio Communication), Washington,
DC, USA, Sept. 2014.
5GMF, “5G Mobile Communications Systems for 2020 and Beyond,”
5GMF White Paper, version 1.01, 2016.
3GPP TS36.300 v14.0.0: “Evolved Universal Terrestrial Radio
Access (E-UTRA) and Evolved Universal Terrestrial Radio AccessVol. 15 No. 3 Mar. 2017

[4]
[5]
[6]

Network (E-UTRAN); Overall description; Stage 2,” 2016.
3GPP TS23.402 v14.1.0: “Architecture Enhancements for Non-3GPP
Accesses,” 2016.
RFC 6824: “TCP Extensions for Multipath Operation with Multiple
Addresses,” 2013.
A. Inoki, B. A. H. S. Abeysekara, M. Matsui, T. Ichikawa, M. Mizoguchi, A. Kishida, A. Yamada, Y. Morihiro, T. Asai, and Y. Okumura,“Starved Wireless LAN Device Identifying Scheme Using Beacon
Signals,” Proc. of WPMC 2016 (the 19th International Symposium on
Wireless Personal Multimedia Communications), Shenzhen, China,
Nov. 2016.

6

Feature Articles

Kenichi Kawamura

Senior Research Engineer, Wireless Access
Systems Project, NTT Access Network Service
Systems Laboratories.
He received a B.E. in electrical engineeringand electronics and an M.E. in informatics from
Kyoto University in 1999 and 2001. He joined
NTT Access Network Service Systems Laboratories in 2001 and engaged in research and development (R&D) of high-quality wireless communications. He is currently involved in developing
services for public WLANs. He is a member of
the Institute of Electronics, Information andCommunication Engineers (IEICE).

Koichi Ishihara

Research Engineer, Wireless Access Systems
Project, NTT Access Network Service Systems
Laboratories.
He received a B.E., M.E., and Ph.D. in communications engineering from Tohoku University, Miyagi, in 2004, 2006, and 2011. He joined
NTT Network Innovation Laboratories in 2006.
His current research interests include digital signal transmission techniques for broadband wireless and fiber-optic communication systems. He
received the Young Engineer’s Award fromIEICE in 2009 and the IEICE RCS (Radio Communication Systems) Active Research Award in
2011. He is a member of IEICE and IEEE.

Junichi Iwatani

Research Engineer, Wireless Access Systems
Project, NTT Access Network Service Systems
Laboratories.
He received a B.E. and M.E. in electronics
engineering from the University of Tokyo in
1994 and 1996. Since joining NTT Wireless
Systems Laboratories in 1996, he has been
engaged in R&D of wireless access systems. In
2006, he moved to NTT Service Integration
Laboratories, where he researched next-generation networks. In 2010, he joined NTT Communications, where he was involved in developing
global network services. He has been with NTT
Access Network Service Systems Laboratories
since 2013. He is currently researching andworking on standardization of high-efficiency
wireless LAN systems. He is a member of IEICE
and IEEE.

Shoko Shinohara

Research Engineer, Wireless Access Systems
Project, NTT Access Network Service Systems
Laboratories.
She received a B.S. in information and electrical engineering from Kyoto Institute of Technology in 2007 and an M.S. in information engineering from Nara Institute of Science and Technology in 2009. She joined NTT Network Innovation Laboratories in 2009 and moved to NTT
Access Network Service Systems Laboratories in
2014. She is currently researching wireless LAN
access control technology. She is a member of
IEICE and IEEE.

7

Mamoru Akimoto

Senior Research Engineer, Wireless Access
Systems Project, NTT Access Network Service
Systems Laboratories.
He received a B.E. and M.E. from Tsukuba
University, Ibaraki, in 1989 and 1991. He joined
NTT Radio Communication Systems Laboratories (now NTT Access Network Service Systems
Laboratories) in 1991. He is currently engaged in
R&D of wide-area wireless access systems. He isa member of IEICE.

Yasuhiko Inoue

Senior Research Engineer, Wireless Access
Systems Project, NTT Access Network Service
Systems Laboratories.
He received a B.E. and M.E. in electrical engineering from Keio University, Kanagawa, in
1992 and 1994. He joined NTT Wireless Systems
Laboratories in 1994, where he engaged in R&D
of a personal handy-phone packet data communication system. He is currently involved in R&D
of IEEE 802.11 wireless LAN systems and theirstandardization activities. He was a visiting
scholar at Stanford University, USA, from 2005
to 2006. He received the Young Engineer Award
from IEICE in 2001 and a contributor award forthe IEEE 802.11j standard from the IEEE Standards Association in 2004. He is currently serving as the secretary of the IEEE 802.11 Task
Group ax. He is a senior member of IEICE and amember of IEEE.

Takeo Ichikawa

Senior Research Engineer, Supervisor, Wireless Access Systems Project, NTT Access Network Service Systems Laboratories.
He received a B.E. and M.E. in electrical engineering from Waseda University, Tokyo, in 1991
and 1993. Since joining NTT in 1993, he has
been involved in R&D of PHS-based packet
systems and high-speed wireless LAN systems.
He received the Young Researcher’s Award fromIEICE in 1999. He is a member of IEICE andIEEE.

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 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

Feature Articles: Wireless Access Technology to Meet
Diverse Needs from IoT/M2M to Broadband

Development Efforts on Wide Area
Wireless Access to Accelerate Its Use
for M2M
Nobuaki Mochizuki, Hajime Katsuda, Yosuke Fujino,
Kazunori Akabane, Hideki Nagaoka, Takashi Murao,
and Shinji Yonesaka
Abstract

The Internet of Things (IoT), in which everything is connected to the Internet, has recently been
attracting a lot of attention. NTT has been undertaking various initiatives such as research and development and field trials to realize IoT services. This article describes the requirements of IoT/M2M(machine-to-machine) services and outlines the low power wide area (LPWA) network. It also introduces a field trial utilizing the LPWA network implemented by NTT WEST and a wide-area and highcapacity radio relay system developed by NTT.
Keywords: IoT/M2M, LPWA, smart metering

1. Introduction
Machine-to-machine (M2M) communication systems have been attracting a lot of attention recently.
The concept of the Internet of Things (IoT) involves
connecting everything around us to the Internet [1].
The information that comes from these multiple components will be collected and analyzed in a network.
This information will then be utilized as big data andis expected to lead to the creation of new value services.
In an IoT/M2M service, the terminals to be connected are dotted everywhere, and it is necessary toaccommodate them efficiently. Examples are thesmart metering of electric power and gas meters currently being introduced as well as agriculture sensors.
Since their terminals will be widely dispersed, awide-area but high-capacity network is necessary.
NTT WEST is working on the creation of IoT usage
scenes and the development of new services in conjunction with partners in various fields. A key development is field trials with a low power wireless1

access (LPWA) network that adopts LoRaWAN*1.
NTT Network Innovation Laboratories has developed
a wide-area and high-capacity radio relay system fora smart metering service and has concluded tests on aliquefied petroleum (LP) gas centralized monitoring
system.
2. Requirements of M2M services andoverview of LPWA
M2M services clearly create different wirelessloads from those of personal communication devices
such as cellular phones. Most M2M service terminals
cannot move independently and thus remain stationary for long periods. The typical communication
environment expected for M2M services is depicted
in Fig. 1. We assume the wireless environment has
the following three requirements:*1 LoRaWAN: An IoT communication standard formulated bymembers of the LoRa Alliance such as Semtech Corporation(semiconductor supplier) and IBM Corporation. LoRaWAN is atrademark of Semtech Corporation.

NTT Technical Review

Feature Articles

Changes in propagation
environment

High-density environmentLong-time use

Homeelectronics
3–7 years

Building blocking
radio waves

Movement ofmany terminals

SimultaneoustransitMetering devices
10 yearsSocial infrastructure
10+ years

Wide variety
of use places
Wide areaconnection

High capacity

Long battery life

Fig. 1. Typical communication environment expected for IoT/M2M services.

1)	Wide area: A terminal cannot be moved easily
even if its wireless environment is inadequate.
For example, the access point and terminal may
not have a direct line of sight. Thus, communication must be established even in difficult circumstances.
2)	High capacity: It is assumed that each accesspoint may have to support many terminals thatare likely to transmit simultaneously after adisaster. Therefore, it is necessary to be able tocommunicate with many terminals.
3)	Long battery life: It is assumed that a terminal
may have no AC (alternating current) power
supply, or that some terminals will be hosted by
the social infrastructure so that their operation
will be managed over a decade-long period
without replacing the battery. Therefore, a long
battery life must be achieved while maintaining
low power consumption.
To satisfy these requirements, wireless communication standards have been formulated to suit various
usages [2]. For 3GPP (3rd Generation Partnership
Project), standardization of Cat. NB1 and Cat. M
were completed in 2016 as IoT standards, and development is now underway for their commercialization.
LPWA using the sub-gigahertz band, which is theunlicensed band, is beginning to be used mainly inEurope and the United States. Since the sub-gigahertzband is lower than the 2.4-GHz band used in wirelessVol. 15 No. 3 Mar. 2017

LANs (local area networks), it has less attenuationand offers high diffraction performance, so it cancover a wide area with one base station. It can alsoachieve low power consumption of terminals and canaccommodate many terminals. LoRaWAN [3] andSigfox [4] are major LPWA networks currently being
widely used in Europe. They are used for infrastructure monitoring and other services. In Japan, theAssociation of Radio Industries and Businesses(ARIB) issued standard STD-T108 [5] for the 920MHz band as an unlicensed band in 2012. It is usedby Wi-SUN (Wireless Smart Utility Network). NTT
Network Innovation Laboratories has developed a920-MHz band radio relay system for a smart metering service. The characteristics of the major LPWA
networks are described below and summarized inTable 1.
(1) LoRaWAN
LoRaWAN achieves long distance communicationof up to 2 km by using a variable transmission ratefrom 0.3 to 50 kbit/s. In addition, it offers highaccommodation performance by multiplexing signalsof different transmission rates using chirp spreading.
Three protocols, Class A to C, are prescribed, but
Class A is the main type being considered. Class A
achieves a battery life of more than 10 years by
eliminating the periodic reception operation of theterminal and sending downlink data together with theuplink response. Since downlink communication is2

Feature Articles

Table 1. Comparison of LPWA networks.
LoRaWAN (Class A)

SigfoxRadio relay system (NTT)Transmission speed

0.3−50 kbit/s

100 bit/s

10 kbit/s

Transmission
distance*1

About 2 km

About 3 km

About 1 km

High capacity
technology

Multiplexes signals byusing chirp spreading
methodMultiplexes signals byrandomly hopping
frequencies

Reduces control signal bymanaging transmission
timingResponse time

Uplink: several seconds
Downlink: best effort*2

Uplink: several seconds
Downlink: best effort*2

Uplink/Downlink: several
seconds

Battery life

More than 10 years

More than 10 years

More than 10 years

Mainly uplink and very
low frequency and low
data volume

Bidirectional comm.
with high responsewith similar uplink anddownlink frequency

Suitable service

Mainly uplink, or
control only bydownlink*1 Base station height: 30 m; Terminal installed outdoors; Deterioration rate: 0.1%
*2 Downlink data sent with response of uplink

best effort, it is assumed that it will be applied to services that mainly collect sensor information (such as
infrastructure management, logistics, and agriculture) and services that realize control only throughdownlink communication.
(2) Sigfox
Sigfox achieves long distance communication of
about 3 km by lowering the transmission rate to 100
bit/s. It achieves high accommodation performanceby randomly hopping frequencies at the time of transmission and by multiplexing a large number of uplink
data flows in the same frequency band. Furthermore,similar to Class A of LoRaWAN, the downlink communication is best effort, thereby realizing low power
consumption of terminals. It is assumed that it isapplied to services using uplink communication andvery low frequency and low data volumes (such as
state monitoring).
(3) Radio relay system
The radio relay system developed by NTT Network
Innovation Laboratories is a specification guaranteeing the downlink response time. For this reason, it
will be used for bidirectional communication, in
which the frequencies of uplink communication anddownlink communication are about the same andhigh response performance is required (services
include smart metering, equipment control, etc.).
The communication requirements of M2M services
are diverse. For example, for infrastructure management and environmental monitoring, the main traffic
consists of uplink communication that collects sensor
information. Also, in applications where a delay ofmore than several seconds is allowable, it is possible
to perform downlink communication by responding3

to periodic uplink communication. Meanwhile, forsmart metering, high-response bidirectional communication is necessary in order to acquire meter information, determine the state, and control the meter. To
satisfy the communication requirements of thesediverse M2M services, it is important to select anduse the appropriate wireless system for each service
from among the wireless systems available.
3. NTT WEST field trial
In these trials, NTT WEST provides an LPWA network that uses LoRaWAN, whose specifications havebeen formulated and promoted by the LoRa Alliance,with which approximately 400 IoT-related companies
are affiliated [6]. A depiction of the trials is shown in
Fig. 2. The trial partners prepare devices, sensors,
platforms, and other elements on their own and connect them to the LPWA network provided by NTT
WEST. The trials are being conducted in order toverify functions and operational requirements for IoT
services on LoRaWAN and to create usage scenes of
IoT services and verify business possibilities. Semtech Corporation (USA), which is in the process of
formulating Japanese standards of LoRaWAN, isproviding information on the outline and technicalrequirements of LoRaWAN.
The first trial was conducted in Yasu City, Shiga
Prefecture. The trial implemented agricultural remotecontrol systems such as those for water supply management, water level, and temperature control utilizing multifunctional automatic water faucets. Due tolarge-scale farming and conversion of single cropping fields (rice paddies etc.) into multiple cropping
NTT Technical Review

Feature Articles

Application
serverData utilization(analysis)

Internet
Application programming interface
Connection interface

Data

Authentication
functionManagementof terminaland circuitCore
network

Relay servers

LPWA networks(offered by NTT WEST)

Access network

Sensoraccommodating
gateway

Connection interface
IoT devices,sensors, etc.

Fig. 2. Elements of field trial.

fields, labor saving in water management anddemand-led water use have become necessary. In
response to this situation, the feasibility of water
management to suit different forms of agricultural
management will be investigated. The intent of thecommunication carrier is to verify radio wave propagation characteristics and power saving compared toexisting communication methods such as Wi-Fi*2.
4. Radio relay system suitable forsmart metering
Smart metering systems that can automate monthly
meter reading and enable visualization of electricity
consumption in conjunction with HEMS (homeenergy management systems) are now being introduced by various electric power companies [7]. A gas
smart metering system for the automation of gas
meter reading and visualization of gas usage is being
actively targeted. The U-Bus Air system has beenstandardized by the nonprofit organization Japan
Vol. 15 No. 3 Mar. 2017

Utility Telemetering Association in order to achieve agas smart metering system [8].
In response, we have developed a wide-area andhigh-capacity IoT/M2M radio relay system that suits
the gas smart metering system. The system configuration is shown in Fig. 3. The system uses a multi-layer
relay system; the lower layer utilizes the user network
to connect directly with the user terminal such as thegas meter, and the relay layer is our developed radio
relay system. The system offers a flexible area designin conjunction with the user network. Furthermore,since a large number of user terminals can be aggregated, it is possible to economically connect moreuser terminals to the server with one public line suchas 3G or LTE (Long Term Evolution).
However, the following problems with the relay
layer system need to be solved in order to satisfy thecommunication requirements of IoT/M2M.
1)	Compatibility of a broadening area and high
*2 Wi-Fi is a registered trademark of Wi-Fi Alliance.

4Feature Articles

Meter(user terminal)

User networks
such as U-Bus Air system(lower layer)

Radio relay system(relay layer)• Wide area (about 1 km max.)• Terminal power saving(10-year battery life)* Operating condition: assuming smart meter
communication once a day

Wirelessterminal

Wirelessterminal

Relayterminal
Relay basestationWirelessterminal

Relayterminal

3G/LTE
terminal

3G/LTE line

ServerRelay
terminal

Fig. 3. System configuration of radio relay system.

capacity accommodation
2)	Power savings from the relay terminals
In order to broaden the area of the relay layer, it isnecessary to lower the transmission speed. The
amount of data that can be transmitted decreases dueto the lower transmission speed, so the accommodation capacity of the relay layer decreases. Furthermore, transmission and reception times of the relay
terminal will increase due to the reduced transmission speed, which will increase the power consumption. For that reason, it is necessary to strike the rightbalance between a wide area, high capacity, and low
power consumption.
Intermittent operation is required in order to maximize power savings. The radio relay system that performs intermittent operation normally needs to send
and receive control signals in order to maintain synchronization between the relay base station and therelay terminal. However, as the number of relay terminals increases, interference due to control signals
from the relay terminal increases. To avoid this interference, it is necessary to reduce the transmission
frequency of the control signal from the relay terminal. However, if the transmission frequency of control signals is reduced, the transfer delay will increase
and fewer applications will be supported.
In order to reduce the transmission frequency of thecontrol signal without degrading the transfer delay,
the radio relay system uses a method that enables therelay base station to manage the transmission timing

5of all subordinate relay terminals. As a result, interference can be reduced, so throughput is improved, as
is capacity. Furthermore, since the transmission frequency of the control signal decreases, power savingof the relay terminal becomes possible.
Under the assumption that each user terminal generates communication traffic of 1 kbyte per day, eachrelay base station can support up to 2000 user terminals. Furthermore, each relay terminal can have abattery life of 10 years. As a result, the radio relay
system can satisfy the requirements of the LP gas
centralized monitoring service (wide area with low
user density) and the urban gas smart metering service (with many gas meters). In addition to gas smart
metering, it is also being applied to other M2M services that require interactivity such as social infrastructure control.
5. Future overview
NTT WEST is planning to consider the usage
scenes of the LPWA network in various IoT services
based on the network technology know-how obtained
from the field trials. NTT Network Innovation Laboratories will continue its research and development
activities to further enhance the wide area, highcapacity, and power savings of radio relay systems, inorder to enable them to be shared by various M2M
services beyond gas smart metering (Fig. 4).

NTT Technical Review

Feature Articles

Cellular access
point

Server
Cloud

lar

Cellu

• Efficiently accommodates many terminals
and traffic• Sharing by various IoT/M2M services

FiberCarrier network

Relay base station

Relay
terminal

Relay layer

Radio relay network

Integration

Lower layer(user network:
Wi-SUN etc.)

Socialinfrastructure
management

Watching overthe elderly

Vehicle, facility
management

Environment,
disasters

Agriculture

Distribution

Fig. 4. Sharing by various M2M services.

References
[1]

[2]
[3]

Ministry of Internal Affairs and Communications, Japan, “White
Paper on Information and Communications in Japan 2015,” 2015.
http://www.soumu.go.jp/johotsusintokei/whitepaper/eng/
WP2015/2015-index.html“Special Feature: Waiting for 5G Is slow! New and Old Confrontation
over IoT Communication,” Nikkei Communications, No. 627, pp.
12–15, 2016 (in Japanese).
LoRa Alliance, https://www.lora-alliance.org/

Vol. 15 No. 3 Mar. 2017

[4]
[5]

[6]
[7]
[8]

Sigfox, https://www.sigfox.com/
ARIB STD-T108: “920MHz-band Telemeter, Telecontrol and Data
Transmission Radio Equipment,” Version 1.0, 2012.
http://www.arib.or.jp/english/html/overview/doc/5-STD-T108v1_0E2.pdf
Press release issued by NTT WEST on June 29, 2016 (in Japanese).
http://www.ntt-west.co.jp/news/1606/160629a.html
Ministry of Economy, Trade and Industry, “Welcome to ‘Smart Community,’” METI Journal, October/November 2011 (in Japanese).
Japan Utility Telemetering Association, http://www.teleme-r.or.jp/ubus/en/index.html

6Feature Articles

Nobuaki Mochizuki

Senior Research Engineer, Wireless Systems
Innovation Laboratory, NTT Network Innovation
Laboratories.
He received a B.E. and M.E. from Tohoku
University, Miyagi, in 1992 and 1994. He joined
NTT in 1994 and engaged in research and development (R&D) of a modulation/demodulation
scheme for a broadband 5-GHz wireless accesssystem and synchronization scheme for coded
OFDM (orthogonal frequency division multiple
access). He is currently engaged in developing
M2M wireless access systems. He is a member of
the Institute of Electronics, Information andCommunication Engineers (IEICE).

Hajime Katsuda

Hideki Nagaoka

Project Manager, Innovative Development
Promotion Group, NTT WEST R&D Center.
He received a B.E. and M.E. from Osaka Institute of Technology in 1994 and 1996. He joined
NTT WEST in 1996. He is currently engaged indeveloping LPWA wireless access systems.

Takashi Murao

Wireless System Innovation Laboratory, NTT
Network Innovation Laboratories.
He received a B.E. and M.E. from Tohoku
University, Miyagi, in 2011 and 2013. Since joining NTT Network Innovation Laboratories in
2013, he has been involved in R&D of wirelesstechnology for M2M wireless access systems. He
is a member of IEICE.

Assistant Manager, Innovative Development
Promotion Group, NTT WEST R&D Center.
He received a B.E. and M.E. from Okayama
University in 2004 and 2006. He joined NTT
WEST in 2006. He is currently engaged in developing LPWA wireless access systems.

Yosuke Fujino

Shinji Yonesaka

Research Engineer, Wireless Systems Innovation Laboratory, NTT Network Innovation Laboratories.
He received a B.E. and M.E. in electrical andelectronic engineering from Shizuoka University
in 2002 and 2004. He joined NTT Network Innovation Laboratories in 2004 and engaged inresearch on multiuser detection for a multiantenna, multi-user wireless system. He is currently researching a low latency and low power
MAC (media access control) protocol for IoT
wireless systems. He is a member of IEICE.

User Service Group, NTT WEST R&D Center.
He received a B.E. and M.E. from Osaka University in 2009 and 2011. He joined NTT WEST
in 2011. He is currently engaged in developing
LPWA wireless access systems.

Kazunori Akabane

Senior Research Engineer, Supervisor, Wireless Systems Innovation Laboratory, NTT Network Innovation Laboratories.
He received a B.E. and M.E. from Keio University, Kanagawa, in 1994 and 1996. He joined
NTT Wireless Systems Laboratories in 1996,
where he has been involved in R&D of wirelesspersonal communication systems and softwaredefined radio systems. He is currently engaged in
R&D of IoT/M2M wireless access systems andnear-field high-speed wireless communication
systems using the 60-GHz band. He is a memberof IEICE.

7NTT Technical Review

Feature Articles: Wireless Access Technology to Meet
Diverse Needs from IoT/M2M to Broadband

A Study on Wireless Technologies to
Improve Communication
Performance by Utilizing Multifrequency Bands
Daisei Uchida, Takafumi Fujita, Ken Hiraga,
Takashi Shimizu, and Kazunori Akabane
Abstract

The wireless access network for the Internet of Things must offer ultra-broadband communication inorder to transfer broadband data such as video data to be displayed for user assistance. High-reliability
and low-latency communication is also necessary to remotely and automatically control the instrumentsof vehicles and robots in real time. This article describes our study on wireless technologies designed toachieve ultra-broadband, high-reliability, and low-latency communications by utilizing new frequency
bands.
Keywords: ultra-broadband, high-reliability/low-latency, multiband

1. Introduction
The Internet of Things (IoT) is expected to enable
numerous applications including machine-tomachine (M2M) narrowband applications such as
smart metering and environment sensing. It is also
expected to enable M2M broadband applications
such as utilization of stored video data for maintenance of outdoor infrastructures, as well as vehicle
control and M2M control applications such as remotecontrol of instruments. For example, to analyze driving data captured by cars as big data in cloud servers,
an ultra-broadband wireless access system is necessary to achieve sufficiently short data transfer speeds.
Moreover, high-reliability and low-latency communication will be required for autonomous vehicle driving systems that can respond to the environment surrounding the vehicle and control the vehicle in realtime. This article describes a study on wireless technologies that promise the ultra-broadband, high-reliability, and low-latency communication desired for1

the IoT.
2. Ultra-broadband wireless access system
In this section, we explain the use cases, frequency
band details, and specifications of the ultra-broadband wireless access system.
2.1 Use cases
Examples of leveraging ultra-broadband data areconsidered below and depicted in Fig. 1.
(1)	Batch transfer of log data: broadband log datasuch as driving data of cars and video data
recorded outdoors are transferred to the cloud
network.
(2)	Gathering information on the surrounding
environment: information needed for automatic vehicle and robot control such as threedimensional (3D) maps and pedestrian positioning information is provided from cloudservers just in time.
NTT Technical Review

Feature Articles(1) Batch transferring log data

(2) Providing information on
surrounds for control system(3) Downloading rich content

Fig. 1. Example of utilizing ultra-broadband data.

(3)	Downloading rich content: rich content suchas movie data and games is downloaded touser mobile devices when needed so that users
can enjoy it without accessing the Internet, forexample, on planes.
In these use cases, log data and database information saved on disk must be exchanged between thecloud database and the user anywhere and anytime
through wireless networks. This requires disk-to-disk(D2D) communication, which until now has mainly
been used to back up data on hard disks, between
multiple devices and instruments that are far fromeach other through wireless networks. We set the target wireless transmission speed for this new D2D
communication to 100 Gbit/s, which is 10 times
faster than Wi-Fi*1.
2.2 60-GHz unlicensed band
We have adopted the 60-GHz unlicensed band as
the frequency band for wireless transmission speedsof 100 Gbit/s. This unlicensed band has the advantage
of easily forming ultra-high-speed wireless accesslinks because the wireless access points can be placed
freely, and the wireless function can be installed onany device without a license. The characteristics of
the 60-GHz band are described below.
(1) Broadband bandwidth of more than 1 GHz(2)	High antenna directivity (antenna size is several centimeters)(3)	Short communication range (greater propagation loss and shielding than the microwave
band)
The large bandwidth makes it possible to achieve
the maximum transmission speed of 11.4 Gbit/s witha single stream [1]. For reference, the transmission
speed of Wi-Fi is less than 1 Gbit/s [2]. By applying
multiple-input multiple-output (MIMO) techniques,a transmission speed of 100 Gbit/s can be expected.
While the short communication range results in
Vol. 15 No. 3 Mar. 2017

Transmitting antennas

Receiving antennas

Multiple streams are transmitted simultaneously
by utilizing the differences in propagation path
lengths among antenna elements.

Fig. 2. S ystem image: using short range MIMO
techniques.

smaller communication coverage than that of the
microwave band, the high antenna directivity enables
interference signals from other stations to be reducedby both propagation loss and antenna directivity.
Therefore, a stable wireless transmission speed of
100 Gbit/s is expected regardless of the other stations.
To achieve a transmission speed of 100 Gbit/s in the
60-GHz band, more than ten streams must be simultaneously transmitted via space division multiplexing
because the maximum transmission speed with asingle stream is 11.4 Gbit/s. We consider short range
MIMO to be one of the most promising approaches tomeet the requirements. The system image is shown in
Fig. 2.
The MIMO technique is often used in multipathrich environments, and with space division multiplexing, the number of streams can equal the number of*1 Wi-Fi is a registered trademark of Wi-Fi Alliance.

2

Feature Articles

Table 1. Standards for 60-GHz band.
(For reference)
LTE-advanced(For reference)
TransferJet(For reference)
IEEE802.11ac

Frequency band

800 MHz/1.5 GHz/1.7 GHz/2 GHz/3.5 GHz

4.5 GHz

5 GHz

60 GHzChannel bandwidth(Without channel
bonding)

1.4, 3, 5, 10, 15,
20 MHz560 MHz

20 MHz2.16 GHz

Channel bonding(Channel bandwidth)

Available (*1)

Not available

Available(Up to 20 MHz x 8)

Not available

Available(Up to 2.16 GHz
× 4)

Available(To be discussed)

MIMO(Number oftransmitted streams)

Available(Up to 4)

Not available

Available(Up to 8)

Not available

Available(Up to 16)

Available(To be discussed)

Transmission speed

Up to 375 Mbit/s(*2)

560 Mbit/s

866 Mbit/s (*3)
(Up to 6.93
Gbit/s)Up to 4.6
Gbit/s (*4)

11.4 Gbit/s (*5)
(Up to 138
Gbit/s)About 100 Gbit/s(To be discussed)

Main use cases

Cellular

Close proximity
communication

Spot

Short range

Close proximity
communication(Higher speed
than TransferJet)

Close proximity
communication;
Short range;
BackhaulProgress instandardization
activities

Completed

Completed

Completed

Completed

To be completed
in 2017

To be completed
in 2019

IEEE802.11ad

IEEE802.15.3e

IEEE802.11ay*1: Carrier aggregation is also possible.
*2: Specifications of Premium4G services of NTT DOCOMO in September 2016.
*3: The user terminal is a smartphone, assuming that the number of antennas is 2 and channel bandwidth is 20 MHz x 4.
*4: When single-carrier physical layer is used.
*5: When single streams are transmitted.

multipath waves. However, general MIMO techniques cannot support more than ten streams because
it is difficult to ensure more than ten multipath waves
regardless of the propagation environment.
The short range MIMO technique is used in very
close proximity environments in which the direct
propagation path lengths between transmitter andreceiver antennas are different from each other. The
multiple streams can be simultaneously transmitted
on direct paths by utilizing different phases on the
propagation paths. When the linear antenna array isadopted as the MIMO antenna configuration, thecarrier-to-interference ratio (CIR)*2 degrades as thenumber of multiple streams increases. Moreover, theCIR strongly depends on the relative positioning of
the transmitter and receiver, so the CIR significantly
degrades when the positioning is not optimum.
To solve these problems, we are studying the use of
two-dimensional antenna arrays and multiple MIMO
configurations, the optimal design of those configurations, and adaptive selection of the MIMO configuration depending on the number of multiple streams
and the positioning relationship at the time of use.

3

2.3 Standard specifications in 60-GHz band
The standard specifications in the 60-GHz band are
introduced in this section. The key standards are listed in Table 1. IEEE802.15.3e is the next-generation
standard specified by the Institute of Electrical andElectronics Engineers (IEEE). It has higher transmission speeds than TransferJet*3 [3], and its target use isclose proximity communication. IEEE802.11ad
offers a higher transfer speed than Wi-Fi [2], andIEEE802.11ay is the next-generation standard withhigher transmission speeds than IEEE802.11ad [4].
The standard IEEE802.11ad was completed in 2013,
but IEEE802.15.3e and IEEE802.11ay are still inprogress. All standards adopt the channel bandwidthof 2.16 GHz. Channel bonding*4 and multiple stream
transmission (with a MIMO function) will be standardized in IEEE802.15.3e and IEEE802.11ay. NTT*2 CIR: Ratio of the received powers of desired signal to interference signal.
*3 TransferJet: Short range wireless technologies for close proximity communication. TransferJet is a trademark licensed by theTransferJet Consortium.
*4 Channel bonding: Combines multiple channels and transmits
them as one.

NTT Technical Review

Feature Articles

Network Innovation Laboratories is active in contributing to IEEE802.15.3e and has proposed the MIMO
function to achieve a transmission speed of 100
Gbit/s and a faster link setup of 0.2 seconds.
3. High-reliability and low-latency
communications
The IoT is expected to support various social
activities. Thus, high-reliability and low-latency
communications will be demanded from the viewpoint of productivity and efficiency, especially inindustrial fields. For monitoring and control systems
in factories for example, the transfer of application
data between servers and devices should be completed within the required time periods in order toacquire real-time sensing information from the field
and to give appropriate feedback for control.
Proprietary industrial Ethernet specifications suchas Ethernet for Control Automation Technology(EtherCAT)*5, which combines a standardized physical layer and a proprietary MAC (media access control) layer, are available. Another approach that isprogressing is Time-Sensitive Networking, which
extends the present Ethernet specifications. In theautomotive industry, automotive Ethernet standards
are being discussed such as IEEE 802.3bw [5] forbroadband low-latency communication for vehicle
harnesses. Accordingly, wireless communications
should match the expectations of high-reliability andlow-latency communications.
3.1 Advanced ITS
The development of an advanced intelligent transport system (ITS) is continuing in Japan, and industry, government, and academia are promoting thedevelopment of autonomous vehicle driving technologies for 2030 [6]. Any autonomous vehicle driving
system requires cooperative technologies such aspredicting events using vehicle-to-vehicle/roadsidewireless communications and artificial intelligence,as well as self-driving technologies such as collision
avoidance using on- and off-board vehicle sensors.
One cooperative ITS technology that has alreadybeen launched is the safe driving support service
called ITS Connect, which uses the 760-MHz band inJapan.
In North America and Europe, a similar DSRC
(Dedicated Short Range Communications) service
will be introduced in the 5.9-GHz band in the next
several years. Both of these systems adopt IEEE
802.11p wireless local area network (LAN) standards
Vol. 15 No. 3 Mar. 2017[1] for vehicle-to-vehicle/roadside communications.
Meanwhile, the development and standardizationof cellular communication systems called LTE (LongTerm Evolution) V2X [7] are also being discussed.
Since LTE V2X can utilize the existing LTE cellularinfrastructure, additional investment is minimal.
As shown in the case of the autonomous driving
systems, a variety of wireless communication technologies in both licensed and unlicensed bands isbeing studied. Of course, existing wireless LANs arealso promising radio access technologies (RATs) for
these systems. The characteristics or performance of
wireless communications depends on the radio environments such as the frequency band, the RAT
adopted, network infrastructure availability, terminal
device position, and radio channel conditions such aswhether there is interference. Here, it is assumed thataccess point 1 with RAT-a running on band A andaccess point 2 with RAT-b running on band B areavailable (Fig. 3). Even if RAT-a has good performance (such as transmission speed or latency performance) as a RAT, its actual communication performance (such as throughput) will fall dramatically due
to the presence of many terminals, heavy interference, or obstacles in the channel. In contrast, RAT-b
offers lower ultimate performance but is more consistent. This suggests that establishing more reliable
radio connections requires the optimal RAT to be
selected from several candidates according to themultiband radio environments.
3.2 M
 ultiband propagation evaluation technologies
NTT Network Innovation Laboratories is promoting the study of multiband propagation evaluation
technologies and applied wireless access technologies. An overview is shown in Fig. 4. First, the radio
propagation characteristics for each band (channel)
are evaluated by simulating radio intensity and arrival
time of multipath waves. For this, we consider 3D
data of buildings and objects around the access points
and terminal devices (Fig. 4(a)). Then the propagation results are combined to permit network simulations of different RATs for each band. These canevaluate overall system parameters such as processing latency and packet loss characteristics, taking into
consideration the connection density of devices andother interference effects (Fig. 4(b)). These evaluation results make it possible to design multiband*5 EtherCAT is a registered trademark and patented technology, licensed by Beckhoff Automation GmbH, Germany.

4

Feature Articles

Access point 1- Band A (RAT-a)- Many accommodated
devices- Large interference- Many obstacles on radio
channelInterference source

Access point 2- Band B (RAT-b)- Few accommodated
devices- Small interference- Few obstacles on radio
channelInterference

Radio links(Band A)

Radio links(Band B)

Obstacles(e.g., building)
IoT devices
Other accommodated
devicesOther
accommodated
devicesFig. 3. Multiband utilization approach for high-reliability and low-latency communication.

(a) 3D model around intersections (left) and simulated results of radio intensity (right)

Single RAT

Throughput

Throughput

Latency

Latency

Packet loss

Multiple RATs

Packet loss(b) Combined simulation for radio propagation and network performanceFig. 4. Multiband propagation evaluation technologies.

wireless systems that make maximum use of multiple
bands and the diversity in available RATs.

4. Conclusion
This article described work on wireless network
technologies to realize ultra-broadband, high-reliability, and low-latency communication for the future

5

NTT Technical Review

Feature Articles

IoT. For ultra-broadband capacity, short range MIMO
techniques are being applied to achieve the wirelesstransmission speed of 100 Gbit/s in the 60-GHz unlicensed band. The effective use of multi-frequency
bands and RATs is being investigated to achieve highreliability and low-latency communication, and astudy is underway on multiband propagation evaluation technologies and applied wireless access technologies.
In the future IoT, both use-case scenarios andrequirements will become more diversified. We aim
to establish wireless technologies to meet theserequirements.

Daisei Uchida

Senior Research Engineer, Wireless Systems
Innovation Laboratory, NTT Network Innovation
Laboratories.
He received a B.E and M.E. in applied physics
from Tokyo Institute of Technology in 1994 and1997. Since joining NTT Wireless Systems Laboratories in 1997, he has worked on the researchand development (R&D) of SDM-COFDM(space division multiplexing - coded orthogonalfrequency division multiplexing) systems for
wireless LANs and wireless access systems for
low-power wide area networks. He is currently
engaged in R&D of an ultra-broadband wirelessaccess system by utilizing unlicensed millimeterwave bands. He received the Young Engineers
Award from the Institute of Electronics, Information and Communication Engineers (IEICE) in2001. He is a member of IEICE.

Takafumi Fujita

Senior Research Engineer, Wireless Systems
Innovation Laboratory, NTT Network Innovation
Laboratories.
He received a B.E. and M.E. from the University of Tokyo in 1999 and 2001. He joined NTT
in 2001 and engaged in R&D of wireless LANs,
low-power wide-area networks, and networkarchitectures for M2M communications. He iscurrently involved in R&D of high-reliability andlow-latency radio communication systems for
IoT. He is a member of IEICE.

References
[1]
[2]
[3]
[4]
[5]
[6]

[7]

IEEE P802.15.3e/D05: “IEEE Draft Standard for High Data Rate
Wireless Multi-Media Networks,” 2015.
IEEE Std 802.11-2016: “Part 11: Wireless LAN Medium AccessControl (MAC) and Physical Layer (PHY) Specification,” 2016.
TransferJet, https://www.transferjet.org/index.html
IEEE 802.11ay, http://www.ieee802.org/11/Reports/tgay_update.htm
OPEN Alliance (One-Pair Ether-Net) Special Interest Group, http://
www.opensig.org/
The Strategic Headquarters for the Promotion of an Advanced Information and Telecommunications Network Society, “Public-Private
ITS Initiative/Roadmaps 2016,” May 2016.
http://japan.kantei.go.jp/policy/it/2016/itsinitiative_roadmaps2016.
pdf
Press release issued by 3GPP on March 27, 2015.
http://www.3gpp.org/news-events/3gpp-news/1675-lte_automotive

Takashi Shimizu

Senior Research Engineer, Supervisor, NTT
Network Innovation Laboratories.
He received a Dr.E. in electrical engineeringfrom Tokyo Institute of Technology in 1996.
From 2011 to 2014, he was affiliated with theresearch laboratory at NTT DOCOMO, INC. andwas engaged in R&D of network virtualization.
In 2014, he returned to NTT Network Innovation
Laboratories, where he has been researching 5G
systems, focusing on network convergence. He isa member of IEEE, the Association for Computing Machinery, and IEICE.

Kazunori Akabane

Senior Research Engineer, Supervisor, NTT
Network Innovation Laboratories.
He received a B.E. and M.E. from Keio University, Kanagawa, in 1994 and 1996. He joined
NTT Wireless Systems Laboratories in 1996,
where he has been involved in R&D of wirelesspersonal communication systems and softwaredefined radio systems. He is currently engaged in
R&D of IoT/M2M wireless access systems andnear-field high-speed wireless communication
systems using the 60-GHz band. He is a memberof IEICE.

Ken Hiraga

Research Engineer, Wireless Systems Innovation Laboratory, NTT Network Innovation Laboratories.
He received a B.E., M.S., and Ph.D. in electronics and information engineering from Hokkaido University in 2003, 2005, and 2013. Since
2005, he has been involved in research and standardization of high-speed wireless systems. He
received Young Engineers Awards from IEICE in2010 and from IEEE AP-S Tokyo Chapter in2012. He is a member of IEICE and IEEE.

Vol. 15 No. 3 Mar. 20176

Regular Articles

An Interpersonal Sentiment
Quantification Method Applied toWork Relationship Prediction
Miyuki Imada, Kei Hirose, Manabu Yoshida,
Sun Yong Kim, Naoya Toyozumi, Guillaume Lopez,
and Yutaka Kano
Abstract

For a business to be successful, it is important for people in the business to consider how other people
feel, that is, to consider interpersonal sentiment. Our research goal is to quantitatively predict thestrength of interpersonal sentiment by analyzing a small amount of data on office employees, forexample, their gender or age group, and data on events such as giving positive feedback on work done
and sexual or power harassment without directly asking someone about their change in sentiment. In thisarticle, we propose an interpersonal-sentiment-changing model for this quantification and propose two
new analysis methods for developing prediction formulas. These methods can be used even if 90% of
data is missing and in environments in which it is difficult to gather data in a comparatively short time.
We also implement two visualization systems to predict how interpersonal sentiment changes for each
event based on actual office data.
Keywords: interpersonal-sentiment prediction, sparse regression, factor regression, visualization,
missing data, personal relationships in offices

1. Introduction
For a business to be successful, it is important forthe people involved to consider a mental model forwork relationships. Let us consider one office scenario (Fig. 1). There are two people, a boss (Boss)and a subordinate (Mike). The relationship between
the two is normally positive. One day, Boss yelled atMike in front of everyone. Boss believed that being
strict is important for guiding subordinates and gains
a sense of superiority by acting in this manner toward
Mike. However, Mike feels he is being harassed andfeels contempt toward Boss. Thus, Boss and Mike
have opposite interpersonal sentiments, and thisaffects the other people in the office. These unmatchedemotions can negatively affect relationships if nothing is done. Therefore, it is important to make Boss1

aware that Mike feels contempt toward him in these
instances. In other words, Mike’s interpersonal sentiment for Boss can be predicted, and a function isneeded that conveys Mike’s sentiments to Boss.
In this article, we describe a way to predict thestrength of interpersonal sentiment quantitatively. We
statistically predict the strength of interpersonal sentiment by using as little data as possible about office
employees, for example, just their gender or age
group, without directly asking them whether or nottheir sentiment has changed. We describe related
work and our motivation for this research in section
2. In section 3, we define the interpersonal sentimentdiscussed in this article. We give an overview of ourapproach in section 4. In section 5, we explain the
proposed interpersonal-sentiment-changing model,
and in section 6, we describe how to select investigation
NTT Technical Review

Regular Articles

Being strict isimportant for
guiding
subordinates.

Feeling ofsuperiority

Contempt

The boss always
makes harshcomments.

Unmatched

Making Boss aware

Subordinate Mike

Boss

Fig. 1. One office scenario.

items in actual environments through statistical analysis using large-scale data. In section 7, we deriveprediction formulas using data from target offices.
We evaluate and discuss the estimation accuracy ofinterpersonal sentiment using these analysis methods.
We explain two types of visualization systems foroffice managers and for office employees in section
8. Finally, we give concluding remarks in section 9.
2. Related work and motivation for this research
In order for businesses that focus on human relationships to succeed, the businesses cannot ignore
mental information such as whether people like or
dislike something. They also must focus their efforts
on a wide range of people (e.g., office employees,
students, and teachers) and develop practical interpersonal-sentiment-estimation tools. Interpersonalsentiment has been investigated from two viewpoints,
psychological and engineering, and extensive
research has been done on the effects of interpersonalsentiment on behavior in personal relationships, forexample, parent-child interactions, conflict, negotiation, and leadership. A framework that can accountfor current findings and guide future research, called
the emotions-as-social-information model, has been
proposed [1, 2]. Many psychology studies have been
conducted from micro- and internal viewpoints andhave involved fragmentary trend analysis limited tofemale students or senior citizens.
There has also been extensive engineering research
involving data analysis for automatically recording
human behavior using sensors or radio frequency
identifiers and web social data [3–5]. Emotion estimation has been investigated using voice and image
data analyses [6–8]. Such research goals can beachieved through engineering technology with whichpeople can superficially judge their own emotions byVol. 15 No. 3 Mar. 2017

sight. That is, if a person smiles but is inwardly angry,
engineering analysis is adequate in only detecting thesmile. Thus, many engineering studies have been carried out from macro- and superficial viewpoints.
However, the aspect of psychology has not been usedin the engineering field.
As mentioned above, we aim to be able to use mental information in applications targeted for humans.
However, many psychology studies involved havefocused on trend analysis rather than quantification,
so psychology research results are difficult to use inapplications involving the controlling of human relationships in the workplace. There is currently a wide
gap between psychology research and engineeringresearch. We have combined psychology with engineering using statistics, and we use these statistics for
sentiment quantification.
3. Interpersonal sentiment
In this article, we describe interpersonal sentimentas the way another person/other people feel. Interpersonal sentiment changes depending on events, and thesentiment has two main aspects, negative and positive. In psychology, interpersonal sentiment consistsof several factors [9–13]. We focus on three such factors: like-dislike, respect-contempt, and relief-fear
because they greatly concern office managers withregard to their subordinates.
4. Approach for predicting strength ofinterpersonal sentiment
We applied a four-step approach (explained below)
and used the application candidates indicated inFig. 2. With these candidates, we first clarify theinterpersonal sentiment of one-to-one (1:1) relations
in single and bi-directions. The application candidates
2

Regular Articles(1) Designing interpersonalsentiment-changing model

Four-step
approach(2) Selecting investigation items in
actual field through statistical analysis
using large-scale data
(3) Derivation of prediction
formula using actual target
office data

1:1
Single/Bi
direction
Application
candidates(4) Visualization

1:n

Human relations in office,manager training, care support,
and fortune-telling applications

n:m

Support to prevent bullying,evaluation of personal magnetism,
evaluation of meetings andpresentations, and informing ofbreach of public manners

Research phases

Evaluation between company
structures or different companies andremote-meeting support

Fig. 2. Approach used in sentiment quantification.

are human relations in offices (target for this article),manager training, care support, and fortune-telling.
The next phase involves clarification of interpersonalsentiment in one-to-many relationships (1:n), forexample, teacher to students. The application candidates for this type of relationship are prevention ofbullying, evaluation of personal magnetism, evaluation of meetings or lectures, and notifying people ofbreaches in public manners. In the final phase, theapproach is expanded to many-to-many relationships(n:m), for example, department-to-department relations. The application candidates are evaluations
between company structures or different companies,
and remote-meeting support. In this study, we targeted the 1:1 relation of the first phase.
It has not been previously clarified which data areneeded to predict the strength of interpersonal sentiment in actual work relationships. We used a statistical method in a top-down approach to identify whattypes of data are needed for the sentiment prediction.
In this approach, we list every conceivable type ofdata and select from those that are useful (variables)using regression analysis. We follow the following
four steps. Steps (1) to (3) are repeated several times
to increase the prediction accuracy.
(1)	Design of interpersonal-sentiment-changing
model(2)	Selection of investigation items in actual
offices through statistical analysis using large3

scale data(3)	Derivation of prediction formula using actual
target office data(4)	Visualization on personal computers (PCs)and smartphones
These steps are described in more detail in the following sections.
5. Design of interpersonal-sentimentchanging model
We developed a model reflecting how our interpersonal sentiments are constructed. A conceptual diagram for changing interpersonal sentiment dependingon the event is shown in Fig. 3. The x-axis is the flowof time, and the y-axis is the strength of interpersonalsentiment y. In Fig. 3, we use the interpersonal sentiment factor of respect-contempt as an example. The
strength of the sentiment is on a 5-point scale, where
1 represents great respect and 5 represents great contempt, with 3 being neutral.
Let us consider the scenario using Fig. 1. Twopeople, Boss and Mike, a subordinate, are in an
office. They initially respect each other. When event
A occurs in which Boss yells at Mike in front of
everyone, Mike feels bad due to Boss’ action (stage1). The strength of interpersonal sentiment regarding
respect-contempt before this event y(tA) decreases
from 2 (respect) to 4 (contempt) after the event
NTT Technical Review

Regular Articles

Basic model based on event A

Basic model based on event B

Great respect
Strength ofinterpersonal sentimenty(t)1y (tA)

Event B occurs

Event A occurs
Stage 1y (tB + t)y (tB)

3tA
y (tA + t)

Time tWith time
Stage 2

5
Great contempty (tn): Strength of interpersonal sentiment at time tn
tA: Before event A occurredtB: Before event B occurredFig. 3. Interpersonal-sentiment-changing model.

y(tA+Δt). However, with time, we usually cool down
and return from contempt to a more positive sentiment, 3 (neutral), before the next event (event B) y(tB)(stage 2). In other words, Mike’s sentiment toward
Boss becomes stable over time. After a certain period,
event B occurs with similar results to event A. Thus,
we argue that stable interpersonal-sentiment resultsby repeating the combination of stages 1 and 2 several times. Thus, we consider the basic model as
consisting of two stages depending on the event.
The prediction goals are as follows.
(1) Small number of variables needed for prediction
We use a regression formula for the interpersonalsentiment prediction. The regression formula developed using general regression analysis has many
variables. Therefore, when we calculate the prediction using the regression formula, we need to prepare
the data for these variables. When considering theload for the formula user, we have to reduce the number of variables. Our goal is to have fewer than ten
variables.
(2) Estimation accuracy
The estimation accuracy of interpersonal-sentiment
strength should be approximately 60–70% with a +/–0.5 margin of error when using a 5-point Likert scale
to rate the three factors mentioned in section 3. We
preliminarily interviewed office managers regarding
prediction accuracy, and they said that achieving alevel of accuracy of approximately 80% was not necessary at first because there has been no quantification of interpersonal sentiment in office environments. They wanted to detect signs of deteriorating
situations in the workplace, even if they might beVol. 15 No. 3 Mar. 2017

wrong. Therefore, prediction accuracy should be set
to higher than 60% on a 5-point Likert scale, although
the accuracy rate is 20% stochastically.
6. Selection of investigation items in actual
offices through statistical analysis usinglarge-scale data
In this section, we explain the process used togather and analyze data.
6.1 Acquisition of large-scale data
We gathered calculation data based on our interpersonal-sentiment-changing model illustrated in Fig. 3.
We gathered 130 potential explanatory variables forpredicting interpersonal sentiment that were referred
to in previous psychology and engineering research.
(1) Static variables of responses about oneself•	Gender, age group, marital status, with/withoutchildren, relatives living together, personality•	Occupation, number of employees in company
and office, one’s status in the office, one’s value
judgment of others(2)	Static variables of responses about others in theofficeThe others’ statuses in the office, gender, age (older
or younger), distance of desk from others (whether
two people sit near each other), things in common(e.g., school, hometown, and experiences), how they
spend private time, communication tools often usedwith others (e.g., LINE (social networking app),voice, or email) and duration (e.g., 10 minutes or 1hour), and how much private information is shared4Regular Articles

Responses

Question items

About 90% missing data

Fig. 4. Large amount of missing data.

via work email (e.g., if the other is on familiar terms,
messages may contain private information such as aninvitation to dinner or news about the family.).
(3) Interpersonal sentiment-related variables•	We included 83 types of events that may changeinterpersonal sentiment in the office, for example, power harassment or consoling others
regarding work problems.
•	Respondents’ sentiment when the event occurredand its strength on a 5-point scale. We use Plutchik’s eight primary emotions [14] as the measure of one’s emotions, that is, rage, terror, vigilance, ecstasy, admiration, amazement, grief,
and loathing.
•	Respondents’ condition such as health and deadlines•	Initial strength of interpersonal sentiment foreach factor with regard to other office staff members•	First impressions of others
A large amount of data is needed for statisticalanalysis. We used a web questionnaire as an efficientdata-gathering tool for the short term. The web questionnaire was conducted in January 2012. We
obtained about 9000 responses.
6.2 F eatures of web questionnaire data and problem from viewpoint of statistical analysis(1) Features
Sentiment is subjective; therefore, we have to prepare various choices in order to obtain the truest possible responses. In the first-impression investigation
mentioned in section 6.1, we prepared a multiplechoice question asking respondents to choose 10 out
5of 94 items on what is important regarding first
impressions. Each person reacts differently to words,
so even if the same impression term was shown, forexample, on the appearance of a good-looking man,we provided several words such as cool, cute, good,and smart. In our questionnaires, the respondents all
chose the first 6 question items, and the other 4 wererandom throughout the remaining 88 items.
(2) Problem
As a result, 84 items were not chosen, resulting in ahuge amount of missing data, about 90% (shaded
region in Fig. 4). Therefore, the method of conventional statistical analysis makes analysis difficult.
6.3 S
 parse factor regression analysis with large
amount of missing data
We used a missing-data algorithm to estimate several of the regression-formula parameters. Regarding
our data problem described in section 6.2, we have todetermine why the data are missing. Rubin classified
the following three types of missing-data mechanisms [15]. The analysis method differs depending on
which mechanism is assumed. These mechanisms
describe the relationships between measured variables and the probability of missing data.
(1) Missing completely at random (MCAR)
The MCAR mechanism means that data are missing independently of both observed and unobserved
data.
(2) Missing at random (MAR)The MAR mechanism means that when theobserved data are given, data are missing independently of unobserved data.
(3) Missing Not at Random (MNAR)NTT Technical Review

Regular Articles

Investigation candidates based oninterpersonal-sentiment-changing
model in Fig. 3
Identity
attributes

Statistics

Formulas used
in applications

Factors ofinterpersonalsentiment

Factor analysis+
Sparse factor
regression

Prediction
formulaPrediction
formulaPrediction
Prediction formula
formulaPrediction formulaLike-Dislike
Respect-Contempt
Relief-Fear

Attributes ofother person

Frequency ofcommunication

Missingvalue

Types of events (8 types)
Factor regression
Strength of interpersonalsentiment before events

Fig. 5. Overview of factor regression.

The MNAR mechanism means that missing observations are related to values of unobserved data.
We assume that the missing data (unobserved data)
are due to the variable not being chosen from theresponse. Therefore, we assume the MAR mechanism can be used. We conducted a factor analysis thatcan be used even if there is a large amount of missing
data. Moreover, the factors not derived from factor
analysis must be used in regression analysis. We want
to minimize the number of useful explanatory variables for interpersonal-sentiment prediction discussed in section 5, even though we listed many
candidate variables. Because variables (explanatory
variables in regression analysis) may be used as input
items in business, for example, in human-relation
predictions in an office, having a large number of
explanatory variables makes the prediction formula
cumbersome and complicated.
The variables chosen from sparse regression modeling are used as the explanatory variables of theinterpersonal-sentiment-prediction formula. The
number of variables is small, which leads to better
privacy protection in service systems because such
systems do not need to keep user data that are notnecessary for sentiment prediction. As the number ofvariables decreases, the input load decreases whenusing the prediction formula.
In this article, we focus on sparse factor regression
analysis, and we describe the proposed analysis
method based on a large amount of missing data as
follows. Let the predictor and responses be xn and yn,
respectively. In the factor regression model, prediction can be done directly by m latent factors fn as folVol. 15 No. 3 Mar. 2017lows:
xn = Λfn + ξn, yn = ΘTfn + εn,(1)

where Λ and Θ are factor loadings, and ξn and εn are
error variables. The joint distribution (xn, yn) can be
expressed as
xn
Λ
ξn
µ
(2)
yn = a + ΘT fn + εn .
The above equation can be regarded as a standard
factor model:(3)x̃n = µ̃ + Λ̃fn + ξ˜n,( ) ( ) ( ) ( )where x̃n = (xTn , yTn )T, µ̃ = (µT, aT)T, Λ̃ = (ΛT, Θ)T, and˜ξn = (ξTn , εTn )T. Therefore, we can directly use an estimation algorithm of the factor-analysis model toobtain the solution. An efficient algorithm when alarge amount of missing data is involved is detailed inprevious papers [16, 17].
6.4 Evaluation results
We proposed the regression formulas for calculating y(tA+Δt) in Fig. 3 using the method described in
section 6.3 (Fig. 5). There are several regression
analysis models for obtaining prediction formulas(regression formulas). We used a linear regression
model because it is easier to interpret with derived
explanatory variables than a non-linear model. Moreover, when we create a graph of discrete values foreach variable, the graph shape is not a curve fitting a
non-linear model.
The regression formulas and their measured accuracy of interpersonal sentiment are listed in Table 1.
The strength of interpersonal sentiment y based on
6Regular Articles

Table 1. Regression formulas and accuracy rates of interpersonal sentiment factors.
Strength of interpersonal sentiment yy = b + a1x1 + a2x2 + a3x3 + … + aixi
Factor ofinterpersonalsentiment

Accuracy(%)

Constant
b

a1

a2

a3

a4

Like/Dislike

62.6

3.8

–1.5

0.4

0.1

0.4

Respect /Contempt

63.3

3.6

–1.2

0.2

Relief /Fear

64.3

3.8

–1.5

0.1

Coefficient ai of explanatory variable xi

–0.5

a5

a6

0.2

0.1

0.2

0.2

0.1

a7

–0.2

Note: without segmentation
x1: strength of interpersonal sentiment before event occurred; x2: type of event; x3: type of one’s sentiment when event occurs;x4: one’s judgment value of others; x5: personality; x6: one’s circumstances; x7: relatives living together

the linear model shown on the y-axis in Fig. 3 isexpressed as follows:y = b + a1x1 + a2x2 + a3x3 + … + aixi,

(4)

where b is a constant and ai is a coefficient of explanatory variable xi (input items of prediction formulas).
Three factors of interpersonal sentiment are describedin section 3. Our prediction goal was to have ten or
fewer variables to reduce the formula user’s load, as
explained in section 5. In selecting explanatory variables, we first ensure that the absolute value of ai is
not close to zero since we are using sparse factor
regression. Greater absolute values of ai are useful toestimate y for each factor of interpersonal sentiment.
The prediction accuracies listed in Table 1 are theaverages of 5-fold cross-validation. We evaluated theprediction accuracy with and without segmentationof all data.
As a result, the prediction accuracy for all three factors was more than 60% without segmentation. We
confirmed a prediction accuracy of approximately
70–80% when the data set was divided into several
segments using explanatory variables of greater absolute values of ai, types of events x2, or initial strengthof interpersonal sentiment before an event occurredx1.
We also found that the approximately 60% accuracy was for all three factors involving only four to six
variables (see Table 1, the number of ai variables).
The useful variables were x1, the initial strength ofinterpersonal sentiment before an event occurred, x2,
types of events, and x4, one’s judgment value of others. That is, only up to ten input items at most can beobtained from the prediction formula in which theprediction accuracy is higher than 60%. Conversely,
about 120 input candidate items out of the 130 items
7

described in section 6.1 were not useful for predicting
interpersonal sentiment.
7. Derivation of prediction formula using
actual target office data
Here, we describe how we derived prediction formulas using actual data.
7.1 P roblems in gathering data in actual officeenvironment
Statistical analysis is generally necessary for large
amounts of data to derive prediction formulas. However, there are two problems with the field data ofactual target offices, as shown in Fig. 6 and as follows.
(1) Problem 1
In an actual office, events in which interpersonalsentiments change do not occur frequently. Therefore, a long investigation period such as more thanhalf a year is necessary to acquire an adequate
amount of sample data for general statistical analysis.
During a long investigation period, however, personnel changes occur regarding those who are gathering data to develop the prediction formulas. Moreover, office workers who are the subjects of such aninvestigation bear a heavy burden; thus, the quality ofgathered data decreases.
(2) Problem 2
We generally used Bayesian estimation for timeseries and small-scale data. The derived prediction
formulas using conventional Bayesian estimationconsist of many variables. In conventional Bayesianestimation, Markov chain Monte Carlo (MCMC)
methods can often be used. MCMC methods find an
optimized answer from repeated computations.
NTT Technical Review

Regular Articles+ a ix iy = a1x1 + a2x2

+b

aj : regression coefficient, xj : explanatory
variable, b: constant
Input a value to xj .
Prediction formula for the target officeProblem 1
Event

Derivation of target
prediction formula

Event

Event

Strength ofinterpersonalsentiment y

Event

Time
Time-series and small-scale data

Problem 2

Fig. 6. Process of deriving prediction formula for actual office.

Comparison
Large-scale
data

Actual target office data

Data set 1

Data set thathas similarfeatures toactual office

Sparse factor
regression analysis

Actual target
office data
Sparse Bayesian estimation method
Prior distribution
Regression
coefficient
distribution

×

Likelihood
function

Prediction
formulaFig. 7. Small-scale data analysis method using large-scale data.

However, they have heavy computational loads.
In contrast, when we want to minimize the numberof variables of a prediction formula, we generally use
sparse regression modeling. However, the conventional Bayesian estimation cannot use the standard
lasso algorithm (sparse regression modeling).
7.2 Sparse Bayesian estimation method
In this subsection, we describe the method of sparse
Bayesian estimation.
7.2.1 Prior distribution of Bayesian estimation
using the analysis results of large-scale data
The proposed Bayesian estimation method is
depicted in Fig. 7. To overcome problem 1, we use
Vol. 15 No. 3 Mar. 2017

the analysis results of large-scale data through Bayesian estimation. As mentioned in section 7.1, conventional Bayesian estimation is useful to obtain an
optimized answer from repeated computations suchas with an MCMC method. To obtain an optimized
answer with fewer repeated computations, it isimportant to set appropriate values to the prior distribution. To set the appropriate prior distribution, we
propose using the analysis results of sparse factor
regression analysis of a large-scale data set gatheredfrom web questionnaires, as described in section 6.1.
The data set involves the same kinds of jobs as in
actual target offices. Bayesian estimation is generally
used as a normal distribution for the prior distribution.
8

Regular Articles

Our proposed method uses the regression coefficient
distribution obtained from sparse regression analysis
for the prior distribution of Bayesian estimation developed using data set 1 in Fig. 7.
7.2.2 Sparse Bayesian estimation method
To overcome problem 2, we use our method called
the sparse Bayesian estimation method to carry out
Bayesian estimation and sparse regression.
We focus on the logarithm of posterior distribution
on Bayesian estimation used in sequential learningfrom a few events, which becomes a quadratic function. Bayesian estimation is unified sparse regressionthat can refine the variables of a prediction formula.
We estimate the posterior mode instead of the posterior mean. The estimation of the posterior mode corresponds to the penalized maximum likelihood estimation. The prior distribution is assumed to be amultivariate-normal distribution; therefore, the posterior distribution is also a multivariate-normal distribution, which implies the standard algorithm used in
the lasso-type estimation, for example, the coordinate
descent algorithm, can be directly used.
The detailed algorithm is described below. The
explanatory variables (e.g., gender and age) before an
event are denoted as X, and the response variable(degree of connectiveness after the event) is denoted
as Y. We conduct linear regression analysis to estimate Y from X, but the number of observations isoften small. In this case, the estimator θ̂ may beunstable. To address this issue, we obtain a stableestimate by using θ̂web, the estimator of parameter θ.
Specifically, we use the following posterior distribution:p(θ|θ̂web, Y, X) ∝ p(Y|θ, θ̂web, X)p(θ|θ̂web). (5)Note that both likelihood function p(Y|θ, θ̂web, X) andprior distribution p(θ|θ̂web) are multivariate-normal
distributions; therefore, the posterior distribution is
also a multivariate-normal distribution. The logarithm of likelihood function can then become quadratic.
We consider the sparse estimation of θ via L1 regularization such as the lasso. Instead of using the negative log-likelihood −log p(Y|θ, θ̂web), we use −log p(Y|θ, θ̂web)−log p(θ|θ̂web). As described above, log p(Y|θ, θ̂web)−log p(θ|θ̂web) takes a quadratic form withrespect to θ. When we substitute the above quadratic
function with the loss function of the lasso, we candirectly use the standard algorithm of the lasso.

9

7.3 Diary method
We gathered the actual target office data using thediary method that we designed based on the interpersonal-sentiment-changing model shown in Fig. 3.
Office employees had to keep a daily diary that
includes information such as other staff members’
names, events, and the changes in the strength of theirinterpersonal sentiments. Approximately 30 of the
130 candidate investigation items were selected
through the sparse regression results described in section 6.4, including correction items for the target
office.
The 30 investigation items were as follows.
(1) One-time investigation items•	Gender, age-group, marital status, with/withoutchildren, personality•	Types of occupation, one’s status in the office,one’s value judgment of others•	Strength of three factors mentioned in section 3
at the initial state for every office staff member(2) Everyday investigation items•	An event that was selected from 83 types of
events and Plutchik’s eight emotions mentioned
in section 6.1, a 5-point scale on the strength offactors for interpersonal sentiment, and respondents’ condition and situation such as overall
health and deadlines.
•	Staff member’s name involved in the event•	Strength of three factors for interpersonal sentiment mentioned in section 3 after the event for
all office staff members.
The prediction was y(tA+Δt) as shown in Fig. 3, andthe correct answer values of y(tA+Δt) are the strengthof the three factors after the event.
The data obtained using the diary method to investigate human relations were gathered in four actual
offices of an NTT Group company from Oct. 24 toDec. 15, 2014. The data were input using an HTML(Hypertext Markup Language) interface. Since theinterpersonal-sentiment data of office employees
were very sensitive, the employees were allowed toanswer the questionnaires in the privacy of theirhomes, as more accurate human-relation data could
be gathered.
The basic analysis results of gathering data were as
follows.
(1) Office staff (respondent) attributes•	Offices (departments): Marketing x 2, Development x 2; total of 4 offices•	Males: 17, females: 7•	Positions: department managers: 4, department
chiefs: 2, staff members: 14, temporary staff
NTT Technical Review

Regular Articles

Proposed method
80

Prediction accuracy (%)

70
60
Desired value

50
40
30

Conventional
method

20
10
0

0

20

40

60

80

100

120

140

160

Number of events

Fig. 8. Prediction accuracy of proposed and conventional methods regarding respect-contempt factor without
segmentation.

members: 4(2)	Number of events that occurred over twomonths: 167• Positive events: 136• Negative events: 31(3)	Examples of main positive events•	Had a positive conversation (mainly marketing
offices)•	Participated in the same event, drink party, or
workshop (all offices)•	Helped someone with his/her work (all offices)(4)	Examples of main negative events•	Looked down on people (mainly marketing
offices)•	Refused to take responsibility for problems(mainly development offices)•	Exhibited bad manners (mainly marketing offices)
7.4 Evaluation results and discussion
The evaluation results are shown in Fig. 8. The
x-axis is the number of events, and the y-axis is theprediction accuracy. We analyzed 167 events that
occurred during the two-month investigation period.
The factor of interpersonal sentiment was respectcontempt. Data set 1 consisted of about 3100 data
samples. The straight green line is the prediction
accuracy using sparse factor regression analysis discussed in section 6.3 by using all 167 events. The blueline is the prediction accuracy using the conventional
Vol. 15 No. 3 Mar. 2017

Bayesian estimation method, and the grey line is theprediction accuracy using the proposed sparse Bayesian estimation method. The weight coefficient τ of theinitial distribution with the proposed sparse Bayesianestimation method was 1.0. The initial distribution
with the conventional Bayesian estimation method
was assumed to be uniform (τ = 0).
We checked which of the two methods converged
more quickly to the straight line of 67% (desired
value) estimation accuracy. Calculations using bothmethods were started after ten events. The input items
were sequentially input using both methods in theorder the events occurred. The prediction accuracy of
the two methods took an average of five times for
two-fold cross-validation of the data of all 167 events.
Both methods began to converge from about 85
events. However, the estimation with the proposed
method was more stable than that with the conventional method. That is, the proposed method had lessdispersion in estimation accuracy than the conventional method. Therefore, the convergent start pointof both methods was almost the same; however, theproposed method became stable faster than the conventional method. There were four useful variables:
the initial strength of interpersonal sentiment before
an event occurred, types of events, types of occupation, and personality.
Next, we estimated the accuracy enhancement
regarding the respect-contempt factor. We focused on
the regression coefficient of all data using our diary10Regular Articles

method. The explanatory variables of a large regression coefficient were gender (= 0.2) and event type (=0.4). In this article, we have focused on event type,which were segmented into positive and negative
events. There were 136 positive events and 31
negative events out of the total 167 events. There
were too few negative events for our sparse Bayesianestimation method; therefore, we used the 136 positive events. After data cleansing, we analyzed 111
positive events. The prediction accuracy takes anaverage of five times for two-fold cross-validation of
office data for the 111 positive events. The prediction
accuracy was 77%—about 10% higher—by segmentation. Thus, we achieved our prediction goals of
obtaining more than 60% prediction accuracy andhaving ten or fewer variables.
The proposed method converged at about 60 events,
and the conventional method converged at about 80
events. That is, the proposed method converged with
about one-fourth fewer events than the conventional
method. The diary method we used required 30 days
to gather the data on 60 events. Thus, we obtained a
prediction formula after at least 20 days of learning
using the proposed method. This 20-day learning
period is sufficient for practical use.
8. Visualization on PCs and smartphones
To our knowledge, no studies have been done on thevisualization of human personal relationships from a
psychological point of view. We implemented two
types of visualization systems. One is for office managers or human resources staff, and the other is forgeneral office staff.
8.1 Visualization system for office managers
We attempted to visualize the change in the prediction of invisible interpersonal sentiment as a tool for
managers. We implemented a visualization system asshown in Fig. 9. We prepared two types of visualization displays, a single (Fig. 9(a)) and a comparativevisualization screen (Fig. 9(b)). We assumed an
actual office scenario in which interpersonal sentiment changes and is easy to visualize in an actual
office. In this visualization system, the privacy of
responses and an interface for observers were notconsidered. We will investigate these issues as future
work. The comparative visualization display (b) has
the same functionality as display (a). The differenceis that the interpersonal-sentiment variation in two
different departments can be compared for the same
events. For example, we can see the difference in
11interpersonal-sentiment variation between an engineering department and a marketing department.
The system requirements for visualization are as
follows.
Operating system (OS): Mac OS X 10.8.4, HTTP
(Hypertext Transfer Protocol) server: Apache
2.2.22, runtime: JDK (Java Development Kit)
7.0_45, browser: Firefox 25, processor: 1.3-GHz
dual core Intel Core i5, memory: 4-GB 1600-MHz
LPDDR3, storage: 256 GB.
8.2 Visualization system for office staff
In this section, we describe the system to visualize
how an office staff member is feeling. The visualization target is the sentiment of a person, so it is very
sensitive. It is necessary to carefully investigatechanging interpersonal sentiments, depending on
who observes the human relations that are visualized.
It may be necessary in some cases, for example, when
the information made visible is viewed by individual
office workers in the workplace, to add minor falsifications of the true predicted values tuned to improve
human relations, or to refrain from showing direct
information about the people involved in order toprotect privacy. To prevent information displayed
from being seen by others in the workplace, we
implemented the system on smartphones; therefore,
users can input data in a private space.
The following components are involved in the visualization process.
First, the gender, age, and position of the person wewant to visualize in the organization have to be set. To
simplify the relationship information, we decided toallow only the following two types of individuals tobe set:(1) One who provoked an event(2)	One who is affected by having witnessed theevent
The prediction of the sentiments between the two
set individuals was executed by using the formulamentioned in section 7.2. The data collected from thediary method during in-context experiments wereused.
The visualization system was designed as an application for smartphones and implemented on a smartphone. The specifications of the smartphone were as
follows.
Smartphone: AQUOS SERIE mini SHV31, central
processing unit (CPU): MSM9874AB 2.3-GHz
Quad-core, memory: 2 GB, storage: 16 GB, OS:
Android 4.4.
In consideration of client-side processing constraints
NTT Technical Review

Regular Articles

Engineering dept.
Actor: boss

NeutralNeutralRegression coefficientsand their explanatory
variables

Like
Observer 2:
mid-career
staff member

Observer 1:
new recruit

Receiver: new recruit

Strength ofinterpersonalsentiment andprediction
accuracy ofreceiver

Personal profile
of receiver (age,gender, job,
relationshipto actor)

Helped me with a task
Scenario

Time-series of events(a) Single display

Engineering dept.

Marketing dept.

Actor: boss

Actor: boss

Direction

Fear

Contempt

Fear

DislikeInterpersonal-sentimentfactors (Like-Dislike,
Relief-Fear, RespectContempt)

Dislike
Prediction
strength

Observer 2:
mid-career
staff member

Observer 1:
new recruit

Receiver: female staff member

Observer 2:
mid-career
staff member

Observer 1:
new recruit

Receiver: female staff member

Helped me with a task
Scenario(b) Comparative display

Fig. 9. Visualization for office managers.

and the high privacy level of manipulated data andresults, we implemented the proposed sparse Bayesian estimation method mentioned in section 7.2 using
R language, and events or user information files wererun or stored on an Apache server.
The server specifications were as follows.
CPU: Intel Core i5-2400 3.1-GHz Quad-core,
memory: 8 GB, storage: 128 GB, OS: Windows 10
Pro 64 bit, HTTP: Apache 2.4.17.
The displayed images are shown in Fig. 10. The
display combined actual and predicted data; the predicted data were displayed after displaying the actual
data collected as the training data set. Specifically,
Vol. 15 No. 3 Mar. 2017

relationships should be expressed through a quizstyle interface such as “Here is the current relationship status; choose the next action and see how the
relationship turns out” (Fig. 10(a)). A warning may
be displayed when a prediction mismatch occurs(chosen action leads to relationship deterioration).
The scenario illustrates the case of monitoring therelationship between colleagues or between full-timeand temporary employees. Therefore, the application
is suitable for both managerial and subordinate positions. For example, it is possible to input an event
such as one that may lead to power harassment,
whether you are the actor (the person who provoked
12

Regular Articles

times, and it was well received based on the opinionsof attendees.
9. Conclusion(a) Next-action-selection displays(b) Prediction result display(general feeling on left, details on right)

Fig. 10. Visualization for office workers.

the event), the receiver (the person who suffered fromthe event), or the observer (the person who observed
the event). The interface to visualize the prediction of
the relationship depending on an event can be aquiz-style interface such as the one mentioned above:“Here is the current relationship status; choose thenext action and see how the relationship turns out.”
The major functionalities are as follows.
•	Input the identification of the person(s) whose
relationship you care about.
•	Input the related event.
•	For the user interface, only the top ten most frequent events are displayed first.
•	For screen size and readability, the event category is selected first; then five to ten concrete
events are listed.
•	Select what action you think would be appropriate next to improve the relationship.
•	Display the quiz results and prediction accuracy
for reference by pressing the relationship-prediction button (Fig. 10(b)).
We used simple sentiments to more easily understand the prediction results, that is, whether theselected next action would be a positive choice. We
have conducted demonstrations of this system many13

The utilization of mental data is important for abusiness to be successful. In this article, we explained
an interpersonal-sentiment-changing model and proposed two analysis methods to quantitatively estimatechanges in interpersonal sentiments in an office byusing psychological research and statistical data. The
first proposed method is a data analysis method thatassumes the MAR mechanism, even if about 90% of
data is missing. This method is also simultaneously
used for sparse regression modeling and factor
regression analysis. The second method was a sparse
Bayesian estimation method for time-series andsmall-scale data. For appropriating prior distributionof the method, we used large-scale data that had
similar attributes to actual workers in the target
offices. The large-scale data were gathered using web
questionnaires, and the small-scale data of actual
target offices were gathered using a diary method.
With these methods, we achieved a prediction accuracy of more than 60% without segmentation and ten
or fewer variables. When we segmented a data set
using a variable of a large regression coefficient, theprediction accuracy increased by about 10%.
We finally implemented two types of visualization
systems for office managers and for office staff members. Managers can monitor a subordinate’s humanrelations on a PC, and staff members can monitor
relations on smartphones. We demonstrated the visualization systems using actual office data. The demonstrations were well received based on the comments of attendees.
References
[1]
[2]
[3]
[4]
[5]

[6]
[7]

G. A. Van Kleef, “How Emotions Regulate Social Life: The Emotions
as Social Information (EASI) Model,” Curr. Dir. Psychol. Sci., Vol.
18, No. 3, pp. 184–188, 2009.
M. Sinaceur and L. Z. Tiedens, “Get Mad and Get More Than Even:
When and Why Anger Expression is Effective in Negotiations,” J.
Exp. Soc. Psychol., Vol. 42, pp. 314–322, 2006.
S. Kumar, F. Morstatter, and H. Liu, “Twitter Data Analytics,”
SpringerBriefs in Computer Science (Online), pp. 53–57, 2014.
facebook, https://www.facebook.com/
K. Kazama, M. Imada, and K. Kashiwagi, “Characteristics ofInformation Diffusion in Blogs, in Relation to Information Source
Type,” Neurocomputing, Vol. 76, No. 1, pp. 84–92, doi:10.1016/j.
neucom.2011.04.036, 2012.
K. Han, D. Yu, and I. Tashev, “Speech Emotion Recognition Using
Deep Neural Network and Extreme Learning Machine,” Interspeech
2014, pp. 223–227, Singapore, Sept. 2014.
X. Han, R. Elbarougy, M. Akagi, J. Li, T. D. Ngo, and T. D. Bui, “A

NTT Technical Review

Regular Articles

Study on Perception of Emotional States in Multiple Languages on
Valence-Activation Approach,” Proc. of 2015 RISP International
Workshop on Nonlinear Circuits, Communications and Signal
Processing (NCSP’15), pp. 86–89, Kuala Lumpur, Malaysia, 2015.
[8] S. Kumano, K. Otsuka, D. Mikami, M. Matsuda, and J. Yamato,“Analyzing Interpersonal Empathy via Collective Impressions,” IEEE
Trans. Affective Computing, Vol. 6, No. 4, pp. 324–336, 2015.
[9] S. J. Breckler, E. C. Wiggins, “Affect Versus Evaluation in the
Structure of Attitudes,” J. Exp. Soc. Psychol., Vol. 25, No. 3, pp.
253–271, 1989.
[10] D. P. French, S. Sutton, S. J. Hennings, J. Mitchell, N. J. Wareham, S.
Griffin, W. Hardeman and A. L. Kinmonth, “The Importance ofAffective Beliefs and Attitudes in the Theory of Planned Behavior:
Predicting Intention to Increase Physical Activity,” J. Appl. Soc.
Psychol., Vo. 35, No. 9, pp. 1824–1848, 2005.
[11] K. Niven, P. Totterdell, and D. Holman, “A Conceptual Classificationof Controlled Interpersonal Affect Regulation Strategies,” Emotion,
Vol. 9, No. 4, pp. 498–509, doi: 10.1037/a0015962, 2009.
[12] J. Zaki, W. C. Williams, “Interpersonal Emotion Regulation,”
Emotion, Vol. 13, No. 5, pp. 803–810, doi:10.1037/a003383, 2013.
[13] K. Niven, D. Garcia, I. Löwe, D. Holman, and W. Mansell, “Becoming
Popular: Interpersonal Emotion Regulation Predicts Relationship
Formation in Real Life Social Networks,” Front Psychol., Vol. 6, doi:
10.3389/fpsyg.2015.01452, published online 2015.

Vol. 15 No. 3 Mar. 2017[14] R. Plutchik, “Emotion: A Psychoevolutionary Synthesis,” Harper &Row, New York, 1980.
[15] D. B. Rubin, “Inference and Missing Data,” Biometrika, Vol. 63, No.
3, pp. 581–592, 1976.
[16] K. Hirose, S. Kim, Y. Kano, M. Imada, M. Yoshida, and M. Matsuo,“Full Information Maximum Likelihood Estimation in Factor
Analysis with a Lot of Missing Values, arXiv:1312.5458, 2013.
[17] K. Hirose and M. Imada, “Sparse Factor Regression via Penalized
Maximum Likelihood Estimation,” Stat. Papers, online first, 2016.
http://link.springer.com/article/10.1007 /s00362-016-0781-8

Trademark notes

Android is a trademark of Google Inc.
Apache is a trademark of the Apache Software Foundation.
AQUOS is a registered trademark of Sharp Corporation.
Intel Core is a trademark of Intel Corporation in the United States and/or
other countries.
Java is a registered trademark of Oracle and/or its affiliates.
Mac and OS X are trademarks of Apple Inc., registered in the U.S. and othercountries.
Windows is a registered trademark of Microsoft Corporation in the UnitedStates and/or other countries.

14Regular Articles

Miyuki Imada

Senior Researcher, Ubiquitous Service Systems Laboratory, NTT Network Innovation Laboratories.
She received an M.E. in education from Yokohama National University in 1990 and a Dr.Eng.
in production and systems from Waseda University, Tokyo, in 2005. Since joining NTT Network
Service System Laboratories in 1990, she has
mainly been involved in researching technology
for fault tolerant systems, network security, service emergencies, privacy protection, and pervasive networking. Her current interests include
conducting research in the interdisciplinary
fields of psychology, engineering, and statistics.
Dr. Imada is a member of the Institute of Electronics, Information and Communication Engineers (IEICE).

Kei Hirose

Associate Professor, Kyushu University.
He received a Doctor of Functional Mathematics from Kyushu University, Fukuoka, in 2011.
His research interests include statistical modeling and machine learning.

Manabu Yoshida

Research Engineer, Ubiquitous Service Systems Laboratory, NTT Network Innovation Laboratories.
He received a B.S. in earth sciences and an
M.E. in creative science and engineering fromWaseda University, Tokyo, in 2008 and 2010. He
joined NTT Network Innovation Laboratories in2010. Since then, his research interests haveincluded ubiquitous computing and affective
computing. He was with NTT DOCOMO, where
he was involved in service planning, development, and data analytics for marketing from 2013
to 2016. He received the best poster award fromthe Academy of Human Informatics in 2014. He
is a member of IEICE and The Japanese Society
for Artificial Intelligence.

Sun Yong Kim

Technical Manager, DOCOMO gacco, Inc.
She received her B.E. and M.E. in information
science from The University of Tokyo in 2002
and 2004. She was engaged in multivariate analysis and data mining for understanding humanbehavior and emotions at NTT Network Innovation Laboratories from 2004 to 2013. Since joining DOCOMO gacco, Inc., she has been involvedin launching and managing the Japanese MOOC
(massive open online course) system named
gacco.

15

Naoya Toyozumi

Graduate Student, Graduate School of Scienceand Engineering, Aoyama Gakuin University.
He received a B.E. in engineering from Aoyama Gakuin University, Tokyo, in 2015. His
research interests include signal processing andhuman machine interfaces.

Guillaume Lopez

Associate Professor, College of Science andEngineering, Aoyama Gakuin University.
He received an M.E. in computer engineeringfrom INSA Lyon, and an M.Sc. and Ph.D. inenvironmental studies from The University ofTokyo in 2000, 2002, and 2005. After working at
the Nissan Mobility Research Laboratory for
four years, he became Assistant Professor dedicated to the JST CREST Anti-Metabolic syndrome Project (CAMP) in March 2009. He
joined Aoyama Gakuin University in 2013,
directing the Wearable Environment Information
Systems Laboratory. He received the Aoki Award
from the Horological Institute of Japan in 2006.
His research interests include lifestyle enhancement and healthcare support based on intelligent
information systems using wearable sensing
technology. His professional memberships
include the Academy of Human Informatics, theInstitute of Electrical and Electronics Engineers,
the Information Processing Society of Japan, theJapan Society of Mechanical Engineers, and theSociety of Instrument and Control Engineers.

Yutaka Kano

Professor of Statistics, Graduate School ofEngineering Science, Osaka University.
He received a D.Eng. in statistics from Osaka
University in 1986. He has worked for several
schools and universities in Japan including the
Department of Mathematics at Tsukuba University and the Graduate School of Human Scienceand Graduate School of Engineering Science at
Osaka University. His interests include statisticalscience, statistics in social sciences, and statistical education. He was awarded a Research
Achievement Prize from the Japan StatisticalSociety in 2013 and a Lifetime Achievement
Prize from the Behaviormetric Society of Japan
in 2014. He has also worked as an associate editor for many international academic journals.

NTT Technical Review

Global Standardization Activities

Report on WTSA-16 (World
Telecommunication Standardization
Assembly 2016)
Yoshinori Goto, Kazuhiro Takaya, Noriyuki Araki,and Hideyuki Iwata
Abstract

The World Telecommunication Standardization Assembly was held from October 25 to November 3
in 2016 in Yasmine Hammamet, Tunisia. This article gives an overview of the meeting along with information about the main topics that were discussed.
Keywords: WTSA, IMT, digital object architecture

1. Introduction
The World Telecommunication Standardization
Assembly (WTSA) is the highest decision-making
body of the International Telecommunication Union
Telecommunication Standardization Sector (ITU-T),
which oversees telecommunication standardizationin the ITU. The assembly convenes every four years(Fig. 1). Decision-making organizations above
WTSA include the Plenipotentiary Conference andthe Council (Fig. 2). WTSA formulates the action
policies of ITU-T, organizes Study Groups (SGs),
and appoints the chairs and vice-chairs of SGs and theTelecommunication Standardization Advisory Group(TSAG).
The SGs are the key organizations engaged in ITUT’s standardization activities. Since discussions at
this level require a high level of technical expertise,
experts from private companies play an importantrole. In contrast, most topics discussed in WTSA
concern policy or organizational operation matters.
Consequently, government officials play a larger role,
although technical knowledge is required when discussing what topics should be addressed by the SGs.
A feature of WTSA is, therefore, that its discussions
require collaboration between government and theprivate sector.
1

The outcomes of the discussions in WTSA usuallytake the form of resolution documents. Unlike the
ITU Constitution, ITU Convention, or ITU Administrative Regulations (Radio Regulations and International Telecommunication Regulations), which aretreated as international law, these documents are notlegally binding. However, since they define the policies governing ITU-T activities, they are not only ofgreat importance in and of themselves but also serve
as guidelines for the working methods adopted by
ITU-T.
Under its plenary, WTSA forms five committees(Fig. 3), each of which discusses specific topics. Particularly important are COM3, which studies working methods, and COM4, which discusses the work
program.
2. SG restructuring
The restructuring of SGs is the most important
issue to be addressed by WTSA. ITU-T currently has
11 SGs. To improve the efficiency of ITU-T operations, it was proposed that some SGs with decreasing
workloads should be merged with other SGs. Specifically, the merger of SG9 (cable television (CATV))and SG15/SG16, and the merger of SG11 (protocols
and testing) and SG13, were considered.
NTT Technical Review

Global Standardization Activities

Fig. 1. Scenes from the conference.

Plenipotentiary
Conference

Council

ITU-D(ITU Telecommunication
Development Sector)

ITU-R(ITU Radiocommunication
Sector)ITU-T(ITU Telecommunication
Standardization Sector)
WTSA(WorldTelecommunication
Standardization
Assembly)

WCIT(World Congresson International
Telecommunications)

Study
Groups(SGs)

SG Plenary

WP

WP

WP

Question

Question

Question

WP: Working Party

Fig. 2. Position of WTSA within the ITU structure.

Members from the USA, Canada, and CEPT (European Conference of Postal and Telecommunications
Administrations) proposed the merger of SG11 andSG13, but the Regional Commonwealth in the fieldof Communications (RCC; Russia and other counVol. 15 No. 3 Mar. 2017

tries in the region) and the Asia-Pacific Telecommunity (APT) proposed to retain SG11 as a separate
entity. Support for this merger from Member States
was insufficient, so it was agreed to retain SG11 as anindependent study group.
2

Global Standardization Activities

Plenary
Chair: Moktar Mnakri (Tunisia)
Vice-chairs: 3 including Yoichi Maeda (TTC, Japan)
COM1: Steering Committee
Chair: Moktar Mnakri (Tunisia)
Vice-chairs: 3 including Yoichi Maeda (TTC, Japan)
COM2: Budget control
Chair: Weiling Xu (China)
COM3: Working methods of ITU-T
Chair: Steve Trowbridge (USA)WG3A Chair: Ahmed Sharaf Al-Raghy (Egypt)
WG3B Chair: Bruce Gracie (Canada)
COM4: Work program and organization
Chair: Kwame Baah-Acheamfour (Ghana);
3 vice-chairs
WG4A Chair: Fabio Bigi (Italy)
WG4B Chair: Jeferson Nacif (Brazil)
COM5: Editorial committee
Chair: Rim Belhaj (Tunisia); 5 vice-chairs
TTC: Telecommunication Technology Committee

Fig. 3. WTSA structure.

Initially, no organizations other than APT expresseda desire to keep SG9. However, when Japan argued
for the retention of SG9 based on the peculiarities ofthe standardization of CATV, which is subject toregulations that are different from other regulations
applied to the telecommunications industry, Russia
changed its stance to support the continuation of
SG9. With this as a trigger, nations in the Middle
Eastern and African regions also changed their positions to support the retention of SG9. There was anextended discussion between these parties and the
USA and the member European nations, which proposed for SG9 to be merged. On October 29, the latterbacked off and agreed to the retention of SG9.
Although it was decided to retain SG9, the USA
and European nations insisted on transferring SG9’s
question on quality to SG12 and the question onhome networking to SG15. The developing nations
that supported the retention of SG9 had no strong
opinion on this matter. In particular, while the African
nations supported the retention of SG9, they requested the transfer of the question on quality. The chair of
COM4, which was in charge of SG restructuring at
this WTSA, was a Ghanaian. He tried to push thetransfer of the question through, but Japan resisted his
attempt by expressing a reservation (an act of retaining the right to restart a specific discussion), claiming
3

that there was a problem in the manner in which thediscussion had proceeded. In the end, it was agreed totransfer the questions on quality and home networking, but the responsibilities of the related SGs wererearranged to enable these SGs to take those actions
deemed necessary.
Other agreements include the transfer of the question on human factors from SG2 to SG16 and themerger of the Review Committee, which had been
studying the reorganization of ITU-T, into TSAG.
The results of SG restructuring were laid out in Resolution 2. An overview of SG restructuring is shown in
Fig. 4.
3. Appointments of chairs and vice-chairs
The chairs and vice-chairs appointed are listed inTable 1. New chairs were appointed for SGs 2, 5, 9,
11, 16, and 17. These appointments took regional balance into consideration, so no more than one chair
was appointed from the same nation except for Japan,
which has two chairs (SG3 and SG9).
Resolution 35 contains provisions on the appointments of chairs and vice-chairs. This WTSA revisedsome of the provisions to limit the number of vicechairs from the same region to three in each SG. In
view of the trend of there being too many vice-chairs
NTT Technical Review

Global Standardization Activities

TSAG operation andadministration
RevCom: Review oforganization and work
method of ITU-T

TSAG operation andadministration

Absorbed into TSAG
Responsibilities for
performance testing
clarified

SG11: Protocols andtesting

SG11: Protocols andtesting

SG12: Quality

SG12: Quality
Video qualitytransferred

SG9: CATV

SG9: CATV
Home networking transferred

SG15: Transporttechnology and access

SG15: Transporttechnology and access

SG16: Multimedia

SG16: Multimedia
Human factors* transferred

SG2: Numbering

SG2: Numbering

SG13: Future
networks

SG20: IoT

SG5: Environment

SG17: Security

More or less the status quo

SG13: Future
networks

SG20: IoT

SG5: Environment

SG17: Security

SG3: Tariff

SG3: Tariff* Human factors: Issues related to improving the quality of life through international telecommunications
RevCom: Review Committee

Fig. 4. Overview of SG restructuring.

Table 1. Officers appointed (Chairs and Vice-chairs).
SGs

ChairsVice-chairs

TSAG

Bruce Gracie (Canada)

8

SG2 (Numbering, administration)

Phil Rushton (UK)8

SG3 (Tariff)

Seiichi Tsugawa (KDDI, Japan)

13

SG5 (Environment)

Maria Victoria Sukenik (Argentina)

9 including Kazuhiro Takaya (NTT, Japan)

SG9 (CATV)

Satoshi Miyaji (KDDI, Japan)

3

SG11 (Protocols and testing)

Andrey Kucheryavy (Russia)

8

SG12 (Quality)

Kwame Baah - Acheamfour (Ghana) 12

SG13 (Future networks)

Leo Lehmann (Switzerland)

10 including Yoshinori Goto (NTT, Japan)

SG15 (Transport technology and access) Steve Trowbridge (USA)

10 including Noriyuki Araki (NTT, Japan)

SG16 (Multimedia)

Zhong Luo (China)

7 including Hideki Yamamoto (OKI, Japan)

SG17 (Security)

Heung Youl Youm (South Korea)

9 including Masaru Miyake (KDDI, Japan)

SG20 (IoT)

Nasser Al Marzouqi (UAE)

13 including Takafumi Hashitani (Fujitsu, Japan)

appointed from developing countries, the intention ofthis revision was to enhance the quality of vice-chairs
by applying some restrictions. At first, some members proposed limiting the number of vice-chairsVol. 15 No. 3 Mar. 2017from the same region to two, but in view of the factthat several Asian nations such as Japan, China, andKorea are more active than others, the number was
raised to three. For TSAG, the number of vice-chairs
4Global Standardization Activities

Transmit a bar code (handle ID).

ICT
deviceReceive a verification code.
Hash value of product model
name, serial number, IMEI,
and MAC address

Verify authenticity of theverification code attached toa product.

Management of information
about product distribution
routes may be assumed.

Detect falsified serial number.

ICT: information and communication technology
ID: identification
IMEI: International Mobile Equipment Identity
MAC: media access control

Fig. 5. Overview of DOA.

from a single region is limited to two.
In recent years, as elsewhere, the increased participation of women in the field of standardization has
been noticeable. For example, the chair of SG5 is an
Argentinean woman. Some expressed the need toseek a gender balance in the appointments of chairs
and vice-chairs. In Japan, it will be necessary to foster
the greater participation of women in the area of standardization activities.
4. Promotion of study on IMT and cooperation
with open source communities
International Mobile Telecommunications (IMT) isa standardization topic concerning mobile communications. It is a unique technical field where wireless
and wired technologies converge. Expectation has
been growing recently for the development of a fifthgeneration mobile communication network called
IMT-2020. IMT is being studied not only by ITU but
by a variety of standardization bodies. In order toaccelerate the standardization of the non-wirelesspart of IMT-2020, ITU-T established the Focus
Group on IMT-2020 (FG-IMT2020) in May 2015. It
has been analyzing existing standards, studying existing technologies such as front/backhaul networks andsoftware implementation of networks, collaboratingwith open source communities, and promoting prototyping.
Since collaboration with ITU-R (ITU Radiocommunication Sector), which is responsible for wirelesstechnology, is important when studying IMT, collaboration with different ITU sectors was defined in
5

Resolution 38. Advances in the activities of FGIMT2020 have led to the formulation of a new resolution, which clarified the tasks of related SGs in addition to collaboration between ITU sectors in order toaccelerate the study on IMT. As a replacement toexisting Resolution 38, this resolution strengthens thecontent of Resolution 38.
FG-IMT2020 is studying the possibility of cooperating with open source communities to gain their
participation in standardization efforts. The cooperation between standardization groups and open source
communities is expected to expand in many areas
beyond IMT. With this trend taken into consideration,a new resolution was adopted to strengthen cooperation with open source communities.
5. DOA and countermeasures against
counterfeit products
Considerable time was spent at WTSA-16 discussing how to incorporate issues related to digital objectarchitecture (DOA) and its applications into resolutions. The DOA is a system used to identify the serial
number and other details of a product based on a twodimensional bar code. It can be used to detect imitation (counterfeit) products and manage the distribution channels of products (Fig. 5).
Imitation communication devices such as mobile
phones are said to be spreading particularly quickly
in developing countries. They represent a variety ofproblems, including infringement of intellectual
property rights and lack of due attention to safety andenvironmental protection. Some products are actually
NTT Technical Review

Global Standardization Activities

authentic but have been stolen with their serial numbers overwritten. However, it is necessary to note that
other than illicitly modified products, stolen products
have been properly certified at point of manufacture,and thus their problems are slightly different fromthose of imitation products. SG11 is studying whatcountermeasures can be taken to combat imitation
products and is proposing solutions, including one
that uses an International Mobile Equipment Identity(IMEI) database. An IMEI is assigned to each mobile
terminal. Another solution it proposes is the use of the
DOA.
The DOA is referred to in a number of resolutionsin this WTSA: Resolution 50 (cybersecurity), Resolution 60 (identifications for Internet of Things),
Resolution 78 (e-health), a new resolution related toimitation products, and a new resolution related tostolen mobile devices. The discussion centered onwhether to accept the DOA in these resolutions.
There were two opposing parties. One party, consisting of the USA and European nations, argued that the
DOA was just an example of implementation technologies, and that adopting it in resolutions without
studying its technical details is tantamount to denying
the existence of similar technologies that have been
adopted in recommendations.
The other party, consisting of Russia and the African nations, stated that the DOA was highly genericand useful technology and insisted on promoting its
study by explicitly referring to it in resolutions. Thisdivision was partly due to differences between thetwo parties in the interpretation of Resolution 188 of
the Plenipotentiary Conference, which refers to Recommendation ITU-T X.1255, which stated that it was
based on the DOA.
This matter was assigned to COM4, which discussed it in both formal and informal meetings.
Although a draft compromise with some modifications to the description of the DOA was informally
proposed, no conclusion was reached. The matter was
carried over to the next plenary conference. On thenight of November 2, with only two days left for the
conference, this matter was included in a new draft
resolution on imitation products. When modifications
to the description of the DOA recurred, the chairmade a hurried attempt to approve the draft resolution. The USA and European nations opposed thisattempt and raised a point of order, which was a
motion of objection.
The dispute over the DOA has several possible
causes. First, a top-down approach of adopting a
resolution in a plenary meeting was attempted in an
Vol. 15 No. 3 Mar. 2017

effort to deal with a technical issue that should more
properly be addressed by technical experts holding
detailed discussions in SGs. Second, a Memorandumof Understanding existed between ITU and the
DONA (Digital Object Numbering Authority) Foundation, which is promoting the DOA. Third, there wasa competition between member groups to obtainleadership in Internet-related technologies.
6. Discussions on the Internet
At the previous WTSA in 2012, there was greatinterest in international governance of the Internet
because the conference was to be followed immediately by the World Congress on International Telecommunications (WCIT), which is in charge of making revisions to the International Telecommunication
Regulations (ITR). At WTSA-16, discussions on theInternet were not as intense as before because there
was still no schedule for the next WCIT. The African
nations proposed a draft resolution promoting a study
on levying a tax on over-the-top (OTT) providers.
This was because telecommunication carriers indeveloping countries are seeing their revenues dwindle due to the expansion of OTT, and they want torecover them through taxation. While it is evident that
there is competition between OTT providers and telecommunication carriers, some argued that taxation
was not an appropriate matter for ITU-T, a body
dedicated to technical discussions, because responsibility for taxation lies with the sovereign governments of individual nations. In the end, the new draft
resolution was not adopted. The only decision thatwas made was to request SG2 and SG3 to study management and financial matters related to numberingresources.
The Middle Eastern nations and the RCC proposeda resolution relating to ITU-T’s participation inanother revision of the ITR. Although the Council
was studying revision of the ITR, it had made no
decision on specific directions for the revision. Since
the ITR has a major impact on ITU-T, some insisted
on holding discussions on this matter within ITU-T
by adopting a new resolution. In the end, WTSA
adopted a resolution to study the ITR through TSAG
based on the resolutions of the Plenipotentiary Conference and the Council.
In addition to the above, Resolution 52 on countermeasures against spam, Resolution 64 on the transition to IPv6 (Internet Protocol version 6), and Resolution 69 demanding unlimited access to Internet
resources, were updated.
6

Global Standardization Activities

7. Future prospects
At this WTSA, there was a sharp division of opinions, including those on the DOA, between developed
and developing countries, leading to intense disputes
not seen in recent years. However, with new people

Yoshinori Goto

Senior Research Engineer, Network Technology Project, NTT Network Technology Laboratories.
He received a B.E. and M.E. in applied physics
from Tohoku University, Miyagi, in 1992 and1994. He joined NTT Basic Research Laboratories in 1994. He has been researching and developing cable television systems, Internet protocol
television (IPTV), and machine-to-machine technology. He has been engaged in the standardization work for IPTV in ITU-T as a member of the
IPTV Focus Group and Global Standards Initiative since 2006. He has also served as Rapporteurof Question 11 of ITU-T SG9, Questions 5 and
25 of ITU-T SG13, and Question 21 of ITU-T
SG16. He has been a vice-chair of ITU-T SG13
since 2013. He is a member of the Institute ofElectronics, Information and Communication
Engineers (IEICE).

Kazuhiro Takaya

Senior Research Engineer, Supervisor, Environmental Technology and Management Project,
NTT Network Technology Laboratories.
He received a B.E. and M.E. in electrical andelectronic engineering from Okayama University
in 1993 and 1995. He joined NTT Telecommunication Network Laboratories in 1995. He is
researching electromagnetic interference in wireless and wired communication systems anddisaster prevention countermeasures using communication systems. At WTSA-16, he wasappointed as a vice-chair of ITU-T SG5. He is amember of IEICE.

7

appointed to chairmanships and vice-chairmanships
in many SGs, ITU-T will undertake its activities overthe next four years with updated missions adapted tothe current circumstances of the telecommunicationsindustry. We will contribute to the global expansionof NTT technologies.

Noriyuki Araki

Senior Research Engineer, Access Media Project, NTT Access Network Service Systems Laboratories.
He received his B.E. and M.E. in electrical andelectronic engineering from Sophia University,
Tokyo, in 1993 and 1995. He joined NTT AccessNetwork Service Systems Laboratories in 1995,
where he researched and developed operationand maintenance systems for optical fiber cable
networks. He has been contributing to standardization efforts in ITU-T SG6 since 2006. He was
the rapporteur of Question 6 in ITU-T SG6 from2006 to 2008 and the rapporteur of Question 17
in ITU-T SG15 from 2008 to 2012. He also
served as the chairman of the ITU-T Focus
Group on Disaster Relief Systems and Network
Resilience and Recovery. He has been a vicechair of ITU-T SG15 since 2013. He also contributes to the activities of IEC (International
Electrotechnical Commission) TC86 (TechnicalCommittee 86: Fibre optics). He received the
ITU-AJ award from the ITU Association ofJapan in 2012. He is a member of IEICE.

Hideyuki Iwata

Senior Research Engineer, Supervisor,
Research and Development Planning Department, NTT.
He received a Ph.D. in electrical engineeringfrom Yamagata University in 2011. From 1993 to
2000, he conducted research on high-density andaerial optical fiber cables at NTT Access Network Service Systems Laboratories. Since 2000,
he has been responsible for standardization strategy planning for NTT research and development.
He has been a delegate of IEC SC (Subcommittee) 86A (optical fiber and cable) since 1998 andof the ITU-T Telecommunication Standardization Advisory Group since 2003. He is a vicechair of the Expert Group on Bridging the Standardization Gap in the Asia-Pacific Telecommunity Standardization Program Forum. In 2004, hereceived an award from the IEC Activities Promotion Committee of Japan for his contributions
to standardization work in the IEC.

NTT Technical Review

External Awards
FY2016 Award of Director-General for Industrial Scienceand Technology Policy and Environment Bureau, METI, toRecognize Contributions to Industrial Standardization
Winner: Hisashi Izumita, NTT Access Network Service Systems
Laboratories
Date: October 6, 2016
Organization: Ministry of Economy, Trade and Industry (METI)
For his contributions to international standardization on the International Electrotechnical Commission (IEC) Technical Committee
86 (Fibre optics).
Excellent Presentation
Winner: Masaya Nohara, Yoko Ono, Mikayo Iwata, MasahikoHayashi, and Takeshi Komatsu, NTT Device Technology Laboratories
Date: December 7, 2016
Organization: EcoDesign Products & Service (EcoDePS) Symposium 2016
For “Proposal and Basic Performance of New Concept Batteries
which Consist of Fertilizer Components.”
Published as: M. Nohara, Y. Ono, M. Iwata, M. Hayashi, and T.
Komatsu, “Proposal and Basic Performance of New Concept Batteries which Consist of Fertilizer Components,” EcoDePS 2016, Tokyo,
Japan, Dec. 2016.
2016 IEICE Communications Society OCS Young Researchers Award
Winner: Masanori Nakamura, NTT Network Innovation Laboratories
Date: December 20, 2016
Organization: The Institute of Electronics, Information and Communication Engineers (IEICE) Communications Society, TechnicalCommittee on Optical Communication Systems (OCS)
For “A Construction Method and Basic Characteristic Evaluationof Optical 8-dimensional Modulation Using Square-QAM.”
Published as: M. Nakamura, M. Yoshida, K. Yonenaga, and A.
Hirano, “A Construction Method and Basic Characteristic Evaluation

1of Optical 8-dimensional Modulation Using Square-QAM,” IEICE
Tech. Rep., Vol. 115, No. 276, OCS2015-53, pp. 59–64, 2015.
Microsoft Most Valuable Professional Award (Data Platform Category)
Winner: Tsuyoshi Ozawa, NTT Software Innovation Center
Date: January 1, 2017
Organization: Microsoft Corporation
For his contributions to open source software projects concerning
Apache Hadoop and enhancement of the Apache Hadoop ecosystem.
*Microsoft is a registered trademark of Microsoft Corporation in the
United States and/or other countries. Apache Hadoop is a registered trademark of the Apache Software Foundation in the United States and/or othercountries.

Young Scientist Presentation Award
Winner: Megumi Kurosu, NTT Basic Research Laboratories
Date: March 14, 2017 (awards ceremony to be held)
Organization: The Japan Society of Applied Physics (JSAP)
For “Dispersion Effects on Phonon Temporal Waveforms in a
Phononic Crystal Waveguide.”
Published as: M. Kurosu, D. Hatanaka, K. Onomitsu, and H. Yamaguchi, “Dispersion Effects on Phonon Temporal Waveforms in a
Phononic Crystal Waveguide,” The 77th JSAP Autumn Meeting,Niigata, Japan, Sept. 2016.
Young Scientist Presentation Award
Winner: Takuya Ikuta, NTT Basic Research Laboratories
Date: March 14, 2017 (awards ceremony to be held)
Organization: The Japan Society of Applied Physics (JSAP)
For “Implementation of Quantum State Tomography for Highdimensional Time-bin Entanglements.”
Published as: T. Ikuta and H. Takesue, “Implementation of Quantum
State Tomography for High-dimensional Time-bin Entanglements,”
The 77th JSAP Autumn Meeting, Niigata, Japan, Sept. 2016.

NTT Technical Review

Papers Published in Technical Journals andConference Proceedings
Channel Model Considering Frequency Dependency
Based on Propagation Measurements with Multiple Frequencies for 5G Systems
M. Sasaki, M. Inomata, W. Yamada, N. Kita, T. Onizawa, and M.
Nakatsugawa
Proc. of the European Wireless 2016, pp. 15–20, Oulu, Finland,
May 2016.
A path loss model is described and the frequency dependency
characteristics of path loss are presented. The characteristics are
obtained from measurement results in multiple frequency bands,
including those above 6 GHz. In order to analyze the frequency
dependency, measurements are carried out with multiple frequency
bands from 0.8 GHz to 37.1 GHz. The measurement environments
are a street canyon and an indoor office, which respectively correspond to an urban micro cell and an indoor hot spot of mobile communication system scenarios. On the basis of the obtained measurement results, path loss characteristics are clarified and path loss
models are proposed for the street canyon and indoor office environments. The models validity is also verified.

Path Loss Characteristics between Different Floors from
0.8 to 37 GHz in Indoor Office Environments
M. Sasaki, M. Inomata, W. Yamada, N. Kita, T. Onizawa, M.
Nakatsugawa, K. Kitao, and T. Imai
Proc. of ISAP 2016 (International Symposium on Antennas andPropagation), pp. 66–67, Okinawa, Japan, October 2016.
This paper describes analytical results obtained for floor penetration loss characteristics and their frequency dependency by measuring in multiple frequency bands, including those above 6 GHz, in anindoor office environment. Measurement and analysis results showthat the floor penetration loss depends on two dominant components:
paths through floors and outside buildings. It was clarified that thecharacteristics of these paths determine the frequency dependency of
the floor penetration loss.

Contextual Analysis on Geminate/Singleton Identification
Difficulties for L2 Learners of Japanese Based on Perceptual Features
Y. Zhang, H. Nakajima, M. Sonu, H. Kato, and Y. Sagisaka
Proc. of the 5th Joint Meeting of the Acoustical Society of America and Acoustical Society of Japan, Honolulu, HI, USA, November/
December 2016.
It is widely known that Japanese geminate/singleton consonant
identification is one of the biggest problems for L2 learners. We havebeen analyzing identification error characteristics based on theirperceptually motivated features.

Ancilla-driven Instantaneous Quantum Polynomial Time
Circuit for Quantum Supremacy
Y. Takeuchi and Y. Takahashi
Physical Review A, Vol. 94, 062336, December 2016.
Instantaneous quantum polynomial time (IQP) is a model of (prob-

Vol. 15 No. 3 Mar. 2017ably) nonuniversal quantum computation. Since it has been proventhat IQP circuits are unlikely to be simulated classically up to a multiplicative error and an error in the l1 norm, IQP is considered as oneof the promising classes that demonstrates quantum supremacy.
Although IQP circuits can be realized more easily than a universal
quantum computer, demonstrating quantum supremacy is still difficult. It is therefore desired to find subclasses of IQP that are easy to
implement. In this paper, by imposing some restrictions on IQP, we
propose ancilla-driven IQP (ADIQP) as the subclass of commuting
quantum computation suitable for many experimental settings. We
show that even though ADIQP circuits are strictly weaker than IQP
circuits in a sense, they are also hard to simulate classically up to a
multiplicative error and an error in the l1 norm. Moreover, the properties of ADIQP make it easy to investigate the verifiability of ADIQP
circuits and the difficulties in realizing ADIQP circuits.

Vibration of the Feet Soles Inducing a Walk Sensation
Expands Peripersonal Space
T. Amemiya, Y. Ikei, K. Hirota, and M. Kitazaki
Transactions of the Virtual Reality Society of Japan, Vol. 21, No.
4, pp. 627–633, December 2016 (in Japanese).
The representation of peripersonal space is remapped by body
actions such as integrating tactile stimuli from the body’s surface
with multisensory stimuli presented within a limited distance fromthe body. Previous research showed that the boundaries of peripersonal space extend while walking when listening to a looming sound,but it is unclear whether the boundaries change when a sensation ofwalking is induced with no physical body motion. Here, we examine
the change using a technique to induce a sensation of pseudo-walking
by presenting vibrotactile stimuli of the sound of recorded footsteps
at the soles of the feet. Experiments were performed to compare thereaction times to detect a vibrotactile stimulus on the chest while
listening to a sound looming toward the body, taken as a proxy of theperipersonal space boundary. Experimental evaluations showed thatthe peripersonal space seems to expand when a sensation of pseudowalking was clearly induced.

Resource Allocation Method of Service Chaining for Guaranteeing Minimum Bandwidth and High Resource Utilization
H. Yamazaki, K. Mochizuki, S. Homma, K. Sugisono, and M.
Omotani
IEICE Transactions on Communications, Vol. E100-B, No. 1, pp.
98–109, January 2017.
Service chaining (SC) is a method for realizing a service by transferring flows among several service functions (SFs) that process
packets. A route among SFs is called a service path (SP). Service
chaining is being developed to reduce costs, increase flexibility, andshorten time-to-market. SC technologies are expected to be applied
to carrier networks so that large communication carriers benefit fromthem. We assume that SPs process the traffic of services that treat allusers in the same way such as an Internet access service for homeusers. An SP processes flows from several users. We do not assume
that each SP is assigned to a user. Because a carrier network2

Papers Published in Technical Journals and Conference Proceedings

accommodates many users, each service will be heavily utilized.
Therefore, it is assumed that the amount of traffic of a service islarger than the resource of an SF apparatus. Several SPs are required
to process the traffic. SPs are supposed to meet two requirements.
One is guaranteeing minimum bandwidth. The other is reducing thenumber of SF apparatuses, i.e., high resource utilization. Resource
utilization depends on the combination of the resource quantities ofSF apparatuses. Network operators have to determine the bandwidthof each SP within the range from the minimum bandwidth to theresource quantities of SF apparatuses to maximize resource utilization. Methods for determining the bandwidth of each SP have notbeen proposed for meeting the two requirements. Therefore, we propose a resource allocation method for this purpose. The proposed
method determines the bandwidth of each SP on the basis of the
combination of the resource quantities of SF apparatuses for guaranteeing the minimum bandwidth and maximizing resource utilization
and allocates necessary resources to each SP. We also evaluate theproposed method and confirm that it can guarantee the minimum
bandwidth of SPs and achieve high resource utilization regardless ofthe combination of resource quantities of SF apparatuses. Although
SF apparatuses are generally produced without considering the combinations of resource quantities of SF apparatuses in SPs, the proposed method can provide more options for selecting SF apparatuses.

Visual Area V5/hMT+ Contributes to Perception of Tactile
Motion Direction: a TMS Study
T. Amemiya, B. Beck, V. Walsh, H. Gomi, and P. Haggard
Scientific Reports, Vol. 7, 40937, January 2017.
Human imaging studies have reported activations associated withtactile motion perception in visual motion area V5/hMT+, the primary somatosensory cortex (SI), and the posterior parietal cortex(PPC; Brodmann areas 7/40). However, such studies cannot establish
whether these areas are causally involved in tactile motion perception. We delivered double-pulse transcranial magnetic stimulation

3(TMS) while moving a single tactile point across the fingertip, andused signal detection theory to quantify perceptual sensitivity tomotion direction. TMS over both SI and V5/hMT+, but not the PPC
site, significantly reduced tactile direction discrimination. Our results
show that V5/hMT+ plays a causal role in tactile direction processing, and strengthen the case for V5/hMT+ serving multimodal motion
perception. Further, our findings are consistent with a serial model ofcortical tactile processing, in which higher-order perceptual processing depends upon information received from SI. By contrast, ourresults do not provide clear evidence that the PPC site we targeted(Brodmann areas 7/40) contributes to tactile direction perception.

Ad-hoc Mobile Network Architecture Using Distributed
P-GW on Unlicensed Bands for LTE
K. Kawamura and N. Takaya
Proc. of IEEE CCNC 2017 (the 14th IEEE Annual Consumer
Communications & Networking Conference), Las Vegas, NV, USA,pp. 749–754, January 2017.
Data traffic has been increasing rapidly in recent years on mobile
networks. Several methods using unlicensed bands for LTE are proposed to increase wireless capacity. Stand-alone unlicensed LTE is
also proposed as a technology that provides LTE service with only
unlicensed bands. In this paper, we propose distributed P-GW systems for stand-alone unlicensed LTE base stations that can provide
future mobile services by using an ad-hoc network architecture.
Additionally, we evaluate the effectiveness of our proposal in termsof network load, handover latency, and user data plane latency incomparison with the full Cloud EPC model and IPsec model. We
reveal that the proposed architecture has an advantage in terms of
network loads and end-to-end latency and has an issue with handoverlatency.

NTT Technical Review