Advanced Wireless & Communication
Research Center

Message from the Director, Prof. Takeo Fujii
2020 was a difficult year for research due to the blow of Covid-19. Many international anddomestic conferences were canceled and shifted to virtual conferences. The activities of Advanced
Wireless Communication Research Center (AWCC) were slightly decreased caused by the stateof emergency in Japan. Many national projects however were continued without a big change of
research plan and the research activities become increased in the latter part of the year. All
professors, staffs, and students of AWCC continue their activities by considering safe of theirhealth and maximizing the research output in the limited research environment. I hope we canovercome Covid-19 and return to a peaceful life.
For the years, AWCC has been aiming and conducting four missions that are;- Dedication to advanced research on wireless communications; offering more unique results.
- Education in graduate school for cultivating specialty in engineering; specialized and universal
education in the area.
- Active collaboration / joint research with industries and government; transferring the outcomesto the society.
- Constant acquisition of competitive research funds; for self-supported operation.
The current vision of AWCC is “Ambient Wireless in Connected Community (AWCC).” AWCC
intends to cover broader research area including not only wireless communications but also any
promising aspects of “wireless” and “communications”. As an academic institute, it is our mission
to pursue basic research in science and technology. Toward the goals, AWCC will enhance its
force and strengthen its presence in the world.
We are focusing the following four research sectors.
(1) Wireless Technology as Social Infrastructure
Wireless technologies will have more importance in the society as the base of safe, secure andsmart life for the individual and the community. Intelligent Transport System (ITS) is one of the
focused topics, anticipating the great demand for automated driving.
(2) Innovative Hardware for Wireless & Communication
Demands for broadband and high-capacity mobile communication systems are very strong,
and 5G and beyond system is being developed in the world. The 5G and beyond system introduces
new usage of frequency spectra called multi-band multi-access, which requires innovation in RF
hardware to achieve higher-accuracy signal transmission with flexibility. Also, wireless powertransfer is another hot and important topic.
(3) Advanced Wireless System & Networks
We have developed many fundamental technologies such as distributed dynamic multi-hop
network, cognitive radio, fault-tolerant network operation, and radio environment-aware
communications. From now on, we integrate the technologies and establish an ultimate wirelessnetwork design.
(4) Exploring Low Power Wireless
By reducing power consumption of wireless system dramatically, applications of wirelesscommunications will spread wider than now. It will make all things connected in the world,
realizing the word "IoT". Innovative low-power technologies are necessary to realize such a world.
As the open research center to the society, AWCC would like to think together with people
and contribute much in research and education of “wireless” and “communications”. We hopeyour kind help and great understanding to AWCC.

-1-１. ABOUT AWCC
1.1 OVERVIEWThe Advanced Wireless Communication
Research Center (AWCC) was launched in
April 2005 with the aims of establishing a
global hub for wireless communications;advancing education in wireless technology;
industrial

collaboration

andtechnology

transfer; and nurturing young engineers
with strong emphasis on both theoreticalandexperimental

aspectsofwireless

communications. In April 2015, the center was re-launched as the Advanced Wireless andCommunication Research Center with the same abbreviation, AWCC, to enhance its remarkablerange of activities over the previous ten years. With funding of approximately 1000 million yen
over nine years, the center consists of 4 full time, 5 concurrent, 20 cooperative, and 6 visiting
professors. In addition, there are 9 visiting professors from industry and more than 100 graduate
students, post-doctoral and research fellows. The center actively contributes to academic societiesand publishes more than 150 papers annually in top journals and proceedings of international
conferences.
The AWCC organizes regular seminars and workshops with the highlight of 2014 being the“Tokyo Wireless Technology Summit” held in March 2014. The meeting focused on the next major
phase of mobile telecommunications called 5th generation (5G) and attracted approximately 240
participants from all over the world.

1.2 FACILITIESAWCC is located on the east-campus of the University ofElectro-Communications in Chofu city, Tokyo near Shinjuku
district in Japan. The center has opened with 10,441 square footof modern research space containing a class room, twoconference rooms, four research offices, and two experimentrooms with a wide range of instruments including FPGA
development platforms, signal generators, vector network
analyzers, spectrum analyzers, software defined radios, and soon. Also, it has extensive computer and network resources
including high-speed workstations and personal computerswhich are integrated with resources of the Univeristy of ElectroCommunications.

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1.3 PEOPLE【Director, Full-time Prof. Takeo Fujii】
Takeo Fujii was born in Tokyo, Japan, in 1974. He received the B.E., M.E.
and Ph.D. degrees in electrical engineering from Keio University,
Yokohama, Japan, in 1997, 1999 and 2002 respectively. From 2000 to 2002,
he was a research associate in the Department of Information andComputer Science, Keio University. From 2002 to 2006, he was an
assistant professor in the Department of Electrical and Electronic
Engineering, Tokyo University of Agriculture and Technology. From 2006
to 2014, he has been an associate professor in Advanced WirelessCommunication Research Center, The University of Electro-Communications. Currently, he is a
professor in Advanced Wireless and Communication Research Center, The University of ElectroCommunications. His current research interests are in cognitive radio and ad-hoc wirelessnetworks. He received Best Paper Award in IEEE VTC 1999-Fall, 2001 Active Research Awardin Radio Communication Systems from IEICE technical committee of RCS, 2001 Ericsson Young
Scientist Award, Young Researcher's Award from the IEICE in 2004, The Young Researcher
Study Encouragement Award from IEICE technical committee of AN in 2009, Best Paper Award
in IEEE CCNC 2013, and IEICE Communication Society Best Paper Award in 2016. He is amember of IEEE and a fellow of IEICE.

【Full-time Associate Prof. Koji Ishibashi (Promoted to Professor in 2021)】
Koji Ishibashi received the B.E. and M.E. degrees in engineering fromThe University of Electro-Communications, Tokyo, Japan, in 2002 and2004, respectively, and the Ph.D. degree in engineering from Yokohama
National University, Yokohama, Japan, in 2007. From 2007 to 2012, he
was an Assistant Professor at the Department of Electrical and Electronic
Engineering, Shizuoka University, Hamamatsu, Japan. Since April 2012,he has been with the Advanced Wireless Communication Research Center(AWCC), The University of Electro-Communications, Tokyo, Japan where
he is currently an Associate Professor. From 2010 to 2012, he was a Visiting Scholar at the Schoolof Engineering and Applied Sciences, Harvard University, Cambridge, MA. Prof. Ishibashi has
contributed more than 100 articles to international journals and conference proceedings. His
current research interests are grant-free access, non-orthogonal multiple access (NOMA),
millimeter wave communications, ultra-low power communications, signal processing, andinformation theory. He is a senior member of IEEE and IEICE.

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【Full-time Associate Prof. Koichi Adachi】
Koichi Adachi received the B.E., M.E., and Ph.D. degrees in engineeringfrom Keio University, Japan, in 2005, 2007, and 2009 respectively. His
research interests include cooperative communications and energy
efficientcommunication technologies. From 2007 to 2010, he was a Japan

Society for the Promotion of Science (JSPS) research fellow. He was thevisiting researcher at City University of Hong Kong in April 2009 and thevisiting research fellow at University of Kent from June to Aug 2009. FromMay 2010 to May 2016, he was with the Institute for Infocomm Research,
A*STAR, in Singapore. Currently, he is an associate professor at The
University of Electro-Communications, Japan. He was an Associate Editor IEEE WirelessCommunications Letters since 2016, IEEE Transactions on Vehicular Technology between 2016
– 2018, IEEE IEEE Transactions on Green Communications and Networking since 2016, andIEEE Open Journal of Vehicular Technology since 2019. He is a senior member of IEEE and amember IEICE. He was recognized as the Exemplary Reviewer from IEEE WirelessCommunications Letters in 2012, 2013, 2014, and 2015. He was awarded excellent editor award
from IEEE ComSoc MMTC in 2013. He is a coauthor of WPMC2020 Best Student Paper Award.

【Concurrent Prof. Koichiro Ishibashi】
Koichiro Ishibashi has been a professor of The University of ElectroCommunications, Tokyo, Japan since 2011. He received PH. D degreefrom Tokyo Institute of Technology in 1985. He joined Central ResearchLaboratory, Hitachi Ltd. in 1985, where he had investigated low power
technologies for Super H microprocessors and high density SRAMs.
From 2004 to 2011, he was in Renesas Electronics where he developed
low power IPs mainly for mobile phone SOCs as a department manager.
He has presented more than 110 academic papers at international conferences including
ISSCC, IEDM invited papers, and IEEE Journals. He was awarded R&D 100 for thedevelopment of SH4 Series Microprocessor in 1999. He is a member of IEICE and a Fellow ofIEEE.
His current interests include design technology of low power LSI, and IoT applications using
the low power LSIs. They include low power design technology using SOI devices and energy
harvesting sensor networks.

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【Concurrent Prof. Takayuki Inaba】
Takayuki Inaba received a B.S. degree from the Department of Physics,
Tokyo Institute of Technology, in 1981, completed the M.E. program inphysics in 1983. He received the Ph.D. degree in engineering from Tokyo
Institute of Technology in 2001. Since April 2008, he has been with the
University of Electro-Communications, where he is a Professor at the
Department of Mechanical and Intelligent Systems Engineering,
Graduate School of Informatics and Engineering. He has been engaged inresearch and development of radar signal processing, and adaptive array signal processing, andautomotive radar systems. He is a senior member of IEEE. He is a recipient of theTelecommunications Advancement Foundation Award (32th), IEEJ Distinguished Paper Award(72th), 2014 IEEE AES Japan-chapter Best Paper Award, IEICE Communications Society
Distinguished Contributions Award 2009, 2006 IEEE AES Japan-chapter Best Paper Award, and
IEICE Communications Society Excellent Paper Award 2006.

.

【Concurrent Prof. Koji Wada】
Koji Wada received the B.E. and M.E. degrees from Kinki University,
Osaka, Japan, in 1991 and 1995, respectively, and the Doctorate degreefrom Yamaguchi University, Yamaguchi, Japan, in 1999. From 1999 to
2004, he was a Research Associate with the Department of ElectricalEngineering and Electronics, Aoyama Gakuin University, Kanagawa,Japan. From 2004 to 2015, he worked as an Associate Professor at the
Department of Electronic Engineering, the University of ElectroCommunications, Tokyo, Japan and he is Currently a Professor at the
Department of Computer and Network engineering, Graduate School ofInformatics and Engineering, the University of Electro-Communications. His research interests
include resonators, filters, multiplexers, multiband circuits, tunable circuits, periodic structure,and metamaterial circuits. Dr. Wada is a member of the Institute of Electronics, Information
and Communication Engineers (IEICE), Japan, Institute of Electrical Engineers of Japan(IEEJ), and Japan Institute of Electronics Packaging (JIEP).

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【Concurrent Prof. Motoharu Matsuura】
Motoharu Matsuura received the Ph.D. degree in electrical engineeringfrom the University of Electro-Communications, Tokyo, Japan, in 2004.
In 2007, he joined the Department of Information and Communication
Engineering at the University of Electro-Communications as an AssistantProfessor. From 2010 to 2011, on leave from the university, he joined the
COBRA Research Institute, Eindhoven University of Technology,
Eindhoven, The Netherlands, as a Visiting Researcher, where he studied
ultrahigh-speed optical signal processing using semiconductor-based
devices. He is currently a Professor with the Graduate School of Informatics and Engineering,
Department ofCommunication Engineering and Informatics, University of Electro-

Communications. His research interests include optical signal processing, photonic subsystems,
and radio-over-fiber transmission systems. He is the author or coauthor of more than 180 papers
published in international refereed journals and conferences. He received the Ericsson Young
Scientist Award in 2008, the FUNAI Information Technology Award for Young Researcher in
2009, and the Telecommunication System Technology Award of the Telecommunications
Advancement Foundation in 2011. He is a member of IEEE, OSA, and IEICE.

【Concurrent Associate Prof. Ryo Ishikawa (Promoted to Professor in 2021)】
Ryo Ishikawa received the B.E., M.E., and D.E. degrees in electronicengineering from Tohoku University, Sendai, Japan, in 1996, 1998, and2001, respectively. In 2001, he joined the Research Institute of ElectricalCommunication, Tohoku University, Sendai, Japan. In 2003, he joined the
University of Electro-Communications, Tokyo, Japan. His researchinterest is the development of microwave compound semiconductor
devices and related techniques. He was the recipient of the 1999 Young
Scientist Award for the Presentation of an Excellent Paper of the Tohoku
Chapter, Japan Society of Applied Physics.

【Visiting Professors】
Prof. Kazuhiko Honjo, Ph.D.
Prof. Yoichiro Takayama, Ph.D.
Prof. Akira Saito, Ph.D.
Prof. Masashi Hayakawa, Ph.D.
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Prof. Hiroshi Suzuki, Ph.D.
Prof. Mitsuo Makimoto, Ph.D.
Prof. Giuseppe T. F. de Abreu, Ph.D
【Cooperative Professors】
Prof. Yasushi Yamao, Ph.D.
Prof. Nobuo Nakajima, Ph.D.
Prof. Haruhisa Ichikawa, Ph.D.
Prof. Kazuo Ohta, Ph.D.
Prof. Sadao Obana, Ph.D.
Prof. Toshihiko Kato, Ph.D.
Prof. Naoto Kishi, Ph.D.
Prof. Tetsuro Kirimoto, Ph.D.
Prof. Kazuo Sakiyama, Ph.D.
Prof. Fengchao Xiao, Ph.D.
Prof. Xi Zhang, Ph.D.
Prof. Cong-Kha Pham, Ph.D.
Associate Prof. Manabu Akita, Ph.D.
Associate Prof. Yoshiaki Ando, Ph.D.
Associate Prof. Hiroyuki Kasai, Ph.D.
Associate Prof. Toshiharu Kojima, Ph.D.
Associate Prof. Hisa-Aki Tanaka, Ph.D.
Associate Prof. Kazuki Nishi, Ph.D.
Associate Prof. Wu Celimuge, Ph.D.
Assistant Prof. Satoshi Ono, Ph.D.
Assitant Prof. Katsuya Suto, Ph.D.

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【Cooperative Professors from Industry】
Prof. Kunio Uchiyama (AIST)
Prof. Yukihiko Okumura (NTT Docomo R&D)
Prof. Yoji Kishi (KDDI Research Inc.)
Prof. Terunao Soneoka (NTT-AT)
Prof. Akinori Taira (Mitsubishi Research Institute Inc.)
Prof. Hiroyuki Tsuji (NICT)
Prof. Hideki Hayashi (Softbank Corp.)
Prof. Hiroyuki Seki (Fujitsu Laboratory Ltd.)
Prof. Yukitsuna Furuya (WiTLa)
Prof. Kenji Yoshida (Intermedia Laboratory Inc.)-8-

2.1 Division of Wireless Technologies as Social Infrastructure
2.1.1 Purpose of Research
Wireless technologies will have more importance in the society as the base of safe,
secure and smart life for individuals and community. Various types of machine tomachine communication such as sensors, IoT devices and vehicular communications will
spread in the society taking little notice but support safety and secureness of society, as
well as creating more comfortable and smarter life. The goal is to develop suchtechnologies.

2.1.2 Research Staffs and Their Specialties
Prof. Takayuki Inaba Division Leader, ITS, Radar
Prof. Takeo Fujii ITS, Radio Environment Analysis (REA), DPRN, Wireless security
Associate Prof. Koichi Adachi (Drone)

2.1.3 Major Research Outcomes in 2020
(A) Intelligent Transport System (ITS)
Advance technologies for Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I)
communications towards automated driving are studied. Since Vehicular communications
are conducted in fully distributed environments, wireless communication techniques forsuch environments are pursued. This work was supported by the Ministry of Internal
Affairs and Communications (MIC) of Japan under the Strategic Innovation Promotion(SIP) program during physical years 2014 to 2016, MIC-1, "Development of V2V and V2I
Communication Technologies necessary for Automated Driving Systems".
On the other hand, in order to realize fully automated driving, it is not enough bystudying only vehicular communication issues, but necessary to discuss and collaborate
with the researchers who study automated vehicle control and stand-alone sensors on thevehicles. Therefore, we established a project with related members to such subjects andcollaborate on the Grants-in-Aid for Scientific Basic Research A, "Basic Research for
Integrated Automated Driving System combining Stand-Alone Sensors and Cooperation by
vehicle communications."[V2V Packet Delivery Ratio Estimation based on Spectrum Database considering Packet Collisionby using Positions and Density of Vehicles] (Fujii Lab.)
V2V communication is expected as one of the technologies to realize connected andautomated driving. However, it is difficult to know the radio environment accurately in V2V
communication and the method for avoiding packet collision and estimating radio
environments is required. The measurement-based spectrum database (MSD) is one of themethods to estimate radio environments. The conventional method using the MSD canestimate radio environment with high accuracy by creating packet delivery ratio (PDR) maps.
However, in that method, the accuracy of PDR maps is expected to be deteriorated
significantly if the vehicle density changes between creating and using the maps. This paper
proposes the method for correcting PDR maps considering packet collision by using positions
and the density of vehicles. Furthermore, we analyze the proposed method by computer
simulation.
We propose a method for estimating PDR considering packet collision by using positions
and density of vehicles. This method corrects a PDR by multiplying the PDR not considering-9-

packet collision by the probability that packet collision does not occur. The PDR consideringcollisions of the packets from the transmitting vehicle to the receiving vehicle is found by
𝑃𝐷𝑅 𝑪 , 𝑪
𝑃𝐷𝑅𝑪 ,𝑪,
𝑃𝐻 𝑪 , 𝑪(1)
where 𝑪 and 𝑪 are the meshes of the transmitting and the receiving vehicle, 𝑃𝐷𝑅𝑪 ,𝑪and 𝑃𝐻 𝑪 , 𝑪
are the PDR not considering packet collision and the probability that thehidden collision does not occur when one vehicle transmits packets at 𝑪 and anothervehicle receives at 𝑪 .
In order to evaluate the performance of the proposed method, computer simulations using
the C++ programming language are executed. Fig. 2.1-1 shows the layout of the simulation
scenario. Each street of the crossroads has 5 m width and 215 m length. The simulation
results show that the proposed method can estimate PDR more accurately than the method
not considering packet collision. Fig. 2.1-2 shows the results of the simulation of therelationship between PDR and CDF for each number of vehicles. The results show that theaccuracy of the proposed method decreases as the number of vehicles increases. The reason
for the accuracy degradation is that the number of packets increased by rescheduling is notconsidered.

Fig. 2.1-1. The layout of simulation scenario

Fig. 2.1-2. The relationship between PDR
and CDF for each number of vehicles[Packet delivery rate information-based routing design for V2V networks with spectrum database](Fujii Lab.)To realize reliable vehicle-to-vehicle (V2V) communications, it is necessary to accurately
estimate the radio propagation fluctuation owing to the mobility of vehicles. The
measurement-based spectrum database (MSD) for distributed environments has attracted
attention as an enabler to accurately estimate radio propagation. The MSD stores theaverage received signal power for each transmission and reception location. This information
can be utilized to appropriately determine the modulation format and relay vehicle in V2V
communications. However, from our previous work, it has been found that there is nocorrelation between the average signal power and the packet delivery rate (PDR) owing to
the shadowing in the dense structures. Therefore, the communication reliability may be poorin this environment. This paper proposes a routing method based on the PDR in the MSD for
multi-hop V2V communications. Furthermore, we analyze the proposed method by computer
simulation.
In this study, the PDR information of each link is obtained from the MSD and used to
select relay vehicles. When the estimated PDR of a link is X [%] or more, the link is extracted
as a candidate for multi-hop V2V communications. Then, using the extracted links, the end-

- 10 -

to-end communication route is constructed based on Dijkstra’s method. We also propose thecompensation method for the error using the confidence interval estimation. In general,
shadowing and fading characteristics are heavily depending on geographical conditions, suchas surrounding structures. Therefore, it is difficult to deterministically formulate theprobability distribution of PDR for various environments. By considering the above fact, theconfidence interval estimation based on Student's t-distribution is used to compensate for
PDR errors.
To evaluate the performance of the proposed method, computer simulations using the C
programming language are executed. Fig. 2.1-3 shows the layout of the simulation scenario.
Each street of the crossroads has a 5 m width and 920 m length. When we use thecompensation method, Fig. 2.1-4 shows the average number of hops, and Fig. 2.1-5 shows theaverage PDR. The simulation results show that we have confirmed that the average PDR canbe accurately compensated using the confidence interval estimation even if the PDR includes
the estimation error.
5m

300m

300m

y
x

Fig. 2.1-3. The layout of the simulation scenario
w/o Error

w/ Errorw/o Error

Margin design

0.98

6.05
6.04

Average PDR

Average hops

Margin design

0.99

6.06

6.03
6.02

0.97
0.96
0.95

6.01

0.94

6.00

0.93

5.99

0.92

5.98
0.025

w/ Error

1.00

6.07

0.050

0.075

0.100
0.125
Standard deviation σ

0.150

0.175

0.200

Fig. 2.1-4. The average number of hops basedon the PDR compensation

0.91
0.025

0.050

0.075

0.100
0.125
Standard deviation σ

0.150

0.175

Fig. 2.1-5. The average PDR based on the
PDR compensation- 11 -

0.200

[ULA Equipped UAV-BS for Low Power
Wireless Networks] (Adachi Lab.)
This research considers a scenario wherean unmanned aerial vehicle (UAV) equippedwith a uniform linear array (ULA) acts as arelay in a cellular system. Due to the line-ofsight (LoS) channel, the UAV can receive asignal with high power from the desired basestation (BS). However, at the same time, theinterference from neighboring BSs becomessignificant. Thus, it is necessary to handle thisinterference so that the UAV can have a better
signal-to-interference plus noise ratio (SINR).
This research introduced coordinated multipoint (CoMP) transmission and applied
minimum mean square error (MMSE) based
beamforming to suppress the interference
from neighboring BSs. By improving the
SINR, the area where the UAV can be deployed
expands. Thus, it can be more flexibly
deployed to increase the system channel
capacity. We have conducted numericalsimulation. Fig. 2.1-6 shows the average SINR
performance at a user equipment (UE). By
deploying UAV, the received SINR increases.
The channel capacity of each UE is shown in
Fig. 2.1-7, which shows that the proposed
approach can improve the channel capacity
due to the flexible deployment of UAV andbeamforming.

Fig. 2.1-6 Received SINR performance.

Fig. 2.1-7 Channel capacity.

(B) Radar Signal Processing[Vehicle Onboard Radar] (Inaba Lab.)We have proposed stepped multiple frequency (SMF) modulation. The unique radar
modulation/demodulation method can achieve a high range resolution and a long-range
detection performance by a narrow receiver bandwidth compared to transmitting bandwidth.
That is why this method achieves not only a high range resolution but a long-range detection
performance. Stepped multiple frequency CPC modulation that is the one of SMF made it
possible to obtain the good range side-lobe in spite of using short code length. Authors havedeveloped the millimeter wave radar using stepped multiple frequency CPC. Thesemillimeter wave radars meet the specified low-power radio station standard of the millimeterwave in Japan. In recent years, Japanese radio wave laws for short range radar has been
modified for use of the ultra-wide bandwidth of 5 GHz in 79 GHz band. The advantage of thismethod itself must be more remarkable for use of the ultra-wide bandwidth. In the case ofultra-wideband, we face the velocity and range ambiguity problems, since both the pulse
repetition interval and the frequency steps should be sparse.
To overcome the problems, we proposed the sparse frequency division scheme, in which
the frequency steps are designed for obtaining good side-lobe characteristics by fewer number- 12 -

of frequency steps without range ambiguity. The target range and velocity are obtained by
the signal processing of iterative signal subtraction and frequency estimation (shown in
Fig.2.1-7). Simulation results indicated that the RMSE of range of each target shows a good
agreement with CRLB. These proposed methods can detect the targets having different
receiving signal level of power among them with the resolution of ultra-wideband without
ambiguity in both velocity and range profiles.

Fig.2.1-7 Block diagram of the signal processing of
iterative signal subtraction and frequency estimation
method in stepped multiple frequency radar.

Fig.
2.1-8
Targetrange
estimation RMSE and CRLB of
the proposed method.

2.1.4 Funds【Grants-in-Aid for Scientific Research】
1. Fund for the Promotion of Joint International Research (Fostering Joint International
Research) “Research on Advanced Wireless Vehicle Networks with Learning Spectrum
Environment for Cooperative Self Driving”
T. Fujii
2. Scientific Research C “Research on Energy Efficient Wireless Communications Network
Using UAV-BS”
K. Adachi【Commissioned Research】
1. Strategic Information and Communications R&D Promotion Program (SCOPE), “R & D
of Ultra-wide band coherent radar technology”
T. Inaba, Y. Yamao and M. Akita【Cooperative Research】
1. "Research on communications technology for machine tools"
K. Adachi and Y. Yamao【Other Funds】
1.

Parameter design for stepped multiple frequency CPC radar”, Academic consulting
T. Inaba
2. “Analysis program of stepped multiple frequency CPC”, License agreement T. Inaba- 13 -

2.2

Advanced Hardware Research Division

2.2.1 Purpose of Research
Research and development of wireless information/power transmission hardware
for next generation mobile communication base stations and terminal devices

2.2.2 Research Staffs and Their Specialties
Prof. Koichiro Ishibashi
Prof. M. Matsuura
Prof. K.Wada
Associate Prof. R. Ishikawa
Visiting Prof.Y. Yamao
Visiting Prof. K. Honjo
Visiting Prof. Y. Takayama
Visiting Prof. A. Saitou

Low Power RF Devices, Sensor Networks
Wideband RoF Systems, Devices, Fibers and Integration
Microwave Filters and Their Applications
Microwave/Millimeterwave Devices and Circuits
Reconfigurable RF Circuit, Nonlinear Compensation
Microwave Engineering, Semiconductor Devices
Microwave Power Amplifier Systems
Electro-Magnetic Wave Engineering, Antennas

2.2.3 Major Research Outcomes in 2020
A [High-efficiency GaN HEMT outphasing power amplifier] Ishikawa Lab.
In recent years, the greater sophistication and diversity in wireless communication systems have become
remarkable, hence further improvements in performance of wireless transmitters in the base stations are
required. Power amplifiers are an important device, which greatly affect distortions for signals and power
consumption. A peak-to-average power ratio (PAPR) of recent digital wireless signals is large as more than
6 dB. A PAPR of 9 dB is often considered in 4G and 5G OFDM (Orthogonal Frequency Division
Multiplexing) / QAM (Quadrature Amplitude Modulation) systems. In such systems, power efficiency atlarge output power back off levels dominates a total power consumption of the amplifiers than that insaturation levels. Thus, high efficiency characteristics are required for wide dynamic range in the amplifiers,
where a special attention should be paid on the efficiency at large output back-off (OBO) power level.
For this issue, we have developed an outphasing power amplifier with a compact combiner. The combiner
could be successfully shrunk by applying a series-load-compensation connection scheme. In addition, adual-power-level design was applied for the power amplifier to increase the dynamic range of its highefficiency power. A fabricated GaN HEMT outphasing power amplifier exhibited a peak drain efficiencyof 77% with a saturation power of 37dBm at 3.92 GHz. In addition, a drain efficiency of more than 50%
was maintained within an output back-off of 7 dB. (Fig. 2.2.1)

Fig. 2.2.1

Fabricated GaN HEMT outphasing power amplifier with a compact combiner, and its characteristics

B A 12-GHz-band loop antenna array with reflector integrated on a Teflon multi-layer substrate
for 4-channel multiplexing OAM communication (Ishikawa Lab.)We have demonstrated that the electro-magnetic wave propagation occurs with a single orbital angularmomentum (OAM) mode when a current distribution of the azimuth angle I-direction for a circular loop
antenna conductor contains only one Fourier expansion coefficient. This condition almost retains at theloop-antenna conductor length of nλ (n is the integer). Based on this principle, we have successfully- 14 -

developed a 4-channel multiplexing OAM communication by using a loop antenna array with reflector
integrated on a Teflon multi-layer substrate at 12-GHz band. The loop antenna array was placed on a Teflon
substrate, and the terminal was pulled out to the back of the reflector with vias. The measured isolation oftransmission for OAM communication at 12.4 GHz is more than 20.9 dB for a distance of 4 mm. (Fig.
2.2.2)

Fig. 2.2.2 Constructed 12-GHz-band 4-channel multiplexing OAM communication system, and its characteristics

C A 12-GHz-band loop antenna array with reflector integrated on a Teflon multi-layer substrate
for 4-channel multiplexing OAM communication (Ishikawa Lab.)A circular loop antenna array system has been successfully developed for a short-range large-capacity
communication and power charging, where both orbital angular momentum (OAM) four-eigen-value
multiplexing communication at 5-GHz band and wireless power transfer (WPT) at megahertz band are
simultaneously realized. To improve a WPT efficiency, a spiral reflector was applied instead of a normal
reflector plane. In a fabricated this system, the WPT efficiency of 82.6% was achieved at 41 MHz, while
keeping a four-value multiplexing OAM communication at 5.23 GHz. (Fig. 2.2.3)

Fig. 2.2.3 Proposed circular loop antenna array system with spiral reflector for simultaneously realizing short-range OAM
multiplex communication and WPT, constructed its system, and its characteristics (left: OAM, right: WPT)- 15 -

D Photonic Digital-to-Analog Conversions (DACs) for RoF Systems (Matsuura Lab.)
Digital-to-analog conversion (DAC) is an essential function that converts a digital signal to an analog signal.
DACs are used in various fields such as communication networks, voice processing, and modern radars.
However, these DACs have complicated schemes, and need additional electrical signal processing toperform the DACs.
Semiconductor optical amplifiers (SOAs) have many advantages in terms of optical signal processing(OSP) applications such as small footprint, low switching energy, high nonlinearity, and ability ofmonolithic integration. In particular, quantum-dot SOAs (QD-SOAs) provides advantages on high speed
operation and broadband gain. By utilizing the property, we have proposed a photonic DAC using blue
chirp in a QD-SOA. However, the resolution improvement by optimizing various parameters is needed forpractical use. In this year, we paper presents a 4-bit resolution of photonic DAC based on all-optical binaryto-hexadecimal conversion using a blue chirp in a QD-SOA and the following blue-shift filtering. We
demonstrate a 10-Gbps DAC and assess the resolution performance.

Fig.2.2.4 Ideal (blue line) and actual (red line)
step functions for 4-bit DAC. Red open circles
show measured amplitude of output 16-level
amplitude signal.

Fig.2.2.5 (a) Differential non-linearity(DNL) and (b) integral non-linearity (INL)of DAC for various input digital codes.

To evaluate the conversion performance of the DAC, we measured the amplitude accuracy of the output
16-level amplitude signal converted by the DAC. Fig. 2.2.4 shows the ideal (blue line) and actual (red line)
step functions for the presented 4-bit DAC. The output pulse amplitude is normalized by 16 steps, and theone step corresponds to the smallest increment step of the DAC output between two adjacent levels. Here,the ideal DAC step is defined as one least significant bit (LSB). Fig. 2.2.5 shows the differential nonlinearity (DNL) and the integral non-linearity (INL) of the presented DAC. The DNL is defined as thedeviation of the actual step from the ideal step, whereas the INL is defined as the deviation of the DAC
output from the ideal transfer function adjusted for the best-fit. The maximum absolute values of the DNL
and the INL were 0.417 LSB and 0.400 LSB, respectively. All the DNL and INL values were within ±0.5
LSB, which can provide no missing codes for the 4-bit photonic DAC.

2.2.4 Funds【Grants-in-Aid for Scientific Research】
1. Grant-in-Aid for Scientific Research (C),”Microwave OAM Antenna” (A. Saitou)【Commissioned Research】
1. MIC, SCOPE, “Research and development of personal area high-speed and large-capacity
wireless communication and wireless power transmission module” (R. Ishikawa, K. Honjo, Y.
Takayama, A. Saitou, H. Suzuki)- 16 -

【Cooperative Research】
1. RF device technologies Inc. “Study on novel devices and its evaluation methods for wirelesscommunications” (R. Ishikawa)
2. SoftBank Group Corp. “Low power consumption amplifier” (R. Ishikawa)
3. Japan Space Systems. “Technical evaluation of high-efficiency power transfer section related to“Research and development project for high efficiency wireless power transfer in space solar
power systems” (R. Ishikawa)- 17 -

2.3 Division of Creating Advanced Wireless Systems
2.3.1 Purpose of Division
R&D of Advanced Wireless Systems and Networks contributing to development of society
by sustainable technologies of wireless systems

2.3.2 Research Staffs and Their Specialties
Prof. Takeo Fujii Division Leader, Future NW, Cognitive Radio, Distributed NW
Prof. Motoharu Matsuura Future NW
Associate Prof. Koji Ishibashi Future NW, Distributed NW
Associate Prof. Koichi Adachi (Future NW)

2.3.3 Major Research Results in 2020
[Low Storage Radio Map Using Statistical Inference] (Fujii Lab.)A crowdsourcing-assisted radio map has attracted attention for providing statisticalinformation about primary users to secondary users. An overview of the crowdsourcingassisted radio map is shown in Fig. 2.3-1. In crowdsourcing, distributed mobile terminals
observe the radio environment in each location and report the observed samples to the cloud.
The radio map is constructed by averaging the received signal power samples in each mesh.
However, if the outdated instantaneous received signal power samples are not deleted, theregistered data size in the cloud may become large. Thus, we propose two methods fordetermining the required sample size to estimate the average received signal power. In thefirst method, the interval estimation and central limit theorem are utilized to estimate therequired sample size in each mesh. As another method, the t-test in hypothesis testing isused. Then, the cloud calculates the average received signal power based on the estimatedsample size. Fig. 2.3-2 shows the average root mean squared error (RMSE). Here, 1
isthe confidence coefficient. In this figure, “w/o deletion” calculates the average power in eachmesh without deletion of samples. “Constant 100” constructs the radio map using 100
samples in each mesh. Additionally, the forgetting factor-based method randomly extracts
the samples for 10% from measured datasets. Meanwhile, Fig. 2.3-3 shows the cumulative
distribution function of the registered data size per mesh. From these results, it can be
confirmed that the proposed methods can accurately estimate the radio environment while
notably reducing the registered data size.

Cloud server
Report radio information
・Received power・Received position

Create radio map

High

Mobile terminals: Transmitter mesh

Tx

Fig. 2.3-1 Overview of radio map.
- 18 -

Low

Interval estimation and CLT (99%)
Interval estimation and CLT (90%)
Forgetting factor

Forgetting factor
Constant 100

3.0

1.0

2.5

0.8

2.0

0.6

CDF

Average RMSE [dB]Interval estimation and CLT
t-testw/o deletion

1.5

0.4

1.0

0.2

0.5
0.90

0.92

0.94

1-

0.96

Fig. 2.3-2 Average RMSE.

0.0
0

0.98

200

400

600

t-test (1%)
t-test (10%)
Constant 100

800

1000 1200

Registered data size rm [bytes]

Fig. 2.3-3 Registered data size per mesh.

[Sequential Control of Allocated Bandwidth According to Required Throughput] (Fujii Lab.)
Accurate knowledge of spectrum opportunities is a key factor for dynamic spectrum sharing.
As an approach to managing spectrum resources to know the spectrum opportunity, anapproach to estimate the spatial white space and aggregate interference by constructing themeasurement-based spectrum database has been considered. As one of the spectrum
sharing methods based on the spectrum database, an algorithm that combines power control
and spectrum band division allocation has been proposed to maximize downlink throughput.
The problem with that research is that it focuses on maximizing throughput, so it gives
extra spectrum resources than the required value of throughput, which leads to a decreasein spectral efficiency. Therefore, the previous research cannot deal with the scenario of
spectrum sharing among many different systems and base stations. To solve this problem,
we proposed a method to control the spectrum bandwidth sequentially according to therequired throughput by centrally managing the required throughput of each operator in the
spectrum database. An overview of the database-managed bandwidth control according to
the required throughput is shown in Figure 2.3-4. In this initial research, the bandwidth of
all operators is increased sequentially until the required throughput was satisfied by all
operators, without considering mutual interference and power control. Figure 2.3-5 shows
the spectral efficiency
andFigure
2.3-6
shows
the
areal
spectralefficiency.
These figures showthat the proposed
algorithm
canincrease the spectral
efficiency compared toprevious researches.
In the future, we willconsider the mutualinterference and thesmart
bandwidth
control method.
Fig. 2.3-4 Overview of database-managed bandwidth control
according to required throughput.

- 19 -

Fig. 2.3-5 Spectral efficiency.

Fig. 2.3-6 Area spectral efficiency.

[Highly Accurate Prediction of Radio Propagation based on Compensation of Clutter Loss forSpectrum Sharing] (Fujii Lab.)ITU-R P.2108 clutter loss model has been proposed to estimate the propagation loss
considering the dominant buildings around Tx and Rx. In recent years, this model has been
considering as the simple method to calculate interference for spectrum sharing. However, in
this model, the propagation loss may be overestimated compared to the actual loss if theclutter height is too high. In this case, interference between different systems occur.
Additionally, this model implicitly assumes that the altitudes are equivalent between thetransmitter and the receiver. Therefore, the estimation accuracy of this model varies
depending on the altitude difference between the transmitter and the receiver. In thisresearch, we propose an improved clutter loss model by considering the dominant path andthe altitude difference. In the proposed method, as shown in Fig. 2.3-7, the building with thelargest elevation angle from the transmitter and receiver is considered as the dominantbuilding, and the clutter loss is calculated on each transmitter and receiver. Then only thesmaller one is considered as the clutter loss. In addition, the height difference between theantenna and the clutter is cut off when the height difference is 50 [m] or more for considering
dominant path. Fig 2.3-8 and Fig 2.3-9 show the cumulative probability distribution (CDF)of the estimated received power error in the urban and suburban environments, respectively.
From these results, it can be seen that the proposed method can reduce the probability ofinterference between different systems to 1% or less compared to other methods.

Fig. 2.3-7 dominant buildings around Tx and Rx- 20 -

Fig. 2.3-8 CDF of the estimated received
power error in the urban environment

Fig. 2.3-9 CDF of the estimated received
power error in the suburban environment.

[Radio and Computing Resource Allocation for Mobile Edge Computing] (Adachi Lab)In this research, we focus onbalancing the energy consumption,
the processing delay and thecommunication quality for multiuser wireless powered-mobile edge
computing (WP-MEC) systems.
MEC can compute alternatively
heavy tasks of wireless devices(WDs) and wireless power transfer(WPT) can charge the batteries ofFig. 2.3-10 A schematic diagram of WP-MEC with
WDs wirelessly. WP-MEC realizes
binary offloading.
power-savingin
IoT
sensor
networks.
In
thissystem,centralized control at the AP can
reduce packet collisions, but it
incurs the overheads because of
exchanging control information.
Thus, this research proposes a
decentralized probabilistic binary
offloading (PBO) strategy. By PBO
strategy, each WD probabilisticallyselects one of two modes, i.e.,
offloading and local computing,instead of centralized control by an
AP as shown in Fig. 2.3-10. Fig. 2.3Fig. 2.3-11 Comparison of PDR performance.
11 shows the comparison of PDR
1, i.e., offloading all tasks. The
with PBO strategy and with offloading probability
figure shows that the proposed PBO can improve the packet delivery rate (PDR)
perforamnce compared to all offloading strategy.

- 21 -

[Q-Learning Based Resource Allocation in
LPWA] (Adachi Lab.)In recent years, with the breakthroughdevelopment of Internet-of-things (IoT) andmachine-to-machine (M2M) communication,
wireless sensor networks are expected to
become more important. Low power wide area
(LPWA) networks such as long-range wide area
network (LoRaWAN) have attracted attention
Fig. 2.3-12 Packet collision due to eventtriggered traffic
as a network standard for wireless sensor
networks. LoRaWAN generally adopts a pure
ALOHA protocol as the MAC layer accessprotocol. However, if multiple wireless devices
transmit packets on the same frequency
channel simultaneously, a packet collision
happens at a fusion center (FC) that collects
wireless devices' information. In particular,
packet collision becomes a serious problem in
event-triggered traffic generated by event
Fig. 2.3-13 Average PDR performance ofdetection as shown in Fig. 2.3-12. To tackle thethe proposed traffic control scheme.
packet collision problem, we have proposed anautonomous decentralized traffic
control by machine learning. This
research installs a reinforcement
learning agent and the event packet
transmission probability in eachdevice. The proposed scheme
consists of two steps. Firstly, thetransmission timing offset of eachdevice is autonomously set by
Fig. 2.3-14 Example of packet transmission in
reinforcement learning. Then, the
PLIM.
event
packettransmission
probability is determined based on
the success rate of event packet
transmission. The performance
evaluationconsidering
the
LoRaWAN network has been
conducted. Fig. 2.3-13 shows thatthe proposed scheme can improve
the average packet delivery rate(PDR) performance compared to theconventional ALOHA protocol.
[Packet-Level Index Modulation for
LPWAN] (Adachi lab)
The long range wide area network(LoRaWAN) is one of the enabling
technologies for low power wide area

Fig. 2.3-15 Improvement of number of bits
conveyed by one packet over conventional- 22 -

(LPWA) networks. Since the packet transmission drains the LoRaWAN nodes’ battery, thepacket collision results in a waste of limited power. In addition, the quantity of data thateach node can transmit is limited by the duty cycle (DC). Thus, it is necessary to develop a
simple but effective transmission strategy that efficiently utilizes limited battery at each
LoRaWAN node. This study proposes packet-level index modulation (PLIM) that conveys
additional information bits by selecting a combination of time slot and frequency channel,i.e., index, as shown in Fig. 2.3-14 The proposed PLIM can compensate for the data rate
limitation above by taking advantage of the sparse transmission in time. Fig. 2.3-15 showsthat the proposed PLIM can increase the information bits conveyed by one data packet. The
PLIM is more effective as the spreading factor (SF) is higher.
[Grant-Free Access for Massive Users] (Koji Ishibashi Lab.)
Future wireless communication such as
Beyond 5G and 6G would accommodate
massive number of devices, as represented byInternet-of-Things (IoT). In order to realize
efficient low-latency data transmission of large
number of devices, designing multiple access
schemes is crucial. As one of the promisingtechniques, grant-free non-orthogonal multiple
access (GF-NOMA) in which the base station
Fig. 2.3-16 GF-NOMA system(BS) does not exclusively assign radio resourcesto active users for data transmission has been
actively investigated. This fiscal year, we haveproposed two GF-NOMA schemes: 1) one withfrequency spreading and 2) one with time-andfrequency spreading, as shown in Fig. 2.3-16.
In the former scheme, a multi-antenna BS
estimates the active users and channels fromthe overlapped pilot signals spread over thefrequency domain via a hyperparameter-freecoordinate descent (CD) algorithm without apriori information of the channels. Fig. 2.3-17
Fig. 2.3-17 NMSE performance ofshows the normalized mean squared error
theproposed GF-NOMA scheme.
(NMSE) performance of GF-NOMA with our
proposed algorithms and demonstrates thatour proposals outperform the conventional
scheme, namely NNLS, and can be comparable
to the ideal maximum likelihood (ML) schemewith the perfect knowledge of the noise
variance. On the other hand, the latter scheme
makes full use of both the time and frequency
domains and employs a spreading pattern thatdiffers between the pilot and transmitted data.
Moreover, the BS estimates the active users
and channels via generalized multiple
Fig. 2.3-18 Effective throughput permeasurement vector approximate message
user of the proposed GF-NOMA.
passing (GMMV-AMP) and the transmitted- 23 -

data via Gaussian belief propagation (GaBP). Fig. 2.3-18 shows the effective throughput peruser of the proposed scheme where 50 of 500 users are active. As seen from the figure, theproposed scheme can achieve the higher effective throughput than 100[bytes/msec] eventhough it estimates both the active users and channels. These results have been alreadypublished in and submitted to IEEE journals, respectively.
[Beamforming Design for Cell-Free Massive MIMO Systems] (Koji Ishibashi Lab.)
Massive MIMO (mMIMO) systems will provide enormous degree of freedom, whichenables the next-generation of wireless systems such as Beyond 5G and 6G. Recently, cellfree mMIMO (CF-mMIMO) systems consisted of collaborative spatially distributed accesspoints are discussed in order to make full use of capability of mMIMO systems and address
the limitation due to spatial correlations, as shown in Fig. 2-3-19.
However, the flexible beamforming (BF)
design for CF-mMIMO has not been studied to
IoT Devices
UAV Control
serve multiple user equipment (UEs) with a
Autonomous Driving
different number of antennas or with a different
Smart Factory
Smart Gridup/down transmission requirements. To this end,
Central Processing Unit
(CPU)
Access Point
we proposed 1) BF design for serving user(AP)Optical Fronthaul
Smart Phone
heterogeneous
UEs
using
multi-linear
Service Area
generalized singular value decomposition (MLGSVD) and 2) BF design for dynamic TDD
Fig. 2.3-19 Concept of CF-mMIMO
architecture using Lagragian dual transform(LDT) and quadratic transform (QT). Figure 2.3-20 shows the achievable sum-spectral
efficiency (SE) of our proposed ML-GSVD based BFs and conventional BF (MMSE and MRC)
in downlink scenarios. Figure 2.3-21 shows the achievable sum-SE of our proposed BF andconventional BF (MMSE). Both proposed BF schemes outperform conventional BF in termsof SE. These results have been already reported in domestic conferences.
=0,

+

=50

CDF

L=32, M=1, K=8, q=0.5, N ul =1, N dl =1,

Spectral efficiency [bit/s/Hz]

Fig. 2.3-20 CDF of the total SE for the
proposed and conventional BFs in downlink.

Fig. 2.3-21 CDF of the sum SE for the proposed andconventional BFs in dynamic TDD.

[Robust Beamforming Design for Millimeter-Wave Channels] (Koji Ishibashi Lab.)
Millimeter-wave communications are expected to be foundations of Beyond 5G and 6G
wireless architectures. Recently, several robust beamforming design methods taking into
account random channel blockages have been proposed, as shown in Fig 2.3-22. Although- 24 -

these methods show robustness against suchblockages, the computational complexity increases
exponentially with number of base stations and users.
This is due to the exponential growth of possiblecombinations of blockage patterns. To this end, we
have
proposedthelow-complexity
robustbeamforming design based on weighted sum rate
maximization together with an effective weight
design for the proposed method. From the small
number of constraints in the optimization problem,
it is clear that the proposed method reduces thecomputational complexity by up to 1/100. Figure 2.323 shows effective throughput of our proposed
method where blockage probabilities between eachbase stations and users are randomly chosen from
20% to 60%. As seen in the Figure 2.3-23, ourproposed method can achieve the comparable
performance to the conventional method while
significantly reducing the computational complexity.
This result has been already reported in theinternational conference.

Fig. 2.3-22 System model of CoMP
millimeter-wave
communications
with random blockages

Fig. 2.3-23 Effective Throughput ofthe proposed and conventional
method[Optically Powered Transmission Systems for Optical Access Networks] Matsuura Lab

Fig. 2.3.24 Schematic view of optically powered RoF networks.
Radio-over-fiber (RoF) is an attractive approach for transmitting radio-frequency (RF)signals over an optical fiber laid between a central office (CO) and a remote antenna unit
(RAU), and has the advantages of low loss, wideband, and no electromagnetic interference.
Since the main elements can be centralized in the CO, the configuration of the RAU can be
simplified. However, RoF systems require an external power supply to RAUs, so there is aproblem that the installation place of RAUs is limited and RAUs do not operate when a power
failure occurs. Therefore, power-over-fiber (PWoF) systems that transmit signals and power
supplied to RAUs from a CO over the same optical fiber has attracted much attention asshown in Fig. 2.3.24.

- 25 -

Recently, we have demonstrated the PWoF experiment for RoF applications using a
conventional multimode fiber (MMF). Then, the signal degradation due to the crosstalk
between data signal and feed light was observed, and the degradation depended on the feed
light power and the MMF link length. We also observed the spectrum analysis of the feed
light in a MMF link. For these reasons, we think that it is very important to evaluate
quantitatively the noise due to the crosstalk between data signal and high-power feed lightin MMFs. In this year, we evaluate the relative intensity noise (RIN) for PWoF using MMFs.
The results show that the RIN of feed light strongly depends on the feed light power and the
MMF link length.

Fig. 2.3.25 RIN spectrum of feed light after (a) 0 km, (b) 2 km, and (c) 4 km MMF transmission
when output power of FRL was set to 10 W.
Figure 2.3.25 shows the RIN spectrum of the feed light after 0 km, 2 km, and 4 km MMF
transmission, when the output power of a feed light source generated by a Fiber Raman
laser (FRL) was set to 10 W, respectively. It was found that the noise level was increased as
the transmission distance was increased. In other words, the noise level was increased
during the feed light transmission. On the other hand, even when the transmission length
was increased, the maximum value of RIN was hardly increased. From these results, thenoise characteristics induced by the feed light source strongly depends on the feed light
power and the transmission length, and is closely related to the nonlinear effects in MMFs.

2.3.4 Funds【Grants-in-Aid for Scientific Research】
1. Scientific Research B “Research on Spectrum Sharing among Multiple Systems basedon Correlation of Wireless Environment using Crowd Sensing”
Takeo Fujii
2. Scientific Research Fostering Joint International Research Type B “Research of Smart
Spectrum using Multi-dimensional Wireless Environment Recognition based on
Learning”
Takeo Fujii (PI belongs to other organization)
3. Scientific Research B “Research on Optically Powered Radio-over-Fiber systems”
Motoharu Matsuura
4. Scientific Research A “Massive IoT Device Communications based on Belief
Propagation Detector for Massive Overloaded MIMO Systems.”
Koji Ishibashi (PI belongs to other organization)【Commissioned Research】
1. MIC “Dynamic Spectrum Sharing among Different Radio Services”
Takeo Fujii, Koichi Adachi- 26 -

2. MIC “Highly-Reliable Wireless Access Technology for Advanced 5G Networks”
Koji Ishibashi
3. MIC SCOPE “Flexible LPWA Based on Environmental Dynamics”
Koichi Adachi, Takeo Fujii
4. MIC SCOPE JP-EU Joint Research “5G Enhanced Mobile Broadband Access Networks
in Crowded Environments (5G-Enhance)”
Takeo Fujii, Koji Ishibashi, Koichi Adachi- 27 -

2.4 Division of Exploring Low Power Wireless2.4.1 Purpose of DivisionThis division is aiming at low power wireless device technologies and application systems,
which become fundamentals for future wireless communication systems. We areinvestigating such low power wireless technologies as super low power LSI design, energy
harvesting technology, power transfer technology by optical fiber, as well as low power
networks by theoretical approach. We also create new wireless application systems using
the low power wireless technologies.
2.4.2 Research Staffs and Their Specialties
Prof. Koichiro Ishibashi (Head of Division, Low-power devices)Prof. Motoharu Matsuura (Radio over Fiber)Prof. Takeo Fujii (Smart meters)
Associate Prof. Koji Ishibashi (Green network and communication theory)
Associate Prof. Ryo Ishikawa (RF energy harvesting)
2.4.3 Major Research Outcomes in 2020
[Development of RF Energy Harvesting System from Environment RF signal] (Koichiro
Ishibashi Lab)RF energy harvesting technology which could be inevitable for future Trillion Sensor
Universe has been investigated in the division of Exploring Low Power Wireless. We havebeen investigating to harvest the energy from environmental RF signal so that every sensor
node at environment can operate everywhere and every time. We have developed Energy
Harvesting Rectenna from LTE Mobile phone Signal combined with Yagi Antenna(Fig. 2.4.1).
The power obtained from the LTE mobile phone signal can be increased to 7.6uW(Fig. 2.4.2).

Fig. 2.4.1 Rectenna for 950MHz LTE signal for cell phone.

Fig. 2.4.2 Output power of the Rectenna.

- 28 -

【Grants-in-Aid for Scientific Research】
1. JST/CREST, “Scavenging nW RF energy using Super Steep Transistor and MetaMaterial Antenna” (K. Ishibashi)[Age of Information in Energy Harvesting Wireless Sensor Networks] (Koji Ishibashi Lab.)In recent years, energy harvesting wireless sensor network (EH-WSN), which is equippedwith energy harvesting (EH) technology that harvests power from natural phenomena suchas sunlight and vibration, and aims for maintenance-free operation, has been studied.
However, there is a limit to the average harvested power, and the problem is that the sensor
node (SN) may not be able to transmit information at any given time due to power shortage.
To address this problem, analysis based on the freshness of information (AoI: Age ofInformation) has been discussed. AoI is defined by the elapsed time from the generate timeof observation information that fusion center successfully received to the present time. To thebest of our knowledge, no mechanism to minimize the AoI in EH-WSN has been proposedthus far. Therefore, we investigated the AoI by focusing on time-division multiple access(TDMA) and frequency-division multiple access (FDMA) as grant access where N EH-SNs
communicate simultaneously to a common FC. Moreover, we proposed novel resource
allocation algorithms both in TDMA and FDMA to minimize the corresponding average
aggregate AoI. Fig. 2.4.3 shows the average AoI per SN as a function of the size of data
packets in TDMA and FDMA. From the figure, it can be seen that the grant access that
achieves the minimum average AoI depends on the amount of data packets. From the above,it is necessary to choose TDMA and FDMA according to the conditions of the system. This
result has been already published in IEEE journals.

Fig. 2.4.3 Average AoI as a function of the size of data packets in TDMA and FDMA systems.
[Highly Efficient Encryption-then-Compression Technique Based on Packet Aggregation](Koji Ishibashi Lab.)
Multi-hop transmission has emerged as a promising technique to address the need for highscalability and low power consumption in wireless sensor networks (WSN). Previously, MAC
protocols that improve energy efficiency by operating each node intermittently were proposed,such as IRDT (Intermittent Receiver-Driven Data Transmission). However, in such a protocol,
the bottleneck problem may occur when an intermediate sensor node (SN) has only a single
connection with the destination or to a SN closer to the destination. One option to enhance
the throughput at the SNs is the compression of multiple packets to forward. Nevertheless,this is unfeasible since the packets are encrypted at every SNs for privacy protection. In ourwork, we exploit the idea of the Slepian-Wolf theorem and propose the encryption-thencompression (EtC) scheme with packet aggregation, enabling the intermediate SNs tocompress packets.

- 29 -

Fig. 2.4.4 Relationship between intermittent intervalof a node per correctly received packet.

and average energy consumption

Fig. 2.4.4 shows the relationship between intermittent interval and the average energy
consumption of a node per a correctly received packet at the destination when each nodecommunicates based on IRDT. The results shown in the figure indicate that, whenislarge, the IRDT using the proposed EtC method achieves lower energy consumption than theoriginal IRDT.
[Wide-dynamic-range high-efficiency rectifier with zero-threshold GaAs HEMTs] (Ishikawa
Lab.)
Middle- to long-range microwave wireless power transfer is promising candidate for wirelesspower supply applications, such as an emergency power supply. For these technologies, a
rectifier is one of the key devices to convert RF power into DC power. However, the input
power range with high-efficiency operation is narrow for conventional rectifiers. Since theinput power largely varies in actual usage, a wide-dynamic-range high-efficiency
characteristic is required for the rectifiers. We have developed a wide-dynamic-range highefficiency rectifier configuration by using two types of transistor rectifiers with zerothreshold GaAs HEMTs. The high-efficiency operation is shifted to a higher input power
region by decreasing the gate-bias voltage of the transistor rectifier, whose circuit is carefully
adjusted. Thus, according to an input power increase, a suitable gate-bias voltage was
supplied to the high-efficiency transistor rectifier from a transistor rectifier circuit
generating a negative DC voltage. The fabricated rectifier with zero-threshold GaAs HEMTs
exhibited RF-to-DC power conversion efficiencies of more than 60% and 70% for input powers
from 0 to 25 dBm and 9 to 24 dBm, respectively, at 2.27 GHz. (Fig. 2.4.5)

Fig. 2.4.5 Fabricated wide-dynamic-range GaAs HEMT rectifier, and its characteristic【Grants-in-Aid for Scientific Research】
1. Grant-in-Aid for Scientific Research (C), “Microwave rectifiers for flexible wirelesspower transfer system in emergency” (R. Ishikawa)- 30 -

[Optically Powered Drone for Airborne Base Stations] (Matsuura Lab.)In mobile communications, the interruption is one of the serious problems, and occurs
frequently due to natural disasters such as earthquake and tsunami. In particular, it is very
important to survive base stations (BSs) in the event of disasters. Therefore, unmanned
aerial vehicles such as drones have attracted much attentions, because these can be used as
airborne BSs (ABSs) in such situations, instead of ground BSs. In order to realize ABSs, a
few researchers have been focused on the use of drones. However, conventional drones forABSs have drawback for practical use, because their batteries have the limitation of flighttime and require the long charging time. To solve this problem, we have proposed the use of
power-over-fiber that transmit the power required for driving drones into optical fibers. In
this year, we present the flight control of an entry-type drone using an optical fiber without
using wireless control signals. We show the flight demonstration of the drone by controllinga wired remote controller. Fig. 2.4.6 shows the electrical spectrum of the control signal after
optical fiber transmission. The center frequency was at around 2.45 GHz. The inset shows
the temporally waveform of the signal. It was a pulse train with the repetition rate, whichcoincide with the center frequency of the signal. To show the feasibility of the flight controlof the drone using optical fibers, we executed the flight control demonstration of the drone,
because we enabled to confirm the control signal after optical fiber transmission. The flying
image is shown in Fig. 2.4.7. In this experiment, we demonstrated the flight control by using
only optical fiber transmission in an environment where wireless communication between
the drone and the remote controller (RC) was not possible. In this condition, after turning on
the switch of the drone, the drone and the RC were synchronized, and the LEDs on the drone
lighted up, the drone flighted. The driving voltage and current were 3.81 V and 1.12 A,
respectively. The power consumption was 4.27 W. It should be noted that the black and red
cables were not optical fibers, but electric wire cables. As the drone was too small to carry all
the electrical equipment, these put on the ground, and connected to the drone using theelectrical wire cable.

Fig.2.4.6 Control signal spectrum after optical fiber transmission, Inset shows waveform ofcontrol signal after optical fiber transmission.

Fig.2.4.7 Flight control demonstration of drone using optical fibers.

- 31 -

[Channel Allocation for LoRaWAN Considering Intra-System and Inter-System
Interferences] (Fujii Lab.)In long range wide area network (LoRaWAN), which is one of the low power wide area
network (LPWAN) systems, the number of LoRa nodes has been increasing rapidly.
Additionally, although LoRaWAN uses the same frequency channels as other LPWAN
systems, LoRa nodes randomly select channels for the uplink communications notconsidering the time occupancy of other systems. In such situations, intra-system and intersystem interferences are frequently occurred, which significantly deteriorates thecommunication quality. Thus, to suppress these interferences, it is necessary to accurately
estimate the time occupancy in each channel and adaptively allocate LoRa nodes to channels
based on the estimation results. Furthermore, because the time occupancy strongly depends
on transmission duration, and signal power fluctuations owing to geographical conditions,the radio environment should be accurately predicted in advance. Therefore, we propose achannel allocation method based on the radio environment. First, the LoRa gateway (GW)
observes the inter-system interference signals for a certain period and estimates an empiricalcumulative distribution function (ECDF) of the time occupancy in each channel, as shown in
Fig. 2.4.8. Based on the obtained ECDF, LoRa nodes are allocated to channels in ascendingorder of the time occupancy. Then, the GW reallocates the LoRa nodes to the channels
searchingly based on the formula of intra-system interference probability so that theprobability on each channel becomes lower than the threshold while maintaining the intersystem interference suppression. Fig. 2.4.9 shows the simulation results of the average
packet loss rate (PLR), and it is confirmed that our proposed method can suppress the PLR
compared to the conventional random channel selection.

Fig. 2.4.8 Channel allocation based on the ECDF of the time occupancy of other systems.

Fig. 2.4.9 Average PLR of LoRa nodes for the number of other system nodes.
【Cooperative Research】
1. “Scavenging nW RF energy using Super Steep Transistor and Meta-Material
Antenna,”On JST/CREST “Scientific Innovation for Energy Harvesting Technology”(Koichiro Ishibashi, Ryo Ishikawa, and Koji Ishibashi)
2. “Optimization of Smart-Meter Networks” (Koji Ishibashi and Takeo Fujii)- 32 -

,【1】,, 2020.

【1】 H. Mizutani, R. Ishikawa, and K. Honjo, “Small-signal design consideration
for two-dimensional change-over switch GaN MMICs,” Japanese Journal ofApplied Physics, vol.59, no.SG, pp.GL07-1- GL07-9, Apr. 2020.
【2】 Longfei Yi, Satoshi Ono, and Koji Wada, “A Wide-Stopband Microstrip
Bandpass Filter Using Stepped Impedance Resonators with
Open-Circuited Stubs and Asymmetric Coupling Structure,” Transactions
of
The
Japan
InstituteofElctronics
Packaging,vol.13,
pp.E19-016-01-E19-016-13, May 2020.
【3】 Naoki Aihara, Koichi Adachi, Osamu Takyu, Mai Ohta, and Takeo Fujii,“Generalized Interference Detection Scheme in Heterogeneous Low Power
Wide Area Networks,” IEEE Sens. Lett., vol.4, no.6, pp.1-4, Jun. 2020.
【4】 Guanghao Sun, Nguyen Vu Trung, Le Thi Hoi, Pham Thanh Hiep, Koichiro
Ishibashi, and Takemi Matsui, “Visualization of epidemiological map using
an Internet of Things infectious disease surveillance platform,” Critical
Care vol.24, no.400, Jul. 2020.
【5】 Luong Duy Manh, Phan Thi Bich, Nguyen Thuy Linh, Nguyen Huy Hoang,
Xuan Nam Tran, and Koichiro Ishibashi, “A Concurrent Triple-band RF
Energy Harvesting Circuit for IoT Sensor Networks,” IEEK Transactions
on Smart Processing and Computing, pp.1-9, Jul. 2020.
【6】 Shusuke Narieda, Takeo Fujii, and Kenta Umebayashi, “Energy
Constrained Optimization for Spreading Factor Allocation in LoRaWAN,”

Sensors, 20, 4417, Aug. 2020.
【7】

,

,“
,

C , vol.J103-C, no.8, pp.366-367,

Aug. 2020.
【8】 Thuy-Linh Nguyen, Shiho Takahashi, Van-Trung Nguyen, Yasuo Sato,
Koichiro Ishibashi, “RF Energy Harvesting using Cross-couple Rectifierand DTMOS on SOTB with Phase Effect of Paired RF Inputs”, ECTITransactions on Electrical Engineering, Electronics & Communications,Vol. 18, No. 2, pp. 170 -178, Aug. 2020
【9】 H. Iimori, G.T.F. de Abreu, O. Taghizadeh, R.-A. Stoica, T. Hara, and K.
Ishibashi, “Stochastic Learning Robust Beamforming for Millimeter-Wave- 33 -

Systems with Path Blockage,” IEEE Wireless Commun. Lett., vol.9, no.9,
pp.1557-1561, Sept. 2020.
【10】,,, “CRLH,”, vol.23, no.6, pp.533-538, Sept.
2020.
【11】 Fuminori Sakai, Mitsuo Makimoto, and Koji Wada, “Near-Field Credit
Card-Sized Chipless RFID Tags Using Higher-Order Mode ResonanceFrequencies of Transmission Line Resonators,” IEICE Trans.
Fundamentals, vol.E103-A, no.9, pp.1001-1010, Sept. 2020.
【12】
,
,
,
,
, “
,”
B , vol.J103-B, no.9,
pp.427-439, Sept. 2020.
【13】 Jose-Victor Rodriguez, Takeo Fujii, Leandro Juan-Llacer, Jose-Maria
Molina-Garcia-Pardo, and Ignacio Rodriguez-Rodriguez, “Plane-Wave
UTD-PO Formulations for Multiple Diffraction by Trees and Buildings at
Millimeter-wave Frequencies,” IEEE Antennas Wireless Propag. Lett.,vol.19, no.10, pp.1793-1797, Oct. 2020.
【14】 Tomohiro Tsukushi, Satoshi Ono, and Koji Wada, “Bandpass filter withflat passband and transmission zeros using parallel-connected resistor
loaded hairpin-shaped resonators,” IEICE Electronics Express, vol.17,
no.22, pp.20200320, Oct. 2020.
【15】 N. Hirosawa, H. Iimori, K. Ishibashi, and G.T.F. de Abreu, “Minimizing
Age of Information in Energy Harvesting Wireless Sensor Networks,” IEEE
Access, vol.8, pp.219934-219945, Nov. 2020.
【16】 Shohei Sakai, Satoshi Ono, and Koji Wada, “High isolation characteristics
between two channels using LPF-HPF diplexer composed of chip andpattern elements”, Transactions of The Japan Institute of Electronics

Packaging, vol.13, pp.E19-019-01-E19-019-10, Dec. 2020.
【17】 Shusuke Narieda, Daiki Cho, Hiromichi Ogasawara, Kenta Umebayashi,
Takeo Fujii, and Hiroshi Naruse, “Theoretical Analyses of Maximum Cyclic
Autocorrelation Selection Based Spectrum Sensing,” IEICE Trans.
Commun., vol.E103-B, no.12, pp.1462-1469, Dec. 2020.
【18】 Yuki Nishio, Osamu Takyu, Hayato Soya, Keiichiro Shirai, Mai Ohta, andTakeo Fujii, “Optimal Construction of Access Rate to Superior Channel in
Rendezvous Channel Based on Channel-Occupancy Ratio,” IEICE Trans.
Fundamentals, vol.E104-A, no.1, pp.243-252, Jan. 2021.
【19】
,
,
,
,
, “TaylorTOA,”
B , vol.J104-B,- 34 -

no.1, pp.54-65, Jan. 2021.
【20】 Koichi Adachi, Kohei Tsurumi, Aoto Kaburaki, Osamu Takyu, Mai Ohta,
and Takeo Fujii, “Packet-Level Index Modulation for LoRaWAN,” IEEE
Access, vol.9, pp.12601-12610, Jan. 2021.
【21】 Koya Sato, Katsuya Suto, Kei Inage, Koichi Adachi, and Takeo Fujii,“Space-frequency-interpolated radio map,” IEEE Trans. Veh. Technol.,vol.70, no.1, pp.714-725, Jan. 2021.
【22】
,
,
,
, “F
,”
C , vol.J104-C, no.1, pp.18-24, Jan.
2021.
【23】 H. Iimori, G. T. F. de Abreu, T. Hara, K, Ishibashi, R.-A. Stoica, D.
Gonzalez G., and O. Gonsa, “Robust Symbol Detection in Large-Scale
Overloaded NOMA Systems,” IEEE Open J. Commun. Soc., vol.2,
pp.512-533, Mar. 2021.
【24】
,
,
, “
,”
C , vol.141, no.3, pp.453-463, Mar. 2021.

【1】 H. Mamiya, H. Nomoto, T. Higuchi, and M. Matsuura “Crosstalkevaluation of simultaneous data signal and feed light transmission at
1.55-μm band for power-over-fiber using a double-clad fiber,” in Proc. The

2nd Optical Wireless and Fiber Power Transmission Conference (OWPT2020), OWPTp-06, Yokohama, Japan, Apr. 2020.
【2】 N. Shindo, R. Yazawa, R. Kobayashi, and M. Matsuura, “Flight control ofpower-over-fiber drones using optical fibers for airborne base stations,” inProc. The 2nd Optical Wireless and Fiber Power Transmission Conference(OWPT 2020), OWPTp-07, Yokohama, Japan, Apr. 2020.
【3】 J. Nagai, T. Abe, and Y. Yamao, “Improvement of EVM for Downlink NOMA
with Blind Nonlinear Compensation Scheme,” in Proc. IEEE 91st Veh.
Technol. Conf. (VTC2020-Spring), Antwerp, Belgium, pp.1-4, May, 2020.
【4】 T. Murakami, Y. Kishi, K. Ishibashi, K. Kasai, H. Shinbo, M. Tamai, K.
Tsuda, M. Nakazawa, Y. Tsukamoto, H. Yokoyama, Y. Fujii, Y. Seki, S.
Nanba, T. Hara, F. Adachi, and T. Sotoyama, “Research Project to Realize
Various High-Reliability Communications in Advanced 5G Network,” inProc. 2020 IEEE Wireless Commun. and Netw. Conf. (WCNC), Seoul,
Korea (South) , pp. 1-8, May 2020.
【5】 Keita Katagiri and Takeo Fujii, “Radio Environment Map Updating
Procedure considering Change of Surrounding Environment,” in Proc.

- 35 -

IEEE WCNC Workshop on Smart Spectrum (IWSS 2020), Virtual
Conference, pp.1-6, May 2020.
【6】 T. Hara, H. Iimori, and K. Ishibashi, “Activity Detection for Uplink
Grant-free NOMA in the Presence of Carrier Frequency Offsets,” in Proc.
2020 IEEE Int. Conf. Commun. Workshops (ICC Workshops), Dublin,
Ireland, pp. 1-6, Jun. 2020.
【7】 R. Ogasawara, Y. Takayama, R. Ishikawa, and K. Honjo, “A 3.9-GHz-band
outphasing power amplifier with compact combiner based on
dual-power-level design for wide-dynamic-range operation,” in Proc. 2020
International Microwave Symposium, Tu2F-2, pp.111-114, Los Angeles, CA,
USA, Jun. 2020.
【8】 T. Okada, R. Kobayashi, R. Wang, M. Sagara, and M. Matsuura,“10-GSamples/s cascaded operation of photonic D/A and A/D converters
using frequency chirp in SOAs,” in Proc. OSA Advanced Photonics Congress,
Photonics in Switching and Computing (PSC 2020), PsTh3F.6, Online, Jul.
2020.
【9】 Yuki IWATA, Koichiro ISHIBASHI, Guanghao SUN, Manh Ha LUU, Trong
Thanh HAN, Linh Trung NGUYEN, and Trong Tuan DO, “ContactlessHeartbeat Detection from CW-Doppler Radar using Windowed-Singular
Spectrum Analysis,” in Proc. 2020 42nd Annual International Conferenceof the IEEE Engineering in Medicine & Biology Society (EMBC), Montreal,
QC, Canada, pp.477-480, Jul. 2020.
【10】 Cong-Hoang Diem and Takeo Fujii, “An UAV and distributed STBC for
wireless relay networks in search and rescue operations,” in Proc. EAI
INISCOM 2020, Virtual conference, Aug. 2020.
【11】 W. Wada, R. Ishikawa, A. Saitou, H. Miyake, H. Kikuchi, H. Suzuki, and K.
Honjo, “Loop antenna array system with simultaneous operation of OAM
multiplex communication and wireless power transfer,” in Proc. 50th

European Microwave Conference, EuMC28-5, Utrecht, the Netherlands,
pp.530-533, Sept. 2020.
【12】 J. Yamazaki, R. Ishikawa, and K. Honjo, “Input-power-synchronous
adaptively biased wide-dynamic-range high-efficiency rectifier withzero-threshold GaAs HEMTs,” in Proc. 50th European Microwave
Conference, EuMC22-1, Utrecht, the Netherlands, pp. 436-439, Sept. 2020.
【13】 Y. Takagi, N. Hasegawa, Y. Ohta, R. Ishikawa, and K. Honjo,“High-efficiency asymmetric Doherty power amplifier with spurious
suppression circuit,” in Proc. 50th European Microwave Conference,
EuMC15-4, Utrecht, the Netherlands, pp. 308-311, Sept. 2020.

- 36 -

【14】 K. Suzuki, N. Aihara, K. Adachi, and S. Yamazaki, “Autonomous
Decentralized Frequency Resource Allocation using ACK Signal in
LoRaWAN,” in Proc. 23rd Int. Symp. on Wireless Personal Multimedia
Commun. (WPMC2020), pp.1-6, Oct. 2020.
【15】 T. Nishimura, K. Adachi, O. Nakamura, M. Sakai, M. Iwabuchi, T.
Okuyama, O. Muta, K. Muraoka, F. Maehara, T. Tandai, S. Suyama, and E.
Okamoto, “Overview of the 6G Workshop Organized by IEICE TechnicalCommittee on RCS - We co-create 6G!,” in Proc. 23rd Int. Symp. on

Wireless Personal Multimedia Commun. (WPMC2020) Special Session on
Recent R&D Activities on Further Enhancement of 5G in Japan, Virtual
Conference, pp.1-2, Oct. 2020.
【16】 M. Sagara, T. Okada, W. Rui, and M. Matsuura, “4-bit resolution ofphotonic digital-to-analog conversion by frequency chirp in a QD-SOA,” inProc. 25th Opto-Electronics and Communications Conference 2020 (OECC2020), T2-1.3, Online, pp.1-3, Oct. 2020.
【17】 Y. Kawamura and M. Matsuura, “Evaluation of relative intensity noise forpower-over-fiber using multimode fibers,” in Proc. 25th Opto-Electronics
and Communications Conference 2020 (OECC 2020), VP63, Online, pp.1-3,
Oct. 2020.
【18】 Linh Nguyen, Yasuo Sato, and Koichiro Ishibashi, “7.6 uW Ambient
Energy Harvesting Rectenna from LTE Mobile phone Signal for IoT
Applications”, in Proc. International Conference on Advanced Technologies
for Communications (ATC 2020), Nha Trang, Vietnam, Oct. 2020.
【19】 Shota Ishiguro, Jiro Ida, Takayuki Mori, and Koichiro Ishibashi,“Z01-3595 - CMOS Inverter Transfer Characteristics on Steep SS “PN-Body
Tied SOI-FET,” in Proc. The Electrochemical Society (ECS), Oct. 2020.
【20】 Hiroki Ito, Jiro Ida, Takayuki Mori, and Koichiro Ishibashi, “Z01-3596 Analysis of Drain Current Enhancement on “PN-Body Tied SOI-FET” Bulk Vs Surface Conduction Mode and Perfect Saturation Effect,” in Proc.

The Electrochemical Society (ECS), Oct. 2020.
【21】 H. Watabe, T. Abe, and Y. Yamao, “Communication-Radar Integrated
System Using 5G OFDM Signal and Mismatched Filter Reception,” in Proc.
IEEE 92nd Veh. Technol. Conf. (VTC2020-Fall), Victoria, BC, Canada,
pp.1-5, Nov. 2020.
【22】 Hiroki Ito, Takeo Fujii, and Yasushi Yamao, “Post-Reception
Compensation Performance of Blind Nonlinear Compensator with
Equalizer against Memory Nonlinear Distortion,” in Proc. IEEE 92nd Veh.
Technol. Conf. (VTC2020-Fall), Victoria, BC, Canada, pp.1-5, Nov. 2020.

- 37 -

【23】 Keita Katagiri, Koya Sato, Kei Inage, and Takeo Fujii, “Experimental
Verification of Shadowing Classification for Radio Map,” in Proc. IEEE
92nd Veh. Technol. Conf. (VTC2020-Fall), Victoria, BC, Canada, pp.1-7, Nov.
2020.
【24】 Tetsuki Taniguchi and Takeo Fujii, “Interference Management with
Beamforming Utilizing Spectrum Database for Micro Operators,” in Proc.
IEEE 92nd Veh. Technol. Conf. (VTC2020-Fall), Victoria, BC, Canada,
pp.1-5, Nov. 2020.
【25】 H. Iimori, G. T. F. de Abreu, O. Taghizadeh, and K. Ishibashi“Discrete-Aware Matrix Completion via Proximal Gradient,” in Proc. The
54th Asilomar Conf. Inf. Netw., Nov. 2020.
【26】 Sunao Miyamoto, Keita Katagiri, Koya Sato, Koichi Adachi, and Takeo
Fujii, “Highly accurate prediction of radio propagation based oncompensation of clutter loss for spectrum sharing,” in Proc. ICETC2020,
Virtual Conference, Dec. 2020.
【27】 H. Miyake, A. Saitou, H. Suzuki, R. Ishikawa, and K. Honjo, “Improved
performance for 8-channel multiplexing OAM communication by
suppressing interference,” in Proc. 2020 Asia-Pacific Microwave Conference,
pp. 161-163, Hong Kong, China, pp.161-163, Dec. 2020.
【28】 Syunsuke Nakano, Kei Inage, and Takeo Fujii, “Primary user detection
under superposed TD-LTE signal using multiple reference signals,” in Proc.
ICETC2020, Virtual Conference, Dec. 2020.
【29】 Tetsuki Taniguchi and Takeo Fujii, “Interference Management of Small
Cell System for Micro Operator Using Spectrum Database andMultiobjective Evolutionary Algorithm under Stochastic Constraint,’’ inProc. ICETC2020, Virtual Conference, Dec. 2020.
【30】 H. Iimori, G. T. F. de Abreu, and K. Ishibashi “Full-Duplex MIMO Systems
with Hardware Limitation and Imperfect Channel Estimation,” in Proc.

2020 IEEE Global Commun. Conf. (GLOBECOM), Taipei, Taiwan, pp.1-6,
Dec. 2020.
【31】 Yudai Yamazaki and Takeo Fujii, “Scheduling Algorithm Considering
Interference Interval for LPWA,” in Proc. 2020 Asia-Pacific Signal Inf.
Processing Association Annual Submit Conf. (APSIPA ASC), Auckland,
New Zealand , pp.1507-1512, Dec. 2020.
【32】 R. Okabe, H. Iimori, and K. Ishibashi, “Low-Complexity Robust
Beamforming
with
Blockage
PredictionforMillimeter-Wave
Communications,” in Proc. 2020 Asia-Pacific Signal Inf. Processing
Association Annual Submit Conf. (APSIPA ASC), Auckland, New Zealand,- 38 -

pp. 1453-1459, Dec. 2020.
【33】 R. Yatsu, T. Hara, K. Ishibashi, S. Tsuchiya, and H. Endo, “Packet
Aggregation Based on Encryption-Then-Compression for Highly EfficientMulti-Hop Transmission,” in Proc. 2020 Asia-Pacific Signal Inf. Processing
Association Annual Submit and Conf. (APSIPA ASC), Auckland, New
Zealand, pp. 1466-1471, Dec. 2020.
【34】 Taiki Matsushima and Takeo Fujii “Compensation Method of Received
Signal Power observed by Smartphone for Crowdsensed Spectrum
Database,” in Proc. 2020 Asia-Pacific Signal Inf. Processing Association
Annual Submit Conf. (APSIPA ASC), Auckland, New Zealand,
pp.1477-1482, Dec. 2020.
【35】 Aoto Kaburaki, Koichi Adachi, Osamu Takyu, Mai Ohta, and Takeo Fujii,“Autonomous Decentralized Transmission Timing Control in WirelessSensor Network,” in Proc. 2020 Asia-Pacific Signal Inf. Processing
Association Annual Submit Conf. (APSIPA ASC), Auckland, New Zealand,
pp.1460-1465, Dec. 2020.
【36】 T. Kobayashi and K. Adachi, “Probabilistic Binary Offloading for WirelessPowered Mobile Edge Computing System,” in Proc. 2020 Asia-PacificSignal Inf. Processing Association Annual Submit Conf. (APSIPA ASC),
Auckland, New Zealand, pp.1502-1506, Dec. 2020.
【37】 Gaku Kobayashi, Osamu Takyu, Koichi Adachi, Mai Ohta, and Takeo Fujii,“Estimation of Desired power and Undesired power Using Chirp
Demodulation and Evaluation of Accuracy,” in Proc. 2020 Asia-PacificSignal Inf. Processing Association Annual Submit Conf. (APSIPA ASC),
Auckland, New Zealand, pp.1513-1518, Dec. 2020.
【38】 Tetsuki Taniguchi and Takeo Fujii, “Utilization of Spectrum Database forPower Control in Micro Operator Based Small Cell System by Two-Stage
Stochastic Design,” in Proc. ISSPIT2020, Virtual Conference, Dec. 2020.
【39】 Kohei Natsume, Mai Ohta, Koichi Adachi, Osamu Takyu, and Takeo Fujii,“DCT Compression Scheme for Wireless Sensor Information considering
Spatial Correlation,” in Proc. ICOIN 2021, Online, pp.192-196, Jan. 2021.
【40】 Soraya Mitate, Yudai Yamazaki, Takeo Fujii, and Shusuke Narieda,“Wireless System Selection with Spectrum Database for IoT,” in Proc.
ICOIN 2021, pp. 203-208, Online, pp.203-208, Jan. 2021.
【41】 Han Trong Thanh, Pham Huong Yen, Koichiro Ishibashi, Guanghao Sun,and Tuan Do Trong, “Machine Learning Algorithms for Dengue FeverPatient Classification,” in Proc. 2020 IEEE Eight international conferenceon communications and electronics (ICCE 2020), Phu Quoc, Vietnam, Jan.

- 39 -

2021.
【42】 Nguyen Thuy Linh, Luong Duy Manh, and Koichiro Ishibashi, “Effect ofModulated Waveform on RF Energy Harvesting,” in Proc. The 9thInternational Conference on Green and Human Information, ICGIT 2021,
Jeju Korea, Jan. 2021.
【43】 Manh Ha Luu, Chinh D Nguyen, Guanghao Sun, Anh Q Le, Huong V
Pham, Vu A Tran, Hieu T Tran, Tan D Tran, Trung V Nguyen, Koichiro
Ishibashi, and Trung L Nguyen, “Short Time Cardio-vascular Pulses
Estimation for Dengue Fever Screening via Continuous-Wave Doppler
Radar using Empirical Mode Decomposition and Continuous Wavelet
Transform”, in Proc. Biomedical Signal Processing and Control, vol.65,
pp.102361, Mar. 2021.

【1】 R. Ishikawa and K. Honjo, “High-efficiency DC-RF/RF-DC conversion
based on high-efficiency power amplifier design technique (Invited),” 2020
IEEE International Symposium on Radio-Frequency Integration
Technology, pp. 181-183, Hiroshima, Japan, Sept. 2020.
【2】 Koichiro Ishibashi, “Energy Harvesting from Environment RF for IoT
Applications,” 2020 International Conference on Advanced Technologies forCommunications (ATC 2020), Nha Trang, Vietnam, Oct. 2020.
【3】 Takeo Fujii, “Dense Spectrum Utilization with Spectrum Database for 5G
and Beyond,” IEEE VTC2020-Fall Workshop The 2nd Workshop on 5G and
Beyond Technologies for Ultra-Dense Environments, Dec. 2020.
【4】 K. Adachi, O. Takyu, M. Ohta, and T. Fujii, “Flexible LPWA Based on
Environmental Dynamics,” 2020 Int. Conf. on Emerging Technologies forCommun. (ICETC2020), Dec. 2020.

【1】

,

,

, “
,”
, PN2020-6,

【2】

, “

【3】

, BI-3-2,, “RoF, Jun. 2020.
,”, Sept. 2020.
,”, MWP2020-11,- 40 -, Oct.

2020.
【4】

, “

,”
,

Nov. 2020.
【5】
, “

,”
41

【6】

,

,

,
,
,
20 Gbps,, Jan. 2021.
, “,”, PN2020-49,, Mar. 2021.

【1】 Koichiro Ishibashi, Guanghao Sun, Nguyen Vu Trung, Le Thi Hoi, Nugyen
Van Kinh, Do Trong Tuan, Han Trong Thanh, Nguyen Linh Trung, Luu
Manh Ha, Pham Thanh Hiep, and Takemi Matsui, “Development of
Infection Diseases Screening System by Collaboration between Vietnam
and Japan,” Vietnamese Academic Network in Japan (VANJ Conference2020), Tokyo Japan, Nov. 2020.
【2】 Koichiro Ishibashi, Jiro Ida, Kenji Itoh, Shigeru Makino, Ryo Ishikawa,
and Koji Ishibashi, “Energy harvesting from environment RF for IoT
applications,” International Conference on ICT for Smart Society (ICISS
2021), Bandung, Indonesia, Nov. 2020.

【1】 Takeo Fujii, “Data-driven future wireless network beyond human
intelligence,” IEICE Communication Society-GLOBAL NEWSLETTER,
vol.44, no.3, pp. 2-4, Sept. 2020.
【2】
, “
/
,”
, vol. 65, no.
12, pp. 731-738, Dec. 2020.

【1】

,

,

,

, “

【2】

,”
,

, vol.120, no.10, RCS2020-7, pp.37-42, Apr. 2020.
,
,
, “
,”

- 41 -

MIMO,

vol.120, no.10, RCS2020-8, pp.43-48, Apr. 2020.
【3】,,, “OFDM,”, vol.120, no.10, RCS2020-9, pp.49-54, Apr.
2020.
,

【4】

, “

MIMO
, vol.120, no.29, RCS2020-22,,”

pp.67-72, May 2020.
【5】
,
, “
CDF
, vol.120, no.53, SR2020-2, pp.9-16, Jun. 2020.
【6】
,
,
,
,
,“
LAN

,”

,”
vol.120, no.53, SR2020-7, Jun. 2020.
【7】
,
,
,
,
RCS2020-23, pp.1-6, Jun. 2020.
【8】
,
, “
Powered MEC,”
Jun. 2020. RCS
【9】
,
,
,

,
,”

,

, “
, vol.120, no.74,

Wireless, vol.120, no.74, RCS2020-24, pp.7-12,, “LoRaWAN,”

ACK, vol.120,

no.74, RCS2020-54, pp.187-192, Jun. 2020.
【10】,,, “TD-LTE,”, vol.120, no.90, SR2020-19, pp.57-61, Jul. 2020.
【11】
,
,
,
,“
,”
, vol.120, no.90,
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【12】
,
,
,

【13】

, “OAMGaN HEMT,”, MW2020-33, vol.120, no.101, pp.101-106, Jul. 2020.
,
,
,
,“
IoT
,”
, vol.120, no.90, SR2020-21,

pp.71-78, Jul. 2020.
【14】
,
,
4 bit

,

,“
,”

, vol.120, no.137,,”, vol.120, no.130,

PN2020-13, pp.1-5, Aug. 2020.
【15】
,
,
, “
RCS2020-84, pp.29-34, Aug. 2020.

- 42 -

【16】,,, “Design of Via Connection Coupling Structure
Based Balun and Its Application to Balun filter,”1, vol.20, no.1, pp.7-8,
Aug. 2020.
【17】,
Tied SOI-FET

,
CMOS

,

, “

SS
,”

PN-Body, vol.120, no.126,

SDM2020-8, pp.37-40, Aug. 2020.
【18】
,
,
, “
,”
,

【19】

, 2A4-1, Sept. 2020.
, “OFDM,,”
Sept. 2020.
【20】
,
,
【21】

,

, B-5-11, pp.211,

,

OFDM

, B-2-16, Sept. 2020.
,
, “

,

,”
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,
UAV
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,

,
,

【24】

,

,

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,

,

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,

, “

【28】

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, B-12-8, Sept. 2020.

,, “, B-12-9, Sept. 2020.
,, “
1.55-μm

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,
,

,
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2

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2
2
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【1】
2020
EP2228932

DE602008062524.9- 49 -

4

5

RFID

【2】
6762006

2020

9

【3】
2020-197256

2020

11

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【4】
2020-198025

2020

11

30

【5】
2020-201894

2020

12

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【6】
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2021-025816

2021

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2021

【7】
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68427592021-029542

2021

【8】
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【9】
2021

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6847448【10】2021-042078 2021
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16

【11】
2020
6
25
2020-170283 PCT

2020 108
PCT/JP2021/010539

2020-109410
2021 3

6G

【1】
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17
Beyond 5G/6G【2】,, 20213

2,【3】, 2021

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