Takada Laboratory
Home page: http://www.ap.ide.titech.ac.jp

Professor Jun-ichi Takada
Prof. Jun-ichi Takada was born in 1964, Tokyo, Japan. He received the
B.E. and D.E. degree from Tokyo Institute of Technology in 1987 and1992 respectively. From 1992 to 1994, he was a Research Associate at
Chiba University. From 1994 to 2006, he was an Associate Professor atTokyo Institute of Technology, where he has been a Professor since2006. He is currently with the Department of Transdisciplinary Scienceand Engineering, School of Environment and Society. He is appointed
as Vice President for International Affairs in March 2019. He was also apart time researcher in National Institute of Information andCommunications Technology from 2003 to 2007. His current researchinterests are the radio wave propagation and channel modeling forvarious wireless systems, applied radio measurements and information technology for regional/ruraldevelopment. He is fellow of IEICE, senior member of IEEE, and member of Japan Society forInternational Development (JASID).

Assistant Professor Kentaro Saito
Assistant Professor Kentaro Saito was born in Kanagawa, Japan, in 1977.
He received his B.S. and Ph.D. degrees from the University of Tokyo,
Japan, in 2002 and 2008, respectively. He joined NTT DOCOMO,
Kanagawa, Japan, in 2002. Since then, he has been engaged in the
research of IP networks, transport technologies, MAC technologies, andradio propagation for mobile communication systems. He has been
engaged in the development of the LTE base station. He joined Tokyo
Institute of Technology, Japan in 2015. Since then, he has been engaged
in research of radio propagation measurements and MIMO channel
modeling. He is a senior member of IEICE and a member of IEEE.

Specially Appointed Lecturer Azril Haniz
Dr. Azril Haniz received the B.E. degree in electrical and electronicengineering in 2010, and the M.Eng and Dr.Eng. degrees from the Dept.
of International Development Engineering in Tokyo Institute ofTechnology, Japan in 2012 and 2016, respectively. He is currently
working as a specially appointed associate professor in the same
university. He won the best student paper award in the Singapore-Japan
International Workshop on Smart Wireless Communications(SmartCom) in 2014, and is a recipient of the 2016 Tejima Seiichi
Doctoral Dissertation Award. He also won the best paper award in
ICREST 2019. His research interests include localization, cognitive radio,
sensor networks and signal processing. He is currently a member ofIEEE and IEICE.

Our Research Interests
Takada Laboratory has been conducting research on the characterization and modeling of the radio
propagation channel for wireless communications as well as the application of radio wave technology
in various use cases. In particular, research on the radio propagation channel is conducted throughextensive channel sounding measurements in various environments (indoor hall environment, urban
microcells, subway tunnels, outdoor agricultural fields etc) and various frequency bands (UHF, SHF
bands etc). In addition to measurements, simulations of the radio propagation channel are also
conducted using several electromagnetic simulation techniques such as ray-tracing, physical optics (PO)and the parabolic equation method.
The use of radio wave technology for various applications is also a main topic of research for
Takada Lab. Research on human motion and recognition using radio waves is conducted using
measurement data obtained from commercial off-the-shelf Wi-Fi devices, and also throughelectromagnetic simulations of the human body. Application of radio wave technology for indoor andoutdoor localization is also being considered, with hardware implementations using Raspberry Pi
devices, and specialized hardware developed by collaborating companies.

Recent Research Topics
 Channel sounding, propagation channel measurement and modeling Frequency Dependency Analysis of Clusters in Indoor Hall Environment at SHF
Bands Visual Inspection of Scattering Objects for 11 GHz Urban Microcell Channel
 Superresolution Subspace-based Joint Delay and Angle of Arrival Estimation of
Coherent Signals for Millimeter Wave Channel Sounding Research on the Formation of Radio Channel in Subway Train Control System
 Radio Propagation Channel Analysis and Modeling in Outdoor Agricultural
Environments for Wireless Sensor Networks
 Radio Propagation Prediction in Tunnel using Parabolic Equation Method
 Path Loss Prediction by Artificial Neutral Network for Wireless Network Cell Planning Channel Capacity Evaluation of Large Array MIMO System from Propagation
Parameters based on Directional Channel Model Human motion and gesture recognition using radio waves
 Development of Hand Motion Tracking System using Channel State Informationfrom Wi-Fi Devices
 Studies on Human Motion Recognition through wireless Sensing with Communication
Signals

 Application of radio technology for indoor and outdoor localization
 Device-free indoor localization utilizing BLE devices by controlling advertisingchannels Radio Map Interpolation for Localization of Unknown Radios
5Takada Laboratory
Frequency Dependency Analysis of Clusters in Indoor Hall
Environment at SHF Bands(A collaborative research with Aalborg University)

Due to the rapid increase in high data rate applications, 5G wireless systems exploiting several frequencies
with large bandwidth have been considered. Frequency dependency analysis and characterization ofmultipath clusters is necessary as the channel performance depends strongly how they interact withinteracting objects (IOs) in the environment, which varies across different frequencies. Thus, this study
analyzes the frequency characteristics of clusters at SHF bands with the assistance of physical optics.
Fig. 1 shows the hall environment with the major cluster trajectories, and Table 1 shows the scattering
intensities (SIs) of clusters at 3, 10 and 28 GHz bands, in which the cluster power is normalized by thefree space at the same
SI mean (dB)distance. Reflection from
ObjectMechanism
3 GHz 10 GHz 28 GHz
tube, pillar and elevator was
Tube
Reflection
-9.7
-9.2
-9.8
Reflection, shadowing -2.1
-2.2
-1.1
the major mechanism where
Elevator
3 GHz zone plate
5.3
0.7
0.9
the clusters are frequency
28 GHz zone plate
-2.1
-2.1
1.1
independent,
whereasTube+pillar
Reflection-9.9-10.6 -10.4
Diffraction
-9.4
-14.3
NA
diffraction, scattering andStud+window
10 GHz zone plate
-6.5
-3.3
-8.8
Fresnel zone plate effect from
Brick
Scattering
-5.4
-6.8
-10.9
elevator, stud, brick andScattering
-4.4
-7.3
-9.7
Plasterboard
Shadowing,
scattering
-8.9
-13.1
-10.3
plasterboard resulted in thevariousfrequency Fig. 1: Major cluster trajectories. Table 1: SI of major clusters.
characteristics.

Visual Inspection of Scattering Objects for 11 GHz Urban Microcell
ChannelThe frequency bands in the range below 6 GHz are already congested with a limited bandwidth forfurther use due to the big amount of usage in the wireless services. To satisfy the demand of high data
rates, it requires exploring higher frequency with wider bandwidth. At higher frequency, however, theattenuation due to the obstruction of propagation path
is more obvious. Considering multiple input multiple
output (MIMO) or massive MIMO technology, it isimportant to characterize the spatial property of thescattered paths for the evaluation of the performance
under the line-of-sight obstruction. This study aims at
understanding the governing mechanism of the nonline-of-sight propagation paths in a street microcell
environment by identifying and characterizing theinteracting objects (IO) which are visually identified bysuperposing the identified paths on to the sphericalFig. Spherical view at the Tx and measured
photo (Fig.). Double directional inspections ensure
paths colored with interaction loss.
that we find the identical IO from both sides. The
software S2 Lite A-GIS can identify the 3D location of the object via a GUI, so that empirical ray tracing
is possible betwenen Tx, IO and Rx to calculate the propagation delay time, which then is compared withthe measured delay time for further confirmation.
6

Superresolution Subspace-based Joint Delay and Angle of Arrival
Estimation of Coherent Signals for Millimeter Wave Channel Sounding(A collaborative research with NTT)

Measurement campaign was conducted utilizing a66.5 GHz channel sounder in an anechoic
chamber. In order to estimate the parameters of
the incoming wave, the subspace-based parameter
estimation algorithm, specifically a variant ofmultiple signal classification (MUSIC) called
JADE-MUSIC, is utilized to jointly estimate delay
and angle of the multipaths. Accommodating thelimitation of the algorithm in the coherent
environment with multipaths of the same signal,smoothing preprocessing techniques is done in thefrequency domain to decorrelate the incoming
multipaths. Measured array response based on thecalibration measurement data is also utilized to
improve the estimation. The modifications
towards JADE-MUSIC that was utilized showed
better performance in estimating coherent
multipaths.

Fig. Delay and angle estimation of multipaths
utilizing (a) JADE-MUSIC (b) JADE-MUSIC
with FSP (c) JADE-MUSIC with FSP andmeasured array response.

Research on the Formation of Radio Channel in Subway Train Control
System (A collaborative research with Kyosan Electric MFG. Co., LTD.)
Recently, radio communication is introduced for train control based on the advantage that it can simplify
way side equipment and maintenance. We are studying three issues
for the reliability improvement of the train control system called
Communication-based Train Control, CBTC.
(1) Research on the radio wave propagation in a subway tunnel(2) Research on the radio environment in a railway area, such as
stations and way sides(3) Research on the low rate communication under the congested
environment of radio communication
Subway tunnels are rich in curves and slopes compared to highspeed railway tunnels. We would like to use radio waves to achieve
better train control communication through research of radio
wave propagation and interference. Radio wave propagation
measurement campaign was conducted in a subway, and theFig. Radio wave propagation
congestion situation on a platform was simulated. Based on these characteristics in a subway tunnel
measurements, we aim at developing a design method for suitable with and without railway facilities.
radio base station layout, and a wireless communication system
based on analysis from ray-trace simulations and electromagnetic simulations.

7

Takada Laboratory
Radio Propagation Prediction in Tunnel using Parabolic Equation
Method (A collaborative research with Kyosan Electric MFG. Co., LTD.)

Controlling the train using wireless communication is an alternative approach to the traditional wirebased approach. The knowledge of radio propagation characteristics in a tunnel is useful for designing
such a controlling system. This research developed a radio propagation prediction approach using theAlternate direction implicit Parabolic Equation (ADI-PE) method. By applying the so-called six-points
scheme together with the first order interior interpolation technique on the boundary wall of the tunnel,
the general matrix form of ADI-PE is obtained. This form can be solved numerically. The figures present
one of the validation results in which the developed approach is applied for the straight semi-circularcross section tunnel under the Neumann boundary condition with frequency =3 GHz, a = 2 and meshsize=0.2λ, 0.2λand 5λin the x, y and z directions, respectively. It shows good agreement between thedeveloped approach and the analytic solution of the waveguide theory. The agreement is even better if asmaller mesh size is used.

Radio Propagation Channel Analysis and Modeling in Outdoor
Agricultural Environments for Wireless Sensor Networks(A collaborative research with National Electronics and Computer Technology
Center, Thailand. This work is partly supported by The Fujikura Foundation)
Information of the radio propagation path loss in an agriculture field is useful for wireless sensor network
planning in smart farming. Using themeasurement finding that the magnitude ofvegetation obstruction causes the variation of thepath loss in the field, this research proposed twopath loss models for two common types of thetropical agriculture environments; the vegetation
obstruction (VO) model for the tall food grass
and the equivalent vegetation obstruction (EVO)
Fig. 1: Part of theFig. 2: Measurement result model for the fruit orchard. Especially, thismeasurements infitted with the VO model in research developed the approach to determine
the equivalent vegetation obstruction by usingsugarcane field.
sugarcane field.
the electromagnetic
simulation. The figures present one of thevalidation results of the proposed model where the measurement was conducted in a sugarcane field (tallfood grass type) with different angular directions. This confirms that the proposed model can represent
the measurement results in every angular direction well. Therefore, this model can be used to predict theloss at any point in the field.

8

Path Loss Prediction by Artificial Neutral Network for Wireless
Network Cell Planning (This work is supported by MIC SCOPE No.
185103006)
As global mobile traffic is forecasted to increase, more devices will
be connected to wireless networks. We have to deploy more
efficient wireless networks to satisfy the growing demands. Pathloss information is vital for determination of coverage andoptimization efficiency of wireless networks. Therefore we
propose path loss prediction model based on artificial neutral
network (ANN) and ray tracing (RT) simulations for wirelessnetwork cell planning.
Due to constraint conditions in RT simulations, we obtain
two types of data, continuous and discrete. Since discrete data is
not suitable for directly performing regression, as Fig. 1 shows weproceed in two steps. We first adopt a binary classification ANN
model for predicting whether the receiver is located inside oroutside the targeted area, and then we utilize a regression ANN
model for estimating path loss only for in-area located receivers.
By iteratively utilizing both ANN models we can obtain the pathloss distribution for a relatively complicated environment as Fig. 2
shows.

Fig. 1: ANN structure.

Fig. 2: Path loss distribution.

Channel Capacity Evaluation of Large Array MIMO System from
Propagation Parameters based on Directional Channel Model
In recent years, with the spread of various application services using the network, the demand for highspeed mobile radio communication system is increasing. In such a system, Multi-Input, Multi-Output(MIMO) transmission aiming at realizing a high data rate has attracted attention as one of the maintechnologies. Furthermore, because of the congestion in
low frequency, we need to utilize higher frequency withlarge bandwidth. However, the higher frequency willcause shorter propagation range, so the directional
channel will be applied to solve this problem. Meanwhile,the directional channel parameter used for the evaluationof the MIMO channel needs to be measured withelement spacing of the array antenna being less than halfa wavelength, so it becomes a comparatively small array
antenna and the angular resolution is also restricted.
Therefore, it is not always applicable to large array
antennas. From the above, we aim to evaluate thepredictable range by predicting the channel response
Fig. Eigenvalue percentile ofof a large array antenna using the propagation parameters
measured by a small array antenna, and compare it with the measurement and synthesis data.
actual result measured by the large array antenna. Figure shows the eigenvalue percentile result of themeasurement and synthesis data. From the figure, we can see a very good agreement between themeasurement result and synthesis result.
9

Takada Laboratory
Development of Hand Motion Tracking System using Channel State
Information from Wi-Fi Devices Calibration of CSI Phase Rotation
Utilizing B2B Connection(This work is partly supported by The Fujikura Foundation)

Wi-Fi has been widely leveraged in RF motion sensing due to its low cost, ubiquitous, and easiness to
deploy. In the presence of any motion, the phase component of channel state information (CSI) will
experience temporal rotation due to the change of
propagation delay, and this phenomenon is the key tosense the motion. In practice, however, the timevarying frequency offset of local oscillators (LOs)between two Wi-Fi transceivers due to the absence ofsynchronization obscures the CSI phase. Therefore, it
is virtually impossible to realize motion analysis
without acquiring the parameters that cause thisundesired rotation from Wi-Fi chips.
Fig. 1 × 2 SIMO configuration of the
Our work introduces the use of a back-to-backproposed b2b calibration.
(b2b) channel as the reference CSI to effectively suppress
the phase rotation without contaminating the target CSI itself. An experiment was performed to compare
the calibrated CSI with the channel measured using a Vector Network Analyzer as ground truth. The
results have successfully shown similarity of the CSI phase component relative to the ground truth albeit
with the constant residual phase offset. After removing the constant residual offset, the CSI phase closely
resembled the ground truth with 0.117 radians root mean square error (RMSE).

Studies on Human Motion Recognition through wireless Sensing with
Communication Signals
Electromagnetic signals from communication devices can be also used for wireless sensing. Thispotentially leads to contact-less, device-less, camera-less applications of human motion recognition byubiquitous radio frequency (RF) sensing. However, the time-variant physical phenomena of RF scatteringfrom deformable biological human bodies are complicated, which depend on many factors such as shape,material properties, polarization, geometry, etc. And there is a lack of extensive data for analysis because
it is difficult and costly to get by measurement. In this research, physically plausible large data is generated.
This deterministic and flexible simulator uses inputs of measured motion data, generative human models
in various postures, and a numerical EM high frequency asymptotic method of physical optics (PO).
Results shows the importanceof doppler signature and thegeometry dependent nature of
RF sensing, and a simple
classifier can be built with
inputs of such time-frequency
spectrograms.
Fig. Methodology.

10Device-free indoor localization utilizing BLE devices by controlling
advertising channels
Recently indoor localization systems have become a focus of research and development. In this research,a device-free indoor localization system by utilizing Raspberry Pi2 is established. The fingerprinting
Reproducibility Grouptechnique is employed. In our proposed system, the Received
Signal Strength Indicator (RSSI) of Bluetooth Low Energy
Ideal result(BLE) signals are used as location fingerprints. BLE is a narrow
Conventionalband communication system and transmits advertising packets
on three different frequency channels. In conventional Proposed (combined)
approaches, the RSSI from each frequency channel is not
0
50
100
150
200
250
individually considered, thus it may suffer from large
Mean error distance(cm)fluctuations, resulting in low localization accuracy. Therefore, in
Test Group
this study, two methods are utilized to employ channel specific
Ideal result
features. First, the device is controlled to transmit
advertisements on one pre-set advertising channel. Second, allConventional
the advertisements with known channel numbers are received
Proposed (combined)
one by one. Thus, we can have triple features at one position's
fingerprint, resulting in possibly better localization accuracy.
0
50
100
150
200
250
Mean error distance(cm)Experiments were conducted in office environment and results
Fig. Localization accuracy ofshowed that combining of all the advertising channels with their
proposed system.
channel numbers can achieve better results.

Radio Map Interpolation for Localization of Unknown Radios(A collaborative research with Koden Electronics Co., Ltd.)

Conventional localization based on radio maps, also known as fingerprinting, is only reliable in scenarios
where the center frequency of the target radio is the same asthat used when constructing the radio map. However, forunknown radios, it is very unlikely that the precise center
frequency used is known in advance. In order to address thisissue, a novel approach to interpolate the radio map is proposedin this research. Firstly, the RSS radio map is measured at
multiple frequencies, and hardware architecture which cansupport the sequential measurement of a multi-frequency radio
map is also proposed. Then, a novel algorithm is employed tointerpolate the radio map in the frequency and spatial domains,based on the log-linear frequency characteristics and spatial
correlation of the RSS, respectively. In order to evaluate theproposed algorithm, a measurement campaign was conductedin the Tokyo Tech O-okayama campus area. The radio map
measured at two different center frequencies was used toFig. Measured and interpolated
predict the radio map at 1297MHz. Results showed that the
sproposed algorithm could achieve a RMSE (root mean radio maps in O-okayama campus.
squared error) of about 2.5dB on average, which led to an improvement of localization accuracy by about
5m.
11

Publications
Takada Laboratory
Transactions and Letters[1] Sukhumarn Archasantisuk, Takahiro Aoyagi,
Min-Seok Kim, and Jun-ichi Takada, “Temporalcorrelation model-based transmission power
control in wireless body area network,” IET
Wireless Sensor Systems, (9 p), Apr. 2018.
[2] Shengru Li, Shinobu Yamaguchi, and Jun-ichi
Takada, “The Influence of Interactive Learning
Materials on Self-Regulated Learning and
Learning Satisfaction of Primary School Teachersin Mongolia,” Sustainability, vol. 10, no. 4, 1093
(19 p), Apr. 2018.
[3] Kosorl Thourn, Takahiro Aoyagi, and Jun-ichi
Takada, “Development of Broadband Parametric
Permittivity Model of Dielectric Absorbing
Material for Time-domain Electromagnetic Wave
Simulation,” IEEJ Transactions on Fundamentals
and Materials, vol.138, no.6, pp.302-308, Jun. 2018.
[4] Kentaro Saito, Yunyi Yao, and Jun-ichi Takada,“Parameter estimation refinement of MIMO
propagation channel by nonlinear conjugate
gradient approach,” IEICE Communications
Express, vol. 7, no. 9, pp. 328-333, Sep. 2018.
[5] Brecht Hanssens, Kentaro Saito, Emmeric
Tanghe, Luc Martens, Wout Joseph, and Jun-ichi
Takada, “Modeling the Power Angular Profile ofDense Multipath Components using Multiple
Clusters,” IEEE Access, vol. 6, pp. 56084-56098,
Sep. 2018.
[6] Yu Tao, Azril Haniz, Kentaro Sano, Ryosuke
Iwata, Ryouta Kosaka, Yusuke Kuki, Gia Khanh
Tran, Jun-ichi Takada, Kei Sakaguchi, “A Guide ofFingerprint Based Radio Emitter Localization
Using Multiple Sensors (invited),” IEICE
Transactions on Communications, vol. E101-B,
no. 10, pp. 2104-2119, Oct. 2018.
[7] Yang Miao, Jun-ichi Takada, Kentaro Saito,
Katsuyuki Haneda, Andres Alayon Glazunov, and
Yi Gong, “Comparison of Plane Wave andSpherical Vector Wave Channel Modeling for
52

Characterizing Non-Specular Rough-Surface
Wave Scattering,” IEEE Antennas and WirelessPropagation, vol. 17, no. 10, pp. 1847-1851, Oct.
2018.
[8] Hiroki Ohara, Hirokazu Sawada, Masayuki
Oodo, Fumihide Kojima, Hiroshi Harada,
Kentaro
Saito,
andJun-ichiTakada,“Characterization of
Broadband Mobile
Communication Channel in 200 MHz Band Basedon Saleh-Valenzuela Model,” IEICE Transactions
on Communications, vol. E101-B, no.11, pp.
2277-2288, Nov. 2018.
[9] Kentaro Saito, Qiwei Fan, Nopphon
Keerativoranan, and Jun-ichi Takada, “Verticaland Horizontal Building Entry Loss Measurementin 4.9 GHz Band by Unmanned Aerial Vehicle,”
IEEE Wireless Communications Letters,
accepted.
[10] Tossaporn Srisooksai, Kamol Kaemarungsi,
Jun-ichi Takada, and Kentaro Saito, “Radio
Propagation Measurement and Characterizationin Outdoor Tall Food Grass Agriculture Field for
Wireless Sensor Network at 2.4 GHz Band,”
Progress In Electromagnetics Research C, Vol. 88,
43-58, 2018.
[11] Guojin Zhang, Kentaro Saito, Wei Fan,
Xuesong Cai, Panawit Hanpinitsak, Jun-ichi
Takada,and
Gert
FrolundPedersen,“Experimental Characterization of MillimeterWave Indoor Propagation Channels at 28 GHz,”IEEE Access, vol. 6, pp. 76516-76526, 2018.
[12] Nopphon Keerativoranan, Azril Haniz,
Kentaro Saito, and Jun-ichi Takada, “Mitigationof CSI Temporal Phase Rotation with B2B
Calibration Method for Fine-Grained Motion
Detection Analysis on Commodity Wi-Fi Devices,”
Sensors, vol. 18, no. 11, pp. 3795 (1-18), Nov. 2018.
[13] Yang Miao, Wei Fan, Jun-ichi Takada, Ruisi
He, Xuefeng Yin, Mi Yang, Jose RodriguezPineiro, Andres Alayon Glazunov, Wei Wang, and
Yi Gong, “Comparing Channel Emulation
Algorithms by Using Plane Waves and Spherical
Vector Waves in Multi-Probe Anechoic Chamber
Setups,” IEEE Transactions on Antennas andPropagation, early access.
[14] Tossaporn Srisooksai, Kamol Kaemarungsi,
Junichi Takada, and Kentaro Saito, “Path Loss
Measurement and Prediction in Outdoor Fruit
Orchard for Wireless Sensor Network at 2.4 GHz
Band,” Progress In Electromagnetics Research C,
vol. 90, pp. 237-252, 2019.

International Conference[15] Brecht Hanssens, Emmeric Tanghe, Wout
Joseph, Luc Martens, Kentaro Saito, Jun-ichi
Takada, and Claude Oestges, “Modeling thePower Angular Profile of Dense Multipath
Components Using Multiple Clusters,” 12th
European Conference on Antennas andPropagation (EUCAP 2018), Apr. 2018 (London,
UK).
[16] Kentaro Saito, Panawit Hanpinitsak, Jun-ichi
Takada, Wei Fan, and Gert F. Pedersen,“Frequency Dependency Analysis of SHF band
Directional Propagation Channel in Indoor
Environment,” 12th European Conference on
Antennas and Propagation (EUCAP 2018), Apr.
2018 (London, UK).
[17] Yuji Hirai, Shinobu Yamaguchi, and Jun-ichi
Takada, “Relationship between communication
channels among teachers and the diffusion ofteachers' ICT use in rural Mongolian education,”
62nd Annual Conference of Comparative andInternational Education Society (CIES 2018), Mar.
2018 (Mexico City, Mexico).
[18] Shengru Li, Shinobu Yamaguchi, Jun-ichi
Takada, and Javzan Sukhbaatar, “The effects ofinteractive learning materials on self-regulated
learning and learning outcome in the case ofMongolian primary school teachers,” 62nd
Annual Conference of Comparative andInternational Education Society (CIES 2018), Mar.
2018 (Mexico City, Mexico).
[19] Xin Du, Kentaro Saito, Jun-ichi Takada,
Mitsuki Nakamura, Motoharu Sasaki, and Yasushi
Takatori, “Shadowing Measurement of Various
Obstacles on Millimeter-Wave Bands in Indoor

Environment,” 2018 Asian Workshop on
Antennas and Propagation (AWAP 2018), July
2018 (Pattaya, Thailand).
[20] Kentaro Saito, Qiwei Fan, Nopphon
Keerativoranan, and Jun-ichi Takada, “4.9 GHz
Band Outdoor-to-Indoor Radio Propagation
Measurement by an Unmanned Aerial Vehicle,”2018 Asian Workshop on Antennas andPropagation (AWAP 2018), July 2018 (Pattaya,
Thailand).
[21] Tossaporn Srisooksai, and Jun-Ichi Takada,“Application of Hybrid T-matrix Method to
Predict Vegetation Attenuation in Outdoor
Agriculture Orchard and Comparison with
Measurement,” Progress In Electromagnetics
Research Symposium (PIERS) 2018 Toyama, Aug.
2018 (Toyama, Japan).
[22] Makoto Sumi, and Jun-ichi Takada,“Unsymmetrical Rectangular Loops with Gaps for
GNSS
Applications,”
Progress
In
Electromagnetics Research Symposium (PIERS)
2018 Toyama, Aug. 2018 (Toyama, Japan).
[23] Nopphon Keerativoranan, Kentaro Saito, and
Jun-Ichi Takada, “A Study of Hand Motion
Trajectory Tracking by Utilizing Channel State
Information of off-the-shelf Wi-Fi Devices,”
Progress
In
Electromagnetics
Research
Symposium (PIERS) 2018 Toyama, Aug. 2018
(Toyama, Japan).
[24] Kentaro Saito, Qiwei Fan, Nopphon
Keerativoranan, and Jun-ichi Takada, “4.9 GHz
band Outdoor-to-Indoor Radio Propagation
Measurement by an Unmanned Aerial Vehicle,”2018 IEEE International Workshop on
Electromagnetics (iWEM 2018), Aug. 2018
(Nagoya, Japan).
[25] Kentaro Saito, Panawit Hanpinitsak, Wei Fan,
Jun-ichi Takada, and Gert F. Pedersen,“Frequency Characteristics of Diffuse Scatteringin SHF band in Indoor Environments,” COST
IRACON 8th MCM TD(18)08040, Oct. 2018
(Podgorica, Montenegro).
[26] Djiby Marema Diallo, Jun-ichi Takada, andKentaro Saito, “Identification of Scattering
53

Publications
Objects for 11 GHz Urban Microcell Radio
Channel via Visual Inspection,” The 5thInternational Workshop on Smart WirelessCommunications (SmartCom 2018), IEICE
Technical Report, vol. 118, no. 274, SR2018-77, pp.
41-43, Oct. 2018 (Bangkok, Thailand).
[27] Junming Jiang, Azril Haniz, Kentaro Saito,
and Jun-ichi Takada, “Device-free Indoor
Localization utilizing BLE devices by controlling
advertisement channels,” The 5th International
Workshop on Smart Wireless Communications(SmartCom 2018), IEICE Technical Report, vol.
118, no. 274, SR2018-85, pp. 63-70, Oct. 2018
(Bangkok, Thailand).
[28] Jun-ichi Takada, “Sustainable Engineering
Technology for International Development(session introduction),” 2018 Korean Associationof International Development and Cooperation(KAIDEC) Winter Meeting, Dec. 2018 (Seoul,
Korea).
[29] Jun-ichi Takada, “Challenges of Grassroot
Deployment of ICT - A Case Study in Luang
Prabang World Heritage, Lao PDR (invited),”2018 Korean Association of International
Development and Cooperation (KAIDEC)
Winter Meeting, Dec. 2018 (Seoul, Korea).
[30] Azril Haniz, Gia Khanh Tran, Kei Sakaguchi,
Jun-ichi Takada, Toshihiro Yamaguchi, Tsutomu
Mitsui, and Shintaro Arata, “Construction andInterpolation of a Multi-frequency Radio Map,”
International Conference on Robotics, Electricaland Signal Processing Techniques (ICREST 2019),
Jan. 2019 (Dhaka, Bangladesh).
[31] Tossaporn Srisooksai, Kamol Kaemarungsi,
Jun-ichi Takada, and Kentaro Saito, “Small-fading
and Wideband Propagation Characteristics in
Fruit Orchard at 2.4 GHz for Wireless Network
in Smart Farming Application,” International
Conference on Robotics, Electrical and Signal
Processing Techniques (ICREST 2019), Jan. 2019
(Dhaka, Bangladesh).
[32] Guojin Zhang, Panawit Hanpinitsak,
Xuesong Cai, Wei Fan, Jun-ichi Takada, Kentaro
Saito, and Gert F. Pedersen, “Millimeter-Wave
54

Channel Characterization at Large Hall Scenario
in the 10 and 28 GHz Bands,” 13th European
Conference on Antennas and Propagation(EUCAP 2019), Mar. 2019 (Krakow, Poland).

Domestic Conference[33] Panawit Hanpinitsak, Kentaro Saito, Jun-ichi
Takada, Wei Fan, and Gert F. Pedersen,“Frequency Dependency Analysis of Multipath
Clusters of Indoor Propagation Channel in SHF
Bands,” Japanese URSI-F Meeting, no. 623, May
2018 (Meguro, Tokyo).
[34] Kosuke Murakami, Kentaro Saito, and Junichi Takada, “Development of 12GHz BandSIMO Channel Sounder using Radio-on-Fiber
Technology,” Japanese URSI-F Meeting, no. 623,
May 2018 (in Japanese, Meguro, Tokyo).
[35] Jiayue Cheng, Kentaro Saito, and Jun-ichi
Takada, “Visualization of Indoor Radio Wave
Propagation by Using Augmented Reality
Technology,” 10th Multidisciplinary International
Student Workshop (MISW2018), P-15, Aug. 2018
(Meguro, Tokyo).
[36] Dan Qiao, Kentaro Saito, and Jun-ichi Takada,“Diffuse Scattering Prediction for HighFrequency Band,” 10th Multidisciplinary
International Student Workshop (MISW2018), P20, Aug. 2018 (Meguro, Tokyo).
[37] Ahmad Salaam Mirfananda, Takada Jun-ichi,
and Kentaro Saito, “Dynamic Channel Modelingin Millimeter Wave for Next Generation Wireless
System,” 10th Multidisciplinary International
Student Workshop (MISW2018), A2-4, Aug. 2018
(Meguro, Tokyo).
[38] Tan Yong Hong, Kentaro Saito, and Jun-ichi
Takada, “Rain Attenuation for Millimeter Wave
Fixed
Wireless
AccessSystem,”10th
Multidisciplinary International Student Workshop(MISW2018), C2-2, Aug. 2018 (Meguro, Tokyo).
[39] Zhihang Chen, Kentaro Saito, Jun-ichi
Takada, “SLAM-based Indoor 3D Environment
Modeling for Electromagnetic Simulation,” 10th
Multidisciplinary International Student Workshop

(MISW2018), C2-4, Aug. 2018 (Meguro, Tokyo).
[40] Nopphon Keerativoranan, Kentaro Saito, and
Jun-ichi Takada, “Calibration of Wi-Fi Phase
Channel State Information for Dynamic Motion
Analysis,” 10th Multidisciplinary International
Student Workshop (MISW2018), C2-5, Aug. 2018
(Meguro, Tokyo).
[41] Katsumi Seki, Kentaro Saito, and Jun-ichi
Takada, “Improvement of Position Logging
Accuracy for UAV-based Radio Measurement
System,” 10th Multidisciplinary International
Student Workshop (MISW2018), E1-1, Aug. 2018
(Meguro, Tokyo).
[42] Deepak Gautam, Jun-ichi Takada, andKentaro Saito, “Development of Measurementbased Radio Environment Map for Dynamic
Spectrum Access,” 10th Multidisciplinary
International Student Workshop (MISW2018),
E1-2, Aug. 2018 (Meguro, Tokyo).
[43] Traitip Siriruang, and Jun-ichi Takada,“Effects of the Study Abroad Program in
ASEAN on Thai Students: ASEAN Awareness,”
10th Multidisciplinary International Student
Workshop (MISW2018), E1-4, Aug. 2018
(Meguro, Tokyo).
[44] Kosuke Murakami, Kentaro Saito, and Junichi Takada, “Performance Analysis of Channel
Sounding System Based on Radio-on-Fiber
Technology,” 10th Multidisciplinary International
Student Workshop (MISW2018), E1-5, Aug. 2018
(Meguro, Tokyo).
[45] Haitao Pang, and Jun-ichi Takada, “Gesture
Recognition with Wireless Devices,” 10th
Multidisciplinary International Student Workshop(MISW2018), E2-1, Aug. 2018 (Meguro, Tokyo).
[46] Djiby Marema Diallo, Jun-ichi Takada, andKentaro Saito, “Visual Inspection Of Scattering
Objects for 11 GHz Urban Microcell Channel,”
10th Multidisciplinary International Student
Workshop (MISW2018), E2-2, Aug. 2018
(Meguro, Tokyo).
[47] Azril Haniz, Gia Khanh Tran, Kei Sakaguchi,
Jun-ichi Takada, Toshihiro Yamaguchi, Tsutomu
Mitsui, Shintaro Arata, “Localization of

Unknown Radios in Urban Environments -- Oncampus Experiment Results,” IEICE Technical
Report, SRW2018-20, Aug. 2018 (Okayama).
[48] Kentaro Saito, Yukiko Kishiki, Jun-ichi
Takada, “3-Dimensional Map Generation by User
Camera and Depth Sensor Data and Application
to Radio Propagation Simulation,” 2018 IEICE
Society Conference, BP-2-1, Sep. 2018 (inJapanese, Kanazawa, Ishikawa).
[49] Azril Haniz, Gia Khanh Tran, Kei Sakaguchi,
Jun-ichi Takada, Toshihiro Yamaguchi, Tsutomu
Mitsui, Shintaro Arata, “Multi-frequency Radio
Map Database Construction and its Application in
Positioning,” 2018 IEICE Society Conference,BP-2-3, Sep. 2018 (Kanazawa, Ishikawa).
[50] Haitao Pang, Jun-ichi Takada, “Human
Motion Recognition through Sensing with
Telecommunication Devices,” IEICE Multiple
Innovative Kenkyu-kai Association for WirelessCommunication (MIKA 2018), Sep. 2018 (Ito,
Shizuoka).
[51] Kosuke Murakami, Kentaro Saito, Jun-ichi
Takada, “Performance Analysis of 1x16 SIMO
Channel Sounding System in 12 GHz Based onRadio-on-Fiber Technology,” IEICE Multiple
Innovative Kenkyu-kai Association for WirelessCommunication (MIKA 2018), Sep. 2018 (Ito,
Shizuoka).
[52] Itsuki Uemura, Shinobu Yamaguchi, and Junichi Takada, “Analysis of Landscape Change inWorld Heritage Site - Case of Luang Prabang, Lao
P.D.R,” 29th Annual Conference of JASID, Nov.
2018 (Tsukuba, Ibaraki).
[53] Yiqiong Mai, Shinobu Yamaguchi, Jun-ichi
Takada, and Shengru Li, “The influence ofinteractive learning materials on Self-Regulated
Learning (SRL) processes and learning outcomesof primary school teachers,” 29th Annual
Conference of JASID, Nov. 2018 (Tsukuba,
Ibaraki).
[54] Shengru Li, Shinobu Yamaguchi, and Jun-ichi
Takada, “Understanding the self-regulatory
processes affecting learning satisfaction andintention to apply learning contents in the case of55

Publications
Mongolian primary school teachers,” 29th Annual
Conference of JASID, Nov. 2018 (Tsukuba,
Ibaraki).
[55] Zhihang Chen, Kentaro Saito, Wataru
Okamura, Yukiko Kishiki, and Jun-ichi Takada,“3D Point Cloud Data Simplification Method forElectromagnetic
SimulationinIndoorEnvironment,” IEICE Technical Report,
SRW2018-49, Jan. 2019 (Kamakura, Kanagawa).
[56] Wataru Okamura, Yuki Matsuyama, Yuikiko
Kishiki, Zhihang Chen, Kentaro Saito, Jun-ichi
Takada, “Modeling Method for Ray Trace
Simulation using 3D Point Cloud Data and
Evaluation of Radiowave Propagation in Indoor
Environment,” IEICE Technical Report,
AP2018-173, Feb. 2019 (Osaka).
[57] Nopphon Keerativoranan, and Jun-ichi
Takada, “Development of Doppler CSI model for
Wi-Fi based hand gesture detection application,”
IEICE Technical Report, ASN2018-111, Mar.
2019 (Meguro, Tokyo).
[58] Xin Du, Kentaro Saito, Jun-ichi Takada,
Mitsuki Nakamura, Motoharu Sasaki, and Yasushi
Takatori, “Shadowing Effect Prediction based on
Physical Optics Approach for Millimetre Wave
Mobile Communication,” IEICE Technical
Report, AP2018-192, Mar. 2019 (Fukushima).
[59] Jun-ichi Takada, Kentaro Saito, On thePrediction of the Over-the-Rooftop Propagation
Loss in Urban Macrocellular Environments,”
2019 IEICE General Conference BS-1-12, Mar.
2019 (Shinjuku, Tokyo),[60] Yongri Jin, Kentaro Saito, Jun-ichi Takada,“Path Loss Prediction by Artificial Neural
Network,” 2019 IEICE General Conference, B19-7, Mar. 2019 (Shinjuku, Tokyo).

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