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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 20 of 222  /  [Next]  
Committee Date Time Place Paper Title / Authors Abstract Paper #
SeMI 2021-01-20
13:20
Online Online A Study of Online Training Method for Image-based Wireless Link Quality Prediction
Sohei Itahara (Kyoto Univ), Takayuki Nishio (Tokyo Tech), Masahiro Morikura, Koji Yamamoto (Kyoto Univ) SeMI2020-44
Machine-learning-based prediction of future wireless link quality is an emerging technique that can potentially improve ... [more] SeMI2020-44
pp.7-9
SRW, SeMI, CNR
(Joint)
2020-11-26
16:00
Online Online [Poster Presentation] Feature Extraction Method for Wireless LANs Channel Selection Based on Contextual Bandit Learning
Kota Yamashita (Kyoto Univ.), Shotaro Kamiya (Sony Corp.), Koji Yamamoto (Kyoto Univ.), Takayuki Nishio (Tokyo Tech.), Masahiro Morikura (Kyoto Univ.) SeMI2020-24
 [more] SeMI2020-24
pp.39-42
SRW, SeMI, CNR
(Joint)
2020-11-26
16:00
Online Online [Poster Presentation] Adversarial Reinforcement Learning-based Robust Access Point Coordination Against Uncoordinated Interference
Yuto Kihira, Yusuke Koda, Koji Yamamoto (Kyoto Univ.), Takayuki Nishio (Tokyo Tech.), Masahiro Morikura (Kyoto Univ.) SeMI2020-25
 [more] SeMI2020-25
pp.43-46
SeMI 2020-01-31
10:00
Kagawa   [Poster Presentation] Communication-Efficient Federated Learning Using Non-Labeled Data
Souhei Itahara, Takayuki Nishio, Masahiro Morikura, Koji Yamamoto (Kyoto Univ) SeMI2019-109
Federated learning (FL) is a machine learning setting where many mobile devices collaboratively train a machine learning... [more] SeMI2019-109
pp.47-48
SeMI 2020-01-31
10:00
Kagawa   [Poster Presentation] Experimental Evaluation of Federated Learning in Real Networks
Naoya Yoshida, Takayuki Nishio, Masahiro Morikura, Koji Yamamoto (Kyoto Univ.) SeMI2019-110
Federated Learning (FL) is a decentralized learning mechanism, which enables to train machine learning (ML) models using... [more] SeMI2019-110
pp.49-50
SeMI 2020-01-31
10:00
Kagawa   [Poster Presentation] A Study of MCS Index Prediction Using Depth Images for Bit-Rate Control in mmWave Communications
Tomoya Mikuma, Takayuki Nishio, Masahiro Morikura (Kyoto Univ.), Yusuke Asai, Ryo Miyatake (NTT) SeMI2019-111
In millimeter-wave (mmWave) communications, link quality is decreased seriously when a line-of-sight (LOS) path is block... [more] SeMI2019-111
pp.51-52
SeMI 2020-01-31
10:00
Kagawa   [Poster Presentation] Sequential WLAN Channel Selection for Mitigating Performance Degradation of Factors Outside System
Kazuki Ohtsu, Shotaro Kamiya, Koji Yamamoto, Takayuki Nishio, Masahiro Morikura (Kyoto Univ.), Noriyasu Kato (Allied Telesis) SeMI2019-106
We propose a sequential channel allocation method based on multi-objective multi-armed bandit problems.
The proposed me... [more]
SeMI2019-106
pp.41-42
RISING
(2nd)
2019-11-26
14:10
Tokyo Fukutake Learning Theater, Hongo Campus, Univ. Tokyo [Poster Presentation] A Study for Knowlage Distillation Based Semi-Supervised Federated Learning with Low Communication Cost
Sohei Itahara, Takayuki Nishio, Masahiro Morikura, Koji Yamamoto (Kyoto Univ)
Federated Learning is a decentralized learning mechanism, which enables to train machine learning (ML) model using the r... [more]
RISING
(2nd)
2019-11-27
13:55
Tokyo Fukutake Learning Theater, Hongo Campus, Univ. Tokyo [Poster Presentation] Handover Control for mmWave Networks with Proactive Performance Prediction Using Depth Images and Deep Reinforcement Learning
Yusuke Koda, Kota Nakashima, Koji Yamamoto, Takayuki Nishio, Masahiro Morikura (Kyoto Univ.)
 [more]
RISING
(2nd)
2019-11-27
13:55
Tokyo Fukutake Learning Theater, Hongo Campus, Univ. Tokyo [Poster Presentation] A Study of mmWave Received Power Prediction from Depth Images
Takayuki Nishio, Koji Yamamoto, Masahiro Morikura (Kyoto Univ.)
 [more]
SRW, SeMI, CNR
(Joint)
2019-11-06
13:25
Tokyo Kozo Keisaku Engineering Inc. [Poster Presentation] A Study of Received Power Prediction Using Ray-tracing Simulation and Deep Learning for mmWave Communications
Masahiro Iwasaki, Takayuki Nishio, Masahiro Morikura, Koji Yamamoto (Kyoto Univ) SRW2019-34 SeMI2019-78 CNR2019-28
Machine learning based received power prediction has been studied. These methods learn Features such as surrounding map ... [more] SRW2019-34 SeMI2019-78 CNR2019-28
pp.53-54(SRW), pp.73-74(SeMI), pp.51-52(CNR)
MIKA
(2nd)
2019-10-03
11:15
Hokkaido Hokkaido Univ. [Poster Presentation] A Study of Transfer Learning for Received Power Prediction Using Depth Images in mmWave Communications
Tomoya Mikuma, Takayuki Nishio, Masahiro Morikura (Kyoto Univ.), Yusuke Asai, Ryo Miyatake (NTT)
The received power prediction scheme using camera images in mmWave communications has been proposed. The prediction sche... [more]
MIKA
(2nd)
2019-10-03
11:15
Hokkaido Hokkaido Univ. [Poster Presentation] Deep Reinforcement Learning-Based Channel Allocation for Wireless LANs -- Investigation of sampling method of replay buffer --
Kota Nakashima, Shotaro Kamiya, Ohtsu Kazuki, Koji Yamamoto, Takayuki Nishio, Masahiro Morikura (Kyoto Univ.)
We have proposed the deep reinforcement learning-based channel allocation approach for wireless area local networks to i... [more]
MIKA
(2nd)
2019-10-03
11:15
Hokkaido Hokkaido Univ. [Poster Presentation] Improving Learning Efficiency of Graph-Based Reinforcement Learning for Wireless LAN Channel Selection
Kazuki Ohtsu, Shotaro Kamiya, Koji Yamamoto, Takayuki Nishio, Masahiro Morikura (Kyoto Univ.)
This report proposes to improve learning efficiency with graph isomorphism for reinforcement learning-based wireless loc... [more]
RCS 2019-06-19
14:55
Okinawa Miyakojima Hirara Port Terminal Building Policy Gradient Reinforcement Learning for Reducing Transmission Delay in EDCA
Masao Shinzaki, Yusuke Koda, Koji Yamamoto, Takayuki Nishio, Masahiro Morikura (Kyoto Univ.) RCS2019-52
This paper proposes a packet mapping algorithm among Access Categories (ACs) in Enhanced Distributed Channel Access (EDC... [more] RCS2019-52
pp.91-96
RCS 2019-06-19
15:15
Okinawa Miyakojima Hirara Port Terminal Building A Study for Improving Prediction Accuracy on Federated Learning with Non-IID Data in Wireless Networks
Naoya Yoshida, Takayuki Nishio, Masahiro Morikura, Koji Yamamoto (Kyoto Univ.), Ryo Yonetani (OMRON SINIC X Corp.) RCS2019-54
Federated Learning (FL) is a decentralized learning mechanism, which enables to train machine learning (ML) model using ... [more] RCS2019-54
pp.103-108
RCS 2019-06-19
15:25
Okinawa Miyakojima Hirara Port Terminal Building Optimal Path Learning for mmWave Communications in Smart Factory
Mayu Mieda, Shotaro Kamiya, Kota Nakashima, Koji Yamamoto, Takayuki Nishio, Masahiro Morikura (Kyoto Univ.) RCS2019-55
 [more] RCS2019-55
pp.109-112
ASN 2019-01-28
14:40
Kagoshima Kyuukamura Ibusuki Deep Reinforcement Learning-Based Optimum Channel Control for Wireless LAN
Kota Nakashima, Syotaro Kamiya, Kazuki Ohtsu, Koji Yamamoto, Takayuki Nishio, Masahiro Morikura (Kyoto Univ.) ASN2018-80
This report proposes deep reinforcement learning-based channel selection method when access points (APs) are located den... [more] ASN2018-80
pp.13-18
MoNA 2019-01-16
10:05
Kyoto T. B. D. A Study on Application of Contextual Bandit Problem to Wireless LAN Access Point Selection
Taichi Sakakibara, Takayuki Nishio, Masahiro Morikura, Koji Yamamoto (Kyoto Univ.), Toshihisa Nabetani (TOSHIBA) MoNA2018-58
This paper models access point (AP) selection problem wireless LAN as an bandit problem and evaluate a performance of an... [more] MoNA2018-58
pp.7-11
MoNA, IN, CNR
(Joint) [detail]
2018-11-15
13:40
Saga Karatsu Civic Exchange Plaza Geo-fencing System for Wireless LANs with Camera
Go Yamanaka, Takayuki Nishio, Masahiro Morikura (Kyoto Univ.), Yuichi Maki, Shinichiro Eitoku, Takuya Indo (NTT) MoNA2018-27 CNR2018-24
Location aware services and networking are attracting a great deal of attentions.
This paper proposes a geo-fencing sys... [more]
MoNA2018-27 CNR2018-24
pp.31-36(MoNA), pp.37-42(CNR)
 Results 1 - 20 of 222  /  [Next]  
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