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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| 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]
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 15:05 |
Okinawa | Miyakojima Hirara Port Terminal Building | Joint Channel Control and Spatial Reuse Towards Starvation Mitigation in WLANs Hiroyasu Shimizu, Bo Yin, Koji Yamamoto (Kyoto Univ.), Hirantha Abeysekera (NTT) RCS2019-53 |
This paper proposes a decentralized scheme to improve the energy efficiency (EE) in dense wireless local area networks (... [more] | RCS2019-53 pp.97-102 |
| 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 |
| RCS, IN, NV (Joint) |
2019-05-16 13:45 |
Kanagawa | Keio University | [Tutorial Lecture]
Frameworks Against Uncertainty in WLANs: Part1 Resource Management for WLANs Koji Yamamoto (Kyoto Univ.) IN2019-4 RCS2019-25 |
[more] | IN2019-4 RCS2019-25 p.17(IN), p.29(RCS) |
| RCS, IN, NV (Joint) |
2019-05-16 14:10 |
Kanagawa | Keio University | [Tutorial Lecture]
Frameworks Against Uncertainty in WLANs: Part2 Stochastic Geometry Koji Yamamoto (Kyoto Univ.) IN2019-5 RCS2019-26 |
[more] | IN2019-5 RCS2019-26 p.19(IN), p.31(RCS) |
| RCS, IN, NV (Joint) |
2019-05-16 14:35 |
Kanagawa | Keio University | [Tutorial Lecture]
Frameworks Against Uncertainty in WLANs: Part3 Reinforcement Learning Koji Yamamoto (Kyoto Univ.) IN2019-6 RCS2019-27 |
[more] | IN2019-6 RCS2019-27 p.21(IN), p.33(RCS) |
| RCS, SR, SRW (Joint) |
2019-03-08 09:25 |
Kanagawa | YRP | [Invited Lecture]
Site Engineering and Machine Learning for Next Generation Radio Communication Systems Koji Yamamoto (Kyoto Univ.) RCS2018-321 |
For the efficient operation of radio communication systems, parameters should be optimized when the communication qualit... [more] | RCS2018-321 p.203 |
| NC, MBE (Joint) |
2019-03-05 15:00 |
Tokyo | University of Electro Communications | On the spatiotemporal information contained in spike responses of mouse retina ganglion cells Yuki Kashiwagi, Koji Yamamoto, Tetsuya Yagi, Yuki Hayashida (Osaka Univ.) MBE2018-99 |
Information in the visual scene projected on the retina is encoded into the digital-like electrical signal called “spike... [more] | MBE2018-99 pp.65-70 |
| 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 |
| IT | 2018-12-18 14:05 |
Fukushima | Spa Resort Hawaiians | [Invited Talk]
Application of Game Theory to Radio Resource Management Koji Yamamoto (Kyoto Univ.) IT2018-31 |
Radio resource management in radio communication systems is required to manage co-channel interference which can be trea... [more] | IT2018-31 pp.1-6 |
| ASN, NS, RCS, SR, RCC (Joint) |
2018-07-12 10:55 |
Hokkaido | Hakodate Arena | [Poster Presentation]
Supervised Learning-based Primary Exclusive Region Update Robust Against Imbalanced Data for Spectrum Sharing Aogu Yamada, Takayuki Nishio, Masahiro Morikura, Koji Yamamoto (Kyoto Univ.) RCC2018-44 NS2018-57 RCS2018-102 SR2018-41 ASN2018-38 |
In spectrum sharing, secondary users (SUs) utilize a licensed frequency band under a condition to avoid interference wit... [more] | RCC2018-44 NS2018-57 RCS2018-102 SR2018-41 ASN2018-38 pp.97-98(RCC), pp.103-104(NS), pp.115-116(RCS), pp.107-108(SR), pp.113-114(ASN) |
| ASN, NS, RCS, SR, RCC (Joint) |
2018-07-12 10:55 |
Hokkaido | Hakodate Arena | [Poster Presentation]
Optimal Primary Exclusive Region Design for Cognitive Radio VANETs Yuxiang Fu, Keiji Yoshikawa, Shota Yamashita, Koji Yamamoto, Takayuki Nishio, Masahiro Morikura (Kyoto Univ.) RCC2018-45 NS2018-58 RCS2018-103 SR2018-42 ASN2018-39 |
[more] | RCC2018-45 NS2018-58 RCS2018-103 SR2018-42 ASN2018-39 pp.99-100(RCC), pp.105-106(NS), pp.117-118(RCS), pp.109-110(SR), pp.115-116(ASN) |
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