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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| SeMI | 2024-01-19 11:05 |
Yamanashi | Raki House Kaiji | 5G throughput prediction for 28GHz cell area using surrounding spatial information Hisashi Nagata, Riichi Kudo, kahoko takahashi, Fujita Takafumi (NIPPON TELEGRAPH AND TELEPHONE CORPORATION), Yuya Aoki, Morihiro Yoshifumi (NTT DOCOMO, INC.)  SeMI2023-67 |
Every thing is connected to the network, and the amount of mobile traffic is increasing year by year. Therefore, the use... [more] | SeMI2023-67 pp.94-99 |
| AP | 2023-10-20 11:30 |
Iwate | Iwate University (Primary: On-site, Secondary: Online) |
[Tutorial Lecture]
Wireless link quality prediction using spatial information Riichi Kudo, Kahoko Takahashi, Hisashi Nagata, Tomoya Kageyama (NTT) AP2023-124 |
The development of wireless communication technologies is accelerating to widespread the various connected devices and e... [more] | AP2023-124 pp.139-140 |
| SeMI, IPSJ-ITS, IPSJ-MBL, IPSJ-DPS | 2023-05-19 09:15 |
Okinawa | Okinawa Institute of Science and Technology (OIST) (Primary: On-site, Secondary: Online) |
An experimental evaluation of millimeter-wave link quality prediction using Wi-Fi CSI and supervised learning Shoki Ohta, Kanare Kodera, Takayuki Nishio (Tokyo Tech) SeMI2023-10 |
This study experimentally evaluates our 60 GHz band millimeter-wave (mmWave) link quality prediction method using 5 GHz ... [more] | SeMI2023-10 pp.42-45 |
| SeMI, SeMI (Joint) |
2023-01-19 17:25 |
Tokushima | Naruto grand hotel (Primary: On-site, Secondary: Online) |
[Short Paper]
An Empirical Study of Data Reduction Method for Point Cloud-based Millimeter-wave Link Quality Prediction Shoki Ohta, Takayuki Nishio (Tokyo Tech), Riichi Kudo, Kahoko Takahashi, Hisashi Nagata (NTT) SeMI2022-93 |
This study experimentally evaluates a tradeoff between prediction accuracy and the number of points on a millimeter-wave... [more] | SeMI2022-93 pp.96-100 |
| RCS, NS (Joint) |
2022-12-15 15:00 |
Aichi | Nagoya Institute of Technology, and Online (Primary: On-site, Secondary: Online) |
[Invited Lecture]
Multi-Radio Proactive Control Technologies (Cradio) Kenichi Kawamura, Motoharu Sasaki, Toshirou Nakahira, Naoki Shibuya, Daisuke Murayama, Takatsune Moriyama (NTT) NS2022-137 RCS2022-194 |
In the Beyond 5G/6G era, wireless access will require not only the evolution of individual wireless systems but also the... [more] | NS2022-137 RCS2022-194 p.47(NS), p.50(RCS) |
| CQ, IMQ, MVE, IE (Joint) [detail] |
2022-03-09 15:55 |
Online | Online (Zoom) | Model selection for link quality prediction based on physical space information Hisashi Nagata, Riichi Kudo, Kahoko Takahashi, Tomoaki Ogawa (NTT) CQ2021-106 |
With the development of wireless communication technology, it is expected that all things will be connected to the netwo... [more] | CQ2021-106 pp.31-36 |
| RCS, SIP, IT | 2022-01-20 15:10 |
Online | Online | [Invited Talk]
Wireless link quality prediction using physical space information in Society 5.0 Riichi Kudo, Kahoko Takahashi, Hisashi Nagata, Tomoki Murakami, Tomoaki Ogawa (NTT) IT2021-44 SIP2021-52 RCS2021-212 |
Thanks to the great advances in wireless communication systems, many types of the wireless terminals are available. It i... [more] | IT2021-44 SIP2021-52 RCS2021-212 pp.93-94 |
| CQ, MIKA (Joint) |
2021-09-10 10:40 |
Online | Online | Link Quality Prediction using Multiple cameras in Indoor Environment for Wireless LAN Systems Kahoko Takahashi, Riichi Kudo, Tomoaki Ogawa (NTT) CQ2021-53 |
This paper proposes a received power prediction scheme that uses deep-neural-network based camera image object detection... [more] | CQ2021-53 pp.77-81 |
| SIP, IT, RCS | 2021-01-22 12:30 |
Online | Online | Deep Learning based Link Quality Prediction for Autonomous Mobility Robots Riichi Kudo, Kahoko Takahashi, Tomoki Murakami, Tomoaki Ogawa (NTT) IT2020-102 SIP2020-80 RCS2020-193 |
Highly advanced mobility robots are expected to be managed, monitored, or efficiently controlled by using wireless commu... [more] | IT2020-102 SIP2020-80 RCS2020-193 pp.218-223 |
| SR, NS, SeMI, RCC, RCS (Joint) |
2020-07-10 10:20 |
Online | Online | Performance evaluation of SHF throughput prediction based on deep neural network detector Riichi Kudo, Kahoko Takahashi, Takeru Inoue, Kohei Mizuno (NTT) SeMI2020-12 |
(To be available after the conference date) [more] | SeMI2020-12 pp.51-56 |
| MIKA (2nd) |
2019-10-03 11:15 |
Hokkaido | Hokkaido Univ. | [Poster Presentation]
Vision information based wireless link quality prediction Kahoko Takahashi, Riichi Kudo, Takeru Inoue, Kohei Mizuno (NTT) |
The advancement of wireless communication technologies is accelerating the widespread use of the various connected devic... [more] | |
| CQ | 2019-08-27 16:15 |
Hokkaido | Hakodate arena | [Invited Talk]
Potentiality of machine learning based next generation wireless communication systems for smart connected devices Riichi Kudo, Kahoko Takahashi, Takeru Inoue, Kohei Mizuno (NTT) CQ2019-72 |
The connected devices which are autonomously operated need to recognize its position and surrounding environment by usin... [more] | CQ2019-72 pp.79-84 |
| ASN, NS, RCS, SR, RCC (Joint) |
2018-07-12 10:55 |
Hokkaido | Hakodate Arena | [Poster Presentation]
Deep Learning Based RSS Prediction Using RGB-D Camera for mmWave Communications Kota Nakashima, Yusuke Koda, Koji Yamamoto, Hironao Okamoto, Takayuki Nishio, Masahiro Morikura (Kyoto Univ.) RCC2018-37 NS2018-50 RCS2018-95 SR2018-34 ASN2018-31 |
This paper experimentally finds the optimum number of input images of a machine learning-based mmWave received signal st... [more] | RCC2018-37 NS2018-50 RCS2018-95 SR2018-34 ASN2018-31 pp.75-76(RCC), pp.81-82(NS), pp.93-94(RCS), pp.85-86(SR), pp.91-92(ASN) |
| SR | 2018-05-24 10:30 |
Tokyo | Tokyo big sight | [Invited Talk]
Wireless Link Quality Prediction And Wireless Control Through Machine Learning Takayuki Nishio (Kyoto Univ.) SR2018-1 |
In this talk, wireless link quality prediction and control methods based on supervised learning from sensing information... [more] | SR2018-1 pp.1-6 |
| NS, IN (Joint) |
2016-03-04 13:30 |
Miyazaki | Phoenix Seagaia Resort | Prediction Accuracy Evaluations of Mobile Throughput Achieved by Different Video Buffering Strategies Makoto Nobe, Kenji Kanai, Jiro Katto (Waseda Univ.) NS2015-237 |
Appropriate history data collection (e.g., throughput) is important for mobile throughput prediction because communicati... [more] | NS2015-237 pp.399-403 |
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