| Presentation | 2022-01-20 [Invited Talk] Wireless link quality prediction using physical space information in Society 5.0 Riichi Kudo, Kahoko Takahashi, Hisashi Nagata, Tomoki Murakami, Tomoaki Ogawa, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | Thanks to the great advances in wireless communication systems, many types of the wireless terminals are available. It is expected that various novel services emerge in Society 5.0 that is based on Internet of Things (IoT) by a high degree of convergence between cyberspace (virtual world) and physical space (real world). This report discusses the potential of the physical space information use for the future wireless communication systems in Society 5.0. In wireless LAN systems, the throughput prediction was conducted using physical space information such as robot position information and camera images. We generated the prediction models using deep learning algorithms including recurrent neural network (RNN) and the indoor experiments were conducted for the evaluation. The results showed that the physical space information enabled the long term prediction. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Society 5.0 / Thanks to the great advances in wireless communication systems, many types of the wireless terminals are available. It is expected that various novel services emerge in Society 5.0 that is based on Internet of Things (IoT) by a high degree of convergence between cyberspace (virtual world) and physical space (real world). This report discusses the potential of the physical space information use for the future wireless communication systems in Society 5.0. In wireless LAN systems, the throughput prediction was conducted using physical space information such as robot position information and camera images. We generated the prediction models using deep learning algorithms including recurrent neural network (RNN) and the indoor experiments were conducted for the evaluation. The results showed that the physical space information enabled the long term prediction. / Bounding box / link quality prediction / machine learning |
| Paper # | IT2021-44,SIP2021-52,RCS2021-212 |
| Date of Issue | 2022-01-13 (IT, SIP, RCS) |
| Conference Information | |
| Committee | RCS / SIP / IT |
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| Conference Date | 2022/1/20(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Chair | Eiji Okamoto(Nagoya Inst. of Tech.) / Yukihiro Bandou(NTT) / Tadashi Wadayama(Nagoya Inst. of Tech.) |
| Vice Chair | Toshihiko Nishimura(Hokkaido Univ.) / Tomoya Tandai(Toshiba) / Fumihide Kojima(NICT) / Toshihisa Tanaka(Tokyo Univ. Agri.&Tech.) / Takayuki Nakachi(Ryukyu Univ.) / Tetsuya Kojima(Tokyo Kosen) |
| Secretary | Toshihiko Nishimura(NEC) / Tomoya Tandai(Panasonic) / Fumihide Kojima(Xiaomi) / Toshihisa Tanaka(Takushoku Univ.) / Takayuki Nakachi(Tokyo Univ. Agri.&Tech.) / Tetsuya Kojima(Saitamai Univ.) |
| Assistant | Koichi Adachi(Univ. of Electro-Comm.) / Osamu Nakamura(Sharp) / Manabu Sakai(Mitsubishi Electric) / Masashi Iwabuchi(NTT) / Tatsuki Okuyama(NTT DOCOMO) / Taichi Yoshida(UEC) / Seisuke Kyochi(Univ. of Kitakyushu) / Masanori Hirotomo(Saga Univ.) |
| Paper Information | |
| Registration To | Technical Committee on Radio Communication Systems / Technical Committee on Signal Processing / Technical Committee on Information Theory |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | [Invited Talk] Wireless link quality prediction using physical space information in Society 5.0 |
| Sub Title (in English) | |
| Keyword(1) | Society 5.0 |
| Keyword(2) | Thanks to the great advances in wireless communication systems, many types of the wireless terminals are available. It is expected that various novel services emerge in Society 5.0 that is based on Internet of Things (IoT) by a high degree of convergence between cyberspace (virtual world) and physical space (real world). This report discusses the potential of the physical space information use for the future wireless communication systems in Society 5.0. In wireless LAN systems, the throughput prediction was conducted using physical space information such as robot position information and camera images. We generated the prediction models using deep learning algorithms including recurrent neural network (RNN) and the indoor experiments were conducted for the evaluation. The results showed that the physical space information enabled the long term prediction. |
| Keyword(3) | Bounding box |
| Keyword(4) | link quality prediction |
| Keyword(5) | machine learning |
| 1st Author's Name | Riichi Kudo |
| 1st Author's Affiliation | NTT(NTT) |
| 2nd Author's Name | Kahoko Takahashi |
| 2nd Author's Affiliation | NTT(NTT) |
| 3rd Author's Name | Hisashi Nagata |
| 3rd Author's Affiliation | NTT(NTT) |
| 4th Author's Name | Tomoki Murakami |
| 4th Author's Affiliation | NTT(NTT) |
| 5th Author's Name | Tomoaki Ogawa |
| 5th Author's Affiliation | NTT(NTT) |
| Date | 2022-01-20 |
| Paper # | IT2021-44,SIP2021-52,RCS2021-212 |
| Volume (vol) | vol.121 |
| Number (no) | IT-327,SIP-328,RCS-329 |
| Page | pp.pp.93-94(IT), pp.93-94(SIP), pp.93-94(RCS), |
| #Pages | 2 |
| Date of Issue | 2022-01-13 (IT, SIP, RCS) |