Presentation | 2022-07-27 [Invited Lecture] RNN Based Proactive Prediction of Received Power Using Environmental Information Motoharu Sasaki, Naoki Shibuya, Kenichi Kawamura, Nobuaki Kuno, Minoru Inomata, Wataru Yamada, Takatsune Moriyama, |
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PDF Download Page | PDF download Page Link |
Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | We report a method for predicting received power using a GRU (Gated Recurrent Unit), which is one of the RNNs (Recurrent Neural Networks). For the training and validation data, RSSI data of 5.6 GHz band wireless LAN measured in the indoor environment of NTT Yokosuka Communication Laboratory in Yokosuka City, Kanagawa Prefecture was used. As input data, we use the information on the distance between transmitter and receiver, and whether the Line-of-Sight or Non-Line-of-Sight at the predicted target position, in addition to 50 points of RSSI data about every 0.1 seconds. The median value of RSSI after 5 seconds was predicted. The median is derived using RSSI data at 50 points (about 5 seconds). Due to the proposed method, the RMSE (Root Mean Squared Error) for the validation data is 1.4 dB, which is 1.4 dB and 0.6 dB for the prediction using the latest observations and the prediction using only the newest RSSI, respectively. The prediction accuracy has been improved by the proposed model. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Deep learning / RNN / GRU / received power prediction / Wi-Fi |
Paper # | AP2022-36 |
Date of Issue | 2022-07-20 (AP) |
Conference Information | |
Committee | AP / SANE / SAT |
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Conference Date | 2022/7/27(3days) |
Place (in Japanese) | (See Japanese page) |
Place (in English) | Asahikawa Taisetsu Crystal Hall |
Topics (in Japanese) | (See Japanese page) |
Topics (in English) | Remote sensing, Sattelite Communication, Radio propagation, Antennas and Propagation |
Chair | Hiroshi Yamada(Niigata Univ.) / Toshifumi Moriyama(Nagasaki Univ.) / Tetsushi Ikegami(Meiji Univ.) |
Vice Chair | Mitoshi Fujimoto(Fukui Univ) / Makoto Tanaka(Tokai Univ.) / Takeshi Amishima(Meiji Univ..) / Masashi Kamei(NHK) / Takana Kaho(Shonan Inst. of Tech.) |
Secretary | Mitoshi Fujimoto(National Defense Academy) / Makoto Tanaka(Mitsubishi Electric) / Takeshi Amishima(Mitsubishi Electric) / Masashi Kamei(ENRI) / Takana Kaho(NTT) |
Assistant | Tomoki Murakami(NTT) / Shang Fang(Univ.. of Electro-Comm.) / Riichiro Nagareda(KDDI Research) / Yuuki Koizumi(NHK) |
Paper Information | |
Registration To | Technical Committee on Antennas and Propagation / Technical Committee on Space, Aeronautical and Navigational Electronics / Technical Committee on Satellite Telecommunications |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | [Invited Lecture] RNN Based Proactive Prediction of Received Power Using Environmental Information |
Sub Title (in English) | |
Keyword(1) | Deep learning |
Keyword(2) | RNN |
Keyword(3) | GRU |
Keyword(4) | received power prediction |
Keyword(5) | Wi-Fi |
1st Author's Name | Motoharu Sasaki |
1st Author's Affiliation | NTT(NTT) |
2nd Author's Name | Naoki Shibuya |
2nd Author's Affiliation | NTT(NTT) |
3rd Author's Name | Kenichi Kawamura |
3rd Author's Affiliation | NTT(NTT) |
4th Author's Name | Nobuaki Kuno |
4th Author's Affiliation | NTT(NTT) |
5th Author's Name | Minoru Inomata |
5th Author's Affiliation | NTT(NTT) |
6th Author's Name | Wataru Yamada |
6th Author's Affiliation | NTT(NTT) |
7th Author's Name | Takatsune Moriyama |
7th Author's Affiliation | NTT(NTT) |
Date | 2022-07-27 |
Paper # | AP2022-36 |
Volume (vol) | vol.122 |
Number (no) | AP-135 |
Page | pp.pp.12-16(AP), |
#Pages | 5 |
Date of Issue | 2022-07-20 (AP) |