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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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
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
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)