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| Presentation | 2022-07-27 10:15 [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 (NTT)  AP2022-36 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (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) |
| (in English) | Deep learning / RNN / GRU / received power prediction / Wi-Fi / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 122, no. 135, AP2022-36, pp. 12-16, July 2022. | |
| Paper # | AP2022-36 | |
| Date of Issue | 2022-07-20 (AP) | |
| ISSN | Online edition: ISSN 2432-6380 | |
| Copyright and reproduction |
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) | |
| Download PDF | AP2022-36 |
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| Conference Information | |
| Committee | AP SANE SAT |
| Conference Date | 2022-07-27 - 2022-07-29 |
| 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 |
| Paper Information | |
| Registration To | AP |
| Conference Code | 2022-07-AP-SANE-SAT |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | 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 |
| Keyword(6) | |
| Keyword(7) | |
| Keyword(8) | |
| 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) |
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| Speaker | Author-1 |
| Date Time | 2022-07-27 10:15:00 |
| Presentation Time | 25 minutes |
| Registration for | AP |
| Paper # | AP2022-36 |
| Volume (vol) | vol.122 |
| Number (no) | no.135 |
| Page | pp.12-16 |
| #Pages | 5 |
| Date of Issue | 2022-07-20 (AP) |
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