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| Presentation | 2021-07-29 13:05 [Invited Lecture] Prediction of Path Loss Fading Distribution using RNN Motoharu Sasaki, Nobuaki Kuno, Toshiro Nakahira, Minoru Inomata, Wataru Yamada, Takatsune Moriyama (NTT)  AP2021-37 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | We report a method for predicting fading distribution of path loss using GRU (Gated Recurrent Unit), which is one of RNN (Recurrent Neural Network) as deep learning. The training data and verification data use path loss measured in Yokosuka City, Kanagawa Prefecture, and the measurement frequency is 4.7 GHz. Using 100 points of fast fading data about every 0.1 seconds, the median data of path loss and the K factor of Nakagami-Rice distribution after 1 second were predicted. The median data and the K factor are derived using the fast fading data of 100 points (about 10 seconds). According to the prediction method using GRU, the RMSE (Root Mean Squared Error) for the verification data is about 1.7 dB for the median path loss and about 0.5 dB for the K factor. The prediction accuracy was improved by 0.9 dB and 0.1 dB compared to the case of using latest observed values. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Deep learning / RNN / GRU / path loss / Nakagami-Rice distribution / K factor / Sub6 / | |
| Reference Info. | IEICE Tech. Rep., vol. 121, no. 126, AP2021-37, pp. 76-80, July 2021. | |
| Paper # | AP2021-37 | |
| Date of Issue | 2021-07-21 (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 | AP2021-37 |
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| Conference Information | |
| Committee | AP SANE SAT |
| Conference Date | 2021-07-28 - 2021-07-30 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| 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 | 2021-07-AP-SANE-SAT |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Prediction of Path Loss Fading Distribution using RNN |
| Sub Title (in English) | |
| Keyword(1) | Deep learning |
| Keyword(2) | RNN |
| Keyword(3) | GRU |
| Keyword(4) | path loss |
| Keyword(5) | Nakagami-Rice distribution |
| Keyword(6) | K factor |
| Keyword(7) | Sub6 |
| Keyword(8) | |
| 1st Author's Name | Motoharu Sasaki |
| 1st Author's Affiliation | NTT (NTT) |
| 2nd Author's Name | Nobuaki Kuno |
| 2nd Author's Affiliation | NTT (NTT) |
| 3rd Author's Name | Toshiro Nakahira |
| 3rd Author's Affiliation | NTT (NTT) |
| 4th Author's Name | Minoru Inomata |
| 4th Author's Affiliation | NTT (NTT) |
| 5th Author's Name | Wataru Yamada |
| 5th Author's Affiliation | NTT (NTT) |
| 6th Author's Name | Takatsune Moriyama |
| 6th Author's Affiliation | NTT (NTT) |
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| Speaker | Author-1 |
| Date Time | 2021-07-29 13:05:00 |
| Presentation Time | 25 minutes |
| Registration for | AP |
| Paper # | AP2021-37 |
| Volume (vol) | vol.121 |
| Number (no) | no.126 |
| Page | pp.76-80 |
| #Pages | 5 |
| Date of Issue | 2021-07-21 (AP) |
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