Paper Abstract and Keywords |
Presentation |
2021-01-22 11:10
[Invited Lecture]
Path Loss Variation Prediction at 2-26 GHz Band using LSTM Motoharu Sasaki, Nobuaki Kuno, Toshiro Nakahira, Minoru Inomata, Wataru Yamada, Takatsune Moriyama (NTT) AP2020-112 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
We report a method for predicting variation in path loss using Long Short-Term Memory (LSTM) as deep learning. The training data and validation data are path loss data measured in Yokosuka City, Kanagawa, and the measurement frequencies are in three frequency bands of 2.2 GHz, 4.7 GHz, and 26.4 GHz. The median data of the path loss after 1 second was predicted using 100 points of fast fading data obtained about every 0.1 seconds. The median data is derived using fast fading data of 100 points (about 10 seconds). By the prediction method using LSTM, the RMSE (Root Mean Squared Error) for the validation data is about 2.2 dB at 2.2 GHz, about 2.1 dB at 4.7 GHz, and about 2.4 dB at 26.4 GHz. The prediction errors are improved by 1 dB or more than predictions using the latest observed values. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Deep learning / LSTM / path loss / Sub6 / millimeter wave / / / |
Reference Info. |
IEICE Tech. Rep., vol. 120, no. 325, AP2020-112, pp. 50-55, Jan. 2021. |
Paper # |
AP2020-112 |
Date of Issue |
2021-01-14 (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) |
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AP2020-112 |
Conference Information |
Committee |
WPT AP |
Conference Date |
2021-01-21 - 2021-01-22 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Online |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
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Paper Information |
Registration To |
AP |
Conference Code |
2021-01-WPT-AP |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Path Loss Variation Prediction at 2-26 GHz Band using LSTM |
Sub Title (in English) |
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Keyword(1) |
Deep learning |
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LSTM |
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path loss |
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Sub6 |
Keyword(5) |
millimeter wave |
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1st Author's Name |
Motoharu Sasaki |
1st Author's Affiliation |
NTT (NTT) |
2nd Author's Name |
Nobuaki Kuno |
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NTT (NTT) |
3rd Author's Name |
Toshiro Nakahira |
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NTT (NTT) |
4th Author's Name |
Minoru Inomata |
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NTT (NTT) |
5th Author's Name |
Wataru Yamada |
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NTT (NTT) |
6th Author's Name |
Takatsune Moriyama |
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NTT (NTT) |
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Speaker |
Author-1 |
Date Time |
2021-01-22 11:10:00 |
Presentation Time |
25 minutes |
Registration for |
AP |
Paper # |
AP2020-112 |
Volume (vol) |
vol.120 |
Number (no) |
no.325 |
Page |
pp.50-55 |
#Pages |
6 |
Date of Issue |
2021-01-14 (AP) |
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