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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
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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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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)  
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)  
Keyword(1) Deep learning  
Keyword(2) LSTM  
Keyword(3) path loss  
Keyword(4) 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  
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 2021-01-22 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
Date of Issue 2021-01-14 (AP) 


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