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Paper Abstract and Keywords
Presentation 2021-11-12 14:00
[Invited Lecture] Prediction of Path Loss Fading Distribution using RNN at 2-4 GHz
Motoharu Sasaki, Nobuaki Kuno, Toshiro Nakahira, Minoru Inomata, Wataru Yamada, Takatsune Moriyama (NTT) AP2021-128 RCS2021-174
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 2.2 GHz and 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 validation data of 2.2 GHz and 4.7 GHz is respectively about 2.3 dB and 2.1 dB for the median path loss and about 1.3 dB and 1.0 dB for the K factor. The prediction accuracy was improved by 1.5 dB and 1.3 dB for the median path loss, and 0.3 dB and 0.2 dB for the K factor 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. 233, AP2021-128, pp. 142-147, Nov. 2021.
Paper # AP2021-128 
Date of Issue 2021-11-03 (AP, RCS) 
ISSN Online edition: ISSN 2432-6380
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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)
Download PDF AP2021-128 RCS2021-174

Conference Information
Committee AP RCS  
Conference Date 2021-11-10 - 2021-11-12 
Place (in Japanese) (See Japanese page) 
Place (in English) NBC-Bekkan (Nagasaki) 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Adaptive Antenna, Equalization, Interference Canceler, MIMO, Wireless Communications, etc. 
Paper Information
Registration To AP 
Conference Code 2021-11-AP-RCS 
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 at 2-4 GHz 
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 2021-11-12 14:00:00 
Presentation Time 25 minutes 
Registration for AP 
Paper # AP2021-128, RCS2021-174 
Volume (vol) vol.121 
Number (no) no.233(AP), no.234(RCS) 
Page pp.142-147(AP), pp.164-169(RCS) 
#Pages
Date of Issue 2021-11-03 (AP, RCS) 


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