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Paper Abstract and Keywords
Presentation 2022-03-04 10:20
[Encouragement Talk] A Study on the Possibility of Estimating Multiple Communication Environment Information by Deep Learning
Shun Kojima (Utsunomiya Univ.), Kazuki Maruta (Tokyo Tech.), Yi Feng (Aptiv), Takashi Yokota, Kanemitsu Ootsu (Utsunomiya Univ.), Chang-Jun Ahn (Chiba Univ.), Vahid Tarokh (Duke Univ.) RCS2021-285
Abstract (in Japanese) (See Japanese page) 
(in English) In the next generation mobile radio communication systems, it is essential to obtain the communication environment information accurately and quickly in order to implement appropriate control such as adaptive modulation and coding for realizing high-speed, high-capacity and low-delay communication. SNR, Doppler shift, and K-factor are some of the communication environment parameters that have a significant impact on the performance of adaptive modulation and coding. In the past, it has been difficult to introduce these parameters into adaptive modulation and coding for high-speed and large-capacity communications because the estimation of these parameters requires a huge amount of computation, a reference signal, and large-scale signal sampling. In this paper, we propose a method for estimating these multiple communication environment parameters on a per-packet basis without using reference signals by using convolutional neural networks from spectrogram images of the received signal. From the simulation results, we clarify the effectiveness of the proposed method in terms of the estimation accuracy of SNR, Doppler shift, and K-factor when they are estimated independently and the estimation accuracy when these three parameters are estimated simultaneously.
Keyword (in Japanese) (See Japanese page) 
(in English) Spectrogram / CNN / SNR estimation / Doppler shift estimation / K-factor estimation / / /  
Reference Info. IEICE Tech. Rep., vol. 121, no. 391, RCS2021-285, pp. 164-169, March 2022.
Paper # RCS2021-285 
Date of Issue 2022-02-23 (RCS) 
ISSN Online edition: ISSN 2432-6380
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 RCS SR SRW  
Conference Date 2022-03-02 - 2022-03-04 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Mobile Communication Workshop 
Paper Information
Registration To RCS 
Conference Code 2022-03-RCS-SR-SRW 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Study on the Possibility of Estimating Multiple Communication Environment Information by Deep Learning 
Sub Title (in English)  
Keyword(1) Spectrogram  
Keyword(2) CNN  
Keyword(3) SNR estimation  
Keyword(4) Doppler shift estimation  
Keyword(5) K-factor estimation  
1st Author's Name Shun Kojima  
1st Author's Affiliation Utsunomiya University (Utsunomiya Univ.)
2nd Author's Name Kazuki Maruta  
2nd Author's Affiliation Tokyo Institute of Technology (Tokyo Tech.)
3rd Author's Name Yi Feng  
3rd Author's Affiliation Aptiv (Aptiv)
4th Author's Name Takashi Yokota  
4th Author's Affiliation Utsunomiya University (Utsunomiya Univ.)
5th Author's Name Kanemitsu Ootsu  
5th Author's Affiliation Utsunomiya University (Utsunomiya Univ.)
6th Author's Name Chang-Jun Ahn  
6th Author's Affiliation Chiba University (Chiba Univ.)
7th Author's Name Vahid Tarokh  
7th Author's Affiliation Duke University (Duke Univ.)
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Speaker Author-1 
Date Time 2022-03-04 10:20:00 
Presentation Time 25 minutes 
Registration for RCS 
Paper # RCS2021-285 
Volume (vol) vol.121 
Number (no) no.391 
Page pp.164-169 
Date of Issue 2022-02-23 (RCS) 

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