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Paper Abstract and Keywords
Presentation 2021-01-22 15:15
A Study on the Possibility of Estimating Multiple Communication Environment Information by Deep Learning Method Using Received Signal Spectrogram
Shun Kojima (Chiba Univ.), Kazuki Maruta (Tokyo Tech.), Chang-Jun Ahn (Chiba Univ.) IT2020-97 SIP2020-75 RCS2020-188
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. 120, no. 322, RCS2020-188, pp. 188-193, Jan. 2021.
Paper # RCS2020-188 
Date of Issue 2021-01-14 (IT, SIP, RCS) 
ISSN Print edition: ISSN 0913-5685  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)
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Conference Information
Committee SIP IT RCS  
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 RCS 
Conference Code 2021-01-SIP-IT-RCS 
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 Method Using Received Signal Spectrogram 
Sub Title (in English)  
Keyword(1) Spectrogram  
Keyword(2) CNN  
Keyword(3) SNR estimation  
Keyword(4) Doppler shift estimation  
Keyword(5) K factor estimation  
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1st Author's Name Shun Kojima  
1st Author's Affiliation Chiba University (Chiba Univ.)
2nd Author's Name Kazuki Maruta  
2nd Author's Affiliation Tokyo Institute and Technology (Tokyo Tech.)
3rd Author's Name Chang-Jun Ahn  
3rd Author's Affiliation Chiba University (Chiba Univ.)
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Speaker
Date Time 2021-01-22 15:15:00 
Presentation Time 25 
Registration for RCS 
Paper # IEICE-IT2020-97,IEICE-SIP2020-75,IEICE-RCS2020-188 
Volume (vol) IEICE-120 
Number (no) no.320(IT), no.321(SIP), no.322(RCS) 
Page pp.188-193 
#Pages IEICE-6 
Date of Issue IEICE-IT-2021-01-14,IEICE-SIP-2021-01-14,IEICE-RCS-2021-01-14 


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