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Paper Abstract and Keywords
Presentation 2021-04-23 09:45
Improving Classification Accuracy in Multi-User Communication Environment Information Estimation by Machine Learning
Shun Kojima (Utsunomiya Univ.), Yi Feng (Duke Univ.), Kazuki Maruta (Tokyo Tech.), Chang-Jun Ahn (Chiba Univ.), Vahid Tarokh (Duke Univ.) RCS2021-10
Abstract (in Japanese) (See Japanese page) 
(in English) Recently, due to the increasing demand for wireless data traffic, highly efficient multiple access methods such as OFDMA have been attracting a great deal of attention. In uplink OFDMA, it has a problem of communication performance degradation due to the carrier frequency offset (CFO) of each user, which causes inter-carrier interference (ICI) and multiple user interference (MUI). In such an environment, adaptive modulation and coding (AMC) is essential to optimize the transmission rate of each user, and feedback of SNR information indicating the communication environment of each user is required to perform AMC. In the conventional SNR estimation method, the accuracy of SNR estimation is greatly degraded in the presence of CFO, and the communication performance deteriorates. In order to solve this problem and improve the communication performance, we propose a method to classify the SNR of each user in the presence of CFO and perform AMC by applying deep learning based only on the received signal waveform without using the reference signal. Since the proposed method can realize a highly robust network, it is expected to contribute to reducing the computational load and speeding up the signal processing. The effectiveness of the proposed method is demonstrated by simulation results.
Keyword (in Japanese) (See Japanese page) 
(in English) OFDMA / SNR estimation / carrier frequency offset / convolutional neural network / / / /  
Reference Info. IEICE Tech. Rep., vol. 121, no. 7, RCS2021-10, pp. 42-47, April 2021.
Paper # RCS2021-10 
Date of Issue 2021-04-15 (RCS) 
ISSN Print edition: ISSN 0913-5685  Online edition: ISSN 2432-6380
Copyright
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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)
Download PDF RCS2021-10

Conference Information
Committee RCS  
Conference Date 2021-04-22 - 2021-04-23 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Railroad Communications, Inter-Vehicle Communications, Road to Vehicle Communications, Radio Access Technologies, Wireless Communications, etc. 
Paper Information
Registration To RCS 
Conference Code 2021-04-RCS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Improving Classification Accuracy in Multi-User Communication Environment Information Estimation by Machine Learning 
Sub Title (in English)  
Keyword(1) OFDMA  
Keyword(2) SNR estimation  
Keyword(3) carrier frequency offset  
Keyword(4) convolutional neural network  
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1st Author's Name Shun Kojima  
1st Author's Affiliation Utsunomiya University (Utsunomiya Univ.)
2nd Author's Name Yi Feng  
2nd Author's Affiliation Duke University (Duke Univ.)
3rd Author's Name Kazuki Maruta  
3rd Author's Affiliation Tokyo Institute of Technology (Tokyo Tech.)
4th Author's Name Chang-Jun Ahn  
4th Author's Affiliation Chiba University (Chiba Univ.)
5th Author's Name Vahid Tarokh  
5th Author's Affiliation Duke University (Duke Univ.)
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Speaker
Date Time 2021-04-23 09:45:00 
Presentation Time 25 
Registration for RCS 
Paper # IEICE-RCS2021-10 
Volume (vol) IEICE-121 
Number (no) no.7 
Page pp.42-47 
#Pages IEICE-6 
Date of Issue IEICE-RCS-2021-04-15 


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