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 |
Online edition: ISSN 2432-6380 |
Copyright and 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) |
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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) |
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Keyword(1) |
OFDMA |
Keyword(2) |
SNR estimation |
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carrier frequency offset |
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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 |
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Duke University (Duke Univ.) |
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Speaker |
Author-1 |
Date Time |
2021-04-23 09:45:00 |
Presentation Time |
25 minutes |
Registration for |
RCS |
Paper # |
RCS2021-10 |
Volume (vol) |
vol.121 |
Number (no) |
no.7 |
Page |
pp.42-47 |
#Pages |
6 |
Date of Issue |
2021-04-15 (RCS) |
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