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Paper Abstract and Keywords
Presentation 2022-03-02 10:25
Investigation on Beamforming for IRS-Assisted MIMO-OFDM Communication using Machine Learning
Julian Webber, Kazuto Yano, Norisato Suga, Yoshinori Suzuki (ATR) SR2021-86
Abstract (in Japanese) (See Japanese page) 
(in English) There has recently been considerable interest in intelligent reflective surface (IRS) which can improve the capacity of communications links by facilitating creation of additional communication paths. The performance of an IRS system depends on the accuracy of estimating the channel and hence ability to compute accurate weights which degrade in the presence of interference. Computing the beamformer weights requires high complexity that scales with the array size. Machine learning is a promising technique for learning the multipath environment and computing the optimized weights that achieve almost the same achievable rates as when the channel is known perfectly at the IRS. In this work we investigate the factors affecting IRS performance for an array size of up to 28X28 using software simulation and ray-tracing channel data.
Keyword (in Japanese) (See Japanese page) 
(in English) MIMO-OFDM / intelligent reflective surface (IRS) / ray-tracing / machine-learning / neural network / multi-layer perceptron / /  
Reference Info. IEICE Tech. Rep., vol. 121, no. 392, SR2021-86, pp. 6-13, March 2022.
Paper # SR2021-86 
Date of Issue 2022-02-23 (SR) 
ISSN 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 SR2021-86

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 SR 
Conference Code 2022-03-RCS-SR-SRW 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Investigation on Beamforming for IRS-Assisted MIMO-OFDM Communication using Machine Learning 
Sub Title (in English)  
Keyword(1) MIMO-OFDM  
Keyword(2) intelligent reflective surface (IRS)  
Keyword(3) ray-tracing  
Keyword(4) machine-learning  
Keyword(5) neural network  
Keyword(6) multi-layer perceptron  
Keyword(7)  
Keyword(8)  
1st Author's Name Julian Webber  
1st Author's Affiliation Advanced Telecommunications Research Institute International (ATR)
2nd Author's Name Kazuto Yano  
2nd Author's Affiliation Advanced Telecommunications Research Institute International (ATR)
3rd Author's Name Norisato Suga  
3rd Author's Affiliation Advanced Telecommunications Research Institute International (ATR)
4th Author's Name Yoshinori Suzuki  
4th Author's Affiliation Advanced Telecommunications Research Institute International (ATR)
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Speaker Author-1 
Date Time 2022-03-02 10:25:00 
Presentation Time 25 minutes 
Registration for SR 
Paper # SR2021-86 
Volume (vol) vol.121 
Number (no) no.392 
Page pp.6-13 
#Pages
Date of Issue 2022-02-23 (SR) 


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