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 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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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) |
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Keyword(1) |
MIMO-OFDM |
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intelligent reflective surface (IRS) |
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ray-tracing |
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machine-learning |
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neural network |
Keyword(6) |
multi-layer perceptron |
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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 |
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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 |
8 |
Date of Issue |
2022-02-23 (SR) |