Paper Abstract and Keywords |
Presentation |
2023-03-02 10:40
Memory-Driven MLP-Mixer Deep Learning Model for Intelligent Reflecting Surface-Assisted mmWave Systems Beam Prediction Taisei Urakami, Haohui Jia, Na Chen, Minoru Okada (NAIST) MW2022-159 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
Although deep learning (DL) based beam prediction for intelligent reflecting surface (IRS)-assisted millimeter wave (mmWave) system is capable of improving the transmission efficiency, it has drawback in limited learning efficiency, robustness for the noise, and large training overhead. In order to address these issues, this paper proposes the memory-driven multi-perceptron-mixer (MDMLP-Mixer) DL model, which consists of the autoregressive encoder (ARE) and spatial MLP-Mixer. Specifically, we use a gated recurrent unit (GRU) as ARE to extract frequency features based on the channel state information (CSI) dataset. In the spatial MLP-Mixer, spatial correlations can be extracted to obtain global features from the frequency and spatial domains by individually mixing the frequency features from each element at IRS index. By the channel matrix compression method the sparsity of CSI, our proposal realizes high beam prediction accuracy and small training overhead, since it achieves a sufficient average accuracy of 80.7% with 10min 52s of simulation time and 14k of parameters. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
mmWave / Intelligent Reflecting Surface / Beam Prediction / Deep Learning / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 122, no. 411, MW2022-159, pp. 13-18, March 2023. |
Paper # |
MW2022-159 |
Date of Issue |
2023-02-23 (MW) |
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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MW2022-159 |
Conference Information |
Committee |
MW |
Conference Date |
2023-03-02 - 2023-03-03 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Tottori Univ. |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
Microwave, etc. |
Paper Information |
Registration To |
MW |
Conference Code |
2023-03-MW |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Memory-Driven MLP-Mixer Deep Learning Model for Intelligent Reflecting Surface-Assisted mmWave Systems Beam Prediction |
Sub Title (in English) |
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Keyword(1) |
mmWave |
Keyword(2) |
Intelligent Reflecting Surface |
Keyword(3) |
Beam Prediction |
Keyword(4) |
Deep Learning |
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1st Author's Name |
Taisei Urakami |
1st Author's Affiliation |
Nara Institute of Science and Technology (NAIST) |
2nd Author's Name |
Haohui Jia |
2nd Author's Affiliation |
Nara Institute of Science and Technology (NAIST) |
3rd Author's Name |
Na Chen |
3rd Author's Affiliation |
Nara Institute of Science and Technology (NAIST) |
4th Author's Name |
Minoru Okada |
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Nara Institute of Science and Technology (NAIST) |
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Speaker |
Author-1 |
Date Time |
2023-03-02 10:40:00 |
Presentation Time |
25 minutes |
Registration for |
MW |
Paper # |
MW2022-159 |
Volume (vol) |
vol.122 |
Number (no) |
no.411 |
Page |
pp.13-18 |
#Pages |
6 |
Date of Issue |
2023-02-23 (MW) |
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