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Paper Abstract and Keywords
Presentation 2022-12-15 09:45
Efficient Beam Prediction for Intelligent Reflecting Surface-Assisted mmWave Systems based on Memory Driven Simple Transformer Deep Learning Model
Taisei Urakami, Haohui Jia, Na Chen, Minoru Okada (NAIST) RCS2022-185
Abstract (in Japanese) (See Japanese page) 
(in English) In this paper, we propose a memory driven simple transformer (MDST) deep learning (DL) model with the autoregressive module and spatial attention method, and channel matrix compression method to realize the high accuracy with small data collecting and low training overhead for intelligent reflecting surface (IRS)-assisted millimeter wave (mmWave) wireless communication system. Specifically, in the channel matrix compression, we convert the channel state information (CSI) into the sparse angular-delay domain, then the data size is significantly compressed in the delay domain. In the MDST-DL model, first, compressed CSI is input to the gated recurrent unit (GRU) to extract the frequency features. After that, these frequency features are input to the simple spatial attention to obtain the global features based on the frequency features extracted from each element. As the results show, in the case of compressed channel, the MDST-DL model can achieve the sufficient average accuracy of 80.0% with 17min 12s in comparison with the average accuracy of original channel of 85.7% with 22min 10s.
Keyword (in Japanese) (See Japanese page) 
(in English) mmWave / intelligent reflecting surface / beam prediction / deep learning / / / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 311, RCS2022-185, pp. 1-6, Dec. 2022.
Paper # RCS2022-185 
Date of Issue 2022-12-08 (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)
Download PDF RCS2022-185

Conference Information
Committee RCS NS  
Conference Date 2022-12-15 - 2022-12-16 
Place (in Japanese) (See Japanese page) 
Place (in English) Nagoya Institute of Technology, and Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Multi-hop/Relay/Cooperation, Disaster-resistant wireless network, Sensor/Mesh network, Ad-hoc network, D2D/M2M, Wireless network coding, Handover/AP switching/Connected cell control/Load balancing among base stations/Mobile network dynamic reconfiguration, QoS/QoE assurance, Wireless VoIP, IoT, Edge computing, etc. 
Paper Information
Registration To RCS 
Conference Code 2022-12-RCS-NS 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Efficient Beam Prediction for Intelligent Reflecting Surface-Assisted mmWave Systems based on Memory Driven Simple Transformer Deep Learning Model 
Sub Title (in English)  
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  
4th Author's Affiliation Nara Institute of Science and Technology (NAIST)
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Speaker Author-1 
Date Time 2022-12-15 09:45:00 
Presentation Time 25 minutes 
Registration for RCS 
Paper # RCS2022-185 
Volume (vol) vol.122 
Number (no) no.311 
Page pp.1-6 
#Pages 6 
Date of Issue 2022-12-08 (RCS) 

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