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Paper Abstract and Keywords
Presentation 2022-07-14 13:25
Deep Reinforcement Learning-based IRS-aided Wireless Communication without Channel State Information
Hashida Hiroaki, Kawamoto Yuichi, Kato Nei (Tohoku Univ.), Iwabuchi Masashi, Murakami Tomoki (NTT) RCS2022-85
Abstract (in Japanese) (See Japanese page) 
(in English) Intelligent reflecting surfaces (IRSs) have attracted attention as devices that enable radio propagation, which has been originally treated as an uncertain element, to be treated as a variable element in wireless communication systems. In previous studies, optimization methods for IRS reflective coefficients based on channel state information (CSI) have been mainly investigated. However, due to the passive nature of IRS, it is difficult to estimate CSI explicitly. To address this problem, in this paper, we propose a deep reinforcement learning (DRL)-based algorithm that learns the precoding vector of BS and IRS phase shift from the wireless environment. In the algorithm, we develop a beam pattern-based learning framework that indirectly maps the wireless environment to the phase shift to deal with the huge state-action space caused by the large number of elements of BS and IRS. Simulation results reveal that the proposed algorithm is able to learn from the environment and obtain a transmission strategy that improves the transmission rate of the user. Results also verify that the proposed algorithm based on the beam pattern learning framework is more efficient and scalable to the number of IRS elements compared to the method that builds a mapping to the phase shift directly.
Keyword (in Japanese) (See Japanese page) 
(in English) Intelligent Reflecting Surface (IRS) / Deep reinforcement learning. / / / / / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 106, RCS2022-85, pp. 84-89, July 2022.
Paper # RCS2022-85 
Date of Issue 2022-07-06 (RCS) 
ISSN Print edition: ISSN 0913-5685  Online edition: ISSN 2432-6380
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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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Conference Information
Committee NS SR RCS SeMI RCC  
Conference Date 2022-07-13 - 2022-07-15 
Place (in Japanese) (See Japanese page) 
Place (in English) The Kanazawa Theatre + Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Distributed Wireless Network, M2M (Machine-to-Machine),D2D (Device-to-Device),IoT(Internet of Things), etc 
Paper Information
Registration To RCS 
Conference Code 2022-07-NS-SR-RCS-SeMI-RCC 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Deep Reinforcement Learning-based IRS-aided Wireless Communication without Channel State Information 
Sub Title (in English)  
Keyword(1) Intelligent Reflecting Surface (IRS)  
Keyword(2) Deep reinforcement learning.  
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1st Author's Name Hashida Hiroaki  
1st Author's Affiliation Tohoku University (Tohoku Univ.)
2nd Author's Name Kawamoto Yuichi  
2nd Author's Affiliation Tohoku University (Tohoku Univ.)
3rd Author's Name Kato Nei  
3rd Author's Affiliation Tohoku University (Tohoku Univ.)
4th Author's Name Iwabuchi Masashi  
4th Author's Affiliation NTT Corporation (NTT)
5th Author's Name Murakami Tomoki  
5th Author's Affiliation NTT Corporation (NTT)
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Speaker Author-1 
Date Time 2022-07-14 13:25:00 
Presentation Time 25 minutes 
Registration for RCS 
Paper # RCS2022-85 
Volume (vol) vol.122 
Number (no) no.106 
Page pp.84-89 
#Pages
Date of Issue 2022-07-06 (RCS) 


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