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Double deep learning for joint phase-shift and beamforming based on cascaded channels in RIS-assisted MIMO networks

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posted on 2023-01-23, 11:08 authored by Kaiyue LiKaiyue Li, Chong Huang, Yu GongYu Gong, Gaojie Cheng

This paper investigates machine learning approach for the joint optimal phase shift and beamforming in the reconfigurable intelligent surface (RIS) assisted multiple-input and multiple-output (MIMO) network, consisting of one source node, one RIS panel and one destination node. If individual sourceto-RIS and RIS-to-destination channels are known, the joint optimization is similar to that in the traditional MIMO network, which has been well studied. However, the channel estimation for the individual channels is complicated and often inaccurate. On the other hand, while estimating the cascaded channels for the source-RIS-destination links are more accessible, the corresponding joint optimization is complicated. In this paper, we propose a novel double deep learning network model which is superior to the conventional reinforcement learning in the RIS joint optimization. Numerical simulations are given to verify the proposed algorithm.

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Published in

IEEE Wireless Communications Letters

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Version

AM (Accepted Manuscript)

Rights holder

© IEEE

Publisher statement

© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Acceptance date

2023-01-10

Publication date

2023-01-19

Copyright date

2023

ISSN

2162-2337

eISSN

2162-2345

Language

en

Depositor

Dr Yu Gong. Deposit date: 22 January 2023