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Deep reinforcement learning based relay selection in intelligent reflecting surface assisted cooperative networks

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journal contribution
posted on 04.03.2021, 09:08 by Chong Huang, Gaojie Chen, Yu Gong, Miaowen Wen, Jonathon Chambers
This paper proposes a deep reinforcement learning (DRL) based relay selection scheme for cooperative networks with the intelligent reflecting surface (IRS). We consider a practical phase-dependent amplitude model in which the IRS reflection amplitudes vary with the discrete phase-shifts. Furthermore, we apply the relay selection to reduce the signal loss over distance in IRS-assisted networks. To solve the complicated problem of joint relay selection and IRS reflection coefficient optimization, we introduce DRL to learn from the environment to obtain the solution and reduce the computational complexity. Simulation results show that the throughput is significantly improved with the proposed DRL-based algorithm compared to random relay selection and random reflection coefficients methods.

Funding

Communications Signal Processing Based Solutions for Massive Machine-to-Machine Networks (M3NETs)

Engineering and Physical Sciences Research Council

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History

School

  • Mechanical, Electrical and Manufacturing Engineering

Published in

IEEE Wireless Communications Letters

Publisher

IEEE

Version

AM (Accepted Manuscript)

Rights holder

© IEEE

Publisher statement

© 2021 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.

Publication date

2021-02-02

Copyright date

2021

ISSN

2162-2337

eISSN

2162-2345

Language

en

Depositor

Dr Yu Gong. Deposit date: 2 March 2021

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