Deep reinforcement learning based relay selection in intelligent reflecting surface assisted cooperative networks
journal contributionposted 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.
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Communications Signal Processing Based Solutions for Massive Machine-to-Machine Networks (M3NETs)
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- Mechanical, Electrical and Manufacturing Engineering