Deep Reinforcement Learning-Based Relay Selection in Intelligent Reflecting Surface Assisted Cooperative Networks
- 2 February 2021
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Wireless Communications Letters
- Vol. 10 (5), 1036-1040
- https://doi.org/10.1109/lwc.2021.3056620
Abstract
This letter 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.Keywords
Funding Information
- Engineering and Physical Sciences Research Council (EP/R006377/1 (“M3NET”))
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