DRL-based RIS phase shift design for OFDM communication systems

Peng Chen, Xiao Li*, Michail Matthaiou, Shi Jin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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This letter investigates a downlink orthogonal frequency division multiplexing (OFDM) transmission system aided by a reconfigurable intelligent surface (RIS). To reduce the system overhead and cost, we consider a 1-bit resolution and column-wise controllable RIS, and aim to design the reflection phase shifts of the elements on the RIS to improve the spectral efficiency. By leveraging a deep Q-network (DQN) framework, a deep reinforcement learning (DRL) based optimization algorithm is proposed in order to design the reflection phase shifts. Simulations illustrate that the proposed DRL-based algorithm can achieve significant performance gains in the spectral efficiency, while greatly reducing the calculation delay.

Original languageEnglish
Pages (from-to)733 - 737
Number of pages5
JournalIEEE Wireless Communications Letters
Issue number4
Early online date06 Feb 2023
Publication statusPublished - Apr 2023


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