DRL-Based RIS Phase Shift Design for OFDM Communication Systems

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Abstract
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.
Funding Information
  • National Natural Science Foundation of China (62231009, 61971126)
  • Natural Science Foundation of Jiangsu Province (BK20211511)
  • Jiangsu Province Frontier Leading Technology Basic Research Project (BK20212002)
  • Research Grant from the Department for the Economy Northern Ireland through the U.S.–Ireland Research and Development Partnership Programme
  • National Natural Science Foundation of China (62261160576, 61921004)

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