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When Visible Light Communication Meets RIS: A Soft Actor-Critic Approach
  • +2
  • Long Zhang ,
  • Xingliang Jia,
  • Ni Tian,
  • Choong Seon Hong,
  • Zhu Han
Long Zhang

Corresponding Author:[email protected]

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Xingliang Jia
Ni Tian
Choong Seon Hong
Zhu Han

Abstract

This letter considers a reconfigurable intelligent surface (RIS) aided indoor visible light communication system, where a mirror array-based RIS is deployed to assist the communication from a light-emitting diode (LED) to multiple user terminals (UTs). We aim to maximize the sum-rate in an entire serving period by jointly optimizing the orientation of the RIS reflecting unit, the time fraction for the UT, and the transmit power at the LED, subject to the communication and illumination intensity requirements. To solve this high-dimensional non-convex problem, we first transform it as a constrained Markov decision process. Then, a soft actor-critic (SAC)-based deep reinforcement learning (DRL) algorithm is proposed with the objective of maximizing both the average reward and the expected policy entropy. Simulation results show that the proposed SAC-based joint optimization design outperforms the existing DRL-based baselines in terms of the sum-rate and long-term average reward. Index Terms Visible light communication, reconfigurable intelligent surface, deep reinforcement learning, soft actor-critic.
15 Jan 2024Submitted to TechRxiv
26 Jan 2024Published in TechRxiv