A Meta-DDPG Algorithm for Energy and Spectral Efficiency Optimization in STAR-RIS-Aided SWIPT

Abstract

This letter studies a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted wireless system where a multi-antenna base station (BS) transmits both wireless information and energy-carrying signals to single-antenna users. To explore the trade-off between spectral efficiency (SE) and energy efficiency (EE) in this system, a multi-objective optimization problem (MOOP) is formulated to maximize SE and EE. The beamforming vector at the BS, the power splitting ratio at each user, phase shifts and amplitude coefficients of the STAR-RIS are jointly optimized, subject to the constraints of the maximum transmit power of the BS and the minimum harvested energy of users. To tackle this MOOP, we propose a Meta-DDPG algorithm that combines deep deterministic policy gradient (DDPG) and meta-learning approaches. Simulation results demonstrate that the Meta-DDPG algorithm outperforms the classic DDPG and genetic algorithms in terms of EE. Besides, via simulation results, it is illustrated that Meta-DDPG reaches a close performance to the exhaustive search and optimization-based solutions.