Resource Allocation in STAR-RIS-Aided SWIPT With RSMA via Meta-Learning

Abstract

Simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) is a cutting-edge concept for the sixth-generation (6G) wireless networks. In this paper, we propose a novel system that incorporates STAR-RIS with simultaneous wireless information and power transfer (SWIPT) using rate splitting multiple access (RSMA). The proposed system facilitates communication from a multi-antenna base station (BS) to single-antenna users in a downlink transmission. The BS concurrently sends energy and information signals to multiple energy harvesting receivers (EHRs) and information data receivers (IDRs) with the support of a STAR-RIS. Furthermore, an optimization is introduced to strike a balance between users’ sum rate (SR) and the total harvested energy. To achieve this, an optimization problem is formulated to optimize the energy/information beamforming vectors at the BS, the phase shifts at the STAR-RIS, and the common message rate. Subsequently, we employ a meta deep deterministic policy gradient (Meta-DDPG) approach to solve the complex problem. Simulation results demonstrate that the proposed Meta-DDPG algorithm performance compared to other base lines, yielding higher data rates and improved harvested energy. This advancement in algorithmic performance substantiates the effectiveness of the proposed STAR-RIS system with SWIPT and RSMA, positioning it as a promising solution for the resource-efficient, high-performance demands of 6G networks.