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

TL;DR

This paper introduces a novel STAR-RIS-aided SWIPT system with RSMA, employing meta-learning to optimize beamforming and phase shifts, significantly improving data rates and energy harvesting in 6G wireless networks.

Contribution

It proposes a new system integrating STAR-RIS with SWIPT and RSMA, and develops a meta-learning based optimization method for resource allocation.

Findings

Meta-DDPG algorithm outperforms conventional DDPG in simulations.

The system achieves a better balance between data rate and energy harvesting.

Simulation results demonstrate significant performance improvements.

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 deployed STAR-RIS. Furthermore, an optimization is introduced to strike a balance between users' sum rate 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 validate that the proposed algorithm significantly enhances both data rate and harvested energy in comparison to conventional DDPG.