Optimization of STAR-RIS-assisted WET Systems Based on IoT Device Selection

In this paper, we evaluate a Wireless Energy Transfer (WET) system assisted by a Simultaneous Transmission and Reflection Reconfigurable Intelligent Surfaces (STAR-RIS) and consider the concept of beamsharing, which allows IoT devices to harvest energy even while others are actively being charged. Then, determining the optimal charging sequence is critical, as it directly impacts the total time required to charge all devices. To tackle this challenge, we propose an Ant Colony Optimization (ACO)-based approach to optimize the charging order, minimizing the overall system charging time. Furthermore, given that perfect Channel State Information (CSI) is challenging and often unavailable in practical scenarios, we propose a design methodology based on Statistical CSI (S-CSI) to optimize both the beamforming at the power beacon (PB) and the phase and amplitude configurations of the STAR-RIS elements. Our results demonstrate that the proposed solution achieves near-optimal performance and reduces the total charging time by at least 23% compared to a random selection benchmark.