Adaptive Optimization of Active RIS-Assisted ISCPT Network: A Hybrid MoE Scheme

This paper investigates the active reconfigurable intelligent surface (RIS)-assisted integrated sensing, communication, and power transfer (ISCPT) networks, where rate-splitting multiple access (RSMA) scheme is employed to serve multiple downlink communication users. To promote the energy efficiency (EE) of such a system, we formulate an EE maximization problem by jointly optimizing the beamforming matrix, the sensing matrix, the active RIS matrix, the power splitting (PS) ratio vector, and the common rate allocation vector. Due to the non-convexity of the problem, we first design a successive convex approximation scheme with alternating optimization method (named SCA-AO) to solve it. As SCA-AO operates in an iterative manner, which is with relatively high computational complexity, we then design a mixture of experts (MoE)-based deep reinforcement learning (DRL) scheme with smooth clipping function (named MoE-SCF). In comparison, SCA-AO is able to achieve higher solution accuracy, while MOE-SCF has a shorter online execution response time. In order to integrate the advantages of both presented SCA-AO and MoE-SCF simultaneously, we further propose a hybrid MoE (H-MoE) scheme, where both the SCA-AO and the MoE-SCF are employed as expert strategies, and an opportunistic activator (OPA) is designed to dynamically select the best strategy generated by all expert combinations according to the performance evaluation function. Simulation results demonstrate that the proposed H-MoE promotes the system's EE by about 18.14% compared to traditional MoE, with similar response time. Additionally, compared to the SCA-AO, H-MoE significantly decreases the response time by approximately 56.17%, while only marginally compromising the EE performance by less than 3.1%.