Joint mode switching and resource allocation in wireless-powered RIS-aided multiuser communication systems

This paper investigates a wireless-powered hybrid reflecting intelligent surface (hybrid RIS)-assisted multiple access system, where the RIS can harvest energy from energy station (ES) transmitted radio frequency signal (RF), and each reflecting element can flexibly switch between active mode, passive mode, and idle mode. The objective is to minimize the maximum energy consumption of the users by jointly optimizing the operating modes of each reflecting element, the amplification factor of active elements, the transmit power, and transmission time allocation, subject to quality-of-service (QoS) of each user and the available energy constraint of RIS. In the formulated optimization problem, the operating modes of each reflecting element are highly coupled with the amplification coefficient of the active reflecting elements, making it a challenging mixed-integer programming problem. To solve this problem, a hierarchical optimization method based on deep reinforcement learning is proposed, where the operating modes of each reflecting element and the amplification coefficient of active elements are obtained by solving the outer sub-problem using proximal policy optimization (PPO), and the transmit power and transmission time allocation are obtained by solving the inner sub-problem using convex optimization methods. Simulation results show that compared to the baseline scheme, the proposed scheme can reduce user energy consumption by $70 \%$.