Beamforming and resource allocation for reconfigurable intelligent surface assisted wireless powered communication network

In view of the energy constraints and diverse quality of service (QoS) requirements of numerous wireless sensor devices in the future Internet of Things, as well as the “doubly near-far" problem in wireless powered communication network (WPCN), this paper investigates a WPCN system model with a reconfigurable intelligent surface (RIS) assisted separated architecture, aiming to maximize the weighted sum energy efficiency of devices by jointly optimizing active beamforming, passive beamforming and resource allocation. To address this optimization problem, a beamforming and resource allocation scheme based on the accelerated proximal distance algorithm (A-PDA) is proposed. This scheme uses fractional programming to decouple the optimization problem and alternately optimizes each subproblem until convergence. Notably, the A-PDA is employed for the first time to derive the closed-form solution of the RIS phase shift vector. Simulation results demonstrate that, compared with classical optimization schemes, the proposed scheme achieves faster convergence, significantly reduces the average running time, and exhibits superior weighted sum energy efficiency performance, particularly under low transmission power conditions.