Performance Evaluation of Downlink IRS and Uplink Cluster NOMA-Aided WPCN

This paper studies the performance of integrated intelligent reflecting surface (IRS) and non-orthogonal multiple access (NOMA)-aided wireless powered communication networks (WPCNs). The problem of maximizing the sum-throughput of the system under realistic operating conditions is formulated as a non-convex optimization problem. Given the complexity of jointly optimizing the decision variables for sum-throughput maximization, we propose a two-stage algorithm to achieve a solution. The algorithm first establishes an active DL energy beamforming matrix and IRS phase shift vector to maximize the sensors’ aggregated received power, followed by optimizing DL and UL time slots and UL energy resources. The numerical results demonstrate that the proposed algorithm increases the average sum-throughput by $45 \%$ in the perfect channel state information (CSI) scenarios. However, its performance declines with an increase in IRS elements under imperfect CSI, due to increased channel estimation errors.