Multi-IRS Assisted Multi-Cluster Wireless Powered IoT Networks

This paper proposes a multi-cluster wireless powered Internet of Things (WP-IoT) network assisted by multiple intelligent reflecting surfaces (multi-IRS). In this network, power station (PS) first broadcasts energy to the distributed IoT devices grouped into clusters. The then use harvested convey their information an access point (AP), based on hybrid time- and frequency-division (TDMA-FDMA) protocol. Furthermore, IRSs are deployed perform anomalous reflection for transfer, improve harvesting data transmission capabilities. Under constraints unit-modulus phase shifts, time shared among clusters bandwidth in each cluster, considered system is optimized maximizing its sum throughput. optimization problem non-convex with complicatedly coupled variables. To solve problem, we propose apply Lagrange dual method Karush-Kuhn-Tucker (KKT) conditions derive closed-form solutions scheduling allocation, quadratic transformation (QT) alternating (AO) algorithm introduced downlink uplink IRS whilst Majorization-Minimization (MM) Riemannian Manifold Optimization (RMO) methods applied iteratively solutions. Additionally, provide benchmark scheme facilitate design, where can control “on/off” state aid transmissions condition at most one activated during certain duration. Finally, simulation results presented verify optimality our proposed highlight beneficial role IRS.