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