Joint Frequency-Time Allocation and Phase-Shift Optimization in Intelligent Reflecting Surface Assisted Multigroup WPCN

In this article, we introduce a wireless-powered communication network (WPCN) which is composed of a base station (BS), an access point (AP), and an N-element intelligent reflecting surface (IRS). Specifically, several groups of Internet of Things (IoT) users will collect energy radiated from BS in the wireless energy transmission (WET) time scheduling, and transfer their collected information for AP in the wireless information transmission (WIT) time scheduling in the direct/reflecting way. Hybrid frequency-time division multiple access (HFTDMA) transmission protocol is adopted, and an optimization problem is formulated to maximize the sum throughput of the system. Since the optimization variables are strongly coupled together, the optimization problem is nonconvex. Therefore, we utilize the Lagrangian function and Karush–Kuhn–Tucker (KKT) conditions to derive the optimal bandwidth allocation and the optimal WIT time scheduling. Then, the Lambert $\mathcal {W}$ function is applied to obtain the optimal WET time scheduling. Finally, we propose alternating direction method of multipliers-based alternating optimization (A2O) algorithm to acquire the optimal WET/WIT phase-shift matrices. Numerical results corroborate that the proposed algorithm significantly outperforms the benchmark schemes, and the tradeoff between energy harvesting and information transmission plays the pivotal role in the IRS-assisted multigroup WPCNs.