Energy Efficiency Maximization for IRS-Aided WPCNs

Abstract

Intelligent reflecting surface (IRS) is a revolutionary technology for achieving spectral- and energy-efficient wireless networks. By adjusting the phase shifts at the IRS, a high passive beamforming gain can be achieved, which is particularly appealing for improving the spectral efficiency of wireless powered communication networks (WPCNs). Inspired by this, we study IRS-aided WPCNs, where the IRS is leveraged to help the energy transmitter (ET) broadcast the energy signals to the distributed devices in the downlink (DL), and meanwhile help the distributed devices forward the information signals to the information receiver (IR) in the uplink (UL). By taking account into the system energy consumption and achievable system throughput, we aim to maximize the energy efficiency (EE) of WPCNs by jointly optimizing phase shifts at the IRS, the beamformer at the ET, time allocation, and the transmit power of devices while guaranteeing the minimum achievable rate requirements for the devices. Since the formulated optimization problem is a nonlinear fractional programming problem, which is non-convex. To tackle this difficulty, an alternating optimization (AO) algorithm is proposed. In particular, a Dinkelbach-based algorithm is proposed to optimize the resource allocation, and a penalty-based algorithm based on the difference of convex (DC) programming and successive convex approximation (SCA) techniques is proposed to solve the phase shift optimization. Simulation results unveil that the IRS is capable of improving the system EE compared to the benchmarks.