Utility Maximization for The IRS-Assisted Wireless Powered Communication Network with Multiple Service Providers

Abstract We consider a multi-device wireless-powered communication network, where an intelligent reflecting surface (IRS) is deployed to assist the wireless energy transfer (WET) from the power station (PS) to the information transmitter (IT) and the wireless information transfer (WIT) from the IT to the devices. The IRS, IT, and PS belong to different service providers, where the PS receives revenue from the IT for WET and pays fees to its energy source and the IRS, and the IT receives revenue from the devices for WIT and pays fees to the PS and the IRS. We model the interactions between the IT and the PS through a Stackelberg game and aim to achieve a win-win situation between them in terms of utility. Specifically, we solve a follower problem that maximizes the utility of the PS by jointly optimizing the IRS reflect beamforming and the transmit power of the PS, and a leader problem that maximizes the utility of the IT by jointly optimizing the energy price, the transmit power allocation of the IT, as well as the time allocation and the IRS reflect beamforming in the WET and WIT phases. To solve these problems, we propose efficient algorithms based on the alternating optimization, closed-form fractional programming, penalty-based equivalent transformation, and successive convex optimization techniques. Simulation results show that the proposed algorithm can significantly improve the PS and IT's utilities compared to the benchmark schemes.