Joint Dynamic Passive Beamforming and Resource Allocation for IRS-Aided Full-Duplex WPCN

TL;DR

This paper explores IRS-assisted full-duplex wireless-powered networks, proposing three IRS beamforming schemes and joint optimization algorithms to enhance throughput, demonstrating significant performance gains over half-duplex systems with effective self-interference suppression.

Contribution

It introduces three IRS beamforming configurations for FD-WPCN and develops joint optimization algorithms for system throughput maximization under different SIC conditions.

Findings

IRS significantly improves FD-WPCN performance

Partially dynamic IRS beamforming balances performance and complexity

Proper SIC suppression enables FD-WPCN to outperform half-duplex systems

Abstract

This paper studies intelligent reflecting surface (IRS)-aided full-duplex (FD) wireless-powered communication network (WPCN), where a hybrid access point (HAP) broadcasts energy signals to multiple devices for their energy harvesting in the downlink (DL) and meanwhile receives information signals in the uplink (UL) with the help of IRS. Particularly, we propose three types of IRS beamforming configurations to strike a balance between the system performance and signaling overhead as well as implementation complexity. We first propose the fully dynamic IRS beamforming, where the IRS phase-shift vectors vary with each time slot for both DL wireless energy transfer (WET) and UL wireless information transmission (WIT). To further reduce signaling overhead and implementation complexity, we then study two special cases, namely, partially dynamic IRS beamforming and static IRS beamforming. For the former case, two different phase-shift vectors can be exploited for the DL WET and the UL WIT, respectively, whereas for the latter case, the same phase-shift vector needs to be applied for both DL and UL transmissions. We aim to maximize the system throughput by jointly optimizing the time allocation, HAP transmit power, and IRS phase shifts for the above three cases. Two efficient algorithms based on alternating optimization and penalty-based algorithms are respectively proposed for both perfect self-interference cancellation (SIC) case and imperfect SIC case by applying successive convex approximation and difference-of-convex optimization techniques. Simulation results demonstrate the benefits of IRS for enhancing the performance of FD-WPCN, and also show that the IRS-aided FD-WPCN is able to achieve significantly performance gain compared to its counterpart with half-duplex when the self-interference (SI) is properly suppressed.