Throughput Maximization for Active Intelligent Reflecting Surface Aided Wireless Powered Communications

TL;DR

This paper introduces the use of active IRS in wireless powered communication networks, demonstrating significant improvements in throughput, energy efficiency, and coverage over passive IRS, with various beamforming strategies analyzed.

Contribution

It is the first work to incorporate active IRS in WPCNs, proposing optimization techniques for beamforming and showing performance benefits over passive IRS.

Findings

Active IRS greatly improves weighted sum throughput.

Active IRS enhances energy efficiency and coverage.

Dynamic beamforming strategies outperform static ones.

Abstract

This paper considers an active intelligent reflecting surface (IRS)-aided wireless powered communication network (WPCN), where devices first harvest energy and then transmit information to a hybrid access point (HAP). Different from the existing works on passive IRS-aided WPCNs, this is the first work that introduces the active IRS in WPCNs. To guarantee fairness, the problem is formulated as an amplifying power-limited weighted sum throughput (WST) maximization problem, which is solved by successive convex approximation technique and fractional programming alternatively. To balance the performance and complexity tradeoff, three beamforming setups are considered at the active IRS, namely user-adaptive IRS beamforming, uplink-adaptive IRS beamforming, and static IRS beamforming. Numerical results demonstrate the significant superiority of employing active IRS in WPCNs and the benefits of dynamic IRS beamforming. Specifically, it is found that compared to the passive IRS, the active IRS not only improves the WST greatly, but also is more energy-efficient and can significantly extend the transmission coverage. Moreover, different from the symmetric deployment strategy of passive IRS, it is more preferable to deploy the active IRS near the devices.