Throughput Maximization for IRS-Aided MIMO FD-WPCN with Non-Linear EH Model

TL;DR

This paper develops advanced algorithms to optimize throughput in IRS-assisted MIMO full-duplex wireless-powered networks, considering practical non-linear energy harvesting and self-interference, with significant improvements over suboptimal configurations.

Contribution

It introduces novel element-wise and MMSE-based algorithms for joint optimization of IRS phase shifts, time allocation, and precoding in a complex non-convex setting, including practical suboptimal schemes.

Findings

Proposed algorithms outperform baseline methods in throughput.

Efficient IRS phase shift optimization via SOCP.

Suboptimal schemes balance performance and complexity.

Abstract

This paper studies an intelligent reflecting surface (IRS)-aided multiple-input-multiple-output (MIMO) full-duplex (FD) wireless-powered communication network (WPCN), where a hybrid access point (HAP) operating in FD broadcasts energy signals to multiple devices for their energy harvesting (EH) in the downlink (DL) and meanwhile receives information signals from devices in the uplink (UL) with the help of an IRS. Taking into account the practical finite self-interference (SI) and the non-linear EH model, we formulate the weighted sum throughput maximization optimization problem by jointly optimizing DL/UL time allocation, precoding matrices at devices, transmit covariance matrices at the HAP, and phase shifts at the IRS. Since the resulting optimization problem is non-convex, there are no standard methods to solve it optimally in general. To tackle this challenge, we first propose an element-wise (EW) based algorithm, where each IRS phase shift is alternately optimized in an iterative manner. To reduce the computational complexity, a minimum mean-square error (MMSE) based algorithm is proposed, where we transform the original problem into an equivalent form based on the MMSE method, which facilities the design of an efficient iterative algorithm. In particular, the IRS phase shift optimization problem is recast as an second-order cone program (SOCP), where all the IRS phase shifts are simultaneously optimized. For comparison, we also study two suboptimal IRS beamforming configurations in simulations, namely partially dynamic IRS beamforming (PDBF) and static IRS beamforming (SBF), which strike a balance between the system performance and practical complexity.