Weighted Sum-Rate Maximization for Multi-IRS-assisted Full-Duplex Systems with Hardware Impairments

TL;DR

This paper develops an optimization framework for maximizing weighted sum-rate in multi-IRS-assisted full-duplex systems considering hardware impairments, proposing an iterative solution approach and demonstrating performance gains through numerical results.

Contribution

It introduces a joint resource allocation method for multi-IRS-assisted full-duplex systems with hardware impairments, including an efficient iterative algorithm for non-convex optimization.

Findings

Multiple IRSs improve sum-rate performance under hardware impairments.

The proposed iterative algorithm effectively finds suboptimal solutions.

Numerical results validate the performance enhancement with multiple IRSs.

Abstract

Smart and reconfigurable wireless communication environments can be established by exploiting well-designed intelligent reflecting surfaces (IRSs) to shape the communication channels. In this paper, we investigate how multiple IRSs affect the performance of multi-user full-duplex communication systems under hardware impairment at each node, wherein the base station (BS) and the uplink users are subject to maximum transmission power constraints. Firstly, the uplink-downlink system weighted sum-rate (SWSR) is derived which serves as a system performance metric. Then, we formulate the resource allocation design for the maximization of SWSR as an optimization problem which jointly optimizes the beamforming and the combining vectors at the BS, the transmit powers of the uplink users, and the phase shifts of multiple IRSs. Since the SWSR optimization problem is non-convex, an efficient iterative alternating approach is proposed to obtain a suboptimal solution for the design problem considered and its complexity is also discussed. In particular, we firstly reformulate the main problem into an equivalent weighted minimum mean-square-error form and then transform it into several convex sub-problems which can be analytically solved for given phase shifts. Then, the IRSs phases are optimized via a gradient ascent-based algorithm. Finally, numerical results are presented to clarify how multiple IRSs enhance the performance metric under hardware impairment.