Phase Optimization for Massive IRS-aided Two-way Relay Network

TL;DR

This paper proposes three phase optimization methods for IRS-assisted two-way relay networks, significantly improving system rate performance through eigenvalue decomposition and iterative algorithms.

Contribution

It introduces novel phase optimization techniques, including Max-RPS-EVD, Max-Min-R, and Max-SR-GPI, to enhance IRS-aided relay network rates with reduced complexity.

Findings

Max-SR-GPI achieves at least 20% rate gain over random phase.

Optimal rate is attained when relay and IRS are centrally located.

Proposed methods outperform existing random phase approaches.

Abstract

In this paper, with the help of an intelligent reflecting surface (IRS), the source (S) and destination (D) exchange information through the two-way decode-and-forward relay (TW-DFR). We mainly focus on the phase optimization of IRS to improve the system rate performance. Firstly, a maximizing receive power sum (Max-RPS) method is proposed via eigenvalue decomposition (EVD) with an appreciable rate enhancement, which is called Max-RPS-EVD. To further achieve a higher rate, a method of maximizing minimum rate (Max-Min-R) is proposed with high complexity. To reduce its complexity, a low-complexity method of maximizing the sum rate (Max-SR) via general power iterative (GPI) is proposed, which is called Max-SR-GPI. Simulation results show that the proposed three methods outperform the case of random phase method, especially the proposed Max-SR-GPI method is the best one achieving at least 20\% rate gain over random phase. Additionally, it is also proved the optimal rate can be achieved when TW-DFR and IRS are located in the middle of S and D.