论文标题
大规模IRS的双向继电器网络的相优化
Phase Optimization for Massive IRS-aided Two-way Relay Network
论文作者
论文摘要
在本文中，借助智能反射表面（IRS），源（S）和目的地（d）通过双向解码和前向继电器（TW-DFR）交换信息。我们主要关注IRS的相位优化，以提高系统速率性能。首先，通过特征值分解（EVD）提出了最大化的接收功率总和（MAX-RPS）方法，并具有明显的速率增强，这称为Max-RPS-EVD。为了进一步达到更高的速率，提出了一种具有高复杂性的最大最小速率（Max-min-R）的方法。为了降低其复杂性，提出了一种通过一般功率迭代（GPI）最大化总和速率（MAX-SR）的低复杂性方法，这称为MAX-SR-GPI。仿真结果表明，所提出的三种方法的表现优于随机相位方法的情况，尤其是所提出的Max-SR-GPI方法是最好的一种，即在随机相中至少达到20 \％的利率增益。此外，当TW-DFR和IRS位于S和D的中间时，也可以证明可以达到最佳速率。
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.
