Title: Joint Hybrid Precoding and Multi-IRS Optimization for mmWave MU-MISO Communication Network Show Chinese title

Title: 毫米波多用户MISO通信网络中的联合混合预编码和多IRS优化

Abstract: This paper attempts to jointly optimize the hybrid precoding (HP) and intelligent reflecting surfaces (IRS) beamforming matrices in a multi-IRS-aided mmWave communication network, utilizing the Alamouti scheme at the base station (BS). Considering the overall signal-to-noise ratio (SNR) as the objective function, the underlying problem is cast as an optimization problem, which is shown to be non-convex in general. To tackle the problem, noting that the unknown matrices contribute multiplicatively to the objective function, they are reformulated into two new matrices with rank constraints. Then, using the so-called inner approximation (IA) technique in conjunction with majorization-minimization (MM) approaches, these new matrices are solved iteratively. From one of these matrices, the IRS beamforming matrices can be effectively extracted. Meanwhile, HP precoding matrices can be solved separately through a new optimization problem aimed at minimizing the Euclidean distance between the fully digital (FD) precoder and HP analog/digital precoders. This is achieved through the use of a modified block coordinate descent (MBCD) algorithm. Simulation results demonstrate that the proposed algorithm outperforms various benchmark schemes in terms of achieving a higher achievable rate. Show Chinese abstract

Abstract: 本文试图联合优化混合预编码（HP）和智能反射面（IRS）波束成形矩阵，在多IRS辅助毫米波通信网络中，基站（BS）采用Alamouti方案。以整体信噪比（SNR）为目标函数，将其建模为一个优化问题，该问题通常被证明是非凸的。为了解决这个问题，注意到未知矩阵对目标函数呈乘性贡献，因此将其重新表述为两个具有秩约束的新矩阵。然后，利用所谓的内逼近（IA）技术与主从最小化（MM）方法相结合，这些新矩阵可以迭代求解。从其中一个矩阵中，可以有效提取出IRS波束成形矩阵。同时，HP预编码矩阵可以通过一个新的优化问题单独求解，该问题旨在最小化全数字（FD）预编码器与HP模拟/数字预编码器之间的欧几里得距离。这是通过使用改进的块坐标下降（MBCD）算法实现的。仿真结果表明，所提出的算法在实现更高可达到速率方面优于各种基准方案。