系统工程与电子技术 ›› 2022, Vol. 44 ›› Issue (12): 3871-3879.doi: 10.12305/j.issn.1001-506X.2022.12.33

• 通信与网络 • 上一篇    

基于IRS辅助多用户通信系统的信道容量优化

王丹, 刘金枝*, 梅志强, 梁家敏   

  1. 重庆邮电大学通信与信息工程学院, 重庆 400065
  • 收稿日期:2021-04-23 出版日期:2022-11-14 发布日期:2022-11-24
  • 通讯作者: 刘金枝
  • 作者简介:王丹(1982—), 女, 正高级工程师, 博士, 主要研究方向为智能反射表面技术、嵌入式系统(移动通信基带处理系统)、通信软件开发|刘金枝(1997—), 女, 硕士研究生, 主要研究方向为智能反射表面技术和移动通信物理层算法|梅志强(1997—), 男, 硕士研究生, 主要研究方向为移动通信物理层算法|梁家敏(1997—), 女, 硕士研究生, 主要研究方向为智能反射表面技术和移动通信物理层算法
  • 基金资助:
    国家自然科学基金(61701063)

Channel capacity optimization based on IRS-aided multi-user communication system

Dan WANG, Jinzhi LIU*, Zhiqiang MEI, Jiamin LIANG   

  1. School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
  • Received:2021-04-23 Online:2022-11-14 Published:2022-11-24
  • Contact: Jinzhi LIU

摘要:

智能反射表面(intelligent reflecting surface,IRS)通过对无线传播环境的智能配置进而获得极好的信道容量增益。在IRS辅助多用户下行链路通信中,本文通过共同优化基站处受功率限制的预编码器和IRS处受单位模量约束的相移器来最大化信道容量。针对由此产生的非确定性多项式难问题,首先将其转换成等效问题,再利用交替优化算法来求解预编码矩阵和相移向量。当固定相移向量时,优化问题可转换为二阶锥规划问题后直接使用标准优化包获得最优预编码矩阵。当固定预编码矩阵时,单位模量约束是解决问题的难点,本文将其嵌入搜索空间之后提出黎曼信赖域(Riemannian trust-region, RTR)算法来求解。仿真结果表明,与现有方法相比,RTR算法不仅具有性能的提升,还有更快的收敛速度。

关键词: 智能反射面, 多用户, 信道容量, 单位模量约束, 黎曼流形优化

Abstract:

Intelligent reflecting surface (IRS) can achieve unprecedented channel capacity gains by reconfiguring the wireless propagation environment smartly. The channel capacity is maximized by jointly optimizing the power-constrained precoder at the base station and the unit modulus-constrained phase shifter at the IRS in the IRS-assisted multi-user downlink communication. Aiming at the resulting non-deterministic polynomial hard problem, it is converted into an equivalent problem firstly, then the alternative optimization algorithm is used to solve the precoding matrix and phase shift vector. The problem can be converted to a second-order cone programming problem that can be solved by adopting standard optimization packages while the phase shifts vector fixing. The unit modulus constraint is the difficulty to solve this problem, it is decided to embed it into the search space. Then the Riemannian trust-region (RTR) algorithm is proposed to solve this problem while fixing the precoding matrix. Simulation results show that, compared with the existing approaches, RTR algorithm not only has better performance gains but also faster convergence speed.

Key words: intelligent reflecting surface (IRS), multi-user, channel capacity, unit modulus constraint, Riemannian manifold optimization

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