基于多IRS辅助的MIMO网络安全通信
MIMO network secure communication based on multiple IRS assistance
  
DOI:
中文关键词:  MIMO安全通信;多IRS;交替优化;瑞利商
英文关键词:MIMO secure communication; multiple IRS; alternate optimization; Rayleigh quotient
基金项目:国家自然科学基金(61771257)和南京邮电大学射频集成与微组装技术国家地方联合工程实验室开放课题(KFJJ20210101)资助项目
作者单位
朱 睿 南京邮电大学 电子与光学工程学院、柔性电子(未来技术)学院,江苏 南京 210023 
傅友华 南京邮电大学 电子与光学工程学院、柔性电子(未来技术)学院,江苏 南京 210023 
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中文摘要:
      智能反射面(Intelligent Reflecting Surface,IRS)可以通过调整反射元件的相移来反射信号,可以用来增强期望信号或者抑制不期望信号,在安全通信领域有着广阔的应用前景。文中研究了一种由多IRS辅助的多输入多输出(Multiple Input Multiple Output, MIMO)安全通信系统,建立了多IRS辅助安全通信系统中最大化保密速率的优化问题,该优化问题可以通过交替优化将其解耦为两个子问题,然后再分别设计发射端的波束形成矩阵和IRS的反射相移矩阵,从而最大化合法通信者的保密率。基于广义瑞利商,文中提出了一种改进的交替优化算法,在优化IRS的反射相移矩阵时,对所得的最优解进行归一化,使其满足约束,然后代入交替优化中进行循环。仿真结果表明,在IRS反射元件数量规模较大时,该算法的性能要明显优于现有算法。
英文摘要:
      Intelligent reflecting surface (IRS) can reflect the signal by adjusting the phase shift of the reflecting element. It can be used to enhance the expected signal or suppress the undesired signal. It has a broad application prospect in the field of secure communication. In this paper, a multiple input multiple output (MIMO) secure communication system assisted by multiple IRS is studied. The optimization problem of maximizing the security rate in the multi IRS assisted secure communication system is established. The optimization problem can be decoupled into two sub problems through alternating optimization, and then the beamforming matrix at the transmitter and the reflection phase shift matrix of IRS are designed to maximize the security rate of legitimate communicators. Based on the generalized Rayleigh quotient, an improved alternating optimization algorithm is proposed. When optimizing the reflection phase shift matrix of IRS, the obtained optimal solution is normalized to meet the constraints, and then substituted into the alternating optimization for circulation. The simulation results show that the performance of the algorithm is significantly better than those of the existing algorithms when the number of IRS reflective elements is large.
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