高速移动环境下基于RM-Net的大规模MIMO CSI反馈算法  

CSI feedback algorithm based on RM-Net for massive MIMO systems in high-speed mobile environment

作　　者：廖勇[1] 王世义 LIAO Yong;WANG Shiyi(School of Microelectronics and Communication Engineering,Chongqing University,Chongqing 400044,China)

机构地区：[1]重庆大学微电子与通信工程学院,重庆400044

出　　处：《通信学报》2022年第5期166-176,共11页Journal on Communications

基　　金：国家自然科学基金资助项目(No.61501066);重庆市自然科学基金项目(No.cstc2019jcyj-msxmX0017)。

摘　　要：针对高速移动环境信道特征复杂多变,同时存在加性噪声和非线性效应的影响,提出一种残差混合网络(RM-Net)的大规模MIMO CSI反馈算法。RM-Net通过学习高速移动信道的空间结构与时间相关性,具备去除大规模MIMO信道噪声的能力,能显著提高CSI压缩率与恢复质量。系统仿真结果表明,RM-Net可消除高速移动场景加性噪声的影响,学习并适应稀疏、双选衰落信道特征,在高压缩率与低信噪比条件下依然具有较好的性能表现,所提算法性能大幅优于其他基于压缩感知(CS)和深度学习(DL)的CSI反馈算法。Aiming at the complex and changeable channel characteristics in high-speed mobile environment,and the influence of additive noise and nonlinear effects,a residual mixing network(RM-Net)for massive MIMO CSI feedback was proposed.By learning the spatial structure and temporal correlation of high-speed mobile channel,the network was able to remove massive MIMO channel noise,and the CSI compression rate and recovery quality could be significantly improved.System simulation results show that RM-Net can eliminate the influence of additive noise in high-speed mobile scenarios,learn and adapt to the channel characteristics of sparse and double-selective fading channels,and still has good performance under the conditions of high compression rate and low signal-to-noise ratio.The proposed algorithm performance is much better than other CS-based and DL-based CSI feedback algorithms.

关 键 词：高速移动 大规模MIMO CSI反馈 深度学习 去噪 

分 类 号：TN929.5[电子电信—通信与信息系统]