面向大规模MIMO信道信息反馈的模型驱动轻量化神经网络  

Model-Driven Lightweight Network for CSI Feedback in Massive MIMO

作　　者：张阳阳 张席畅 刘毅[1] ZHANG Yangyang;ZHANG Xichang;LIU Yi(State Key Laboratory of Integrated Services Networks,Xidian University,Xi'an,Shaanxi 710071,China)

机构地区：[1]西安电子科技大学综合业务网理论与关键技术国家重点实验室,陕西西安710071

出　　处：《信号处理》2023年第3期381-389,共9页Journal of Signal Processing

基　　金：国家重点研发计划(2021YFA0716500);国家111项目(B08038)。

摘　　要：信道状态信息对于大规模多输入多输出(Multi-Input Multi-Output,MIMO)系统获得高信道容量和能量效率是十分重要的。频分双工系统因为上下行信道缺少互易性,所以需要用户将下行信道反馈至基站来进行预编码等处理。因为反馈的信息量和天线数成正比,所以大规模MIMO系统的反馈量是十分巨大的。很多数据驱动的深度学习神经网络使用编码器压缩信道信息,使用解码器恢复信道信息,但是由于数据驱动神经网络的黑盒子特性,不仅需要很高的复杂度来恢复信道信息,而且性能难以得到进一步提升,尤其增加用户设备处模型的复杂度和计算资源来提升性能是不切实际的。本文提出了一种展开迭代阈值收缩算法(Iterative Shrinkage-Thresholding Algorithm,ISTA)的可解释的模型驱动网络。针对信道信息不严格满足压缩感知稀疏性要求而导致恢复性能下降的问题,引入残差网络,设计了一种非线性稀疏变换来提升性能;为了平衡性能和复杂度,在用户处提出了一个可学习的压缩矩阵来保留更多信道信息;进一步地,本文在不改变已有网络框架的基础上将适用范围从单天线用户扩大到多天线用户,提高了神经网络的兼容性。仿真结果表明,本文提出的方法相比于其他方法具有更好的信道恢复性能以及在用户处具有更低的复杂度。Channel state information is very vital to obtain high channel capacity and energy efficiency in massive Multi-Input Multi-Output(MIMO)systems.Because the uplink and downlink channels lack reciprocity,the user needs to feed back the downlink channel to the base station for processing such as precoding in frequency division duplexing system.The feedback overhead is huge cause the feedback information is proportional to the number of antennas in the massive MIMO systems.Many data-driven deep learning neural networks use encoders to compress channel state information and decoders to restore channel.However,due to the black box characteristics of data-driven neural networks,not only high complexity is required to recover channel state information,but better performance is difficult to obtain,especially improving performance by increasing complexity and computing resources at the user is impractical.This paper proposed an interpretable model-driven network that unfolds the Iterative Shrinkage-Thresholding Algorithm(ISTA).Because channel state information does not strictly meet the sparsity requirements of compressed sensing,which leads to the degradation of recovery performance,a nonlinear sparse transform was designed to improve the reconstruction performance.In order to balance performance and complexity,a learnable compression matrix was proposed at the UE to reserve more channel state information.Moreover,this paper expands the scope of application from single-antenna users to multi-antenna users without changing the existing network framework,which improves the compatibility of neural networks.Simulation results show that the proposed method had better channel state information recovery performance and lower complexity at the user than other methods.

关 键 词：大规模多输入多输出 模型驱动 深度学习 信道反馈 

分 类 号：TN929.5[电子电信—通信与信息系统]