面向多用户大规模MIMO系统的信道估计研究  

Channel Estimation for Multi-User Massive MIMO Systems

作　　者：杨延 安澄全[1] 杨茜 李俊江 YANG Yan;AN Chengquan;YANG Qian;LI Junjiang(School of Information and Communication Engineering,Harbin Engineering University,Harbin,Heilongjiang 150001,China;AVIC First Aircraft Institute,Xi’an,Shaanxi 710089,China)

机构地区：[1]哈尔滨工程大学信息与通信工程学院,黑龙江哈尔滨150001 [2]航空工业第一飞机设计研究院,陕西西安710089

出　　处：《信号处理》2025年第3期484-493,共10页Journal of Signal Processing

基　　金：黑龙江省重点研发计划(2023ZX01A20)。

摘　　要：获取大规模多输入多输出(Multiple-Input Multiple-Output,MIMO)系统的信道状态信息十分关键。频分双工(Frequency Division Duplexing,FDD)模式下,传统的多用户信道估计问题是将多用户MIMO系统分解成多个单用户MIMO系统,利用单用户的信道特性进行估计重构,但随着基站端天线数量和用户数的增加,不仅导频开销和重构算法的误差逐渐增大,计算复杂度也随之上升,导致系统整体性能下降。针对这一问题,本文提出了一种基于压缩感知多测向量(Multiple Measurement Vector,MMV)模型的多用户联合信道估计方案:利用多用户大规模MIMO系统中,地理相邻用户角度域信道之间的共同稀疏性和独立稀疏性结构,首先设计了适用于MMV模型的信道稀疏度估计策略,通过排列稀疏分量的贡献率来获取信道稀疏度,提高了稀疏先验信息未知或不准确条件下重构算法的性能。其次,提出了一种分段残差动态反馈联合匹配追踪(Segmented Residual Dynamic Feedback joint Matching Pursuit,SRDFMP)算法,通过区分不同属性的支撑集并分段估计,有效降低了导频开销;将所有用户的共同支撑集共享,避免部分重复的迭代步骤;针对不同支撑设置两种索引长度更新准则,加快了算法的收敛速度;同时本文算法也考虑了错误原子的纠正问题。最后,构建了标准的空间信道模型(Spatial Channel Model,SCM)来验证所提算法的性能。最后,构建了标准的空间信道模型(Spatial Channel Model,SCM)来验证所提算法的性能。仿真结果表明,相比于传统算法,本文所提算法具有更小的导频开销和优良的信道估计性能,且在多用户条件下联合恢复的效率更高。Obtaining channel state information of massive multiple-input multiple-output(MIMO)systems is crucial.In frequency division duplexing(FDD)mode,the conventional multi-user channel estimation problem is to decompose the multi-user MIMO system into multiple single-user MIMO systems and use the channel characteristics of the single user for estimation and reconstruction.However,with the increase in the number of antennas at the base station and the number of users,not only the pilot overhead and error of the reconstruction algorithm increase gradually,but also the computational complexity increases,resulting in a decline of the overall system performance.To address this problem,this study proposes a multi-user joint channel estimation scheme based on the compressed sensing multiple measurement vector(MMV)model.First,based on the common sparsity and independent sparsity structure among the channels in the angle domain of geographically adjacent users in the multi-user massive MIMO system,a channel sparsity estima‐tion strategy suitable for the MMV model is designed.The channel sparsity estimation strategy is obtained by ranking the contribution rate of sparse components,which improves the performance of the reconstruction algorithm under the con‐dition of unknown or inaccurate sparse prior information.Second,a segmented residual dynamic feedback joint match‐ing pursuit(SRDFMP)algorithm is proposed.The algorithm uses several innovative techniques:support sets with dif‐ferent attributes are differentiated and estimated in segments,effectively reducing the pilot frequency overhead;the common support sets of all users are shared to avoid some redundant iteration steps;and two index length updating crite‐ria are set according to different supports to accelerate the convergence speed of the algorithm.Simultaneously,the prob‐lem of correcting the wrong atoms is also considered.Finally,a standardized spatial channel model(SCM)is con‐structed to validate the performance of the proposed algorithm.Simulation result

关 键 词：大规模多输入多输出 信道估计 压缩感知 多测向量模型 

分 类 号：TN929.5[电子电信—通信与信息系统]