摘要
大规模多输入多输出(multiple input multiple output,MIMO)系统中,信道估计算法复杂度随着基站侧天线数量的增加而急剧增加,针对需要在信道估计算法复杂度与算法性能之间进行折中的问题,提出分布式压缩感知线性最小均方误差(distributed compressed sensing linear minimum mean square error,DCS-LMMSE)算法。该算法利用信道的空时共稀疏性,首先根据先验支撑集信息将接收信号分为密集部分和稀疏部分,然后分别采用不同的算法进行初始信道估计,最后采用奇异值分解代替信道相关矩阵求逆进一步降低DCS-LMMSE算法复杂度。所提算法与传统线性最小均方误差算法相比明显地降低了计算复杂度。仿真结果表明,所提算法与纯压缩感知稀疏信道估计算法相比具有更好的性能。
In massive multiple input multiple output(MIMO)systems,the complexity of channel estimation algorithm increases rapidly with the increase of the number of antennas on the base station side.To solve the problem of tradeoff between the complexity of channel estimation algorithm and the performance of the algorithm,a distributed compressed sensing linear minimum mean square error(DCS-LMMSE)algorithm is proposed.The algorithm takes advantage of the space-time co-sparseness of the channel.Firstly,the received signals are divided into dense part and sparse part according to the prior support set information.Then,the different algorithms are used to estimate the initial channel.Finally,singular value decomposition is used to replace the inverse of channel correlation matrix to further reduce the complexity of DCS-LMMSE algorithm.Compared with the traditional LMMSE algorithm,the proposed algorithm significantly reduces the computational complexity.Simulation results show that the proposed algorithm has better performance than the pure compressed sensing sparse channel estimation algorithm.
作者
李贵勇
于敏
余永坤
LI Guiyong;YU Min;YU Yongkun(School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China)
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2021年第3期823-831,共9页
Systems Engineering and Electronics
基金
国家科技重大专项(2017ZX03001021-004)
重庆教委科学技术研究项目(KJ1500428)资助课题。
关键词
大规模多输入多输出
空时共稀疏性
信道估计
分布式压缩感知线性最小均方误差
massive multiple input multiple output(MIMO)
space-time co-sparseness
channel estimation
distributed compressed sensing linear minimum mean square error(DCS-LMMSE)
