基于PARAFAC分解的大规模MU-MIMO稀疏信道估计-《郑州大学学报(工学版)》

文章信息/Info

Title:: Massive MU-MIMO Sparse Channel Estimation Based on PARAFAC Decomposition

Author(s):: Mu Xiaomin 1; Liu Yali 1; Zhang Jiankang1; Zhao Lingxiao 1; 1. School of Information Engineering, Zhengzhou University; 2. State Key Laboratory of Mobile Communication, Southeast University

Keywords:: Massive MU-MIMO; sparse channel; Parallel factorization; channel estimation; Normalized mean square error

摘要:: 针对大规模MU-MIMO(多用户多输入多输出）系统中上行链路的信道估计问题，提出了一种基于平行因子（PARAFAC）分解的稀疏信道估计算法。该算法利用稀疏数学模型构造稀疏信道模型，将稀疏理论与张量分解相结合，对基站端的接收信号进行PARAFAC建模。在满足唯一性分解条件下，利用双线性交替最小二乘（BALS）拟合算法联合估计出多个用户的信号矩阵与信道矩阵。仿真结果表明: 所提算法的估计性能优于经典的正交匹配跟踪算法等稀疏信道估计算法，与基于导频序列的估计方法相比，其信道估计的的精度大幅提高；仅需要少量导频，降低了导频开销，实现了高频谱效率的通信传输。

Abstract:: For the channel estimation problem of the uplink in a large-scale MU-MIMO systems, a sparse channel estimation algorithm based on parallel factor (PARAFAC) decomposition was proposed. In this paper, a sparse mathematical model was used to construct a sparse channel model, and sparse theory was combined with tensor decomposition to perform PARAFAC modeling of the received signal at the base station. Under the condition of uniqueness decomposition, a bilinear alternating least squares (BALS) fitting algorithm was used to jointly estimate the signal matrix and the channel matrix of multiple users.The simulation results showed that the proposed algorithm had better estimation performance than the classical orthogonal matching tracking algorithm and other sparse channel estimation algorithms. Compared with the pilot sequence based estimation method, the accuracy of the channel estimation was greatly improved.Only a small amount of pilot was needed.The pilot overhead was reduced, and high spectral efficiency communication transmission was realized.