# [1]穆晓敏,刘亚丽,张建康,等.基于PARAFAC分解的大规模MU-MIMO稀疏信道估计[J].郑州大学学报(工学版),2019,40(01):44-49.[doi:10.13705/j.issn.1671-6833.2019.01.010] 　Mu Xiaomin,Liu Yali,Zhang Jiankang,et al.Massive MU-MIMO Sparse Channel Estimation Based on PARAFAC Decomposition[J].Journal of Zhengzhou University (Engineering Science),2019,40(01):44-49.[doi:10.13705/j.issn.1671-6833.2019.01.010] 点击复制 基于PARAFAC分解的大规模MU-MIMO稀疏信道估计() 分享到： var jiathis_config = { data_track_clickback: true };

40卷

2019年01期

44-49

2019-01-10

## 文章信息/Info

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

1. 郑州大学信息工程学院;2. 东南大学移动通信国家重点实验室
Author(s):
1. School of Information Engineering, Zhengzhou University; 2. State Key Laboratory of Mobile Communication, Southeast University

Keywords:
DOI:
10.13705/j.issn.1671-6833.2019.01.010

A

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.