Massive MU-MIMO Sparse Channel Estimation Based on PARAFAC Decomposition
[1]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]
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Journal of Zhengzhou University (Engineering Science)[ISSN
1671-6833/CN
41-1339/T] Volume:
40
Number of periods:
2019 01
Page number:
44-49
Column:
Public date:
2019-01-10
- Title:
- Massive MU-MIMO Sparse Channel Estimation Based on PARAFAC Decomposition
- Keywords:
- Massive MU-MIMO; sparse channel; Parallel factorization; channel estimation; Normalized mean square error
- CLC:
- -
- DOI:
- 10.13705/j.issn.1671-6833.2019.01.010
- 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.
- References:
- -
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Last Update:
2019-02-28