Joint Low Rank and Sparsity-based Channel Estimation for FDD Massive MIMO

Authors

Zhou Jin, Tianjin University of Finance and Economy, School of Science and Technology, Tianjin 300222, China;

Abstract

Abstract: Channel estimation of millimeter wave communication needs large system load. In order to reduce the load, a low-rank and sparse feature of the wireless channel is combined, and a channel estimation algorithm framework based on non-convex low-rank approximation is proposed. Aiming at the large computation of the channel model-based dictionary learning algorithm, a dictionary learning algorithm for deep neural network channel feature classification is designed. The simulation shows that the average square error of the proposed method is better than the channel model-based dictionary learning method, the channel estimation method under the Bayesian framework, and the compressed sensing channel estimation method under the specific city microcellular channel model. The signal-to-noise ratio required by the algorithm is the lowest when the mean square error is the same. The number of pilots required is lower than the above three methods.

Recommended Citation

Jin, Zhou (2021) "Joint Low Rank and Sparsity-based Channel Estimation for FDD Massive MIMO," Journal of System Simulation: Vol. 33: Iss. 1, Article 11.
DOI: 10.16182/j.issn1004731x.joss.19-0629
Available at: https://dc-china-simulation.researchcommons.org/journal/vol33/iss1/11

First Page

99

Revised Date

2020-04-09

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.19-0629

Last Page

108

CLC

TN929.5;TP391

Recommended Citation

Zhou Jin. Joint Low Rank and Sparsity-based Channel Estimation for FDD Massive MIMO[J]. Journal of System Simulation, 2021, 33(1): 99-108.

DOI

10.16182/j.issn1004731x.joss.19-0629