Title | ||
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Bayesian mmWave Channel Estimation via Exploiting Joint Sparse and Low-Rank Structures. |
Abstract | ||
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We consider the problem of channel estimation for millimeter wave (mmWave) systems, where both the base station and the mobile station employ a single radio frequency (RF) chain to reduce the hardware cost and power consumption. Recent real-world channel measurements reveal that the mmWave channels incur a certain amount of spread over the angular domains due to the scattering clusters. The angular spreads give rise to a joint sparse and low-rank channel matrix in the angular domain. To utilize this joint sparse and low-rank structure, we address the channel estimation problem within a Bayesian framework. Specifically, we adopt a matrix factorization formulation and translate the problem of channel estimation into one of searching for two-factor matrices. To encourage a joint sparse and low-rank solution, independent sparsity-promoting priors are placed on entries of the two-factor matrices, which aims to promote sparse factor matrices with only a few non-zero columns. Based on the proposed prior model, we develop a variational Bayesian inference method for the mmWave channel estimation. The simulation results show that our proposed method presents a considerable performance improvement over the state-of-the-art compressed sensing-based channel estimation methods. |
Year | DOI | Venue |
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2019 | 10.1109/ACCESS.2019.2910088 | IEEE ACCESS |
Keywords | Field | DocType |
mmWave channel estimation,angular spread,joint sparse and low-rank,compressed sensing | Base station,Bayesian inference,Mobile station,Matrix (mathematics),Computer science,Matrix decomposition,Algorithm,Communication channel,Prior probability,Compressed sensing,Distributed computing | Journal |
Volume | ISSN | Citations |
7 | 2169-3536 | 1 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
---|---|---|---|
Kaihui Liu | 1 | 5 | 3.14 |
Xingjian Li | 2 | 34 | 2.46 |
Jun Fang | 3 | 1039 | 94.15 |
Hongbin Li | 4 | 1412 | 114.60 |