An Off-Grid Massive MIMO Channel Estimator with Unknown Angular Cluster Prior

Hongyun Chu; Le Zheng; Xiaodong Wang

doi:10.1109/LWC.2020.2969661

An Off-Grid Massive MIMO Channel Estimator with Unknown Angular Cluster Prior

Hongyun Chu, Le Zheng^*, Xiaodong Wang

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

4 Citations (Scopus)

Abstract

By exploiting both the inherent sparsity in the continuous angular domain and the unknown angular cluster prior, we propose a super-resolution channel estimator for massive multiple-input multiple-output (MIMO) systems. The off-grid channel estimation is formulated by a weighted atomic norm minimization problem promoting the angular cluster-sparse prior, rather than known a priori, which is acquired by iteratively updating the angular cluster boundaries within the on-grid angular domain. Simulation results show the superior performance of the proposed algorithm over the existing off-grid estimator.

Original language	English
Article number	8972545
Pages (from-to)	783-786
Number of pages	4
Journal	IEEE Wireless Communications Letters
Volume	9
Issue number	6
DOIs	https://doi.org/10.1109/LWC.2020.2969661
Publication status	Published - Jun 2020
Externally published	Yes

Keywords

MIMO channel estimation
cluster
sparsity
weighted atomic norm
weighted ℓ -norm

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10.1109/LWC.2020.2969661

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abstract = "By exploiting both the inherent sparsity in the continuous angular domain and the unknown angular cluster prior, we propose a super-resolution channel estimator for massive multiple-input multiple-output (MIMO) systems. The off-grid channel estimation is formulated by a weighted atomic norm minimization problem promoting the angular cluster-sparse prior, rather than known a priori, which is acquired by iteratively updating the angular cluster boundaries within the on-grid angular domain. Simulation results show the superior performance of the proposed algorithm over the existing off-grid estimator.",

keywords = "MIMO channel estimation, cluster, sparsity, weighted atomic norm, weighted ℓ -norm",

author = "Hongyun Chu and Le Zheng and Xiaodong Wang",

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year = "2020",

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AU - Zheng, Le

AU - Wang, Xiaodong

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N2 - By exploiting both the inherent sparsity in the continuous angular domain and the unknown angular cluster prior, we propose a super-resolution channel estimator for massive multiple-input multiple-output (MIMO) systems. The off-grid channel estimation is formulated by a weighted atomic norm minimization problem promoting the angular cluster-sparse prior, rather than known a priori, which is acquired by iteratively updating the angular cluster boundaries within the on-grid angular domain. Simulation results show the superior performance of the proposed algorithm over the existing off-grid estimator.

AB - By exploiting both the inherent sparsity in the continuous angular domain and the unknown angular cluster prior, we propose a super-resolution channel estimator for massive multiple-input multiple-output (MIMO) systems. The off-grid channel estimation is formulated by a weighted atomic norm minimization problem promoting the angular cluster-sparse prior, rather than known a priori, which is acquired by iteratively updating the angular cluster boundaries within the on-grid angular domain. Simulation results show the superior performance of the proposed algorithm over the existing off-grid estimator.

KW - MIMO channel estimation

KW - cluster

KW - sparsity

KW - weighted atomic norm

KW - weighted ℓ -norm

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An Off-Grid Massive MIMO Channel Estimator with Unknown Angular Cluster Prior

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Keywords

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