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MmWave Channel Estimation via Compressive Covariance Estimation: Role of Sparsity and Intra-Vector Correlation

Prasanna, D and Murthy, CR (2021) MmWave Channel Estimation via Compressive Covariance Estimation: Role of Sparsity and Intra-Vector Correlation. In: IEEE Transactions on Signal Processing, 69 . pp. 2356-2370.

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Official URL: https://doi.org/10.1109/TSP.2021.3070210


In this work, we address the problem of multiple-input multiple-output mmWave channel estimation in a hybrid analog-digital architecture, by exploiting both the underlying spatial sparsity as well as the spatial correlation in the channel. We accomplish this via compressive covariance estimation, where we estimate the channel covariance matrix from noisy low dimensional projections of the channel obtained in the pilot transmission phase. We use the estimated covariance matrix as a plug-in to the linear minimum mean square estimator to obtain the channel estimate. We present a new Gaussian prior model, inspired by sparse Bayesian learning (SBL), which incorporates parameters to capture the channel correlation in addition to sparsity. Based on this prior, we develop the Corr-SBL algorithm, which uses an expectation maximization procedure to learn the parameters of the prior and update the posterior channel estimates. A closed form solution is obtained for the maximization step based on fixed-point iterations. To facilitate practical implementation, an online version of the algorithm is developed which significantly reduces the latency at a marginal loss in performance. The efficacy of the prior model is studied by analyzing the normalized mean squared error in the channel estimate. Our results show that, when compared to a genie-aided estimator and other existing sparse recovery algorithms, exploiting both sparsity and correlation results in significant performance gains, even under imperfect covariance estimates obtained using a limited number of samples. © 1991-2012 IEEE.

Item Type: Journal Article
Publication: IEEE Transactions on Signal Processing
Publisher: Institute of Electrical and Electronics Engineers Inc.
Additional Information: The copyright for this article belongs to Institute of Electrical and Electronics Engineers Inc.
Keywords: Covariance matrix; Maximum principle; Mean square error; Millimeter waves, Closed form solutions; Covariance estimation; Expectation - maximizations; Fixed point iteration; Linear minimum mean square estimator; Normalized mean squared errors; Sparse Bayesian learning (SBL); Spatial correlations, Channel estimation
Department/Centre: Division of Electrical Sciences > Electrical Communication Engineering
Date Deposited: 09 Mar 2023 06:33
Last Modified: 09 Mar 2023 06:33
URI: https://eprints.iisc.ac.in/id/eprint/80887

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