Downlink channel estimation for massive MIMO systems relying on vector approximate message passing

Abstract

To reduce the pilot overhead of downlink channel estimation in massive multiple-input–multiple-output (MIMO) systems, a sparse recovery algorithm relying on the vector approximate message passing (VAMP) technique is proposed. More specifically, an a-priori channel model characterized by a multivariate Bernoulli-Gaussian distribution is invoked for exploiting the common sparsity of massive MIMO channels, and the VAMP technique is used for jointly estimating the spatially correlated channels. Moreover, the hyperparameters of the a-priori model are learned by invoking the expectation maximization (EM) algorithm. Our numerical results demonstrate that the proposed algorithm is capable of reducing the pilot overhead by 50% in massive MIMO systems.

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Identifiers

ORCID for Lajos Hanzo: orcid.org/0000-0002-2636-5214

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