Abstract
In this study, we propose an improved channel estimator based on vector approximate message passing (VAMP) for quantized millimeter wave (mmWave) massive multiple-input and multiple-output (MIMO) systems. Using channel sparsity and the orthogonality of steering matrix, the proposed VAMP-on method designed for the on-grid scenario not only improves estimation performance but also has better convergence and inverse-free implementation. Furthermore, to achieve better estimation performance under the off-grid scenario, we provide a VAMP-off algorithm to update steering matrix and channel coefficients in an alternating fashion. Finally, simulation results are provided to demonstrate the advantages of the proposed approaches.
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