Super-Resolution Channel Estimation for Massive MIMO via Clustered Sparse Bayesian Learning

This correspondence paper provides a novel super-resolution downlink channel estimation approach for massive multiple-input multiple-output (MIMO) systems, by jointly learning the parametric dictionary and recovering the sparse channel components. Specifically, we exploit a Markov spike and slab prior to characterize the clustered sparse channel structure resulting from small local scatterers in the angular domain. The proposed algorithm is developed within a variational expectation maximization framework and integrated with the generalized approximate message passing technique to calculate the intractable posterior distribution. Simulation results illustrate that our approach attains a significant performance improvement over existing methods.