期刊
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
卷 68, 期 6, 页码 6156-6160出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TVT.2019.2908379
关键词
Approximate message passing; channel estimation; massive MIMO; sparse Bayesian learning
资金
- National Natural Science Foundation of China [61671128, 61801084]
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.
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