Millimeter Wave Channel Estimation Based on Clustering Block Sparse Bayesian Learning

The clustering block sparse Bayesian learning (CB-SBL) algorithm for millimeter wave (mmWave) channel estimation is proposed in this paper. Exploiting the correlation between the mmWave channel, the algorithm adopts the structure prioritization clustering method to cope with the sparse coefficients of the channel space in the mmWave system. The dependence of mmWave channel adjacent estimation coefficients is improved by processing the hyperparameters of the neighborhood, which is used to avoid the suboptimal solutions caused by the entanglement of hyperparameters. The mmWave channel estimation accuracy is improved in this way. Meanwhile the proposed algorithm also avoids the vulnerability of parameter choice in block sparse Bayesian learning (BSBL), which improves the robustness of mmWave channel estimation. The simulation results show that the mmWave channel estimation based on CBSBL outperforms the recently proposed algorithms.