… , clustered channel behavior and unknown cluster structure … learning. In order to overcome
these challenges, we propose two novel sparse Bayesian learning algorithms, ie, the Cluster-…

… We developed a sparse Bayesian learning method for superresolution downlink channel
estimation of massive MIMO by treating the sampled angular grid as the underlying parameter …

… We consider the problem of recovering block-sparse signals whose cluster patterns are …
new sparse Bayesian learning method for recovery of block-sparse signals with unknown cluster …

… optimization purposes, leading to our clustered sparse Bayesian learning algorithm (C-SBL) …
• Development of a robust sparse Bayesian algorithm that adheres to these principles to an …

… clustered sparse signals with unknown cluster sizes and locations by proposing a new sparse
Bayesian learning … -coupled sparse Bayesian learning (PC-SBL) and Clustered Sparse …

… Here we generalize the conventional block sparse model with the unknown block partition.
Based on it, we derive another algorithm, called Cluster-SBL (Type II), which requires little a …

… In this paper, the cluster structured sparse signals are investigated. … cluster structures via a
hierarchical Bayesian model. ► An MCMC sampling is implemented to carry out the Bayesian …

… exhibit clustered-sparse structure, and sparse channel recovery based on the clustered-sparse …
We propose a novel clustered-sparse Bayesian model for sparse channel estimation in …

… clustered-sparse distribution, we propose a partition-based clustered-sparse Bayesian
learning … distribution, we propose the partition-based clustered-sparse Bayesian learning …

… not capable of modeling cluster heterogeneity in cases where … Sparse Bayesian Mixed
Effect ELM (SBME-ELM). The proposed model allows the possible heterogeneity in the clustered …