Compressive Estimation of Cluster-sparse Channels

Cluster-sparse multipath channels, i.e., non-zero taps occurring in clusters, exist frequently in many communication systems, e.g., underwater acoustic (UWA), ultra-wide band (UWB), and multiple-antenna communication systems. Conventional sparse channel estimation methods often ignore the additional structure in the problem formulation. In this paper, we propose an improved compressive channel estimation (CCE) method using block orthogonal matching pursuit algorithm (BOMP) based on the cluster-sparse channel model. Making explicit use of the concept of cluster-sparsity can yield better estimation performance than the conventional sparse channel estimation methods. Compressive sensing utilizes clustersparse information to improve the estimation performance by further mitigating the coherence in training signal matrix. Finally, we present the simulation results to confirm the performance of the proposed method based on cluster-sparse.