Compressed Channel Sensing Algorithm Based on Cluster Sparsity

Abstract:

According to cluster structure feature for wideband sparse channel, the Cluster Regularized Adaptive Matching Pursuit (CRAMP) algorithm is presented based on the study of the original compressed channel sensing estimation algorithm. The proposed algorithm can accurately reconstruct the channel by both the adaptive process which chooses the number of candidate clusters and the regularization process which Support for secondary screening of candidate clusters, although the cluster sparsity of the channel is unknown. Simulation results show that the proposed algorithm’ performance is better than the BP, OMP, BOMP algorithm.

• Periodical: Applied Mechanics and Materials (Volumes 644-650)
• Pages: 4013-4017
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.644-650.4013
• Online since: September 2014
• Authors: Yan Wang*, Li Yi Zhang, Rui Zhang
• Keywords: Channel Estimation, Cluster Sparsity, Compressed Channel Sensing

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