On the reconstruction of block-sparse signals with an optimal number of measurements
IEEE Transactions on Signal Processing
Robust recovery of signals from a structured union of subspaces
IEEE Transactions on Information Theory
Block-sparse signals: uncertainty relations and efficient recovery
IEEE Transactions on Signal Processing
Beyond Nyquist: efficient sampling of sparse bandlimited signals
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Cognitive radio: brain-empowered wireless communications
IEEE Journal on Selected Areas in Communications
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In cognitive radio (CR), compressed sensing (CS) and cooperative sensing are two emerging techniques enabling sub-Nyquist sampling for wide-band sensing and improving the detection performance under fading channels. In this paper, a decentralized cooperative compressed spectrum sensing scheme for block sparse signals is proposed. The traditional block sparse assumption of the multi-band signal is extended to a generalized case viewing the fact that each occupied block may both have zero and nonzero elements. Thus, a Binary Tree-based Block Adaptive Matching Pursuit (BT-BAMP) algorithm is presented at local CR to achieve more accurate detection results. BT-BAMP estimates the signal supports by separating each candidate block into two half smaller sub-blocks iteratively until the final block size is one. At each block size level, adaptive block updating and backtracking based on greedy pursuit are employed to estimate the block sparsity of the multi-band signal, where the block detection results are exchanged among the cognitive radio network (CRN) and merged as a priori information for local iteration. The efficiency and superiority of the proposed approach are validated via simulations.