Efficient limited data multi-antenna compressed spectrum sensing exploiting angular sparsity

In this paper, we propose a novel approach for multiple antenna spectrum sensing based on compressed sensing. Our focus is on the angular sparsity of the received signal given an unknown number of primary user source signals impinging upon the antenna array from different Directions Of Arrival (DOA). Given multiple snapshots over a small time period, multiple measurement vectors are available and a joint sparse recovery is performed to estimate the common sparsity profile over the angular domain. In this estimation process, we employ the regularized M-FOCUSS algorithm [1], which is the noisy multiple snapshot extension of the iterative weighted minimumnorm algorithm, called FOCUSS. The contribution of this paper is to take advantage of the sparse primary user DOA estimation within the detection framework of multiple antenna spectrum sensing. In this scope, an accurate sparse reconstruction is not required and a coarse estimation using a reduced number of snapshots is sufficient to decide about the number of present primary users reflected by the angular sparsity order of the received signal. A simulation study shows significant constant false alarm rate performance gain of the proposed approach compared to the conventional maximum to minimum eigenvalue detector especially when the number of PUs increases.