Efficient cooperative compressive spectrum sensing by identifying multi-candidate and exploiting deterministic matrix

Abstract

Cooperative spectrum sensing exploits the spatial diversity to improve the detection of occupied channels in cognitive radio networks (CRNs). Cooperative compressive spectrum sensing (CCSS) utilizing the sparsity of channel occupancy further improves the efficiency by reducing the number of reports without degrading detection performance. In this paper, we firstly and mainly propose the referred multi-candidate orthogonal matrix matching pursuit (MOMMP) algorithms to efficiently and effectively detect occupied channels at fusion center (FC), where multi-candidate identification and orthogonal projection are utilized to respectively reduce the number of required iterations and improve the probability of exact identification. Secondly, two common but different approaches based on threshold and Gaussian distribution are introduced to realize the multi-candidate identification. Moreover, to improve the detection accuracy and energy efficiency, we propose the matrix construction based on shrinkage and gradient descent (MCSGD) algorithm to provide a deterministic filter coefficient matrix of low t-average coherence. Finally, several numerical simulations validate that our proposals provide satisfactory performance with higher probability of detection, lower probability of false alarm and less detection time.

A cooperative construction method for the measurement matrix and sensing dictionary used in compression sensing

“…The above method is used to update Φ so as to gradually reduce cost function C and finally converge to a local optimal value. The convergence of the above method is proved in [16,17]. The paper above proves that the algorithm performs to the maximal iterations and gets the convergency value when β takes a minimum of 0.001.…”