Parallel cooperative spectrum sensing for cognitive sensor network

Abstract

Cognitive sensor networking is an emerging wireless technology to efficiently utilize the available radio resources for dense deployment sensor nodes. Spectrum sensing is the key enabling of cognitive radio to detect the unoccupied channels for data transmission. In order to deal with shadowing and multipath fading in sensing channels, cooperative spectrum sensing is designed to increase the reliability of the sensed signal. In this paper, an optimised local decision rule is implemented for the case that the received observations from primary user are possibly correlated due to the sensing channel impairments. As the priority information is unavailable in the real systems, Neyman-Pearson criterion is used as the cost function. Then, a discrete iterative algorithm based on Gauss-Seidel process is applied to optimize the local cognitive user decision rules under a fixed fusion rule. This method with low complexity can minimise the cost function using the golden section search in finite number of iterations. ROC curve is depicted using the achieved probability of detection and false alarm by numerical examples to illustrate the efficiency of the suggested algorithm. Simulation results confirm the superiority of the proposed method comparing to the conventional topologies and decision rules.