Cooperative Spectrum Sensing for Cognitive Heterogeneous Networking Using Iterative Gauss-Seidel Process

Abstract

Heterogeneous networks with dense deployment of small cells can employ cognitive features to efficiently utilize the available spectrum resources. Spectrum sensing is the key enabler for 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 accuracy of the sensed signal. In this paper, an optimized local decision rule is implemented for the case that the received data from primary users are possibly correlated due to the sensing channel impairments. Since the prior 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 minimize the cost using the golden section search method in a finite number of iterations. ROC curves are depicted using the achieved probability of detection and false alarm by numerical examples to illustrate the efficiency of the proposed algorithm. Simulation results also confirm the superiority of the proposed method compared to the conventional topologies and decision rules.