Optimized Statistical Beamforming for Cooperative Spectrum Sensing in Cognitive Radio Networks

In cognitive radio (CR), cooperative spectrum sensing (CSS) employs a fusion of multiple decisions from various secondary user (SU) nodes at central center (FC) to detect spectral holes not utilized by the primary (PU). The energy detector (ED) is well-established technique (SS). However, major challenge in designing an detector-based SS requirement correct knowledge for distribution decision statistics. Usually, Gaussian assumption employed received statistics, which true real practice, particularly with limited number samples. Another big CSS task choosing optimal strategy. To tackle these issues, we have proposed beamforming-assisted ED heuristic-optimized that utilizes more accurate statistics employing characterization indefinite quadratic form (IQF). Two heuristic algorithms, genetic algorithm multi-parent crossover (GA-MPC) and constriction factor particle swarm-based optimization (CF-PSO), are developed design optimum beamforming weights can maximize global probability detection pd while constraining false alarm pf below required level. simulation results presented validate theoretical findings asses performance algorithm.