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

Automated summary

Cooperative spectrum sensing (CSS) in cognitive radio (CR) utilizes multiple decisions from secondary user (SU) nodes to detect unused spectral holes. The energy detector (ED) is commonly used for spectrum sensing, but the challenge lies in having accurate knowledge of decision statistics distribution. Another challenge is choosing the optimal fusion strategy in CSS. To address these issues, we propose a beamforming-assisted ED with heuristic-optimized CSS using the characterization of the indefinite quadratic form (IQF). Genetic algorithm with multi-parent crossover (GA-MPC) and constriction factor particle swarm-based optimization (CF-PSO) algorithms are developed to design optimum beamforming and fusion weights for maximum detection probability while constraining false alarm probability.