Genetic Algorithm with Multi-Parent Crossover for cooperative spectrum sensing

Alrefaei, Mohammed A.; Shami, Tareq M.; El‐Saleh, Ayman A.

doi:10.1109/tafgen.2015.7289568

Cited by 3 publications

(2 citation statements)

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“…One of the critical issues in obtaining the optimal fusion weights and beamforming weights is that the objective function of the optimization task is usually highly nonlinear and non-convex, which limits the use of classical optimization techniques. Thus, many heuristic optimization strategies have been employed for the SS in CRN [36][37][38][39][40][41][42]. In [36], genetic algorithm-assisted optimization for finding the optimal fusion combining is proposed.…”

Section: Introductionmentioning

confidence: 99%

“…In [36], genetic algorithm-assisted optimization for finding the optimal fusion combining is proposed. In [38], a modified genetic algorithm (GA) was proposed for SS which employs two additional constraints to limit the number of GA solutions and to reduce the size of the search space, which is termed as genetic algorithm-multi parent crossover (GA-MPC). Another method called cross-entropy has been successfully employed for the SS in [39].…”

Section: Introductionmentioning

confidence: 99%

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Optimized Statistical Beamforming for Cooperative Spectrum Sensing in Cognitive Radio Networks

Al‐Saggaf

Jafarian²,

Alrefaei

et al. 2023

Mathematics

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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.

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