Optimized Statistical Beamforming for Cooperative Spectrum Sensing in Cognitive Radio Networks

Ubaid M. Al-Saggaf, Jawwad Ahmad, Mohammed A. Alrefaei and Muhammad Moinuddin ()
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Ubaid M. Al-Saggaf: Electrical and Computer Engineering Department, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Jawwad Ahmad: Faculty of Electrical Engineering, Usman Institute of Technology, UIT University, Karachi 75300, Pakistan
Mohammed A. Alrefaei: Electrical and Computer Engineering Department, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Muhammad Moinuddin: Electrical and Computer Engineering Department, King Abdulaziz University, Jeddah 21589, Saudi Arabia

Mathematics, 2023, vol. 11, issue 16, 1-16

Abstract: 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 p d while constraining the global probability of false alarm p f to below a required level. The simulation results are presented to validate the theoretical findings and to asses the performance of the proposed algorithm.

Keywords: cognitive radio; beamforming; spectrum sensing; indefinite quadratic form; genetic algorithm multi-parent crossover; particle swarm optimization (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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