Smart Full-Exploitation of Beamforming Fusion assisted Spectrum Sensing for Cognitive Radio

Chaabane, Sarra Ben; Bouallegue, Kaïs; Belazi, Akram; Kharbech, Sofiane; Bouallègue, Ammar

doi:10.1109/wimob55322.2022.9941584

Cited by 1 publication

(2 citation statements)

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“…Compute p d i using (10) and p f i using (11) ∀ i. Compute σ 2 v i using (18) ∀ i. Compute R z using (17). Compute the eigenvalues {λ l } and {ρ l } of the matrix B defined in (20). Compute p d using ( 21) and (23).…”

Section: Pseudo-code Of the Proposed Schemementioning

confidence: 99%

“…Various SS methods have been developed to find spectral holes when they are present. The most wellknown of these methods are energy detector-based SS [4][5][6][7], waveform matching-based SS [8,9], matched filter-based SS [10,11], autocorrelation-based SS [12], cyclostationaritybased SS [13], eigenvalue-based SS [14], wavelet-based SS [15], covariance-based SS [16,17], and beamforming-based SS [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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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