Cognitive Radio Spectrum Sensing using Hybrid MME and Energy Double Thresholding Optimized with Weighted Chimp Optimization Algorithm
Keywords:
Chimp Optimization Algorithm, Cognitive Radio, Energy Detection, Maximum-Minimum Eigenvalue, Spectrum SensingAbstract
The detection of free frequency bands for use by cognitive radio networks without disrupting primary users is essential, making spectrum sensing a crucial technology. The adaptive double-threshold technique modifies the upper and lower thresholds for energy detection, depending on the cognitive nodes' SNR. To calculate the thresholds' weighting coefficient, the SNR of all cognitive nodes in the network is considered. This paper proposes the WCOA based approach for weighting coefficients calculation, which is used to adjust the upper and lower thresholds accordingly. Specifically, when multiplying the weighting coefficient of the upper threshold by a scaling factor to obtain the new upper threshold, and further multiply the weighting coefficient of the lower threshold by another scaling factor to obtain the new lower threshold. The scaling factors are used to ensure that the new thresholds are within a reasonable range and to prevent them from being too sensitive to small changes in the weighting coefficients. The suggested double-threshold algorithm based on a hybrid of Energy and maximum-minimum Eigenvalue (MME), further enhanced with the Weighted Chimp algorithm (WCOA), can efficiently solve the issue of inadequate detection performance encountered by the conventional double-threshold energy detection method, especially at low SNR. By collaborating, cognitive nodes can enhance their detection accuracy, resulting in a shorter spectrum sensing period and a higher probability of detection.
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