Synergistic Spectrum Sensing: MME and Energy Double Thresholding Enhanced byHybrid Whale-Chimp Algorithm

Abstract

Spectrum sensing is necessary for cognitive radio networks to locate usable frequency bands without upsetting core users. To enhance energy detection, the adaptive double-threshold method was developed. It constantly alters higher and lower thresholds based on the SNR of cognitive nodes. We present a unique approach in this research to get the weighting coefficients required for threshold modification. We introduce the hybrid Whale-Chimp Optimization Algorithm (WCOA) for effective threshold adjustments, ensuring their stability within an appropriate range and reduced sensitivity to minor fluctuations in weighting coefficients. Through the integration of the double-threshold algorithm with a hybrid approach combining Energy and maximum-minimum Eigenvalue (MME), further optimized by the Innovative Hybrid Whale-Chimp Algorithm, our technique addresses the limitations associated with conventional double-threshold energy detection methods, particularly under conditions of low SNR. Collaborative interactions among cognitive nodes enhance their detection precision, leading to swifter spectrum sensing and heightened detection probabilities. The proposed method presents a potentially efficient approach to spectrum sensing within cognitive radio networks, all while preserving the primary users' integrity.