Differential Energy-Driven Adaptive Dual-Threshold Collaborative Spectrum Sensing Algorithm

In emergency communication scenarios, ensuring highly reliable and low-latency network transmission is crucial. Efficient spectrum utilization becomes paramount in such situations, necessitating advanced spectrum sensing techniques. Cognitive radio systems rely heavily on spectrum sensing algorithms to detect spectrum occupancy, signal strength, and optimize spectrum usage for improved transmission efficiency. Among various spectrum sensing algorithms, the energy detection algorithm stands out but suffers from performance degradation under noise uncertainty and low signal-to-noise ratio. To address these challenges, we introduce an innovative adaptive dual-threshold collaborative spectrum sensing algorithm based on differential energy. Simulations and experiments demonstrate that our proposed algorithm significantly enhances detection efficiency, especially in low SNR environments, and effectively handles double threshold confusion. This advancement contributes to more robust and dependable spectrum sensing in critical communication systems.