Adaptive Threshold Optimization for Energy Detection-Based Spectrum Sensing in Cognitive Radio Networks Using Swarm Intelligence
This study demonstrates that swarm intelligence-based adaptive threshold optimization, specifically comparing Artificial Bee Colony and Firefly Algorithms, significantly enhances spectrum sensing performance in Cognitive Radio Networks by improving detection probabilities and balancing false alarm rates compared to conventional fixed-threshold energy detection.