Collaborative Spectrum Sensing Based on Energy Detection in Cognitive Radio Networks

This document discusses the application of energy detection-based collaborative spectrum sensing in cognitive radio networks. We examine the relationship between signal-to-noise ratio and detection probability, presenting both theoretical calculations and simulation results for single-node and three-node configurations. The comparison of these values enables performance evaluation of the methodology. From an implementation perspective, the energy detection algorithm typically involves calculating the received signal power over a specific bandwidth and comparing it to a predetermined threshold. For collaborative sensing, decision fusion techniques (such as OR-rule, AND-rule, or majority voting) are implemented to combine individual node decisions. The MATLAB simulation code would include functions for signal generation, noise addition, energy calculation, and probability analysis. We further explore the advantages and limitations of this approach, including its computational simplicity and robustness to unknown signal types versus susceptibility to noise uncertainty. Potential improvement directions discussed include adaptive threshold adjustment mechanisms and advanced fusion strategies. This content aims to enhance understanding and practical application of energy detection-based collaborative spectrum sensing technology in cognitive radio systems.