Spectrum Sensing Based on Energy Detection

In this paper, we explore spectrum sensing based on energy detection and investigate its application in practical communication channels. We recognize that multipath propagation and shadowing effects significantly degrade the performance of individual cognitive users' spectrum sensing capabilities. To address these challenges, we propose a cooperative spectrum sensing mechanism where two users collaborate in spectrum detection. This approach involves implementing energy detection algorithms that calculate received signal power thresholds and employ decision fusion techniques to combine sensing results. Through this collaborative framework, we demonstrate improved primary user detection rates, reduced detection time, and enhanced system agility. The implementation typically involves MATLAB or Python code structures that handle signal energy calculation using FFT-based power estimation, threshold determination based on noise variance, and hard/soft decision combining mechanisms. Our simulation experiments validate the design effectiveness, showing comprehensive results that include receiver operating characteristics (ROC) curves, detection probability under varying SNR conditions, and comparative analysis with single-user sensing. The results conclusively demonstrate that cooperative spectrum sensing effectively counters the challenges posed by multipath and shadowing effects in wireless environments.