Energy Detection ROC Performance Analysis

This experiment conducts energy detection operations across different Signal-to-Noise Ratio (SNR) levels and generates corresponding performance charts. The implementation typically involves calculating the energy threshold based on noise statistics and comparing it with the received signal energy. Key algorithmic steps include signal preprocessing, noise variance estimation, threshold calculation using statistical methods like Neyman-Pearson criterion, and performance metric computation (Probability of Detection vs. Probability of False Alarm). Through systematic signal detection and graphical visualization, we can quantitatively analyze how SNR variations impact energy detection performance. This study provides crucial insights into fundamental signal processing concepts and establishes a foundation for advanced research in detection theory. The code implementation would likely involve MATLAB functions like periodogram for power spectral density estimation, mean/variance calculations for threshold determination, and ROC curve plotting functions for performance visualization.