ROC Performance Curve of Energy Detection Algorithm under AWGN Channel

In AWGN (Additive White Gaussian Noise) channels, the energy detection algorithm serves as a fundamental wireless communication method. This approach detects the presence of target signals by comparing received signal energy against a predefined threshold. To comprehensively evaluate the algorithm's performance, we utilize Receiver Operating Characteristic (ROC) curves, which graphically represent the detector's efficacy with false positive rate (FPR) on the x-axis and true positive rate (TPR) on the y-axis. From an implementation perspective, the algorithm typically involves: 1. Calculating signal energy over a observation window using squared magnitude operations 2. Setting detection thresholds based on noise statistics and desired false alarm probability 3. Generating ROC curves by sweeping threshold values across different SNR conditions The ROC curve visualization enables quantitative analysis of detection performance under varying signal-to-noise ratios (SNR), facilitating algorithm optimization and parameter adjustment. Key MATLAB functions for implementation may include awgn() for channel modeling, periodogram() for energy calculation, and perfcurve() for ROC generation.