Powerful Goodness-of-Fit Test for Spectrum Sensing With Noise Uncertainty

The variability in estimating the noise variance can considerably diminish the effectiveness of the energy detection (ED). This study analyzes the performance of a newly introduced goodness-of-fit test called the modified Anderson–Darling (MAD) test, which shows improved statistical power when noise uncertainty is present. We derive and empirically validate the analytical formulations for the theoretical performance of the MAD regarding false alarm and detection probabilities. Additionally, we compare our developed method with existing techniques to assess its performance, including ED, generalized ED (GED), and a two-sample likelihood ratio statistic test. The MAD surpasses the investigated methods without the need for prior knowledge of a particular set of noise samples. Our findings indicate that the proposed spectrum sensing technique also results in reduced computational complexity. Moreover, we propose the idea of spectrum sensing based on channel bandwidth rather than detecting by frequency bin, which is more appropriate for enhancing the efficiency of tactical radio band detection in tactical radio communications.