Analysis and new implementations of periodogram-based spectrum sensing

In this article, we investigate the performance of spectrum sensing in the frequency domain. Classical and modified periodogram are analyzed based on the Neyman-Pearson (NP) criterion in terms of false alarm and detection probabilities. We introduce two novel modified periodogram algorithms and compare their performances with the existing periodogram methods. It is shown that the new modified periodogram algorithms based on the maximum and minimum statistics perform better than classical one. Although, the average-based periodogram (Bartlett´s or Welch´s) outperforms the proposed minimum-based periodogram, it performs worse than the maximum-based periodogram especially at higher signal-to-noise ratio (SNR) and moderate false alarm probability. Moreover, taking advantage of the binary nature of spectrum sensing, we introduce decision fusion for flexible and more robust detection decision making.