UC San Diego

This thesis proposes a technique for blind modulation classification for spectrum sensingapplications by leveraging second-order cyclostationarity and frequency domain characteristics of signals. A detailed analysis of different cyclostationary features of commonly used digital modulations using the Strip Spectral Correlation Analyzer (SSCA) is carried out. Based on the one-dimensional cyclic features derived from the SSCA estimates, a hierarchical decision tree-based classifier is developed. The algorithm also extracts important signal parameters such as symbol rate. The proposed method follows a pre-processing pipeline similar to a practical spectrum sensing system, and its accuracy is rigorously evaluated with respect to Signal to Noise Ratio and Energy to Noise Ratio of the signals in both simulated and over-the-air data scenarios.