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.