Cyclostationary Feature Detection Algorithm for Cognitive Radio Spectrum Sensing

This code implements a simulation of the cyclostationary feature detection algorithm for cognitive radio spectrum sensing. The algorithm is designed to detect cyclostationary characteristics in radio frequency spectra, enabling identification of periodic patterns within radio signals. Through sophisticated signal analysis and processing techniques, the algorithm extracts valuable information about cyclic features present in the spectrum. The implementation includes key components such as signal preprocessing, cyclic autocorrelation function calculation, and feature extraction modules. In cognitive radio applications, cyclostationary feature detection algorithms play a crucial role in core technologies like spectrum sensing and dynamic spectrum sharing. Understanding and implementing this algorithm is therefore essential for cognitive radio research and development, particularly for advanced spectrum management systems. The code structure comprises signal input handling, spectral correlation computation, detection threshold determination, and performance evaluation metrics. Implementation details include FFT-based spectral processing, cyclic frequency analysis, and statistical hypothesis testing for feature detection.