Cognitive Radio Spectrum Sensing Using Cyclostationary Spectral Estimation

I have developed a MATLAB program for cognitive radio spectrum sensing utilizing cyclostationary spectral estimation techniques. This implementation employs advanced signal processing algorithms to detect and analyze radio spectrum characteristics through cyclostationary properties. The core algorithm computes spectral correlation functions to identify periodic patterns in signal statistics, which enables robust spectrum sensing even in low signal-to-noise ratio conditions. The program architecture includes key modules for signal preprocessing, feature extraction using cyclostationary analysis, and decision-making logic for spectrum occupancy detection. Major functions implement cyclic autocorrelation calculations and spectral coherence analysis through FFT-based processing chains. The system demonstrates how cognitive radio technology can intelligently optimize spectrum utilization by dynamically adapting to radio environment conditions. This approach combines mathematical frameworks for cyclostationary signal analysis with practical implementation considerations for real-time spectrum monitoring. The MATLAB code effectively bridges theoretical concepts with executable spectrum sensing solutions, providing a foundation for developing adaptive wireless communication systems that enhance spectral efficiency and communication performance.