Spectrum Sensing Algorithm Using Cyclostationary Feature Detection

In cognitive radio applications, spectrum sensing algorithms utilizing cyclostationary feature detection can significantly enhance signal detection accuracy. This algorithm performs cyclostationary analysis on signals to better identify their spectral characteristics. The MATLAB implementation includes key functions for computing cyclic autocorrelation and spectral correlation density, which form the core of cyclostationary detection. To further validate the algorithm's effectiveness, we developed a comprehensive MATLAB simulation program that models various real-world signal environments, including different modulation schemes and noise conditions. The simulation incorporates critical components such as signal generation with multiple modulation types (QPSK, BPSK, QAM), additive white Gaussian noise (AWGN) channels, and cyclostationary feature extraction modules. This approach enables more realistic evaluation of the algorithm's performance metrics, including detection probability, false alarm rate, and computational complexity. Through in-depth research and simulation validation of this algorithm, we can better understand its advantages and limitations, providing strong support for its practical implementation in real-world cognitive radio systems. The code structure includes modular design for easy parameter adjustment and performance comparison with conventional energy detection methods.