Signal Detection Based on BPSK Cyclostationary Autocorrelation Function in Cognitive Radio

In cognitive radio systems, signal detection based on BPSK cyclostationary autocorrelation function represents a fundamental time-domain algorithm that has been extensively cited in numerous research papers. This method enables efficient signal detection and plays a crucial role in wireless communication systems. By computing the cyclostationary autocorrelation function of signals, we can accurately determine the presence or absence of target signals. The implementation typically involves calculating the cyclic autocorrelation function (CAF) using mathematical operations on the received signal samples. Key algorithmic steps include: - Signal preprocessing and sampling at appropriate rates - Computation of the time-domain autocorrelation function - Fourier transform implementation to extract cyclic frequencies - Threshold comparison for detection decision making The reliability and accuracy of this approach have been validated through practical applications, making it widely adopted in the wireless communication domain. The method leverages the inherent periodicity in BPSK modulated signals, which manifests as spectral correlation at specific cyclic frequencies, allowing robust detection even in low signal-to-noise ratio conditions.