A cost efficient and flexible cyclostationary feature detector based on Sliding Discrete Fourier Transform for cognitive spectrum sensing

Cyclostationary feature (CSF) detection plays an important role in spectrum sensing for cognitive radio systems, since it has low requirements on a-priori knowledge about the primary user signals and high robustness to noise and interferences. Existing CSF detecting technologies depend on full-size Fast Fourier Transform (FFT), which leads to high implementation cost in case of high carrier frequency and high spectral resolution. Here in this work, we introduce Sliding Discrete Fourier Transform (SDFT) to reduce the computational complexity of CSF detection while enhancing its performance simultaneously.