[en] Spectrum Sensing (SS) is one of the fundamental mechanisms required by a Cognitive Radio (CR). Among several SS techniques, cyclostationary feature detection is considered as an important technique due to its robustness against noise variance uncertainty and its capability to distinguish among different systems on the basis of their cyclostationary features. However, one of the main limitations of this detector in practical scenarios is its performance degradation in the presence of cyclic frequency mismatch, which mainly arises due to the lack of knowledge about the transmitter clock/oscillator errors at the detector. In this context, this paper proposes a novel solution to address the cyclic frequency mismatch problem utilizing the Slepian basis expansion instead of the widely used Fourier basis expansion. It is shown that the proposed approach captures the deviation in the cyclic frequency caused by the aforementioned
imperfections and hence provides a significant improvement in the sensing performance in the presence of cyclic frequency mismatch.
