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Blind detection of cyclostationary features in the context of Cognitive Radio

The methods of dynamic access to spectrum developed in Cognitive Radio require efficient and robust spectrum detectors. Most of these detectors suffer from four main limits: the computational cost required for the detection procedure; the need of prior knowledge of Primary User's (PU) signal features; the poor performances obtained in low SNR (Signal to Noise Ratio) environment; finding an optimal detection threshold is a crucial issue. In this paper, we propose a blind detection method based on the cyclostationary features of communication signals to overcome the four limits of spectrum sensors. In order to reduce the computational cost, the FFT Accumulation Method has been adjusted to estimate the cyclic spectrum of the intercepted signal. Then, the spectrum coherence principle is used to catch the periodicity hidden in the cyclic autocorrelation function of this signal. The hidden periodicity is revealed by the crest factor of the cyclic domain profile. The detection of PU's signal is achieved by comparing the embedded periodicity level with a predetermined threshold related to the crest factor. This threshold varies randomly dependent on the SNR. Then, we have modelized the distribution law of the threshold in order to select the optimal value. Using the crest factor of the cyclic domain profile as a detection criterion has permitted to develop a spectrum sensor which is able to work in a blind context. Simulation results corroborate the efficiency and robustness of the proposed detector compared with the classical Energy Detector.