Blind Detection of Cyclostationary Features in the Context of Cognitive Radio
Résumé
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.