Cyclic Autocorrelation Based Spectrum Sensing in Colored Gaussian Noise

Provided by: Institute of Electrical & Electronic Engineers
Topic: Mobility
Format: PDF
Detection of cyclostationary Primary User (PU) signals in colored Gaussian noise for cognitive radio systems is considered based on looking for a cycle frequency at a particular time lag in the Cyclic Autocorrelation Function (CAF) of the noisy PU signal. The authors explicitly exploit the knowledge that under the null hypothesis of PU signal absent, the measurements originate from colored Gaussian noise with possibly unknown correlation function. They consider both single and multiple antenna receivers. A performance analysis of the proposed detector is carried out. Supporting simulation examples are provided using an OFDM PU signal and they show that their proposed approaches are computationally much cheaper than the Dandawate-Giannakis and related approaches while having quite similar detection performance for a given false alarm rate.

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