Spectrum sensing of wideband signals based on cyclostationary detection and compressive sensing

Compressive sensing is a powerful technique used to overcome the problem of high sampling rate when dealing with wideband signal spectrum sensing which leads to high speed analogue to digital convertor (ADC) accompanied with large hardware complexity, high processing time, long duration of signal spectrum acquisition and high consumption power. Cyclostationary based detection with compressive technique will be studied and discussed in this paper. To perform the compressive sensing technique, discrete cosine transform (DCT) is used as sparse representation basis of received signal and Gaussian random matrix as a sensing matrix, and then 𝓁1- norm recovery algorithm is used to recover the original signal. This signal is used with cyclostationary detector. The probability of detection as a function of SNR and the probability of false alarm as a function of SNR with several compression ratios and processing time are used as performance parameters. The effect of the recovery error of reconstruction algorithm is presented as a function of probability of detection. Simulation results show that the performance of the system is maintained even at high compression.