Cyclostationary detection of 5G GFDM waveform using time smoothing algorithms in cognitive radio transmission

Cognitive radio is one of the most promising technologies in wireless communications. Spectrum sensing is the technique of detection of unused frequencies in order to achieve the efficient use of bandwidth. 5G is the new mobile generation which can be realized by 2020. GFDM is the waveform candidate for 5G physical layer, GFDM has tail biting cyclic prefix which reduces the out of band radiation. Spectrum sensing is the first step for Cognitive radio, it is the process to identify the vacant spectrum band. Cyclostationary sensing is one of the traditional spectrum sensing technique. It is known with the best performance detection in low SNR. This depends on identifying the signal from the surrounding noise due to the repetitive feature of signal caused by modulation technique or cycle prefix. In this paper, we discuss exploring the cyclostationary feature of GFDM using different time smoothing algorithms and different values of SNR, comparing the execution time of the used algorithms and finally detect the effect of roll off factor on the probability of detection of the signal. Our results show that SSCA is time efficient algorithm when calculating the spectral correlation function of GFDM, it achieves the optimal detection of the signal in low SNR. Moreover, the performance detection is getting better while increasing the roll-off factor of pulse shaping filter used in GFDM.