Selective Sampling Based Angular Domain Cyclostationary Feature Detection of ODFM Signals

This paper proposes a novel approach to simplify and improve the Angular Domain Cyclostationary Feature Detection (ADCFD) of OFDM signals for cognitive radio wireless network. The originality is to perform the ADCFD by a selection of CP (Cyclic Prefix) samples to check the non-uniformity of the signal phase when the OFDM signal is present in the spectrum band. The simulation results show that the proposed method improves the performance by increasing the sensitivity of the detector with a gain of about 5 dB in an Additive White Gaussian Noise (AWGN) channel compared to the other methods in the literature. The proposed method reaches the maximum detection probability (100%) from a Signal to Noise Ratio (SNR) equal to -16 dB.