OFDM signal sensing for cognitive radios

In this paper, we consider the detection of OFDM signals used for cognitive radios. Based on the special feature - cyclic prefix embedded in OFDM signals, we propose an optimal Neyman-Pearson likelihood test for the signal detection. We have derived rigorously the probability distribution function of the corresponding decision statistics under frequency selective fading channels and obtained the closed forms of the probability of signal detection and the false alarm rate. The optimal threshold is also derived with respect to a given false alarm rate. Finally, simulations have been conducted to illustrate the promising performance of the proposed detection for OFDM signals under multipath fading environments.