We consider spectrum sensing of OFDM signals. The main concerns are the two cases of completely known, or completely unknown, noise power and signal power. For the case of completely known noise power and signal power, we derive the optimal Neyman-Pearson detector from first principles. The optimal detector exploits the inherent correlation of the OFDM signal, incurred by the repetition of data in the cyclic prefix. We compare the optimal detector to the energy detector numerically. We show that the energy detector is near-optimal (within 1 dB SNR) when the noise variance is known. Thus, when the noise power is known, no substantial gain can be achieved by using any other detector than the energy detector.
For the case of completely unknown noise power and signal power, we propose a GLRT detector based on the correlation of the OFDM signal. The proposed detector exploits the known structure of the signal, and does not require any knowledge of the noise power or the signal power. The GLRT detector is compared to other state-of-the-art OFDM detectors, and shown to improve detection performance with 5 dB SNR in relevant cases.