Optimal and near-optimal spectrum sensing of OFDM signals in AWGN channels

We consider spectrum sensing of OFDM signals in an AWGN channel. For the case of completely unknown noise and signal powers, we derive a GLRT detector based on empirical second-order statistics of the received data. The proposed GLRT detector exploits the non-stationary correlation structure of the OFDM signal and does not require any knowledge of the noise power or the signal power. The GLRT detector is compared to state-of-the-art OFDM signal detectors, and shown to improve the detection performance with 5 dB SNR in relevant cases. For the case of completely known noise power and signal power, we present a brief derivation of the optimal Neyman-Pearson detector from first principles. We compare the optimal detector to the energy detector numerically, and show that the energy detector is near-optimal (within 0.2 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.