Invariant detection of OFDM signals with unknown parameters for cognitive radio applications

We propose a computationally efficient spectrum sensing solution for an Orthogonal Frequency Division Multiplexing (OFDM) signal in a frequency selective fading channel with Additive White Gaussian Noise (AWGN). Our assumption is that the data symbols, channel coefficients and the noise variance are all unknown. The nature of the problem leads us to find an invariant detector, the optimum one is Uniformly Most Powerful Invariant (UMPI); our effort shows that this test does not exist, as the final decision statistic depends on some unknown parameters; though, we derive an MPI detector, implanting these parameters, to provide an upper bound for the detection performance. Instead, we develop the Generalized Likelihood Ratio Test (GLRT), substituting the unknown parameters by their Maximum Likelihood (ML) estimates in the Neyman-Pearson likelihood ratio. Furtheremore, we propose a computationally efficient implementation of the resulting detector. Simulation results show a slight decrease in efficiency while gaining so much computational complexity improvement.