Maximum likelihood frame synchronization for flat fading channels

The author derives the maximum likelihood (ML) decision rule for locating a frame synchronization pattern in data disturbed by flat fading and additive white Gaussian noise. He gives a high signal-to-noise ratio approximation that is simpler to implement and almost reaches the performance of the ML decision rule. The correlation rule is shown to perform catastrophically. Simulation results for the Rayleigh channel show that the new ML decision rule performs about 1 dB better than the Gaussian ML rule. The rules presented require channel state information for each received symbol. In many cases this information is already available or can be obtained with relative ease