Code-aided ML joint delay estimation and frame synchronization

We present a novel maximum-likelihood (ML) algorithm for joint, delay estimation and frame synchronization. The algorithm operates on coded signals and exploits the code properties by accepting soft information from the MAP decoder. Issues of convergence are addressed and we show how computational complexity may be reduced without any performance degradation. Simulation results are presented for convolutional and turbo codes, and are compared to performance results of conventional algorithms both in terms of mean square estimation error (MSEE) and BER. We show that code-aided delay estimation always improves the MSEE, but not necessarily the BER. On the other hand, code-aided frame synchronization is mandatory, in order to avoid either significant BER degradations or the need for very long pilot sequences.