Channel-estimation-based adaptive equalization of underwater acoustic signals

To reduce computational complexity of signal processing and improve performance of data detection, receiver structures that are matched to the physical channel characteristics are investigated. A decision-feedback equalizer is designed which relies on an adaptive channel estimator to compute its parameters. The channel estimate is reduced in size by selecting only the significant components, whose delay span is often much shorter than the multipath spread of the channel. This estimate is used to cancel the post-cursor ISI prior to linear equalization. Optimal coefficient selection (sparsing) is performed by truncation in magnitude. The advantages of this approach are reduction in the number of receiver parameters, optimal implementation of sparse feedback, and efficient parallel implementation of adaptive algorithms for the multichannel pre-combiner, the fractionally-spaced channel estimators and the short feedforward equalizer filters. The receiver algorithm is demonstrated using real data transmitted at 10 kbps over 3 km in shallow water