A channel subspace filtering approach to adaptive equalization of highly dynamic realistic channels

A major challenge while communicating in dynamic channels, such as the underwater acoustic channel, is the large amount of time-varying ISI due to multipath. In realistic channels, the fluctuations between different taps of the sampled channel impulse response are correlated. Traditional least squares algorithms used for adapting channel equalizers do not exploit this correlation structure. A channel subspace post-filtering algorithm is presented that treats the least squares channel estimate as a noisy time series and exploits the channel correlation structure to reduce the channel estimation error. A channel estimate based equalizer structure that uses this improved channel estimate to determine the equalizer coefficients is presented. The improvement in performance of the equalizer is predicted theoretically and demonstrated using both simulation and experimental data.