A novel adaptive equaliser for nonstationary communication channels

The performance of a Kalman decision-feedback equalizer (DFE) that uses a channel estimator based on a least-mean-squares (LMS) algorithm is studied for a variety of stationary and nonstationary communications channels. This structure provides a means of model order reduction by using the residuals of the LMS to provide information on the unmodeled paths in the communication channel, which is then incorporated into the Kalman DFE structure as observation noise. The structure is compared with a conventional DFE that is trained by a Godard-Kalman algorithm with exponential windowing and adaptive Kalman structure previously reported (B. Mulgrew and C.F.N. Cowan, 1987). The results indicate that the best performance, in terms of final MSE (mean square error), is offered by the adaptive Kalman DFE structure, the final MSE being lower than that achieved by the conventional DFE by some 5-10 dB