A performance study of 3 adaptive equalisers in the mobile communication environment

The performance of three equalizer structures are compared in terms of their mean-squared error performance in a simulated mobile communications environment. The equalizers considered are: (a) a Kalman equalizer, which utilizes a least-mean-square (LMS) algorithm as a channel estimator to provide the equalizer with an estimate of the channel impulse response; and (b) a Kalman decision feedback equalizer (DFE) based on the above, but incorporating decision feedback in the structure. Both of these structures provide a means of model order reduction by using the residuals of the LMS to provide information on the unmodeled paths in the communications channel, which is incorporated in the Kalman structure as observation noise. These structures are studied and compared with a conventional decision feedback equalizer (the third equaliser) which is trained by a Godard-Kalman (1974) algorithm with exponential windowing