Showing papers by "Stephen McLaughlin published in 1989"
23 May 1989
TL;DR: The results indicate that the best performance, in terms of final MSE (mean square error), is offered by the adaptive Kalman DFE structure, thefinal MSE being lower than that achieved by the conventional DFE by some 5-10 dB.
Abstract: 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. >
10 citations
11 Jun 1989
TL;DR: Three equalizer structures are compared in terms of their mean-squared error performance in a simulated mobile communications environment and a conventional decision feedback equalizer is compared.
Abstract: 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. >
5 citations