Robust decision feedback equalizer design via the solution of a regularized least squares problem

This paper¹ presents a method to estimate a Decision Feedback Equalizer (DFE) directly from training data, which is robust w.r.t. time-variations in the communication channel. It is based on the indirect method proposed in [15], where the time variations in the channel are modeled as a probabilistic uncertainty. The robust DFE optimizes the performance by minimizing the mean squared error averaged over the distribution of the uncertainty in the channel. We show, that the robust DFE design problem can be solved by a regularized least squares problem. The main advantage of this direct method over [15] is, that no longer a spectral factorization in addition to a least squares problem is necessary. Another advantage is, that a model of the (average) channel and the noise color are not necessary anymore.