Raúl A. Casas

Blind adaptive decision feedback equalization: A class of channels resulting in ill‐convergence from a zero initialization

This paper addresses the ill-convergence of blind adaptive decision feedback equalizers using the decision-directed algorithm under a zero initialization. In particular, the paper identifies analytically a class of channels for which the adaptive equalizer parameters converge to an undesired minimum where incorrect decision estimates can be made under zero noise. This misbehaviour occurs despite the existence of a DFE parameter setting that perfectly equalizes the channel.

Prediction error methods for limit cycle data

Prediction error methods are considered for identification of the forward linear dynamics of nonlinear feedback closed-loop systems which operate in a perturbed stable limit cycle. A model of the signals measured in a neighborhood of the limit cycle is presented and shown to satisfy a quasistationarity property. Quasistationarity is then used to prove that prediction error methods are both convergent and consistent for our data model.

Closed form expression of EMSE for Bussgang equalization with spatio-temporal diversity

Bussgang algorithms are a class of simple stochastic gradient type solutions for blind channel equalization. We investigate the degradation of performance in the source estimation resulting from the stochastic jitter around stationary points of Bussgang algorithms (which correspond to the equalizers of the channel). More precisely, we derive a closed form approximation of the excess mean square error (EMSE) defined as the variance of the jitter which depends on the effects of the channel characteristics, the source distribution and the non-linearity used in the update equation. The analysis is performed in a context of spatiotemporal channel diversity involving a single-input/multiple outputs data model.