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M. Rupp:
"Convergence Properties of Adaptive Equalizer Algorithms";
IEEE Transactions on Signal Processing, 59 (2011), 6; S. 2562 - 2574.

In this paper, we provide a thorough stability analysis of two well known adaptive algorithms for equalization based on a novel least squares reference model that allows to treat the equalizer problem equivalently as system identification problem. While not surprising the adaptive minimum mean-square error (MMSE) equalizer algorithm behaves $l_{2}$-stable for a wide range of step-sizes, the even older zero-forcing (ZF) algorithm however behaves very differently. We prove that the ZF algorithm generally does not belong to the class of robust algorithms but can be convergent in the mean square sense. We furthermore provide conditions on the upper step-size bound to guarantee such mean squares convergence. We specifically show how noise variance of added channel noise and the channel impulse response influences this bound. Simulation examples validate our findings.

Adaptation model , Algorithm design and analysis , Compounds , Convergence , Equalizers , Least squares approximation

http://dx.doi.org/10.1109/TSP.2011.2121905

http://publik.tuwien.ac.at/files/PubDat_197123.pdf

Projektleitung Markus Rupp:
Signal and Information Processing in Science and Engineering - Entwicklungsmethodik