@inproceedings{9ddb6670aa8d4076a2e670168b3e197e,
title = "Nonlinear turbo equalization using context trees",
abstract = "In this paper, we study adaptive nonlinear turbo equalization to model the nonlinear dependency of a linear minimum mean square error (MMSE) equalizer on soft information from the decoder. To accomplish this, we introduce piecewise linear models based on context trees that can adaptively choose both the partition regions as well as the equalizer coefficients in each region independently, with the computational complexity of a single adaptive linear equalizer. This approach is guaranteed to asymptotically achieve the performance of the best piecewise linear equalizer that can choose both its regions as well as its filter parameters based on observing the whole data sequence in advance.",
keywords = "Turbo equalization, context trees, decision feedback, nonlinear equalization",
author = "Nargiz Kalantarova and Kyeongyeon Kim and Kozat, {Suleyman S.} and Singer, {Andrew C.}",
year = "2011",
doi = "10.1109/ITA.2011.5743608",
language = "English (US)",
isbn = "9781457703614",
series = "2011 Information Theory and Applications Workshop, ITA 2011 - Conference Proceedings",
pages = "378--382",
booktitle = "2011 Information Theory and Applications Workshop, ITA 2011 - Conference Proceedings",
note = "2011 Information Theory and Applications Workshop, ITA 2011 ; Conference date: 06-02-2011 Through 11-02-2011",
}