A Hybrid Linear/Nonlinear Approach to Channel Equalization Problems
Abstract
Channel equalization problem is an important problem in high-speed communications. The sequences of symbols transmitted are distorted by neighboring symbols. Traditionally, the channel equalization problem is considered as a channel-inversion operation. One problem of this approach is that there is no direct correspondence between error proba(cid:173) bility and residual error produced by the channel inversion operation. In this paper, the optimal equalizer design is formulated as a classification problem. The optimal classifier can be constructed by Bayes decision rule. In general it is nonlinear. An efficient hybrid linear/nonlinear equalizer approach has been proposed to train the equalizer. The error probability of new linear/nonlinear equalizer has been shown to be bet(cid:173) ter than a linear equalizer in an experimental channel.
Cite
Text
Lee and Pearson. "A Hybrid Linear/Nonlinear Approach to Channel Equalization Problems." Neural Information Processing Systems, 1992.Markdown
[Lee and Pearson. "A Hybrid Linear/Nonlinear Approach to Channel Equalization Problems." Neural Information Processing Systems, 1992.](https://mlanthology.org/neurips/1992/lee1992neurips-hybrid/)BibTeX
@inproceedings{lee1992neurips-hybrid,
title = {{A Hybrid Linear/Nonlinear Approach to Channel Equalization Problems}},
author = {Lee, Wei-Tsih and Pearson, John},
booktitle = {Neural Information Processing Systems},
year = {1992},
pages = {674-681},
url = {https://mlanthology.org/neurips/1992/lee1992neurips-hybrid/}
}