Constant Variance Transversal Filtering for Adaptive Channel Equalization


This paper addresses the problem of channel equalization in communications and channel identification in the context of sonar. In both cases, the study comes from the optimal linear filtering theory, taking out the useful information contained in the received data, that used to be noisy. In this sense, the equalization of a channel in analog or digital communications consists of the characterization of the channel behaviour in such a way that it is possible to correct its frequency response and besides, to reduce the degradating effects of the noise that we are going to consider additive, but not necesarily gaussian. The objective in the problem of channel identification is clearly different. In this case we search for a model of the channel and the desired Information remains just in the coefficients of the model. What is really important is that the two refered topics can not be acomplished by the same techniques. For instance, some kind of gradient-based adaptive algorithms performs much better for equalizing than for identifying the channel, that is, the filtering error exhibits a different convergencetime than the filter weights.

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Cite this paper

@inproceedings{Vzquez2016ConstantVT, title={Constant Variance Transversal Filtering for Adaptive Channel Equalization}, author={Gl{\`o}ria V{\'a}zquez and Jordi Girona Salgado}, year={2016} }