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Data-Selective Volterra Adaptive Filters

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Abstract

This work proposes the use of data-selective schemes in Volterra adaptive filters. By working only with input data that brings novelty to the system, thus avoiding unnecessary parameter updates, one can reduce drastically the high computational burden associated with the use of Volterra series. Considering a nonlinear system identification setup, results show that the proposed data-selective Volterra filters update in only 7.5% of the iterations, whereas in a nonlinear channel equalization setup the update rate is around 27%, without compromising the performance in terms of mean squared error or bit-error rate.

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Notes

  1. Here the weighting matrix \(\mathbf {G}[k]\) is the same as the one employed in the set-membership case, whose update equation is described in (5).

  2. The delay values were chosen based on prior simulations.

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Authors and Affiliations

  1. Department of Electronics and Computer Engineering (DEL/Poli) and Electrical Engineering Program (PEE/COPPE), Federal University of Rio de Janeiro, Rio de Janeiro, RJ, 21941-972, Brazil

    Felipe B. da Silva & Wallace A. Martins

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  1. Felipe B. da Silva
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  2. Wallace A. Martins
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Correspondence to Felipe B. da Silva.

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da Silva, F.B., Martins, W.A. Data-Selective Volterra Adaptive Filters. Circuits Syst Signal Process 37, 4651–4664 (2018). https://doi.org/10.1007/s00034-018-0765-0

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