Traditional approaches to channel equalization are based on the inversion of the global (linear or nonlinear) channel response. However, in digital links the complete channel inversion is neither required nor desirable. Since transmitted symbols belong to a discrete alphabet, symbol demodulation can be effectively recasted as a classification problem in the space of received symbols. In this paper a novel neural network for digital equalization is introduced and described. The proposed approach is based on a decision-feedback architecture trained with a complex-valued discriminative learning strategy for the minimization of the classification error. Main features of the resulting neural equalizer are the high rate of convergence with respect to classical neural equalizers and the low degree of complexity. Its effectiveness has been demonstrated through computer simulations for several typical digital transmission channels. (C) 2001 Elsevier Science B.V. All rights reserved.

Complex discriminative learning Bayesian neural equalizer / Mirko, Solazzi; Uncini, Aurelio; DI CLAUDIO, Elio; Parisi, Raffaele. - In: SIGNAL PROCESSING. - ISSN 0165-1684. - STAMPA. - 81:12(2001), pp. 2493-2502. [10.1016/s0165-1684(01)00129-3]

Complex discriminative learning Bayesian neural equalizer

UNCINI, Aurelio;DI CLAUDIO, Elio;PARISI, Raffaele
2001

Abstract

Traditional approaches to channel equalization are based on the inversion of the global (linear or nonlinear) channel response. However, in digital links the complete channel inversion is neither required nor desirable. Since transmitted symbols belong to a discrete alphabet, symbol demodulation can be effectively recasted as a classification problem in the space of received symbols. In this paper a novel neural network for digital equalization is introduced and described. The proposed approach is based on a decision-feedback architecture trained with a complex-valued discriminative learning strategy for the minimization of the classification error. Main features of the resulting neural equalizer are the high rate of convergence with respect to classical neural equalizers and the low degree of complexity. Its effectiveness has been demonstrated through computer simulations for several typical digital transmission channels. (C) 2001 Elsevier Science B.V. All rights reserved.
2001
bayes decision rule; channel equalization; complex neural networks; decision-feedback equalizer; discriminative learning; neural networks
01 Pubblicazione su rivista::01a Articolo in rivista
Complex discriminative learning Bayesian neural equalizer / Mirko, Solazzi; Uncini, Aurelio; DI CLAUDIO, Elio; Parisi, Raffaele. - In: SIGNAL PROCESSING. - ISSN 0165-1684. - STAMPA. - 81:12(2001), pp. 2493-2502. [10.1016/s0165-1684(01)00129-3]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/252849
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