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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.