Traditional equalizers try to invert the global, linear or non-linear, channel response. However, in digital links, where transmitted symbols belong to a discrete alphabet, the complete channel inversion is neither required, nor desirable. Actually, symbol demodulation can be recasted as a classification problem in the received symbol space. Following this approach, in recent years, neural networks have been used as demodulators. In this paper, we propose a neural architecture, which resorts to a somewhat intermediate approach between the channel inversion and the Bayesian classification. A complex-valued discriminative learning, which attempts to minimize the error risk, is applied to a non-linear decision-feedback network, resulting in fast convergence and low degree of complexity.

Complex Discriminative Learning Bayesian Neural Equalizer / Solazzi, M; Uncini, Aurelio; DI CLAUDIO, Elio; Parisi, Raffaele. - STAMPA. - 5:(1999), pp. 343-346. (Intervento presentato al convegno ISCAS 1999 tenutosi a Orlando, FL, USA nel 30 May-02 Jun 1999) [10.1109/ISCAS.1999.777579].

Complex Discriminative Learning Bayesian Neural Equalizer

UNCINI, Aurelio;DI CLAUDIO, Elio;PARISI, Raffaele
1999

Abstract

Traditional equalizers try to invert the global, linear or non-linear, channel response. However, in digital links, where transmitted symbols belong to a discrete alphabet, the complete channel inversion is neither required, nor desirable. Actually, symbol demodulation can be recasted as a classification problem in the received symbol space. Following this approach, in recent years, neural networks have been used as demodulators. In this paper, we propose a neural architecture, which resorts to a somewhat intermediate approach between the channel inversion and the Bayesian classification. A complex-valued discriminative learning, which attempts to minimize the error risk, is applied to a non-linear decision-feedback network, resulting in fast convergence and low degree of complexity.
1999
0780354710
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/244027
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