This paper presents a black-box model that can be applied to characterize the nonlinear dynamic behavior of power amplifiers. We show that time-delay feed-forward neural networks can be used to make a large-signal input-output time-domain characterization, and to provide an analytical form to predict the amplifier response to multitone excitations. Furthermore, a new technique to immediately extract Volterra series models from the neural network parameters has been described. An experiment based on a power amplifier, characterized with a two-tone power swept stimulus to extract the behavioral model, validated with spectra measurements, is demonstrated.

Time-domain neural network characterization for dynamic behavioral models of power amplifiers / G., Orengo; P., Colantonio; A., Serino; F., Giannini; Ghione, Giovanni; Pirola, Marco; G., Stegmayer. - STAMPA. - Unico:(2005), pp. 189-192. (Intervento presentato al convegno Gallium Arsenide and Related III-V Compounds 2005 tenutosi a Paris, France nel 3-4 Ottobre 2005).

Time-domain neural network characterization for dynamic behavioral models of power amplifiers

GHIONE, GIOVANNI;PIROLA, Marco;
2005

Abstract

This paper presents a black-box model that can be applied to characterize the nonlinear dynamic behavior of power amplifiers. We show that time-delay feed-forward neural networks can be used to make a large-signal input-output time-domain characterization, and to provide an analytical form to predict the amplifier response to multitone excitations. Furthermore, a new technique to immediately extract Volterra series models from the neural network parameters has been described. An experiment based on a power amplifier, characterized with a two-tone power swept stimulus to extract the behavioral model, validated with spectra measurements, is demonstrated.
2005
9782960055108
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/1718934
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