A Digital Predistortion Technique Based on a NARX Network to Linearize GaN Class F Power Amplifiers (poster)
Cargando...
Fecha
2014-08
Autores
Aguilar-Lobo, Lina M.
Reynoso-Hernandez
García-Osorio, Alberto
Loo-Yau, José R.
Ortega-Cisneros, Susana
Moreno, Pablo
Rayas-Sánchez, José E.
Reynoso-Hernández, Apolinar
Título de la revista
ISSN de la revista
Título del volumen
Editor
IEEE
Resumen
Descripción
This work presents a novel Digital Predistortion (DPD) scheme based on a NARX network, suitable for linearizing power amplifiers (PAs). The NARX network is a Recurrent Neural Network (RNN) with embedded memory that allows efficient modeling of nonlinear systems. Its neural architecture is very effective to model long term dependencies, such as the typical memory effects of PAs. To demonstrate the feasibility of the NARX network as a DPD system, a GaN class F PA with two LTE signals with 5 MHz of bandwidth is used. Experimental results show a distortion correction better than 10 dB.
Palabras clave
PA Linearization, Long-term Memory Effects, NARX Network, Recurrent Neural Networks, Nonlinear Systems, Digital Predistortion
Citación
L.M. Aguilar-Lobo, A. Garcia-Osorio, J.R. Loo-Yau, S. Ortega-Cisneros, P. Moreno, J.E. Rayas-Sánchez, and A. Reynoso-Hernández, “A digital predistortion technique based on a NARX network to linearize GaN class F power amplifiers,” in IEEE Int. Midwest Symp. Circuits Syst., College Station, TX, Aug. 2014, poster.