A digital predistortion technique based on a NARX network to linearize GaN class F power amplifiers
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IEEE International Midwest Symposium on Circuits and Systems
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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
Digital Predistortion, Power Amplifiers, Artificial Neural Networks
Citación
L. M. Aguilar-Lobo, Reynoso-Hernández, A.; A. Garcia-Osorio, J. R. Loo-Yau, S. Ortega-Cisneros, P. Moreno, J. E. Rayas-Sánchez, “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, pp. 717-720. (ISSN: 1548-3746, P-ISBN: 978-1-4799-4134-6, INSPEC: 14631538, DOI: 10.1109/MWSCAS.2014.6908515).