A Digital Predistortion Technique Based on a NARX Network to Linearize GaN Class F Power Amplifiers (poster)

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

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Date

2014-08

Author

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

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URI

https://hdl.handle.net/20.500.12032/71099

Description

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.

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