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Behavior Modeling and Digital Predistortion of Mismatched Wireless Transmitters using Convolutional Neural Networks

Publication Type : Journal Article

Publisher : IEEE

Source : IEEE Trans. Circuits Syst. II Exp. Briefs, vol. 70, no. 1, pp. 336-340

Url : https://ieeexplore.ieee.org/document/9895157

Campus : Bengaluru

School : School of Engineering

Department : Electronics and Communication

Year : 2023

Abstract : In modern wireless compact transmitters, Power Amplifier (PA) behavior is considerably affected by the impedance mismatch between the PA’s output and the antenna’s input. This PA’s output mismatch results in a reflection at the PA–antenna interface. In this brief, reflection-aware PA modeling and digital predistortion (DPD) techniques are proposed to mitigate the negative impact of this mismatch on the forward and reverse models of the PA. An Augmented Convolutional neural network model (Γ ACNN) is proposed to linearize a Doherty PA under different values of the output mismatch using a single set of coefficients. The developed DPD shows robust performance metrics like normalized mean square error (NMSE), and adjacent channel power ratio (ACPR) under diverse complex output mismatch levels.

Cite this Research Publication : P. Jaraut et al., “Behavior Modeling and Digital Predistortion of Mismatched Wireless Transmitters using Convolutional Neural Networks,” IEEE Trans. Circuits Syst. II Exp. Briefs, vol. 70, no. 1, pp. 336-340

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