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