Behavioral Modeling of GaN Power Amplifiers Using Long Short-Term Memory Networks

被引：0

作者：

Chen, Peng ^{[1
]}

Alsahali, Sattam ^{[1
]}

Alt, Alexander ^{[1
]}

Lees, Jonathan ^{[1
]}

Tasker, Paul J. ^{[1
]}

机构：

[1] Cardiff Univ, Sch Engn, Cardiff CF24 3AA, Wales

来源：

INTERNATIONAL WORKSHOP ON INTEGRATED NONLINEAR MICROWAVE AND MILLIMETRE-WAVE CIRCUITS (INMMIC) | 2018年

基金：

英国工程与自然科学研究理事会;

关键词：

Artificial neural networks; behavioral modeling; Doherty power amplifier; long short-term memory;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper presents the formulation of a behavioral model for a gallium nitride (GaN) Doherty power amplifier (DPA) using long short-term memory (LSTM) networks. Implemented in TensorFlow, LSTM networks can construct the dynamic behavior with memory effects by learning the useful patterns in the time domain. The behavioral model is built using the measured in-phase and quadrature (I/Q) data of the DPA, under excitation by a 20-MHz LTE signal. A comparative study indicates that the LSTM model is capable of accurately capturing the AM/AM and AM/PM characteristics of the DPA, as well as achieving competitive accuracy when compared to Volterra-based models.

引用

页数：3

共 8 条

[1]

Goodfellow I, 2016, ADAPT COMPUT MACH LE, P1

[2] A comparative analysis of behavioral models for RF power amplifiers [J].