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 50 条

[21] Short-term power load forecasting using integrated methods based on long short-term memory
WenJie Zhang
Jian Qin
Feng Mei
JunJie Fu
Bo Dai
WenWu Yu
Science China Technological Sciences, 2020, 63 : 614 - 624
[22] Long Short-Term Memory Neural Networks for Modeling Nonlinear Electronic Components
Moradi, Mahvash A.
Sadrossadat, Sayed Alireza
Derhami, Vali
IEEE TRANSACTIONS ON COMPONENTS PACKAGING AND MANUFACTURING TECHNOLOGY, 2021, 11 (05): : 840 - 847
[23] Improvements on Long Term Memory Modeling in Power Amplifiers.
Ngoya, Edouard
Quindroit, Christophe
Nebus, Jean-Michel
2009 IEEE/MTT-S INTERNATIONAL MICROWAVE SYMPOSIUM, VOLS 1-3, 2009, : 1357 - 1360
[24] Subclinical tremor differentiation using long short-term memory networks
Nanayakkara, Gerard Ruchin Randil
Chan, Ping Yi
PHYSICAL AND ENGINEERING SCIENCES IN MEDICINE, 2025,
[25] Forecast Customer Flow using Long Short-Term Memory Networks
Yin, Zongming
Zhu, Junzhang
Zhang, Xiaofeng
2017 INTERNATIONAL CONFERENCE ON SECURITY, PATTERN ANALYSIS, AND CYBERNETICS (SPAC), 2017, : 61 - 66
[26] Modular Multitarget Tracking Using Long Short-Term Memory Networks
Verma, Rishabh
Rajesh, R.
Easwaran, M. S.
JOURNAL OF AEROSPACE INFORMATION SYSTEMS, 2021, 18 (10): : 751 - 754
[27] Using long short-term memory networks for river flow prediction
Xu, Wei
Jiang, Yanan
Zhang, Xiaoli
Li, Yi
Zhang, Run
Fu, Guangtao
HYDROLOGY RESEARCH, 2020, 51 (06): : 1358 - 1376
[28] ICU Mortality Prediction Using Long Short-Term Memory Networks
Mili, Manel
Kerkeni, Asma
Ben Abdallah, Asma
Bedoui, Mohamed Hedi
INTELLIGENT DATA ENGINEERING AND AUTOMATED LEARNING - IDEAL 2022, 2022, 13756 : 242 - 251
[29] NONLINEAR BEHAVIORAL OF GaN DOHERTY POWER AMPLIFIERS USING NEURAL MODELING
Liu, Haiwen
MICROWAVE AND OPTICAL TECHNOLOGY LETTERS, 2010, 52 (02) : 307 - 309
[30] Descriptor Free QSAR Modeling Using Deep Learning With Long Short-Term Memory Neural Networks
Chakravarti, Suman K.
Alla, Sai Radha Mani
FRONTIERS IN ARTIFICIAL INTELLIGENCE, 2019, 2

← 1 2 3 4 5 →