Modeling of a high gain two stage pHEMT LNA using ANN with Bayesian regularization algorithm

This paper presents novel way to achieve fast and accurate Artificial Neural Network (ANN) modeling of Radio Frequency (RF) front end Low Noise Amplifier (LNA). Multilayer perceptron neural network is implemented to model high gain, ultra-low noise pseudomorhic High Electron Mobility Transistor (pHE...

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Bibliographic Details
Published in:Wireless networks Vol. 30; no. 4; pp. 2329 - 2342
Main Authors: Thangaraj, Vignesh, Elangeswaran, Srie Vidhya Janani, Subburaman, Bhuvaneshwari, Kulkarni, Jayshri
Format: Journal Article
Language:English
Published: New York Springer US 01.05.2024
Springer Nature B.V
Subjects:
Algorithms
Amplifiers
Artificial neural networks
Bandwidths
Bayesian analysis
Communications Engineering
Computer Communication Networks
Design
Electrical Engineering
Engineering
Feedback
Global positioning systems
GPS
High electron mobility transistors
High gain
IT in Business
Low noise
Modelling
Multilayer perceptrons
Networks
Neural networks
Neurons
Optimization techniques
Original Paper
Receivers & amplifiers
Regularization
Satellite communications
Semiconductor devices
Simulation
Wireless networks
pHEMT
ANN
Bayesian regularization
LNA
ISSN:1022-0038, 1572-8196
Online Access:Get full text
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Summary:This paper presents novel way to achieve fast and accurate Artificial Neural Network (ANN) modeling of Radio Frequency (RF) front end Low Noise Amplifier (LNA). Multilayer perceptron neural network is implemented to model high gain, ultra-low noise pseudomorhic High Electron Mobility Transistor (pHEMT) based LNA. Datasets are developed for input and output that is obtained from the Electro-Magnetic simulator. Different neural networks such as PatternNet, FitNet, and CascadeForwardNet with different algorithms are trained, tested, and compared to learn the behavior of the amplifier, and the best model is analyzed. Neural networks are modeled for LNA S-parameters and NF. It is observed that greater than 99% accuracy is achieved for PatternNet Bayesian regularization algorithm with less number of hidden layers and also it is found that the simulation results are almost similar to the developed ANN.
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ISSN:1022-0038
1572-8196
DOI:10.1007/s11276-024-03654-z