GaN-based Class-F Power Amplifier for 5G Applications

Bhargava, Gaurav; Rath, Pinak Kumar; Majumdar, Shubhankar

doi:10.1109/mapcon56011.2022.10047615

Cited by 4 publications

(1 citation statement)

References 25 publications

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“…Also, simulation-based PA circuit related to RF PA automation is discussed in related literature. 9 After reviewing previous literature on PA design [10][11][12] and automation, we are motivated to automate the design of class EFJ PA using a physics-informed automation method. In this article, it is proposed for the first time that the PA can be designed and validated with the help of a physics-informed neural network (PINN).…”

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confidence: 99%

Physics‐informed neural network assisted automated design of power amplifier by user defined specifications

Bhargava,

Kumari,

Vadalà

et al. 2024

Int J Numerical Modelling

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This article presents a model that can automatically produce a power amplifier's (PA) design parameters, that is, transmission lines (TLs) dimension, from a dataset of user‐specified design goals like gain, efficiency, linearity, and scattering (S‐) parameters. Based on the applied boundary conditions, a synthetic dataset is generated with the best range of design parameters (W and L). This dataset is utilized for training the physics‐informed neural network (PINN) model with user‐specified design goals as input and design parameters as target to produce the optimum value of W and L as the resultant output. Furthermore, utilizing the obtained dimensions, design, simulation, fabrication, and measurement of a PA are performed to validate our proposed model. The results of large signal measurements of PA are drain efficiency (DE) of 26.9%, power added efficiency (PAE) of 24.7%, output power (Pout) of 30.98 dBm at an input power of 19 dBm, and gain of 12.41 dB at an operating frequency of 1.625 GHz. It has been observed that the design parameters produced by the model have a significant agreement with the validated output. Also, the statistical error analysis is done by calculating the error metrics between the validated output and the actual output of the PA design.

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confidence: 99%

Physics‐informed neural network assisted automated design of power amplifier by user defined specifications

Bhargava,

Kumari,

Vadalà

et al. 2024

Int J Numerical Modelling

Self Cite

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Importing Experimental Results via S2D Model in ADS Tool for Power Amplifier Design

Bhargava,

Majumdar,

Medjdoub

2024

IETE Journal of Research

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In this paper, behavioral modelling of a power amplifier is implemented by using S2P and S2D model setup. By using the standard power amplifier for obtaining the measured results of Scattering (S-parameters and large signal parameters. These parameters are imported into the S2P and S2D model to perform the small signal and large signal analysis at 3 GHz frequency. Then, the simulated results are compared with the measured results to verify the efficient utilization of behavioral models. The novelty of this work deals with the simulation study of the characteristics of a siliconbased driver amplifier which are obtained directly from the measurements. This work can be utilized to determine the influence of the driver amplifier properties on the power amplifier measurements via simulation. At last, the study of relative error performance analysis between measured and simulated results of different parameters is carried out and calculated of NMSE (in %) for S11, S12, S21, S22, gain, pout, 1 dB compression point, ACP, 3 rd harmonics, 4 th harmonics, and 5 th harmonics are 0

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Data-Driven Model-Based Design Approach for Class EFJ Power Amplifier with Analog Predistortion Circuit

Dewangan,

Bhargava,

Borah

et al. 2023

2023 IEEE Microwaves, Antennas, and Propagation Conference (MAPCON)

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GaN-based Class-F Power Amplifier for 5G Applications

Cited by 4 publications

References 25 publications

Physics‐informed neural network assisted automated design of power amplifier by user defined specifications

Physics‐informed neural network assisted automated design of power amplifier by user defined specifications

Importing Experimental Results via S2D Model in ADS Tool for Power Amplifier Design

Data-Driven Model-Based Design Approach for Class EFJ Power Amplifier with Analog Predistortion Circuit

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