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ANN modeling for design of a matched low noise pHEMT amplifier for mobile application

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Abstract

For high quality performance, future efficient wireless communication systems require a Broadband Amplifier in the frequency range under consideration. When such an amplifier is plugged into the measuring path it would enable the system to perceive even the weakest of signals. To achieve this, a new Scattering-parameter model that is valid for a wide frequency range has been developed for microwave analysis of a pseudomorphic high electron mobility transistors (pHEMT). The developed neural network model is used for designing a pHEMT power amplifier. The calculated S-parameters, gain and minimum noise figure from the artificial neural networks (ANN) model are the parameters used to design the low noise pHEMT power amplifier. The various gains so obtained from the S-parameters have been plotted with the frequency and it was found to yield a close fit to the simulated model. Neural network training has been done using Levenberg-Marqaurdt back propagation algorithm implemented in ANN toolbox of MATLAB software. All the results have been compared with the experimental data that showed a close agreement and validated our model. The calculated S-parameters, gain and minimum noise figure from the ANN model are the parameters used to design a stabilized and matched LNA.

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Acknowledgements

I sincerely thank UNIVERSITY SCHOOL OF ENGINEERING & TECHNOLOGY, Guru Gobind Singh Indraprastha University, New Delhi for providing the opportunity and guidance for research work.

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Authors and Affiliations

  1. Department of Electronics & Communication Engg., Ajay Kumar Garg Engineering College, 27th Km Stone, Delhi-Hapur Bypass Road, Ghaziabad, 201009, India

    P. K. Chopra

  2. Devas Multimedia, Bangalore, India

    M. G. Chandrasekhar

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  1. P. K. Chopra
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  2. M. G. Chandrasekhar
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Correspondence to P. K. Chopra.

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Chopra, P.K., Chandrasekhar, M.G. ANN modeling for design of a matched low noise pHEMT amplifier for mobile application. J Comput Electron 12, 743–751 (2013). https://doi.org/10.1007/s10825-013-0473-8

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  • DOI: https://doi.org/10.1007/s10825-013-0473-8

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