Influence of Neural Network Learning Algorithms on High Power Amplifier (HPA) Predistortion Performance

Hariony Bienvenu Rakotonirina; Marie Emile Randrianandrasana

doi:doi:10.11648/j.ajnna.20251102.15

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Influence of Neural Network Learning Algorithms on High Power Amplifier (HPA) Predistortion Performance

Hariony Bienvenu Rakotonirina^*

, Marie Emile Randrianandrasana

Published in American Journal of Neural Networks and Applications (Volume 11, Issue 2)

Received: 20 October 2025 Accepted: 3 November 2025 Published: 9 December 2025

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Abstract

In this article, we propose a feedforward neural network model designed to approximate the inverse transfer characteristic of a High-Power Amplifier (HPA) in order to linearize it using Digital Predistortion (DPD). This approach is particularly relevant for next-generation communication systems, such as those employing OTFS (Orthogonal Time Frequency Space) modulation envisioned for 6G, whose signals exhibit large amplitude variations that exacerbate amplifier nonlinearities. The performance of predistortion heavily depends on the learning algorithm used to train the neural model. We compared three optimization algorithms: Gradient Descent, Gauss-Newton, and Levenberg-Marquardt. The amplifier is modeled using the Rapp model. The neural network architecture consists of a single input neuron, a hidden layer with ten neurons using the hyperbolic tangent activation function, and a linear output neuron. Training and simulations were carried out in MATLAB, and the performance of each algorithm was evaluated using the Mean Squared Error (MSE) criterion, which quantifies the deviation between the ideal transfer characteristic of a linear amplifier and the characteristic obtained after predistortion. The results clearly show that the Levenberg-Marquardt algorithm provides the best approximation of the predistortion function, achieving an MSE on the order of 4.2708×10^-8, significantly outperforming Gauss-Newton 1.0481×10^-4 and Gradient Descent (0.0272). This superior performance is attributed to Levenberg-Marquardt’s ability to combine the robustness of Gradient Descent with the fast convergence of Gauss-Newton, while avoiding local minimum and issues related to poor synaptic weight initialization.

Published in	American Journal of Neural Networks and Applications (Volume 11, Issue 2)
DOI	10.11648/j.ajnna.20251102.15
Page(s)	88-96
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2025. Published by Science Publishing Group

Keywords

Predistortion, Neural Network, Training Algorithm, Amplifier, Approximation

References

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[3]	Spano, C., Badini, D., Cazzella, L., & Matteucci, M. (2025). Local and Remote Digital Pre-Distortion for 5G Power Amplifiers with Safe Deep Reinforcement Learning. Sensors, 25(19), 6102. https://doi.org/10.3390/s25196102
[4]	Thys, C., Martinez Alonso, R., Lhomel, A., Fellmann, M., Deltimple, N., Rivet, F., & Pollin, S. (2024). Walsh-domain Neural Network for Power Amplifier Behavioral Modelling and Digital Predistortion. IEEE International Symposium on Circuits and Systems (ISCAS), https://doi.org/10.1109/ISCAS58744.2024.10557970
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APA Style

Rakotonirina, H. B., Randrianandrasana, M. E. (2025). Influence of Neural Network Learning Algorithms on High Power Amplifier (HPA) Predistortion Performance. American Journal of Neural Networks and Applications, 11(2), 88-96. https://doi.org/10.11648/j.ajnna.20251102.15

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ACS Style

Rakotonirina, H. B.; Randrianandrasana, M. E. Influence of Neural Network Learning Algorithms on High Power Amplifier (HPA) Predistortion Performance. Am. J. Neural Netw. Appl. 2025, 11(2), 88-96. doi: 10.11648/j.ajnna.20251102.15

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AMA Style

Rakotonirina HB, Randrianandrasana ME. Influence of Neural Network Learning Algorithms on High Power Amplifier (HPA) Predistortion Performance. Am J Neural Netw Appl. 2025;11(2):88-96. doi: 10.11648/j.ajnna.20251102.15

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@article{10.11648/j.ajnna.20251102.15,
  author = {Hariony Bienvenu Rakotonirina and Marie Emile Randrianandrasana},
  title = {Influence of Neural Network Learning Algorithms on High Power Amplifier (HPA) Predistortion Performance},
  journal = {American Journal of Neural Networks and Applications},
  volume = {11},
  number = {2},
  pages = {88-96},
  doi = {10.11648/j.ajnna.20251102.15},
  url = {https://doi.org/10.11648/j.ajnna.20251102.15},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajnna.20251102.15},
  abstract = {In this article, we propose a feedforward neural network model designed to approximate the inverse transfer characteristic of a High-Power Amplifier (HPA) in order to linearize it using Digital Predistortion (DPD). This approach is particularly relevant for next-generation communication systems, such as those employing OTFS (Orthogonal Time Frequency Space) modulation envisioned for 6G, whose signals exhibit large amplitude variations that exacerbate amplifier nonlinearities. The performance of predistortion heavily depends on the learning algorithm used to train the neural model. We compared three optimization algorithms: Gradient Descent, Gauss-Newton, and Levenberg-Marquardt. The amplifier is modeled using the Rapp model. The neural network architecture consists of a single input neuron, a hidden layer with ten neurons using the hyperbolic tangent activation function, and a linear output neuron. Training and simulations were carried out in MATLAB, and the performance of each algorithm was evaluated using the Mean Squared Error (MSE) criterion, which quantifies the deviation between the ideal transfer characteristic of a linear amplifier and the characteristic obtained after predistortion. The results clearly show that the Levenberg-Marquardt algorithm provides the best approximation of the predistortion function, achieving an MSE on the order of 4.2708×10-8, significantly outperforming Gauss-Newton 1.0481×10-4 and Gradient Descent (0.0272). This superior performance is attributed to Levenberg-Marquardt’s ability to combine the robustness of Gradient Descent with the fast convergence of Gauss-Newton, while avoiding local minimum and issues related to poor synaptic weight initialization.},
 year = {2025}
}

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TY  - JOUR
T1  - Influence of Neural Network Learning Algorithms on High Power Amplifier (HPA) Predistortion Performance
AU  - Hariony Bienvenu Rakotonirina
AU  - Marie Emile Randrianandrasana
Y1  - 2025/12/09
PY  - 2025
N1  - https://doi.org/10.11648/j.ajnna.20251102.15
DO  - 10.11648/j.ajnna.20251102.15
T2  - American Journal of Neural Networks and Applications
JF  - American Journal of Neural Networks and Applications
JO  - American Journal of Neural Networks and Applications
SP  - 88
EP  - 96
PB  - Science Publishing Group
SN  - 2469-7419
UR  - https://doi.org/10.11648/j.ajnna.20251102.15
AB  - In this article, we propose a feedforward neural network model designed to approximate the inverse transfer characteristic of a High-Power Amplifier (HPA) in order to linearize it using Digital Predistortion (DPD). This approach is particularly relevant for next-generation communication systems, such as those employing OTFS (Orthogonal Time Frequency Space) modulation envisioned for 6G, whose signals exhibit large amplitude variations that exacerbate amplifier nonlinearities. The performance of predistortion heavily depends on the learning algorithm used to train the neural model. We compared three optimization algorithms: Gradient Descent, Gauss-Newton, and Levenberg-Marquardt. The amplifier is modeled using the Rapp model. The neural network architecture consists of a single input neuron, a hidden layer with ten neurons using the hyperbolic tangent activation function, and a linear output neuron. Training and simulations were carried out in MATLAB, and the performance of each algorithm was evaluated using the Mean Squared Error (MSE) criterion, which quantifies the deviation between the ideal transfer characteristic of a linear amplifier and the characteristic obtained after predistortion. The results clearly show that the Levenberg-Marquardt algorithm provides the best approximation of the predistortion function, achieving an MSE on the order of 4.2708×10-8, significantly outperforming Gauss-Newton 1.0481×10-4 and Gradient Descent (0.0272). This superior performance is attributed to Levenberg-Marquardt’s ability to combine the robustness of Gradient Descent with the fast convergence of Gauss-Newton, while avoiding local minimum and issues related to poor synaptic weight initialization.
VL  - 11
IS  - 2
ER  -

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Author Information

Hariony Bienvenu Rakotonirina

Department of Telecommunication, High School Polytechnic of Antsirabe, Antsirabe, Madagascar

Contact Email

http://orcid.org/0009-0004-5054-736X
Marie Emile Randrianandrasana

Department of Telecommunication, High School Polytechnic of Antsirabe, Antsirabe, Madagascar

Contact Email

http://orcid.org/0009-0007-2595-5857

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APA Style

Rakotonirina, H. B., Randrianandrasana, M. E. (2025). Influence of Neural Network Learning Algorithms on High Power Amplifier (HPA) Predistortion Performance. American Journal of Neural Networks and Applications, 11(2), 88-96. https://doi.org/10.11648/j.ajnna.20251102.15

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ACS Style

Rakotonirina, H. B.; Randrianandrasana, M. E. Influence of Neural Network Learning Algorithms on High Power Amplifier (HPA) Predistortion Performance. Am. J. Neural Netw. Appl. 2025, 11(2), 88-96. doi: 10.11648/j.ajnna.20251102.15

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AMA Style

Rakotonirina HB, Randrianandrasana ME. Influence of Neural Network Learning Algorithms on High Power Amplifier (HPA) Predistortion Performance. Am J Neural Netw Appl. 2025;11(2):88-96. doi: 10.11648/j.ajnna.20251102.15

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@article{10.11648/j.ajnna.20251102.15,
  author = {Hariony Bienvenu Rakotonirina and Marie Emile Randrianandrasana},
  title = {Influence of Neural Network Learning Algorithms on High Power Amplifier (HPA) Predistortion Performance},
  journal = {American Journal of Neural Networks and Applications},
  volume = {11},
  number = {2},
  pages = {88-96},
  doi = {10.11648/j.ajnna.20251102.15},
  url = {https://doi.org/10.11648/j.ajnna.20251102.15},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajnna.20251102.15},
  abstract = {In this article, we propose a feedforward neural network model designed to approximate the inverse transfer characteristic of a High-Power Amplifier (HPA) in order to linearize it using Digital Predistortion (DPD). This approach is particularly relevant for next-generation communication systems, such as those employing OTFS (Orthogonal Time Frequency Space) modulation envisioned for 6G, whose signals exhibit large amplitude variations that exacerbate amplifier nonlinearities. The performance of predistortion heavily depends on the learning algorithm used to train the neural model. We compared three optimization algorithms: Gradient Descent, Gauss-Newton, and Levenberg-Marquardt. The amplifier is modeled using the Rapp model. The neural network architecture consists of a single input neuron, a hidden layer with ten neurons using the hyperbolic tangent activation function, and a linear output neuron. Training and simulations were carried out in MATLAB, and the performance of each algorithm was evaluated using the Mean Squared Error (MSE) criterion, which quantifies the deviation between the ideal transfer characteristic of a linear amplifier and the characteristic obtained after predistortion. The results clearly show that the Levenberg-Marquardt algorithm provides the best approximation of the predistortion function, achieving an MSE on the order of 4.2708×10-8, significantly outperforming Gauss-Newton 1.0481×10-4 and Gradient Descent (0.0272). This superior performance is attributed to Levenberg-Marquardt’s ability to combine the robustness of Gradient Descent with the fast convergence of Gauss-Newton, while avoiding local minimum and issues related to poor synaptic weight initialization.},
 year = {2025}
}

Copy | Download

TY  - JOUR
T1  - Influence of Neural Network Learning Algorithms on High Power Amplifier (HPA) Predistortion Performance
AU  - Hariony Bienvenu Rakotonirina
AU  - Marie Emile Randrianandrasana
Y1  - 2025/12/09
PY  - 2025
N1  - https://doi.org/10.11648/j.ajnna.20251102.15
DO  - 10.11648/j.ajnna.20251102.15
T2  - American Journal of Neural Networks and Applications
JF  - American Journal of Neural Networks and Applications
JO  - American Journal of Neural Networks and Applications
SP  - 88
EP  - 96
PB  - Science Publishing Group
SN  - 2469-7419
UR  - https://doi.org/10.11648/j.ajnna.20251102.15
AB  - In this article, we propose a feedforward neural network model designed to approximate the inverse transfer characteristic of a High-Power Amplifier (HPA) in order to linearize it using Digital Predistortion (DPD). This approach is particularly relevant for next-generation communication systems, such as those employing OTFS (Orthogonal Time Frequency Space) modulation envisioned for 6G, whose signals exhibit large amplitude variations that exacerbate amplifier nonlinearities. The performance of predistortion heavily depends on the learning algorithm used to train the neural model. We compared three optimization algorithms: Gradient Descent, Gauss-Newton, and Levenberg-Marquardt. The amplifier is modeled using the Rapp model. The neural network architecture consists of a single input neuron, a hidden layer with ten neurons using the hyperbolic tangent activation function, and a linear output neuron. Training and simulations were carried out in MATLAB, and the performance of each algorithm was evaluated using the Mean Squared Error (MSE) criterion, which quantifies the deviation between the ideal transfer characteristic of a linear amplifier and the characteristic obtained after predistortion. The results clearly show that the Levenberg-Marquardt algorithm provides the best approximation of the predistortion function, achieving an MSE on the order of 4.2708×10-8, significantly outperforming Gauss-Newton 1.0481×10-4 and Gradient Descent (0.0272). This superior performance is attributed to Levenberg-Marquardt’s ability to combine the robustness of Gradient Descent with the fast convergence of Gauss-Newton, while avoiding local minimum and issues related to poor synaptic weight initialization.
VL  - 11
IS  - 2
ER  -

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