Exploitation of Feedforward Neural Networks to Improve HPA Predistortion Performance and Application to OTFS Signals

Hariniony Bienvenu Rakotonirina; Marie Emile Randrianandrasana

doi:doi:10.11648/j.awcn.20251001.12

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Exploitation of Feedforward Neural Networks to Improve HPA Predistortion Performance and Application to OTFS Signals

Hariniony Bienvenu Rakotonirina^*

, Marie Emile Randrianandrasana

Published in Advances in Wireless Communications and Networks (Volume 10, Issue 1)

Received: 17 November 2025 Accepted: 4 December 2025 Published: 20 December 2025

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Abstract

6G (Next-generation mobile telephony) communication systems require modulation schemes robust against channel fading, and Orthogonal Time Frequency Space (OTFS) has emerged as a key technology to achieve this goal. However, OTFS exhibits high amplitude variations (high PAPR), making it particularly susceptible to High Power Amplifier (HPA) nonlinearities, which degrade spectral purity (ACPR or Adjacent Channel Power Ratio) and increase the Bit Error Rate (BER). Digital predistortion (DPD) is the most effective method for HPA linearization, but classical polynomial models struggle to capture complex nonlinearities especially when applied to demanding signals like OTFS. In this paper, we propose and evaluate an innovative DPD approach based on a feedforward neural network. A multi-criteria analysis demonstrates that this method significantly outperforms polynomial predistortion: it achieves precise predistortion function approximation with a Mean Squared Error (MSE) of 7.38 × 10^-6, improves ACPR by 22 dB (from -15 dB to -36 dB), and attains a BER nearly identical to that of a linear amplifier even in a Rayleigh fading channel. Moreover, it enables the HPA to operate in saturation (low IBO ou Input Back-off, ~70% efficiency) while preserving optimal transmission quality, thereby breaking the traditional trade-off between energy efficiency and linearity. Our approach is simple, robust, and computationally lightweight, paving the way for highly efficient 6G transmission chains tailored for mobile environments.

Published in	Advances in Wireless Communications and Networks (Volume 10, Issue 1)
DOI	10.11648/j.awcn.20251001.12
Page(s)	9-27
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

HPA, OTFS, PAPR, Predistortion, ANN, Nonlinearity

References

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APA Style

Rakotonirina, H. B., Randrianandrasana, M. E. (2025). Exploitation of Feedforward Neural Networks to Improve HPA Predistortion Performance and Application to OTFS Signals. Advances in Wireless Communications and Networks, 10(1), 9-27. https://doi.org/10.11648/j.awcn.20251001.12

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ACS Style

Rakotonirina, H. B.; Randrianandrasana, M. E. Exploitation of Feedforward Neural Networks to Improve HPA Predistortion Performance and Application to OTFS Signals. Adv. Wirel. Commun. Netw. 2025, 10(1), 9-27. doi: 10.11648/j.awcn.20251001.12

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AMA Style

Rakotonirina HB, Randrianandrasana ME. Exploitation of Feedforward Neural Networks to Improve HPA Predistortion Performance and Application to OTFS Signals. Adv Wirel Commun Netw. 2025;10(1):9-27. doi: 10.11648/j.awcn.20251001.12

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@article{10.11648/j.awcn.20251001.12,
  author = {Hariniony Bienvenu Rakotonirina and Marie Emile Randrianandrasana},
  title = {Exploitation of Feedforward Neural Networks to Improve HPA Predistortion Performance and Application to OTFS Signals},
  journal = {Advances in Wireless Communications and Networks},
  volume = {10},
  number = {1},
  pages = {9-27},
  doi = {10.11648/j.awcn.20251001.12},
  url = {https://doi.org/10.11648/j.awcn.20251001.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.awcn.20251001.12},
  abstract = {6G (Next-generation mobile telephony) communication systems require modulation schemes robust against channel fading, and Orthogonal Time Frequency Space (OTFS) has emerged as a key technology to achieve this goal. However, OTFS exhibits high amplitude variations (high PAPR), making it particularly susceptible to High Power Amplifier (HPA) nonlinearities, which degrade spectral purity (ACPR or Adjacent Channel Power Ratio) and increase the Bit Error Rate (BER). Digital predistortion (DPD) is the most effective method for HPA linearization, but classical polynomial models struggle to capture complex nonlinearities especially when applied to demanding signals like OTFS. In this paper, we propose and evaluate an innovative DPD approach based on a feedforward neural network. A multi-criteria analysis demonstrates that this method significantly outperforms polynomial predistortion: it achieves precise predistortion function approximation with a Mean Squared Error (MSE) of 7.38 × 10-6, improves ACPR by 22 dB (from -15 dB to -36 dB), and attains a BER nearly identical to that of a linear amplifier even in a Rayleigh fading channel. Moreover, it enables the HPA to operate in saturation (low IBO ou Input Back-off, ~70% efficiency) while preserving optimal transmission quality, thereby breaking the traditional trade-off between energy efficiency and linearity. Our approach is simple, robust, and computationally lightweight, paving the way for highly efficient 6G transmission chains tailored for mobile environments.},
 year = {2025}
}

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TY  - JOUR
T1  - Exploitation of Feedforward Neural Networks to Improve HPA Predistortion Performance and Application to OTFS Signals
AU  - Hariniony Bienvenu Rakotonirina
AU  - Marie Emile Randrianandrasana
Y1  - 2025/12/20
PY  - 2025
N1  - https://doi.org/10.11648/j.awcn.20251001.12
DO  - 10.11648/j.awcn.20251001.12
T2  - Advances in Wireless Communications and Networks
JF  - Advances in Wireless Communications and Networks
JO  - Advances in Wireless Communications and Networks
SP  - 9
EP  - 27
PB  - Science Publishing Group
SN  - 2575-596X
UR  - https://doi.org/10.11648/j.awcn.20251001.12
AB  - 6G (Next-generation mobile telephony) communication systems require modulation schemes robust against channel fading, and Orthogonal Time Frequency Space (OTFS) has emerged as a key technology to achieve this goal. However, OTFS exhibits high amplitude variations (high PAPR), making it particularly susceptible to High Power Amplifier (HPA) nonlinearities, which degrade spectral purity (ACPR or Adjacent Channel Power Ratio) and increase the Bit Error Rate (BER). Digital predistortion (DPD) is the most effective method for HPA linearization, but classical polynomial models struggle to capture complex nonlinearities especially when applied to demanding signals like OTFS. In this paper, we propose and evaluate an innovative DPD approach based on a feedforward neural network. A multi-criteria analysis demonstrates that this method significantly outperforms polynomial predistortion: it achieves precise predistortion function approximation with a Mean Squared Error (MSE) of 7.38 × 10-6, improves ACPR by 22 dB (from -15 dB to -36 dB), and attains a BER nearly identical to that of a linear amplifier even in a Rayleigh fading channel. Moreover, it enables the HPA to operate in saturation (low IBO ou Input Back-off, ~70% efficiency) while preserving optimal transmission quality, thereby breaking the traditional trade-off between energy efficiency and linearity. Our approach is simple, robust, and computationally lightweight, paving the way for highly efficient 6G transmission chains tailored for mobile environments.
VL  - 10
IS  - 1
ER  -

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Author Information

Hariniony 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). Exploitation of Feedforward Neural Networks to Improve HPA Predistortion Performance and Application to OTFS Signals. Advances in Wireless Communications and Networks, 10(1), 9-27. https://doi.org/10.11648/j.awcn.20251001.12

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ACS Style

Rakotonirina, H. B.; Randrianandrasana, M. E. Exploitation of Feedforward Neural Networks to Improve HPA Predistortion Performance and Application to OTFS Signals. Adv. Wirel. Commun. Netw. 2025, 10(1), 9-27. doi: 10.11648/j.awcn.20251001.12

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AMA Style

Rakotonirina HB, Randrianandrasana ME. Exploitation of Feedforward Neural Networks to Improve HPA Predistortion Performance and Application to OTFS Signals. Adv Wirel Commun Netw. 2025;10(1):9-27. doi: 10.11648/j.awcn.20251001.12

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@article{10.11648/j.awcn.20251001.12,
  author = {Hariniony Bienvenu Rakotonirina and Marie Emile Randrianandrasana},
  title = {Exploitation of Feedforward Neural Networks to Improve HPA Predistortion Performance and Application to OTFS Signals},
  journal = {Advances in Wireless Communications and Networks},
  volume = {10},
  number = {1},
  pages = {9-27},
  doi = {10.11648/j.awcn.20251001.12},
  url = {https://doi.org/10.11648/j.awcn.20251001.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.awcn.20251001.12},
  abstract = {6G (Next-generation mobile telephony) communication systems require modulation schemes robust against channel fading, and Orthogonal Time Frequency Space (OTFS) has emerged as a key technology to achieve this goal. However, OTFS exhibits high amplitude variations (high PAPR), making it particularly susceptible to High Power Amplifier (HPA) nonlinearities, which degrade spectral purity (ACPR or Adjacent Channel Power Ratio) and increase the Bit Error Rate (BER). Digital predistortion (DPD) is the most effective method for HPA linearization, but classical polynomial models struggle to capture complex nonlinearities especially when applied to demanding signals like OTFS. In this paper, we propose and evaluate an innovative DPD approach based on a feedforward neural network. A multi-criteria analysis demonstrates that this method significantly outperforms polynomial predistortion: it achieves precise predistortion function approximation with a Mean Squared Error (MSE) of 7.38 × 10-6, improves ACPR by 22 dB (from -15 dB to -36 dB), and attains a BER nearly identical to that of a linear amplifier even in a Rayleigh fading channel. Moreover, it enables the HPA to operate in saturation (low IBO ou Input Back-off, ~70% efficiency) while preserving optimal transmission quality, thereby breaking the traditional trade-off between energy efficiency and linearity. Our approach is simple, robust, and computationally lightweight, paving the way for highly efficient 6G transmission chains tailored for mobile environments.},
 year = {2025}
}

Copy | Download

TY  - JOUR
T1  - Exploitation of Feedforward Neural Networks to Improve HPA Predistortion Performance and Application to OTFS Signals
AU  - Hariniony Bienvenu Rakotonirina
AU  - Marie Emile Randrianandrasana
Y1  - 2025/12/20
PY  - 2025
N1  - https://doi.org/10.11648/j.awcn.20251001.12
DO  - 10.11648/j.awcn.20251001.12
T2  - Advances in Wireless Communications and Networks
JF  - Advances in Wireless Communications and Networks
JO  - Advances in Wireless Communications and Networks
SP  - 9
EP  - 27
PB  - Science Publishing Group
SN  - 2575-596X
UR  - https://doi.org/10.11648/j.awcn.20251001.12
AB  - 6G (Next-generation mobile telephony) communication systems require modulation schemes robust against channel fading, and Orthogonal Time Frequency Space (OTFS) has emerged as a key technology to achieve this goal. However, OTFS exhibits high amplitude variations (high PAPR), making it particularly susceptible to High Power Amplifier (HPA) nonlinearities, which degrade spectral purity (ACPR or Adjacent Channel Power Ratio) and increase the Bit Error Rate (BER). Digital predistortion (DPD) is the most effective method for HPA linearization, but classical polynomial models struggle to capture complex nonlinearities especially when applied to demanding signals like OTFS. In this paper, we propose and evaluate an innovative DPD approach based on a feedforward neural network. A multi-criteria analysis demonstrates that this method significantly outperforms polynomial predistortion: it achieves precise predistortion function approximation with a Mean Squared Error (MSE) of 7.38 × 10-6, improves ACPR by 22 dB (from -15 dB to -36 dB), and attains a BER nearly identical to that of a linear amplifier even in a Rayleigh fading channel. Moreover, it enables the HPA to operate in saturation (low IBO ou Input Back-off, ~70% efficiency) while preserving optimal transmission quality, thereby breaking the traditional trade-off between energy efficiency and linearity. Our approach is simple, robust, and computationally lightweight, paving the way for highly efficient 6G transmission chains tailored for mobile environments.
VL  - 10
IS  - 1
ER  -

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