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This paper presents a novel approach to the design and characterization of low-noise amplifiers (LNAs) for sub-6-GHz frequency applications, specifically targeting the 3.1–3.4 GHz spectrum. Utilizing artificial neural networks (ANNs), the behaviour and performance characteristics of LNAs to optimize design parameters that influence gain, noise figure, and linearity is modelled. The ANN model provides a rapid and accurate prediction method, significantly enhancing the design efficiency compared to traditional simulation-based methods. Experimental results validate the ANN model’s predictions, demonstrating improved performance in terms of accuracy and design cycle time.

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Artificial Neural Network Modelling and Characterization of a 3.2–3.8-GHz Low-Noise Amplifier for Sub-6-GHz Applications

  • 会议论文

  • 发表日期：

    2025年

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  • Mfonobong Uko,
  • Sunday Ekpo,
  • Fanuel Elias,
  • Sunday Enahoro,
  • Ubong Ukommi,
  • Rahul Unnikrishnan,
  • Unwana Ubong Iwok,
  • Aniebiet Inyang

摘要

This paper presents a novel approach to the design and characterization of low-noise amplifiers (LNAs) for sub-6-GHz frequency applications, specifically targeting the 3.1–3.4 GHz spectrum. Utilizing artificial neural networks (ANNs), the behaviour and performance characteristics of LNAs to optimize design parameters that influence gain, noise figure, and linearity is modelled. The ANN model provides a rapid and accurate prediction method, significantly enhancing the design efficiency compared to traditional simulation-based methods. Experimental results validate the ANN model’s predictions, demonstrating improved performance in terms of accuracy and design cycle time.