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Optical frequency multiplication using residual network with random forest regression.

Qi Zhang1, Xu Han1, Xinyu Fang1

  • 1College of Electrical and Electronics Engineering, Changchun University of Technology, 2055 Yanan Street, Changchun, 130012, China.

Heliyon
|May 30, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a hybrid deep learning method using ResNet and RFR for optical frequency multiplication. The approach successfully generates high-order mm-wave signals with excellent suppression ratios.

Keywords:
Deep learning algorithmFrequency multiplication modulationRandom forest regressionResidual network

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Area of Science:

  • Photonics
  • Machine Learning
  • Signal Processing

Background:

  • Optical frequency multiplication is crucial for generating high-frequency signals.
  • Traditional methods face challenges in precision and parameter optimization.
  • Deep learning offers a novel approach to complex signal generation tasks.

Purpose of the Study:

  • To develop and validate a hybrid deep learning method for optical frequency multiplication.
  • To demonstrate the generation of 8-, 12-, and 16-tupling mm-wave signals.
  • To analyze the performance and robustness of the proposed method.

Main Methods:

  • Integration of Residual Network (ResNet) and Random Forest Regression (RFR) for parameter prediction.
  • Utilizing three distinct frequency multiplication modulation schemes.
  • Numerical simulation for generating and evaluating mm-wave signals.

Main Results:

  • Achieved high optical sideband suppression ratios (OSSR) around 30 dB for all multiplication orders.
  • Demonstrated excellent radio frequency spurious suppression ratios (RFSSR): 42.29 dB (8-tupling), 36.21 dB (12-tupling), and 34.52 dB (16-tupling).
  • Investigated the impact of amplitude fluctuations and bias voltage drift on signal quality.

Conclusions:

  • The hybrid deep learning approach effectively enables precise optical frequency multiplication.
  • The method provides a robust framework for generating high-quality mm-wave signals.
  • Further research can explore optimization for reduced signal degradation under varying conditions.