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Machine learning for DCO-OFDM based LiFi.

Krishna Saha Purnita1, M Rubaiyat Hossain Mondal1

  • 1Institute of Information and Communication Technology, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh.

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Summary
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Machine learning optimizes the DC bias for optical OFDM in LiFi systems. This approach improves power efficiency and reduces noise by identifying key signal parameters for accurate bias prediction.

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Area of Science:

  • Optical Wireless Communications
  • Machine Learning Applications
  • Signal Processing

Background:

  • Light fidelity (LiFi) systems commonly employ DC biased optical OFDM (DCO-OFDM).
  • DCO-OFDM performance is sensitive to the DC bias level, balancing optical power efficiency against clipping noise.
  • Optimizing DC bias is crucial for efficient and reliable DCO-OFDM based LiFi.

Purpose of the Study:

  • To apply machine learning (ML) algorithms for determining the optimal DC bias in DCO-OFDM LiFi systems.
  • To identify the key signal attributes influencing the optimal DC bias value.
  • To evaluate the effectiveness of regression models in predicting the optimal DC bias.

Main Methods:

  • A dataset for DCO-OFDM was generated using MATLAB.
  • Machine learning algorithms, including linear and polynomial regression, were implemented in Python.
  • ML was used to analyze signal characteristics like minimum, standard deviation, maximum values, and constellation size.

Main Results:

  • The optimal DC bias was found to be dependent on signal statistics and constellation size.
  • Linear and polynomial regression models were successfully applied to predict the optimal DC bias.
  • A second-order polynomial regression model achieved a high coefficient of determination (96.77%).

Conclusions:

  • Machine learning effectively predicts the optimal DC bias for DCO-OFDM LiFi systems.
  • The identified signal attributes are significant factors in DC bias optimization.
  • The high prediction accuracy confirms the efficacy of ML in enhancing DCO-OFDM performance.