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Performance Studies on machine learning based channel modelling for vehicular visible light communication.

L Ramya1, K Umadevi2

  • 1Department of Electronics and Communication Engineering, Sri Ramakrishna College of Engineering, Perambalur, 621 113, TamilNadu, India. yoursrskp@gmail.com.

Scientific Reports
|May 15, 2026
PubMed
Summary
This summary is machine-generated.

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This study introduces a machine learning model for vehicular visible light communication (V2LC) channel modeling. The ML-V2LC-CM framework significantly improves prediction accuracy and robustness for vehicle-to-vehicle communication.

Area of Science:

  • Computer Science
  • Electrical Engineering
  • Transportation Technology

Background:

  • Vehicular Visible Light Communication (V2LC) leverages vehicle lights for data transmission, offering a cost-effective alternative to radio-frequency systems.
  • Traditional optical channel models struggle with the dynamic and non-linear nature of V2LC environments, influenced by factors like LED shape, speed, and atmospheric conditions.
  • Accurate channel modeling is crucial for reliable V2LC performance, addressing signal strength variations and distortions.

Purpose of the Study:

  • To develop and evaluate a novel machine learning-based framework for Vehicular Visible Light Communication (V2LC) channel modeling.
  • To address the limitations of conventional empirical and deterministic models in dynamic V2LC scenarios.
  • To enhance the accuracy, robustness, and efficiency of V2LC channel gain, path loss, and signal distortion estimation.
Keywords:
Hybrid prediction frameworkIntelligent transportation systemsMachine learning channel modellingOptical wireless communicationV2LC performance evaluationVehicular visible light communication

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Main Methods:

  • Proposed a hybrid learning system, Machine Learning-based Vehicular Visible Light Communication Channel Modelling (ML-V2LC-CM).
  • Integrated regression models, ensemble learning, and deep neural predictors for channel parameter estimation.
  • Conducted experimental tests to compare ML-V2LC-CM against baseline empirical models.

Main Results:

  • ML-V2LC-CM demonstrated superior performance with an 18.7% improvement in prediction and a 22.4% reduction in Root Mean Square Error (RMSE).
  • Achieved Signal-to-Noise Ratio (SNR) estimation error below 1.6 dB with a low prediction latency of 4-7ms.
  • Showcased robustness with a 6-9% degradation rate under blockage and over 92% generalization across lighting conditions, outperforming standalone models by 11.3%.

Conclusions:

  • The ML-V2LC-CM framework offers a significant advancement in V2LC channel modeling, outperforming traditional methods.
  • The model exhibits high accuracy, robustness, and efficiency, making it suitable for real-world dynamic V2LC applications.
  • ML-V2LC-CM demonstrates high consistency in channel stability estimation (>95%), even at high vehicle speeds.