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Related Experiment Video

Updated: Jul 27, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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Machine-Learning-Based Indoor Localization under Shadowing Condition for P-NOMA VLC Systems.

Affan Affan1, Hafiz M Asif2, Naser Tarhuni2

  • 1Department of Electrical and Computer Engineering, University of Louisville, Louisville, KY 40292, USA.

Sensors (Basel, Switzerland)
|June 10, 2023
PubMed
Summary
This summary is machine-generated.

Accurate agent localization is key for reliable communication. This study uses visible light communication and machine learning for real-time indoor positioning, improving power allocation in Power-domain Non-Orthogonal Multiple Access systems.

Keywords:
NOMASIClocalizationmachine learningshadowingvisible light communication

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Area of Science:

  • Wireless Communication
  • Machine Learning
  • Indoor Localization

Background:

  • Effective agent localization is vital for maintaining communication quality in collaborative tasks.
  • Power-domain Non-Orthogonal Multiple Access (P-NOMA) systems require accurate environmental information for efficient power allocation.
  • Dynamic environments and signal shadowing pose challenges for real-time agent positioning in P-NOMA.

Purpose of the Study:

  • To develop a real-time indoor localization method for agents using visible light communication (VLC).
  • To enhance power allocation strategies in P-NOMA systems by integrating accurate agent position estimates.
  • To address signal loss due to shadowing using the Euclidean Distance Matrix (EDM).

Main Methods:

  • Utilizing a two-way VLC link to receive agent signal power at the base station.
  • Employing machine learning algorithms for real-time indoor agent position estimation.
  • Implementing the Simplified Gain Ratio Power Allocation (S-GRPA) scheme with a look-up table for resource allocation.
  • Applying the Euclidean Distance Matrix (EDM) for localization when signals are shadowed.

Main Results:

  • Machine learning algorithms achieved a localization accuracy of 0.19 m.
  • The proposed method enables effective power allocation to agents in a dynamic indoor environment.
  • Successful estimation of agent locations even in the presence of signal shadowing.

Conclusions:

  • The integration of machine learning with VLC provides a robust solution for real-time indoor agent localization.
  • Accurate localization significantly improves the performance of P-NOMA systems by enabling precise power allocation.
  • The developed approach enhances communication reliability for collaborative tasks in dynamic indoor settings.