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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.
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.
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.

