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Machine Learning-Based Path Loss Modeling With Simplified Features
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DOI：10.1109/LAWP.2024.3388253.png)
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Abstract
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Propagation modeling is a crucial tool for successful wireless deployments and spectrum planning with the demand for high modeling accuracy continuing to grow. Recognizing that detailed knowledge of the physical environment (terrain and clutter) is essential, we propose a novel approach that uses environmental information for predictions. Instead of relying on complex, detail-intensive models, we explore the use of simplified scalar features involving the total obstruction depth along the direct path from transmitter to receiver. Obstacle depth offers a streamlined, yet surprisingly accurate, method for predicting wireless signal propagation, providing a practical solution for efficient and effective wireless network planning.
Keywords:
Clutter
Predictive models
Data models
Meters
Training
Urban areas
Loss measurement
Machine learning (ML)
path loss modeling
feature selection
drive test measurements
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4.8
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2.8W
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