Electromagnetic–Geometric Coupled Environmental Features-Based Intelligent Path Loss Model in Urban Scenarios

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Abstract
Path loss (PL) is a key channel characteristic that provides theoretical guidance for wireless network planning and optimization. Traditional channel models face an inherent trade-off: empirical models are limited in accuracy due to the omission of environmental factors, while deterministic models suffer from low efficiency due to their reliance on manual environmental modeling. Although deep learning (DL) has gained attention in PL prediction, existing DL-based models still lack expressive environmental representations, limiting improvements in prediction results. To address this limitation, an electromagnetic-geometric (E-G) coupled environmental representation is proposed, which describes the environment by integrating the types of land covers with their elevation information. An intelligent PL model is developed based on E-G environmental representation. Experimental results show that, without cumbersome environmental modeling, E-G features significantly improve prediction accuracy, highlighting the potential of the proposed model to overcome the accuracy-efficiency trade-off in traditional PL models.
Funding Information
  • Beijing Information Science and Technology University (5212410971)

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