Channel modeling is considered as a fundamental step in the design, deployment, and
optimization of vehicular wireless communication systems. For typical vehicular …

Propagation models are essential for the prediction of received signal strength and the
planning of wireless systems in a given environment. The vector parabolic equation (VPE) …

The radio wave propagation channel is central to the performance of wireless
communication systems. In this paper, we introduce a novel machine learning-empowered …

Recently, there has been a surge in the development of data-driven propagation models.
These models aspire to distill knowledge from propagation solvers or measured data and …

Parabolic equation (PE) methods have been widely utilized for modeling radio wave
propagation in tunnels due to their notable efficiency and fidelity. However, as emerging …

A Machine Learning (ML) network based on transfer learning and transformer networks is
applied to wave propagation models for complex indoor settings. This network is designed …

The next decade is poised for a transformative shift in wireless communication technologies,
driven by the increasing demand for data-intensive applications. Innovations in signal …

In this paper, we provide a novel heterogeneous graph neural network (GNN)-based
approach to predict the electromagnetic field (EMF) exposure distribution in an indoor …

Machine learning (ML) methods have been successfully employed to derive efficient
radiowave propapagation models in recent years. Recent efforts have focused on artificial …

Grasping the electric-field (E-field) distribution is challenging yet important for monitoring the
electromagnetic field (EMF) exposure level. In this paper, a graph neural network (GNN) …