Efficient and realistic indoor radio propagation modeling tools are inextricably intertwined
with the design and operation of next-generation wireless networks. Machine-learning (ML) …

Efficient and accurate indoor radio propagation modeling tools are essential for the design
and operation of wireless communication systems. Lately, several attempts to combine radio …

A fundamental challenge for machine learning (ML) models for electromagnetics is their
ability to predict output quantities of interest (such as fields and scattering parameters) in …

A surrogate model that “learns” the physics of radio wave propagation is indispensable for
the efficient optimization of communication network coverages and comprehensive …

Machine learning (ML) and artificial neural networks (ANNs) have been successfully applied
to simulating complex physics by learning physics models thanks to large data. Inspired by …

Wireless propagation loss modeling has gained significant attention due to its critical
importance in forthcoming dynamic wireless technologies. Stochastic and map-based …

In this letter we present our results on how deep learning can be leveraged for outdoor path
loss prediction in the 30GHz band. In particular, we exploit deep learning to boost the …

In modern wireless communication systems, radio propagation modeling has always been a
fundamental task in system design and performance optimization. These models are used in …

In urban scenarios, network planning requires awareness of the notoriously complex
propagation environment by accounting for blocking, diffraction, and reflection on buildings …

Accurate path gain models are critical for coverage prediction and radio frequency (RF)
planning in wireless communications. In many settings irregular terrain induces blockages …