Methods for accurate prediction of radio signal quality parameters are crucial for
optimization of mobile networks, and a necessity for future autonomous driving solutions …

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 …

Radio propagation modeling and path loss prediction have been the subject of many
machine learning-based estimation attempts. Our current work uses deep learning for the …

Path loss prediction is essential for network planning in any wireless communication system.
For cellular networks, it is usually achieved through extensive received signal power …

Accurate channel models are essential to evaluate mobile communication system
performance and optimize coverage for existing deployments. The introduction of various …

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

When deploying communication systems, an accurate wireless propagation model is
important to ensure the quality of service covering the region. Due to the complex radio …

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 this paper we propose a highly efficient and very accurate deep learning method for
estimating the propagation pathloss from a point (transmitter location) to any point on a …

Not only has the volume of mobile traffic been increasing exponentially in recent years,
making various services available, such as IoT and connected cars moving at high speed …