8 papers
From Waves to Graphs: A Ray-Tracing-Inspired Neural Radio Propagation Model
Paul Almasan, Stefanos Bakirtzis, José Suárez-Varela +1
Artificial intelligence-driven radio propagation models provide agile and robust solutions for mobile network operators in their effort to ensure the optimal performance of the wir…
Radio Propagation Modelling: To Differentiate or To Deep Learn, That Is The Question
Stefanos Bakirtzis, Paul Almasan, José Suárez-Varela +5
Differentiable ray tracing has recently challenged the status quo in radio propagation modelling and digital twinning. Promising unprecedented speed and the ability to learn from r…
iPLAN: Redefining Indoor Wireless Network Planning Through Large Language Models
Jinbo Hou, Stefanos Bakirtzis, Kehai Qiu +5
Efficient indoor wireless network (IWN) planning is crucial for providing high-quality 5G in-building services. However, traditional meta-heuristic and artificial intelligence-base…
The First Indoor Pathloss Radio Map Prediction Challenge
Stefanos Bakirtzis, Çağkan Yapar, Kehai Qiu +2
To encourage further research and to facilitate fair comparisons in the development of deep learning-based radio propagation models, in the less explored case of directional radio…
Empowering Wireless Network Applications with Deep Learning-based Radio Propagation Models
Stefanos Bakirtzis, Cagkan Yapar, Marco Fiore +2
The efficient deployment and operation of any wireless communication ecosystem rely on knowledge of the received signal quality over the target coverage area. This knowledge is typ…
Solving Maxwell's equations with Non-Trainable Graph Neural Network Message Passing
Stefanos Bakirtzis, Marco Fiore, Jie Zhang +1
Computational electromagnetics (CEM) is employed to numerically solve Maxwell's equations, and it has very important and practical applications across a broad range of disciplines,…