5 papers
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…
The First Indoor Pathloss Radio Map Prediction Challenge
Stefanos Bakirtzis, ÃaÄkan 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…
A Joint Optimization Approach for Power-Efficient Heterogeneous OFDMA Radio Access Networks
Gabriel O. Ferreira, André F. Zanella, Stefanos Bakirtzis +6
Heterogeneous networks have emerged as a popular solution for accommodating the growing number of connected devices and increasing traffic demands in cellular networks. While offer…
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,…