Stefanos Bakirtzis

Paul Almasan, Stefanos Bakirtzis, José Suárez-Varela et al.

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 wireless ecosystem and support its efficient expansion. In this paper, we introduce GRAPHWAVE, a neural graph-driven propagation solver hinging on the governing principles of ray tracing. The proposed model leverages a digitized version of the propagation environment to build a point cloud and extract an equivalent graph representation of the radio environment. By applying neural message passing over the equivalent graph, it allows the model to accurately infer radio-related quantities, e.g., received signal strength, in a three-dimensional environment. We showcase the use of GRAPHWAVE as a radio environment digital twin and we demonstrate that the model can learn from synthetic and real-world data while achieving low inference times.

Stefanos Bakirtzis, Cagkan Yapar, Marco Fiore et al.

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 typically acquired through radio propagation solvers, which however suffer from intrinsic and well-known performance limitations. This article provides a primer on how integrating deep learning and conventional propagation modeling techniques can enhance multiple vital facets of wireless network operation, and yield benefits in terms of efficiency and reliability. By highlighting the pivotal role that the deep learning-based radio propagation models will assume in next-generation wireless networks, we aspire to propel further research in this direction and foster their adoption in additional applications.

Stefanos Bakirtzis, Çağkan Yapar, Kehai Qiu et al.

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 signal emissions in indoor propagation environments, we have launched the ICASSP 2025 First Indoor Pathloss Radio Map Prediction Challenge. This overview paper describes the indoor path loss prediction problem, the datasets used, the Challenge tasks, and the evaluation methodology. Finally, the results of the Challenge and a summary of the submitted methods are presented.

Stefanos Bakirtzis, Paul Almasan, José Suárez-Varela et al.

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 real-world data, it offers a real alternative to conventional deep learning (DL) models. However, no experimental evaluation on production-grade networks has yet validated its assumed scalability or practical benefits. This leaves mobile network operators (MNOs) and the research community without clear guidance on its applicability. In this paper, we fill this gap by employing both differentiable ray tracing and DL models to emulate radio coverage using extensive real-world data collected from the network of a major MNO, covering 13 cities and more than 10,000 antennas. Our results show that, while differentiable ray-tracing simulators have contributed to reducing the efficiency-accuracy gap, they struggle to generalize from real-world data at a large scale, and they remain unsuitable for real-time applications. In contrast, DL models demonstrate higher accuracy and faster adaptation than differentiable ray-tracing simulators across urban, suburban, and rural deployments, achieving accuracy gains of up to 3 dB. Our experimental results aim to provide timely insights into a fundamental open question with direct implications on the wireless ecosystem and future research.