The future of cellular networks is contingent on artificial intelligence (AI) based automation,
particularly for radio access network (RAN) operation, optimization, and troubleshooting. To …

Machine learning (ML) is increasingly used to support or automate decision processes in
critical decision systems such as self driving cars or systems for medical diagnosis. These …

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

The rapid progress of the artificial intelligence (AI) sector has greatly impacted vehicular
edge components (VECs) in the vehicular ad hoc network (VANET). Various AI applications …

Native artificial intelligence (AI) has played a pivotal role in sha** the evolution of 6G
networks. It must meet stringent real-time requirements and therefore deploying lightweight …

Unmanned aerial vehicles (UAVs) are envisioned to play a pivotal role in modern
telecommunication and wireless sensor networks, offering unparalleled flexibility and …

Accurate and efficient propagation modeling is a key requirement for radio planning in
cellular networks. Here, deep learning has recently shown promising performance in real …

Mesh networks are self-managing wireless systems with dynamic topology. These networks
differ from broadcast and mobile networks because their mesh nodes can directly exchange …

The evolution of wireless cellular networks to support Digital Twins (DTs) requires robust
propagation models. Traditional propagation modeling methods, though fundamental, lack …