IEEE Workshop on AI-Driven Integration of Terrestrial and Non-Terrestrial Networks

After a successful first edition of the AITNTN Workshop at INFOCOM 2025 and second edition at ICC 2025 we are holding the third edition at ICC 2026. Scope: The emergence of the 3rd Generation Partnership Project (3GPP) Release 17 represents a noteworthy milestone in the evolution of communication networks. It empowers 5G operators to extend their services beyond terrestrial boundaries, with implications reaching far beyond traditional communication networks, impacting remote communities, maritime endeavours, and airborne operations. A key advantage of Non-Terrestrial Networks (NTN) (including Unmanned Aerial Vehicles (UAVs), High Altitude Platforms (HAPs) and Satellites) is their extensive coverage capability. Machine-to-machine (M2M) applications, emergency response, enhanced coverage to high-speed platforms (airplanes, trains, ships), spanning agriculture, transportation, environmental monitoring, and asset tracking, can leverage NTN for pervasive and reliable Internet connectivity. However, the integration of NTN into Terrestrial Networks (TN) introduces various technical and regulatory challenges. Unlike stationary base stations in TN, one of the use case of NTN utilize satellites in Low Earth Orbit (LEO) that move at considerable speeds, introducing challenges like Doppler shift and trajectory-dependent frequency variations. Compensating for these shifts and ensuring user equipment is aware of satellite mobility pattern becomes crucial. Additionally, the extended signal path through the atmosphere results in higher path loss, impacting network performance in terms of latency and capacity. An efficient integrated TN-NTN network shall carefully adapt the resources based on the dynamics of the system and the instantaneous demand of the corresponding users. Furthermore, such networks are expected to coexist in spectrum, claiming for efficient dynamic spectrum access strategies. Clearly, the successful integration requires collaborative efforts among satellite operators, network service providers, government agencies, and standards bodies to overcome regulatory barriers. Wireless devices based on TN typically require more resources to establish a robust link with NTN. To address the restricted storage and computing capabilities of these devices, an efficient wireless architecture needs to be devised. Overcoming these challenges propels us beyond conventional rules-based methods, with many now turning to artificial intelligence (AI) as the preferred solution to navigate the intricacies introduced by contemporary systems. AI-supported methods play a pivotal role in operating future 6G networks, empowering NTNs to function optimally in dynamic and unpredictable settings. However, the reliability of AI models hinges on the size and quality of the training dataset. Consequently, this workshop aims to address the challenges associated with the use of AI in bridging the gap between TN and NTN. Find out more