Artificial Intelligence Enabled Converged 3D Networks

With the deployment of 5G, research toward 6G wireless networks has gained significant momentum in both industry and academia. Among the disruptive ambitions for 6G, one of the most promising is the development of integrated ground-air-space three-dimensional (3D) networks that provide seamless global connectivity and advanced computing services. These networks converge satellites, aerial platforms, and terrestrial nodes into a unified system to meet the demands of future communication and computational applications.

Despite progress in areas such as Low Earth Orbit (LEO) satellite communications and aerial platform-based broadband access, the realization of integrated 3D networks poses significant challenges. These include efficient communication across heterogeneous layers and scalable distributed computing systems. Research efforts have recently focused on areas like LEO and aerial-based broadband access, multilayer satellite network-enabled distributed computing, satellite-assisted sensing and communication, IoT-over-satellite frameworks, and aerial IoT solutions.

This PhD project will address these challenges by exploring AI-driven methods to design, optimize, and manage the complexities of 3D converged networks, ensuring efficient resource utilization and seamless operation across ground, air, and space domains.