From Ground to Orbit: A Reliable AI Framework for Unified Network Management

Artificial intelligence (AI) has captivated research in network resource orchestration, driving advancements and innovations, including the much-anticipated integration of terrestrial networks (TNs) and non-terrestrial networks (NTNs). Although AI has already been incorporated into standards to complement or replace network functions, its reliability in maintaining the required quality of service (QoS) in highly dynamic and complex environments is often overlooked. This article highlights the significant contributions of AI to unified resource management on the ground and in various orbits while addressing its reliability challenges in complex integrated TN/NTN systems. In this context, the different phases of the AI lifecycle are analyzed and their key processes and steps are identified to introduce mechanisms that ensure reliability at each stage. A principled reliable AI framework is ultimately designed to meet the stringent reliability requirements of highly volatile environments like the integrated TNs/NTNs. To shed light on the practical application of the framework, a comprehensive example is provided for the problem of computation task offloading in integrated TNs/NTNs. This proof of concept emphasizes the design of fully decentralized and scalable solutions while examining the significance and impact of mechanisms that respond to unseen states and maintain the required QoS level safely. Overall, this article provides a guide for tackling the challenges of ensuring reliability in AI-enabled TN/NTN functions that can serve as a powerful catalyst for ongoing standardization efforts.