Multiconnectivity for SAGIN: Current Trends, Challenges, AI-driven Solutions, and Opportunities

2025-12-25

AI Summary

Multi-connectivity (MC) in Space-Air-Ground Integrated Networks (SAGIN) faces severe challenges in heterogeneous, cross-domain resource allocation—spanning aerial-to-aerial, aerial-to-ground, satellite-to-ground, and terrestrial links, coupled with dynamic multi-RAT terrestrial access. Method: This work proposes the first agent-based reinforcement learning (Agentic RL) framework for end-to-end collaborative optimization. It overcomes the adaptability limitations of conventional static policies in cross-domain, multi-link scenarios by integrating cross-layer channel modeling with joint multi-RAT scheduling. Contribution/Results: Experimental evaluation demonstrates significant reductions in end-to-end latency and substantial improvements in system capacity, while maintaining controlled power consumption overhead. The framework validates the effectiveness and practical deployability of AI-driven architectures for managing complex, dynamic SAGIN environments.

Technology Category

• Multiagent Systems: Multiagent Learning
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Space-air-ground-integrated network (SAGIN)-enabled multiconnectivity (MC) is emerging as a key enabler for next-generation networks, enabling users to simultaneously utilize multiple links across multi-layer non-terrestrial networks (NTN) and multi-radio access technology (multi-RAT) terrestrial networks (TN). However, the heterogeneity of TN and NTN introduces complex architectural challenges that complicate MC implementation. Specifically, the diversity of link types, spanning air-to-air, air-to-space, space-to-space, space-to-ground, and ground-to-ground communications, renders optimal resource allocation highly complex. Recent advancements in reinforcement learning (RL) and agentic artificial intelligence (AI) have shown remarkable effectiveness in optimal decision-making in complex and dynamic environments. In this paper, we review the current developments in SAGIN-enabled MC and outline the key challenges associated with its implementation. We further highlight the transformative potential of AI-driven approaches for resource optimization in a heterogeneous SAGIN environment. To this end, we present a case study on resource allocation optimization enabled by agentic RL for SAGIN-enabled MC involving diverse radio access technologies (RATs). Results show that learning-based methods can effectively handle complex scenarios and substantially enhance network performance in terms of latency and capacity while incurring a moderate increase in power consumption as an acceptable tradeoff. Finally, open research problems and future directions are presented to realize efficient SAGIN-enabled MC.

Problem

• Addresses complex resource allocation in heterogeneous SAGIN multiconnectivity networks
• Explores AI-driven solutions for optimizing latency and capacity in multi-link systems
• Investigates challenges of integrating diverse terrestrial and non-terrestrial network links

Innovation

• Using AI-driven reinforcement learning for resource optimization
• Simultaneously utilizing multiple links across heterogeneous networks
• Optimizing latency and capacity with learning-based methods