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

TL;DR

This paper reviews multiconnectivity in SAGIN, discusses challenges, and demonstrates how AI, especially reinforcement learning, can optimize resource allocation to improve network performance in complex heterogeneous environments.

Contribution

It introduces AI-driven solutions, particularly reinforcement learning, for resource optimization in SAGIN multiconnectivity, addressing heterogeneity and complexity challenges.

Findings

Learning-based methods improve latency and capacity.

AI approaches handle complex SAGIN scenarios effectively.

Moderate power increase is an acceptable tradeoff.

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.