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.

翻译：空天地一体化网络支持的多连接技术正成为下一代网络的关键赋能者，使用户能够同时利用多层非地面网络中的多条链路以及多无线接入技术地面网络。然而，地面网络与非地面网络的异构性带来了复杂的架构挑战，使得多连接的实施变得困难。具体而言，链路类型的多样性——涵盖空对空、空对天、天对天、天对地及地对地通信——使得最优资源分配问题高度复杂。近年来，强化学习与智能体人工智能在复杂动态环境中的最优决策方面展现出显著成效。本文综述了空天地一体化网络支持的多连接技术的最新进展，并概述了其实施过程中的关键挑战。我们进一步强调了人工智能驱动方法在异构空天地一体化网络环境中进行资源优化的变革潜力。为此，我们展示了一个案例研究，探讨了由智能体强化学习驱动的、涉及多种无线接入技术的空天地一体化网络多连接资源分配优化。结果表明，基于学习的方法能有效处理复杂场景，在延迟与容量方面显著提升网络性能，同时以适度的功耗增加作为可接受的权衡。最后，本文提出了实现高效空天地一体化网络多连接的开放研究问题与未来方向。