发表机构
IMT-Atlantique; INPT; Carleton University（IMT大西洋学院; 国立邮电学院; 卡尔顿大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对下一代无线通信中地面网络在灾难和极端需求下易失效的问题，提出基于ORAN的NTN应急通信框架，利用GNN建模拓扑并结合基于MDP的Q学习实现实时控制，显著提升时延和服务可靠性。
AI 中文摘要
在下一代无线通信的灾难场景和极端数据需求期间，传统地面网络（TNs）往往变得不可靠或完全失效，导致关键服务中断。在此类情境下，非地面网络（NTNs）成为实现泛在且弹性连接的一种有前景的解决方案。此外，开放无线接入网（ORAN）范式促进了网络解聚及其关键组件（即中央单元（CU）、分布式单元（DU）和无线单元（RU））之间的灵活功能拆分，这些组件可根据服务需求部署在异构NTN平台上。然而，这种灵活性在网络复杂性和实时控制方面带来了重大挑战。为解决这些挑战，本文提出了一种面向应急通信场景的智能ORAN使能NTN框架。所提系统利用图神经网络（GNNs）对动态网络拓扑进行建模，并采用基于强化学习（RL）的Q学习算法（表述为马尔可夫决策过程（MDP））来实现自适应和实时的网络控制。在该框架中，网络节点被视为状态，基于系统动态学习最优决策。用户设备（UE）的空间分布采用带有拒绝采样技术的非齐次泊松点过程（IPPP）进行建模，以捕捉真实的用户密度变化。仿真结果表明，所提出的GNN增强RL方法在时延和服务可靠性方面显著提升了网络性能，从而能够在应急条件下实现高效且稳健的运行。
英文摘要
During disaster scenarios and periods of extreme data demand in next-generation wireless communications, conventional terrestrial networks (TNs) often become unreliable or fail entirely, leading to critical service disruptions. In such contexts, non-terrestrial networks (NTNs) emerge as a promising solution to ubiquitous and resilient connectivity. Furthermore, the open radio access network (ORAN) paradigm facilitates network disaggregation and flexible functional splitting among its key components, namely the central unit (CU), distributed unit (DU), and radio unit (RU), which can be deployed across heterogeneous NTN platforms according to service requirements. However, this flexibility introduces significant challenges in terms of network complexity and real-time control. To address these challenges, this paper proposes an intelligent ORAN-enabled NTN framework for emergency communication scenarios. The proposed system leverages graph neural networks (GNNs) to model the dynamic network topology and employs a reinforcement learning (RL)-based Q-learning algorithm, formulated as a Markov decision process (MDP), to enable adaptive and real-time network control. In this framework, network nodes are treated as states, and optimal decisions are learned based on system dynamics. The spatial distribution of user equipment (UE) is modeled using an inhomogeneous Poisson point process (IPPP) with a rejection sampling technique, capturing realistic user density variations. Simulation results demonstrate that the proposed GNN-enhanced RL approach significantly improves network performance in terms of latency and service reliability, thereby enabling efficient and robust operation under emergency conditions.
CommentsThe paper accepted for presentation in GLOBECOM 2026
