A GNN-Enhanced Reinforcement Learning Framework for Emergency Communications in ORAN-based Non-Terrestrial Networks
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.