用图神经网络实时优化城市车联网多跳中继选择，速度比传统方法快多个数量级。
AI-Driven Multi-Hop Relay Selection for Smart Urban NR-V2X Networks via Learning-to-Optimize Graph Neural Networks
- 构建带属性的车辆-路侧单元图，用图神经网络学习最优中继决策。
- 在真实城市仿真数据上实现接近最优解的连接率，推理时间降低数个量级。
- 适合智能交通、车载通信系统研发者，可直接部署于高动态城市环境。
在密集城市环境中，可靠且低延迟的NR-V2X通信对智慧出行至关重要。然而，路侧单元（RSU）密度有限、频繁出现非视距传播以及高度动态的车辆拓扑，常导致大量联网自动驾驶汽车（CAVs）无法维持稳定单跳连接。尽管多跳中继通信可扩展基础设施覆盖范围，但在实际流量、容量和连通性约束下实时选择中继链路仍具挑战。混合整数线性规划（MILP）虽能获得最优解，但其计算复杂度随网络密度急剧上升，难以满足实时需求。为此，我们提出一种基于图神经网络（GNN）的“学习优化”（L2O）框架，用于实时NR-V2X中继选择。将车辆通信状态建模为带属性的图，其中车辆与路侧单元为节点，候选无线链路融合传播感知特征。利用离线的MILP最优解作为监督信号，通过边感知的图同构网络（GINE）近似最优决策，推理延迟近乎恒定。在集成SUMO--GEMV2仿真平台生成的大规模城市数据集上实验表明，该方法在连接率上接近MILP最优解，同时执行时间降低多个数量级。该框架通过复用现有车载资源，实现了低成本、可扩展的实时城市V2X通信增强。
原文摘要 · Abstract (English)
Reliable and low-latency NR-V2X communications are essential for smart mobility in dense urban environments. However, limited Road-Side Unit (RSU) density, frequent non-line-of-sight conditions, and highly dynamic vehicular topologies often prevent many Connected and Automated Vehicles (CAVs) from maintaining stable single-hop connectivity. Although multi-hop relay-assisted communication can extend infrastructure coverage, selecting relay links in real time under practical flow, capacity, and connectivity constraints remains challenging. Mixed-Integer Linear Programming (MILP) yields optimal multi-hop relay decisions, but its computational complexity scales sharply with network density, limiting real-time applicability. To address this, we propose a Learning-to-Optimise (L2O) framework based on Graph Neural Networks (GNNs) for real-time NR-V2X relay selection. Vehicular communication states are modeled as attributed graphs, where CAVs and RSUs are nodes and candidate radio links are enriched with propagation-aware features. An offline MILP oracle provides optimal supervision, while an edge-aware Graph Isomorphism Network (GINE) approximates oracle decisions with near-constant inference latency. Experiments on large-scale urban datasets generated by an integrated SUMO--GEMV2 simulation pipeline show that the proposed approach achieves connectivity comparable to that of the MILP oracle while reducing execution time by orders of magnitude. The framework enables cost-effective enhancement of urban V2X connectivity by leveraging existing vehicular assets and supporting scalable, real-time NR-V2X operation in smart city environments.
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