AI 中文总结
研究智能城市NR-V2X网络中多跳中继选择问题，提出基于图神经网络的学习优化框架，将车辆通信状态建模为属性图，通过离线MILP预言机监督、边缘感知图同构网络近似决策，实验证明该方法能在降低执行时间的同时实现与MILP相当的连接性。
AI 中文摘要
可靠且低延迟的NR-V2X通信对密集城市环境中的智能移动性至关重要。然而，路边单元（RSU）密度有限、频繁的非视距条件和高度动态的车辆拓扑结构常常阻碍许多联网和自动驾驶车辆（CAV）维持稳定的单跳连接。多跳中继辅助通信虽可扩展基础设施覆盖范围，但在实际流量、容量和连接性约束下实时选择中继链路仍具挑战性。混合整数线性规划（MILP）能产生最优多跳中继决策，但其计算复杂度随网络密度急剧增加，限制了实时适用性。为解决此问题，我们提出基于图神经网络（GNN）的学习优化（L2O）框架用于实时NR-V2X中继选择。车辆通信状态被建模为属性图，其中CAV和RSU为节点，候选无线链路具有传播感知特征。离线MILP预言机提供最优监督，而边缘感知图同构网络（GINE）以近乎恒定的推理延迟近似预言机决策。在由集成SUMO-GEMV2模拟管道生成的大规模城市数据集上的实验表明，该方法在将执行时间减少几个数量级的同时，实现了与MILP预言机相当的连接性。该框架通过利用现有车辆资产，支持智能城市环境中可扩展的实时NR-V2X操作，实现了具有成本效益的城市V2X连接增强。
英文摘要
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.
Comments7 pages, conference
DOI：10.1109/SmartNets69662.2026.11604994
