AI 中文总结
研究NR-V2X车辆通信低延迟中继选择问题，提出基于带边特征的图同构网络的边缘感知学习优化框架，通过离线MILP预言机监督GINE，还提出GP-MILP策略，实验证明该方法能紧密匹配MILP决策且降低延迟。
AI 中文摘要
在密集城市环境中，可靠的低延迟上行链路连接是C-V2X网络的关键要求，多跳中继可恢复覆盖，但中继链路激活受多种约束导致NP难优化问题，通常用混合整数线性规划（MILP）解决，其运行时间随图大小扩展性差。本文引入用于实时中继选择的边缘感知学习优化框架。将每个V2X快照建模为有向图，节点特征编码车辆状态和交通需求，边特征捕获无线链路容量。离线MILP预言机生成最优中继配置来监督带边特征的图同构网络（GINE），通过单次前向传递实现边级中继激活，推理延迟严格受限。还提出混合GINE-剪枝MILP（GP-MILP）策略，GINE预测可修剪MILP搜索空间。实验表明GINE在链路级与MILP决策紧密匹配，推理延迟严格受限，GP-MILP在保持MILP等效解的同时大幅降低运行时间。
英文摘要
Reliable, low-latency uplink connectivity is a key requirement for C-V2X networks in dense urban environments, where fast channel variations and blockages often degrade direct vehicle-to-infrastructure links. Multi-hop relaying can restore coverage, but relay-link activation under radio, capacity, and routing constraints results in an NP-hard optimisation problem, typically solved via Mixed-Integer Linear Programming (MILP), whose runtime scales poorly with graph size. This paper introduces an edge-aware Learning-to-Optimise framework for real-time relay selection. Each V2X snapshot is modelled as a directed graph: node features encode vehicle state and traffic demand, while edge features capture radio-link capacity. An offline MILP oracle generates optimal relay configurations that supervise a Graph Isomorphism Network with Edge Features (GINE), enabling edge-level relay activation through a single forward pass, with tightly bounded inference latency. To bridge learning and exact optimisation, we also propose a hybrid GINE-Pruned MILP (GP-MILP) strategy in which GINE predictions prune the MILP search space. Experiments on a large-scale dataset generated via an OSM-SUMO-GEMV$^2$ pipeline show that GINE closely matches MILP decisions at the link level (accuracy 0.9589), F1-score (0.9544) on validation) and yields consistent end-to-end connectivity gains over a 1-hop MILP baseline (up to 9.2% with four RSUs and 12% with two RSUs). Inference latency remains tightly bounded, with all evaluated instances completing within 5~ms. Moreover, GP-MILP preserves MILP-equivalent solutions (same objective value) while achieving solver runtimes below 30~ms for more than 98%) of the graph instances, making MILP-grade optimisation compatible with stringent NR-V2X latency budgets.
Comments6 pages, conference
DOI：10.1109/ICCSPA69228.2026.11600864
