Low-Latency Relay Selection in NR-V2X Vehicular Communications via Graph Isomorphism Networks with Edge Features
This paper introduces an edge-aware learning-to-optimize framework for real-time relay selection in NR-V2X vehicular communications. By modeling V2X snapshots as directed graphs and using offline MILP solutions to supervise a Graph Isomorphism Network with Edge Features (GINE), the approach achieves inference latency within 5 ms. The GINE achieves 0.9589 accuracy and 0.9544 F1-score at link level, while a hybrid GP-MILP strategy reduces solver runtime below 30 ms for over 98% of graph instances while preserving optimality.
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[Submitted on 15 Jul 2026]
Title:Low-Latency Relay Selection in NR-V2X Vehicular Communications via Graph Isomorphism Networks with Edge Features
View a PDF of the paper titled Low-Latency Relay Selection in NR-V2X Vehicular Communications via Graph Isomorphism Networks with Edge Features, by Giambattista Amati and Federica Mangiatordi and Emiliano Pallotti and Simone Angelini and Pierpaolo Salvo and Paola Vocca
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Abstract: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.
Comments: 6 pages, conference
Subjects:
Machine Learning (cs.LG); Networking and Internet Architecture (cs.NI)
Cite as: arXiv:2607.14176 [cs.LG]
(or arXiv:2607.14176v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2607.14176
arXiv-issued DOI via DataCite
Submission history
From: Federica Mangiatordi [view email] [v1] Wed, 15 Jul 2026 12:50:48 UTC (1,321 KB)
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