# Low-Latency Relay Selection in NR-V2X Vehicular Communications via Graph Isomorphism Networks with Edge Features

arXiv: https://arxiv.org/abs/2607.14176 · HTML: https://mlsanity.com/paper/2607.14176/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 24 references that are in the map.
- First posted on arXiv 15 Jul 2026.

## Builds on (most cited first, 8 of 8)

- [Machine Learning for Combinatorial Optimization: a Methodological Tour d'Horizon](https://mlsanity.com/paper/1811.06128/) (2018)
- [Co-embedding of Nodes and Edges with Graph Neural Networks](https://mlsanity.com/paper/2010.13242/) (2020)
- [Graph Neural Networks for Distributed Power Allocation in Wireless Networks: Aggregation Over-the-Air](https://mlsanity.com/paper/2207.08498/) (2022)
- [Autonomous Vehicles in 5G and Beyond: A Survey](https://mlsanity.com/paper/2207.10510/) (2022)
- [Learning for Robust Combinatorial Optimization: Algorithm and Application](https://mlsanity.com/paper/2112.10377/) (2021)
- [A survey on road safety and traffic efficiency vehicular applications based on C-V2X technologies](https://mlsanity.com/paper/2403.17851/) (2024)
- [GNN-Based Joint Channel and Power Allocation in Heterogeneous Wireless Networks](https://mlsanity.com/paper/2408.03957/) (2024)
- [Propagation Channels for mmWave Vehicular Communications: State-of-the-art and Future Research Directions](https://mlsanity.com/paper/1812.02483/) (2018)

## Built on by (most cited first, 1 of 1)

- [Learning-to-Optimize as the Missing Architectural Layer of AI-Native Networks](https://mlsanity.com/paper/2609.21519/) (2026)

## Abstract (arXiv)

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-GEMV2 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.