# Graph Neural Networks and Deep Reinforcement Learning Based Resource Allocation for V2X Communications

arXiv: https://arxiv.org/abs/2407.06518 · HTML: https://mlsanity.com/paper/2407.06518/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 19 of its 97 references that are in the map.
- First posted on arXiv 9 Jul 2024.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 19 of 19)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Link Prediction Based on Graph Neural Networks](https://mlsanity.com/paper/1802.09691/) (2018)
- [Graph Embedding based Wireless Link Scheduling with Few Training Samples](https://mlsanity.com/paper/1906.02871/) (2019)
- [A Tutorial on 5G NR V2X Communications](https://mlsanity.com/paper/2102.04538/) (2021)
- [Mobility-Aware Cooperative Caching in Vehicular Edge Computing Based on Asynchronous Federated and Deep Reinforcement Learning](https://mlsanity.com/paper/2208.01219/) (2022)
- [Convergence Analysis and System Design for Federated Learning over Wireless Networks](https://mlsanity.com/paper/2105.00872/) (2021)
- [Generative AI-enabled Vehicular Networks: Fundamentals, Framework, and Case Study](https://mlsanity.com/paper/2304.11098/) (2023)
- [Towards V2I Age-aware Fairness Access: A DQN Based Intelligent Vehicular Node Training and Test Method](https://mlsanity.com/paper/2208.01283/) (2022)
- [Delay-sensitive Task Offloading in Vehicular Fog Computing-Assisted Platoons](https://mlsanity.com/paper/2309.10234/) (2023)
- [URLLC-Awared Resource Allocation for Heterogeneous Vehicular Edge Computing](https://mlsanity.com/paper/2311.18352/) (2023)
- [Cooperative Edge Caching Based on Elastic Federated and Multi-Agent Deep Reinforcement Learning in Next-Generation Network](https://mlsanity.com/paper/2401.09886/) (2024)
- [Semantic-Aware Spectrum Sharing in Internet of Vehicles Based on Deep Reinforcement Learning](https://mlsanity.com/paper/2406.07213/) (2024)
- [Semantic-Aware Resource Allocation Based on Deep Reinforcement Learning for 5G-V2X HetNets](https://mlsanity.com/paper/2406.07996/) (2024)
- [A Swarming Approach to Optimize the One-hop Delay in Smart Driving Inter-platoon Communications](https://mlsanity.com/paper/1807.07301/) (2018)
- [Knowledge-Driven Deep Learning Paradigms for Wireless Network Optimization in 6G](https://mlsanity.com/paper/2402.01665/) (2024)
- [Deep Reinforcement Learning Based Mode Selection and Resource Allocation for Cellular V2X Communications](https://mlsanity.com/paper/2002.05485/) (2020)
- [Dynamic Proximity-aware Resource Allocation in Vehicle-to-Vehicle (V2V) Communications](https://mlsanity.com/paper/1609.03717/) (2016)
- [Joint Optimization of Sensing and Computation for Status Update in Mobile Edge Computing Systems](https://mlsanity.com/paper/2210.17025/) (2022)

## Built on by (most cited first, 18 of 18)

- [DRL-Based Resource Allocation for Motion Blur Resistant Federated Self-Supervised Learning in IoV](https://mlsanity.com/paper/2408.09194/) (2024)
- [DRL-Based Optimization for AoI and Energy Consumption in C-V2X Enabled IoV](https://mlsanity.com/paper/2411.13104/) (2024)
- [Semantic-Aware Resource Management for C-V2X Platooning via Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2411.04672/) (2024)
- [A Survey on Semantic Communications in Internet of Vehicles](https://mlsanity.com/paper/2503.03767/) (2025)
- [A Comprehensive Survey on Joint Resource Allocation Strategies in Federated Edge Learning](https://mlsanity.com/paper/2410.07881/) (2024)
- [Adaptive AI-based Decentralized Resource Management in the Cloud-Edge Continuum](https://mlsanity.com/paper/2501.15802/) (2025)
- [Federated Learning Assisted Edge Caching Scheme Based on Lightweight Architecture DDPM](https://mlsanity.com/paper/2506.04593/) (2025)
- [A Survey on the Role of Artificial Intelligence and Machine Learning in 6G-V2X Applications](https://mlsanity.com/paper/2506.09512/) (2025)
- [Resource Allocation for Twin Maintenance and Computing Task Processing in Digital Twin Vehicular Edge Computing Network](https://mlsanity.com/paper/2407.07575/) (2024)
- [Learning Load Balancing with GNN in MPTCP-Enabled Heterogeneous Networks](https://mlsanity.com/paper/2410.17118/) (2024)
- [V2X-Assisted Distributed Computing and Control Framework for Connected and Automated Vehicles under Ramp Merging Scenario](https://mlsanity.com/paper/2410.22987/) (2024)
- [Enhanced SPS Velocity-adaptive Scheme: Access Fairness in 5G NR V2I Networks](https://mlsanity.com/paper/2501.08037/) (2025)
- [PPO-Based Vehicle Control for Ramp Merging Scheme Assisted by Enhanced C-V2X](https://mlsanity.com/paper/2501.12656/) (2025)
- [GCN-Based Throughput-Oriented Handover Management in Dense 5G Vehicular Networks](https://mlsanity.com/paper/2505.04894/) (2025)
- [Generative Resource Allocation for 6G O-RAN with Diffusion Policies](https://mlsanity.com/paper/2506.07880/) (2025)
- [Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking](https://mlsanity.com/paper/2603.06607/) (2026)
- [iSatCR: Graph-Empowered Joint Onboard Computing and Routing for LEO Data Delivery](https://mlsanity.com/paper/2603.18539/) (2026)
- [AI-Driven Real-Time Relay Optimisation in Smart Urban NR-V2X Networks via Learning-to-Optimise Graph Neural Networks](https://mlsanity.com/paper/2609.20271/) (2026)

## Abstract (arXiv)

In the rapidly evolving landscape of Internet of Vehicles (IoV) technology, Cellular Vehicle-to-Everything (C-V2X) communication has attracted much attention due to its superior performance in coverage, latency, and throughput. Resource allocation within C-V2X is crucial for ensuring the transmission of safety information and meeting the stringent requirements for ultra-low latency and high reliability in Vehicle-to-Vehicle (V2V) communication. This paper proposes a method that integrates Graph Neural Networks (GNN) with Deep Reinforcement Learning (DRL) to address this challenge. By constructing a dynamic graph with communication links as nodes and employing the Graph Sample and Aggregation (GraphSAGE) model to adapt to changes in graph structure, the model aims to ensure a high success rate for V2V communication while minimizing interference on Vehicle-to-Infrastructure (V2I) links, thereby ensuring the successful transmission of V2V link information and maintaining high transmission rates for V2I links. The proposed method retains the global feature learning capabilities of GNN and supports distributed network deployment, allowing vehicles to extract low-dimensional features that include structural information from the graph network based on local observations and to make independent resource allocation decisions. Simulation results indicate that the introduction of GNN, with a modest increase in computational load, effectively enhances the decision-making quality of agents, demonstrating superiority to other methods. This study not only provides a theoretically efficient resource allocation strategy for V2V and V2I communications but also paves a new technical path for resource management in practical IoV environments.