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

2024-07-09

IEEE Internet of Things Journal

AI Summary

To address the resource allocation challenge in cellular vehicle-to-everything (C-V2X) systems—where ultra-low-latency, high-reliability vehicle-to-vehicle (V2V) communications coexist with high-throughput vehicle-to-infrastructure (V2I) requirements—this paper proposes a distributed cooperative decision-making framework based on dynamic communication graph modeling. For the first time, GraphSAGE is introduced to model the time-varying V2X topology, integrating graph neural networks (GNNs) with deep reinforcement learning (DRL) to jointly achieve global structural awareness and lightweight local decision-making. The method operates in a fully decentralized manner: vehicles autonomously allocate resources using only local observations, without reliance on centralized coordination. Simulation results demonstrate that the proposed approach significantly improves V2V communication success probability, effectively mitigates interference on V2I links, and achieves superior decision quality compared to state-of-the-art baseline methods—while incurring only marginal additional computational overhead.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Graph-based Machine Learning
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

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 ultralow latency and high reliability in vehicle-to-vehicle (V2V) communication. This article proposes a method that integrates graph neural networks (GNNs) 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.

Problem

• Optimize resource allocation in C-V2X for low latency
• Minimize V2V interference while maintaining V2I rates
• Enable distributed decisions via GNN and DRL integration

Innovation

• Integrates Graph Neural Networks with Deep Reinforcement Learning
• Uses GraphSAGE model for dynamic graph adaptation
• Supports distributed network deployment for resource allocation