Summary of Graph Neural Networks and Deep Reinforcement Learning Based Resource Allocation For V2x Communications, by Maoxin Ji et al.
Graph Neural Networks and Deep Reinforcement Learning Based Resource Allocation for V2X Communications
by Maoxin Ji, Qiong Wu, Pingyi Fan, Nan Cheng, Wen Chen, Jiangzhou Wang, Khaled B. Letaief
First submitted to arxiv on: 9 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Networking and Internet Architecture (cs.NI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed method integrates Graph Neural Networks (GNN) with Deep Reinforcement Learning (DRL) to address the challenge of resource allocation within C-V2X communication. The model constructs a dynamic graph with communication links as nodes and employs the Graph Sample and Aggregation (GraphSAGE) model to adapt to changes in graph structure. This enables vehicles to extract low-dimensional features that include structural information from the graph network based on local observations and make independent resource allocation decisions. Simulation results indicate that the introduction of GNN effectively enhances the decision-making quality of agents, demonstrating superiority to other methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes a new way to manage resources in Internet of Vehicles (IoV) technology. It uses special computer models called Graph Neural Networks (GNN) and Deep Reinforcement Learning (DRL) to help vehicles decide how to use their communication resources. The model builds a network of connections between vehicles and infrastructure, and then uses this network to make decisions about how to share information. This helps ensure that important safety messages are sent quickly and reliably, while also minimizing interference with other types of communication. The results show that this approach is better than others at making good decisions. |
Keywords
» Artificial intelligence » Gnn » Reinforcement learning
