An Agile Adaptation Method for Multi-mode Vehicle Communication Networks
Fuente:
arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866911975332642816 |
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| author | He, Shiwen Chen, Kanghong Huang, Shiyue Huang, Wei An, Zhenyu |
| author_facet | He, Shiwen Chen, Kanghong Huang, Shiyue Huang, Wei An, Zhenyu |
| contents | This paper focuses on discovering the impact of communication mode allocation on communication efficiency in the vehicle communication networks. To be specific, Markov decision process and reinforcement learning are applied to establish an agile adaptation mechanism for multi-mode communication devices according to the driving scenarios and business requirements. Then, Q-learning is used to train the agile adaptation reinforcement learning model and output the trained model. By learning the best actions to take in different states to maximize the cumulative reward, and avoiding the problem of poor adaptation effect caused by inaccurate delay measurement in unstable communication scenarios. The experiments show that the proposed scheme can quickly adapt to dynamic vehicle networking environment, while achieving high concurrency and communication efficiency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_01429 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | An Agile Adaptation Method for Multi-mode Vehicle Communication Networks He, Shiwen Chen, Kanghong Huang, Shiyue Huang, Wei An, Zhenyu Networking and Internet Architecture Artificial Intelligence Machine Learning This paper focuses on discovering the impact of communication mode allocation on communication efficiency in the vehicle communication networks. To be specific, Markov decision process and reinforcement learning are applied to establish an agile adaptation mechanism for multi-mode communication devices according to the driving scenarios and business requirements. Then, Q-learning is used to train the agile adaptation reinforcement learning model and output the trained model. By learning the best actions to take in different states to maximize the cumulative reward, and avoiding the problem of poor adaptation effect caused by inaccurate delay measurement in unstable communication scenarios. The experiments show that the proposed scheme can quickly adapt to dynamic vehicle networking environment, while achieving high concurrency and communication efficiency. |
| title | An Agile Adaptation Method for Multi-mode Vehicle Communication Networks |
| topic | Networking and Internet Architecture Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2408.01429 |