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When Deep Reinforcement Learning Meets 5G-Enabled Vehicular Networks: A Distributed Offloading Framework for Traffic Big Data

The emerging 5G-enabled vehicular networks can satisfy various requirements of vehicles by traffic offloading. However, limited cellular spectrum and energy supplies restrict the development of 5G-enabled applications in vehicular networks. In this article, we construct an intelligent offloading fra...

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Bibliographic Details
Published in:IEEE transactions on industrial informatics 2020-02, Vol.16 (2), p.1352-1361
Main Authors: Ning, Zhaolong, Li, Ye, Dong, Peiran, Wang, Xiaojie, Obaidat, Mohammad S., Hu, Xiping, Guo, Lei, Guo, Yi, Huang, Jun, Hu, Bin
Format: Article
Language:English
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Summary:The emerging 5G-enabled vehicular networks can satisfy various requirements of vehicles by traffic offloading. However, limited cellular spectrum and energy supplies restrict the development of 5G-enabled applications in vehicular networks. In this article, we construct an intelligent offloading framework for 5G-enabled vehicular networks, by jointly utilizing licensed cellular spectrum and unlicensed channels. A cost minimization problem is formulated by considering the latency constraint of users and is further decomposed into two subproblems due to its complexity. For the first subproblem, a two-sided matching algorithm is proposed to schedule the unlicensed spectrum. Then, a deep-reinforcement-learning-based method is investigated for the second one, where the system state is simplified to realize distributed traffic offloading. Real-world traces of taxies are leveraged to illustrate the effectiveness of our solution.
ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2019.2937079