Efficient transmission control is a challenging issue in vehicular networks due to the highly dynamic network environment. In this paper, we propose a Deep reinforcement learning based adaptive Transmission Scheduling Mechanism (DTSM), which is able to adaptively select different transmission control policies based on the current network status and the history data learning. In particular, we first introduce the adaptive transmission scheduling units (ATSU) in both Software-Defined Vehicular Networking (SDVN) controllers and the corresponding base stations. Based on this architecture, we formulate a mathematical model for optimal decision-making in SDVN controllers. Besides, in ATSUs, we proposed a deep Q-learning based transmission control method to dynamically adapt to the time-varying vehicular network scenarios. Simulation results verify that the proposed DTSM solution outperforms the single transmission control method of four existing benchmarks (e.g., TcpVegas, TcpBic, TcpWestwood, TcpVeno) in terms of average throughput and round-trip time.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Deep Reinforcement Learning based Adaptive Transmission Control in Vehicular Networks


    Contributors:
    Liu, Mingyuan (author) / Quan, Wei (author) / Yu, Chengxiao (author) / Zhang, Xue (author) / Gao, Deyun (author)


    Publication date :

    2021-09-01


    Size :

    622197 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    A Reinforcement Learning Based Adaptive Traffic Signal Control for Vehicular Networks

    Krishnendhu, S. P. / Vigneshwari Reddy, Mainampati / Basumatary, Thulunga et al. | Springer Verlag | 2022


    Deep Reinforcement Learning for Traffic Light Control in Vehicular Networks

    Liang, Xiaoyuan / Du, Xunsheng / Wang, Guiling et al. | ArXiv | 2018

    Free access


    Robust Longitudinal Control for Vehicular Platoons Using Deep Reinforcement Learning

    Alves Neto, Armando / Mozelli, Leonardo Amaral | IEEE | 2024


    Edge Service Migration for Vehicular Networks Based on Multi-agent Deep Reinforcement Learning

    Zhang, Haohan / Li, Jinglin / Yuan, Quan | British Library Conference Proceedings | 2020