Mobile wireless networks revolutionize our lives and livelihoods. Yet, rural areas, characterized with sparse populations and rugged terrain, consistently lag behind in mobile connectivity compared to their urban counterparts. As a result, community-owned networks realized through fixed wireless tech-nologies, have become an increasingly viable Internet option for otherwise disconnected areas. Fixed wireless, however, is inherently designed for residential/stationary access and is not readily applicable for the use of mobile agents that might travel through a rural community. In this paper we explore the extension of fixed wireless networks for mobile access. A key factor for continuous mobile access is efficient rate adaptation. To that end, we develop WideRate- a reinforcement learning framework that employs signal strength measurements for optimal rate adaptation. We showcase WideRate in the context of wide-area Television White Space networks, whereby we design a vehicular mobile unit and carry out an extensive measurement campaign in a real community network. We use the collected traces to motivate the need for rate adaptation and implement a realistic network simulator that aids in our evaluation. We demonstrate that WideRate significantly outperforms counterparts from the literature including a reinforcement learning model.


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    Titel :

    WideRate: Reinforcement Learning Rate Adaptation for Mobile Wide Area Networks


    Beteiligte:
    Doke, Karyn (Autor:in) / Sadeghi, Elham (Autor:in) / Taneja, Vaasu (Autor:in) / Affinnih, Habib (Autor:in) / Bogdanov, Petko (Autor:in) / Zheleva, Mariya (Autor:in)


    Erscheinungsdatum :

    24.06.2024


    Format / Umfang :

    1959851 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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