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.
WideRate: Reinforcement Learning Rate Adaptation for Mobile Wide Area Networks
24.06.2024
1959851 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Rate Adaptation by Reinforcement Learning for Wi-Fi Industrial Networks
BASE | 2020
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