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- W3185785890 abstract "Today's Wireless Local Area Networks (WLANs) rely on a centralized Access Controller (AC) entity for managing a fleet of Access Points (APs). Real-time analytics enable the AC to optimize the radio resource allocation (i.e. channels) online in response to sudden traffic shifts. Deep Reinforcement Learning (DRL) relieves the pressure of finding good optimization heuristics by learning a policy through interactions with the environment. However, it is not granted that DRL will behave well in unseen conditions. Tools such as the WiFi Dynoscope introduced here are necessary to gain this trust. In a nutshell, this demo dissects the dynamics of WLAN networks, both simulated and from real large-scale deployments, by (i) comparatively analyzing the performance of different algorithms on the same deployment at high level and (ii) getting low-level details and insights into algorithmic behaviour." @default.
- W3185785890 created "2021-08-02" @default.
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- W3185785890 date "2021-05-10" @default.
- W3185785890 modified "2023-09-27" @default.
- W3185785890 title "WiFi Dynoscope: Interpretable Real-Time WLAN Optimization" @default.
- W3185785890 cites W1561068705 @default.
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- W3185785890 doi "https://doi.org/10.1109/infocomwkshps51825.2021.9484543" @default.
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