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UID:96@lincs.fr
DTSTART;TZID=Europe/Paris:20150527T140000
DTEND;TZID=Europe/Paris:20150527T150000
DTSTAMP:20170313T171045Z
URL:https://www.lincs.fr/events/tbd-5/
SUMMARY:Learning WiFi Performance
DESCRIPTION:Accurate prediction of wireless network performance is
 important when performing link adaptation or resource allocation. However\,
 the complexity of interference interactions at MAC and PHY layers\, as well
 as the vast variety of possible wireless configurations make it notoriously
 hard to design explicit performance models.In this work\, we advocate an
 approach of ``learning by observation''\, where we use machine learning
 techniques to learn implicit performance models\, from a limited number of
 real-world measurements. While our model does not use information on the
 WiFi mechanism itself\, our results show that the accuracy of performance
 prediction is significantly improved as compared to measurement-seeded
 models based on SINR. To demonstrate that learned models can be useful in
 practice\, we build a new algorithm that uses such a model as an oracle to
 jointly allocate spectrum and transmit power. Our algorithm is
 utility-optimal\, distributed\, and it produces efficient allocations that
 significantly improve performance and fairness.Joint work with Julien
 Herzen (EPFL) and Henrik Lundgren (Technicolor)
CATEGORIES:Seminars,Youtube
LOCATION:LINCS Meeting Room 40\, 23\, avenue d'Italie\, Paris\, 75013\,
 France
GEO:48.8283983;2.3568972000000485
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TZID:Europe/Paris
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DTSTART:20150329T030000
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