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UID:353@lincs.fr
DTSTART;TZID=Europe/Paris:20180103T140000
DTEND;TZID=Europe/Paris:20180103T160000
DTSTAMP:20171129T080532Z
URL:https://www.lincs.fr/events/reinforcement-learning-for-efficient-acces
 s-control-with-m2mh2h-traffic-in-lte-a-networks/
SUMMARY:Reinforcement Learning for Efficient Access Control with M2M/H2H
 traffic in LTE-A networks
DESCRIPTION:Although many advantages are expected from the provision of
 services for M2M communications in cellular networks such as extended
 coverage\, security\, robust management and lower deployment costs\,
 coexistence with a large number of M2M devices is still an important
 challenge\, in part due to the difficulty in allowing simultaneous access.
 Although the random access procedure in LTE-A is adequate for H2H
 communications\, it is necessary to optimize this mechanism when M2M
 communications are considered. One of the proposed methods in 3GPP is
 Access Class Barring (ACB)\, which is able to reduce the number of
 simultaneous users contending for access. However\, it is still not clear
 how to adapt its parameters in dynamic or bursty scenarios\, such as those
 that appear when M2M communications are introduced.We propose a dynamic
 mechanism for ACB based on reinforcement learning\, which aims to reduce
 the impact that M2M communications have over H2H communications\, while at
 the same time ensuring that the KPIs for all users are in acceptable
 levels.
CATEGORIES:Seminars
LOCATION:LINCS Seminars room\, 23\, avenue d'Italie\, Paris\, 75013\,
 France
GEO:48.828400;2.356897
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 Paris\, 75013\, France;X-APPLE-RADIUS=100;X-TITLE=LINCS Seminars
 room:geo:48.828400,2.356897
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TZID:Europe/Paris
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DTSTART:20171029T020000
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
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