BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//wp-events-plugin.com//7.2.3.1//EN
BEGIN:VEVENT
UID:802@lincs.fr
DTSTART;TZID=Europe/Paris:20231129T140000
DTEND;TZID=Europe/Paris:20231129T150000
DTSTAMP:20231211T094325Z
URL:https://www.lincs.fr/events/reinforcement-learning-and-optimization-fo
 r-an-energy-and-resource-efficient-5g-slicing/
SUMMARY:Reinforcement Learning and optimization for an energy and resource
 efficient 5G slicing
DESCRIPTION:This talk\, which will be the rehearsal of my thesis\,
 addresses resource allocation problems in 5G networks. Our objective is to
 leverage network slicing (e.g. the set of techniques based on
 virtualization and network softwarization which allows the network operator
 to provide different amounts of resources to different tenants) in order to
 to improve the energy-efficiency and resource consumption of 5G networks\,
 while guaranteeing Quality of Service constraints. To do so\, we formulate
 and solve optimization problems at the different domains of the network: We
 are first concerned with the placement of slices in the core network. To
 solve the problem\, a new approach combining Monte Carlo Search and
 Neighborhood Search is formulated.\n\nWe show it accepts more core slices
 than state-of-the-art approaches for the core network placement problem.
 Then we shift the focus to energy efficiency in resource allocation in 5G
 networks shared between Physical Network Operators (PNOs) and Mobile
 Virtual Network Operators (MVNOs). This framework jointly considers
 software component placement\, user request routing\, and resource
 dimensioning while meeting Service Level Agreements (SLAs) based on latency
 and reliability constraints.\n\nThrough Column Generation\, we obtain exact
 solutions\, demonstrating energy savings of up to 50\\% in real networks
 compared to existing placement or resource minimization algorithms.
 Finally\, we delve into the realm of energy optimization in Integrated
 Access and Backhaul (IAB) networks\, a key component of dense 5G
 deployments. Leveraging the Open Radio Access Network (O-RAN) framework\,
 our model minimizes active IAB nodes while ensuring a minimum capacity for
 User Equipment (UE).\n\nFormulated as a binary nonlinear program\, this
 approach reduces RAN energy consumption by 47\\%\, while maintaining
 Quality-Of-Service for UEs. Overall\, this thesis provides novel algorithms
 for improving resource and energy efficiency of 5G network slicing. Such
 improvement is studied in different parts of the network\, from the core up
 to the access network.
CATEGORIES:Seminars,Youtube
LOCATION:Room 4B01\, 19 place Marguerite Perey\, Palaiseau\, France
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=19 place Marguerite Perey\,
 Palaiseau\, France;X-APPLE-RADIUS=100;X-TITLE=Room 4B01:geo:0,0
END:VEVENT
BEGIN:VTIMEZONE
TZID:Europe/Paris
X-LIC-LOCATION:Europe/Paris
BEGIN:STANDARD
DTSTART:20231029T020000
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
END:STANDARD
END:VTIMEZONE
END:VCALENDAR