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DTSTART:20210314T030000
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DTSTART:20211107T010000
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BEGIN:VEVENT
DTSTAMP:20210521T031655Z
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DTSTART;TZID=America/Los_Angeles:20210520T120000
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DESCRIPTION:Future wireless networks are expected to support a multitude of
  services demanded by Enhanced\nMobile Broadband (eMBB)\, Ultra-Reliable a
 nd Low-latency Communications (uRLLC)\, and massive\nMachine Type Communic
 ations (mMTC) users. Heterogeneous devices with different quality of servi
 ce\n(QoS) demands will require intelligent and flexible allocation of netw
 ork resources in response to\nnetwork dynamics. To meet these demands\, fu
 ture wireless networks must incorporate a paradigm shift\nin network resou
 rce optimization\, in which efficient and intelligent resource management 
 techniques\nare employed. Artificial intelligence\, or more specifically m
 achine learning algorithms stand as promising\ntools to intelligently mana
 ge the networks such that network efficiency\, reliability\, robustness go
 als are\nachieved and QoS demands of users are satisfied. In this talk\, w
 e will provide an overview of the state-\nof-art in machine learning algor
 ithms and their applications to wireless networks\, in addition to their\n
 challenges and the open issues in terms of their applicability to various 
 functions of future wireless\nnetworks.\n\nSpeaker(s): Associate Professor
  Melike Erol-Kantarci\, \n\nVirtual: https://events.vtools.ieee.org/m/2708
 29
LOCATION:Virtual: https://events.vtools.ieee.org/m/270829
ORGANIZER:anewton@ieee.org
SEQUENCE:3
SUMMARY:AI-enabled Wireless Networks
URL;VALUE=URI:https://events.vtools.ieee.org/m/270829
X-ALT-DESC:Description: <br /><p>Future wireless networks are expected to s
 upport a multitude of services demanded by Enhanced<br />Mobile Broadband 
 (eMBB)\, Ultra-Reliable and Low-latency Communications (uRLLC)\, and massi
 ve<br />Machine Type Communications (mMTC) users. Heterogeneous devices wi
 th different quality of service<br />(QoS) demands will require intelligen
 t and flexible allocation of network resources in response to<br />network
  dynamics. To meet these demands\, future wireless networks must incorpora
 te a paradigm shift<br />in network resource optimization\, in which effic
 ient and intelligent resource management techniques<br />are employed. Art
 ificial intelligence\, or more specifically machine learning algorithms st
 and as promising<br />tools to intelligently manage the networks such that
  network efficiency\, reliability\, robustness goals are<br />achieved and
  QoS demands of users are satisfied. In this talk\, we will provide an ove
 rview of the state-<br />of-art in machine learning algorithms and their a
 pplications to wireless networks\, in addition to their<br />challenges an
 d the open issues in terms of their applicability to various functions of 
 future wireless<br />networks.</p>
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