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DTSTAMP:20210415T012210Z
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DESCRIPTION:Future wireless networks are expected to support a multitude of
  services demanded by Enhanced Mobile Broadband (eMBB)\, Ultra-Reliable an
 d Low-latency Communications (uRLLC)\, and massive Machine Type Communicat
 ions (mMTC) users. Heterogeneous devices with different quality of service
  (QoS) demands will require intelligent and flexible allocation of network
  resources in response to network dynamics. For instance\, a highly reliab
 le and low-latency network is needed to enable rapid transfer of messages 
 between connected autonomous vehicles. At the same time\, the same physica
 l infrastructure is expected to serve users with high-quality video demand
  or even mobile Augmented/Virtual Reality entertainment applications. Next
 -generation wireless networks are expected to accommodate such diverse use
  cases. In addition\, resource efficiency\, reliability\, and robustness a
 re becoming more stringent for 5G and beyond networks. To meet this\, the 
 next generation wireless network\, namely 6G\, must incorporate a paradigm
  shift in network resource optimization\, in which efficient and intellige
 nt resource management techniques are employed. Artificial intelligence\, 
 or more specifically machine learning algorithms stand as promising tools 
 to intelligently manage the networks such that network efficiency\, reliab
 ility\, robustness goals are achieved and quality of service demands are s
 atisfied. The opportunities that arise from learning the environment param
 eters under varying behavior of the wireless channel\, positions AI-enable
 d 5G and 6G\, superior to preceding generations of wireless networks. In t
 his talk\, we will provide an overview of the state-of-art in machine lear
 ning algorithms and their applications to wireless networks\, in addition 
 to their challenges and the open issues in terms of their applicability to
  various functions of future wireless networks.\n\nSpeaker(s): Melike Erol
 -Kantarci\, \n\nSanta Clara University\, Santa Clara\, California\, United
  States\, Virtual: https://events.vtools.ieee.org/m/265026
LOCATION:Santa Clara University\, Santa Clara\, California\, United States\
 , Virtual: https://events.vtools.ieee.org/m/265026
ORGANIZER:bdezfouli@scu.edu
SEQUENCE:5
SUMMARY:AI-Enabled Wireless Networks: A Bridge from 5G to 6G
URL;VALUE=URI:https://events.vtools.ieee.org/m/265026
X-ALT-DESC:Description: <br /><p>Future wireless networks are expected to s
 upport a multitude of services demanded by Enhanced Mobile Broadband (eMBB
 )\, Ultra-Reliable and Low-latency Communications (uRLLC)\, and massive Ma
 chine Type Communications (mMTC) users. Heterogeneous devices with differe
 nt quality of service (QoS) demands will require intelligent and flexible 
 allocation of network resources in response to network dynamics. For insta
 nce\, a highly reliable and low-latency network is needed to enable rapid 
 transfer of messages between connected autonomous vehicles. At the same ti
 me\, the same physical infrastructure is expected to serve users with high
 -quality video demand or even mobile Augmented/Virtual Reality entertainme
 nt applications. Next-generation wireless networks are expected to accommo
 date such diverse use cases. In addition\, resource efficiency\, reliabili
 ty\, and robustness are becoming more stringent for 5G and beyond networks
 . To meet this\, the next generation wireless network\, namely 6G\, must i
 ncorporate a paradigm shift in network resource optimization\, in which ef
 ficient and intelligent resource management techniques are employed. Artif
 icial intelligence\, or more specifically machine learning algorithms stan
 d as promising tools to intelligently manage the networks such that networ
 k efficiency\, reliability\, robustness goals are achieved and quality of 
 service demands are satisfied. The opportunities that arise from learning 
 the environment parameters under varying behavior of the wireless channel\
 , positions AI-enabled 5G and 6G\, superior to preceding generations of wi
 reless networks. In this talk\, we will provide an overview of the state-o
 f-art in machine learning algorithms and their applications to wireless ne
 tworks\, in addition to their challenges and the open issues in terms of t
 heir applicability to various functions of future wireless networks.</p>
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