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DTSTART:20260308T030000
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DTSTART:20261101T010000
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DTSTAMP:20260918T003218Z
UID:F8D0E041-8BD4-4F68-95FF-0155D9912170
DTSTART;TZID=America/New_York:20260917T190000
DTEND;TZID=America/New_York:20260917T200000
DESCRIPTION:In this webinar\, we explore edge intelligence (EI)-empowered i
 ntegrated sensing and communication (ISAC) through two case studies involv
 ing digital twins (DTs). First\, we show that DTs can be used to model the
  stochastic spatial distributions of sensing targets\, which is essential 
 for characterizing service demands and optimizing proactive resource manag
 ement in ISAC. Our DT design adaptively synergizes multiple candidate spat
 ial models for location-based resource reservation. Second\, we show that 
 DTs can enable a user-centric approach to deep neural network (DNN)-based 
 sensing data processing. Given an ISAC device with a small DNN model and a
  mobile edge computing (MEC) server with a large DNN model\, our DT design
  supports continual learning in the presence of data drift. These two case
  studies demonstrate how DTs can be leveraged to support proactive resourc
 e reservation\, continual model adaptation\, and cost-efficient edge intel
 ligence for ISAC.\n\nJoin us to explore how digital twins and edge intelli
 gence can unlock more adaptive\, proactive\, and efficient ISAC systems in
  the next-generation communication networks.\n\nSpeaker(s): \, Jie Gao\n\n
 Virtual: https://events.vtools.ieee.org/m/573023
LOCATION:Virtual: https://events.vtools.ieee.org/m/573023
ORGANIZER:mul224@lehigh.edu
SEQUENCE:35
SUMMARY:Edge Intelligence-Empowered Integrated Sensing and Communication: T
 wo Case Studies with Digital Twins
URL;VALUE=URI:https://events.vtools.ieee.org/m/573023
X-ALT-DESC:Description: <br /><p>In this webinar\, we explore edge intellig
 ence (EI)-empowered integrated sensing and communication (ISAC) through tw
 o case studies involving digital twins (DTs). First\, we show that DTs can
  be used to model the stochastic spatial distributions of sensing targets\
 , which is essential for characterizing service demands and optimizing pro
 active resource management in ISAC. Our DT design adaptively synergizes mu
 ltiple candidate spatial models for location-based resource reservation. S
 econd\, we show that DTs can enable a user-centric approach to deep neural
  network (DNN)-based sensing data processing. Given an ISAC device with a 
 small DNN model and a mobile edge computing (MEC) server with a large DNN 
 model\, our DT design supports continual learning in the presence of data 
 drift. These two case studies demonstrate how DTs can be leveraged to supp
 ort proactive resource reservation\, continual model adaptation\, and cost
 -efficient edge intelligence for ISAC.</p>\n<p>&nbsp\;</p>\n<p>Join us to 
 explore how digital twins and edge intelligence can unlock more adaptive\,
  proactive\, and efficient ISAC systems in the next-generation communicati
 on networks.</p>
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