Digital Twin in Edge Intelligence-empowered Integrated Sensing and Communication

#digital-twin #edge-computing #communication #sensing #neural-networks #6G
Share
In this seminar, we explore the potential role of digital twins (DTs) in Edge Intelligence (EI)-empowered Integrated Sensing and Communication (ISAC) with two case studies. 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 management in ISAC. Our DT design adaptively synergizes multiple candidate spatial models for location-based resource reservation. Second, we show that DTs can enable a user-centric approach to deep neural networks (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 drifts. Leveraging the above role of DT, we can achieve objectives such as minimizing resource reservation or computation costs subject to performance constraints.
 

  Date and Time

  Location

  Hosts

  Registration


  • Add_To_Calendar_icon Add Event to Calendar
  • 245 church St.
  • Toronto, Ontario
  • Canada M5B 2R2
  • Building: ENG
  • Room Number: 460
  • Contact Event Host
  • Starts 18 December 2025 05:00 AM UTC
  • Ends 19 December 2025 02:02 PM UTC
  • No Admission Charge

  Speakers

Carleton University

Topic:

Digital Twin in Edge Intelligence-empowered Integrated Sensing and Communication

In this seminar, we explore the potential role of digital twins (DTs) in Edge Intelligence (EI)-empowered Integrated Sensing and Communication (ISAC) with two case studies. 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 management in ISAC. Our DT design adaptively synergizes multiple candidate spatial models for location-based resource reservation. Second, we show that DTs can enable a user-centric approach to deep neural networks (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 drifts. Leveraging the above role of DT, we can achieve objectives such as minimizing resource reservation or computation costs subject to performance constraints.
 

Biography:

Dr. Jie Gao (Senior Member, IEEE) received the M.Sc. and Ph.D. degrees in electrical engineering from the University of Alberta, Edmonton, AB, Canada, in 2009 and 2014, respectively. He worked as a postdoctoral fellow at Toronto Metropolitan University, a Research Associate with the University of Waterloo, and an Assistant Professor at Marquette University. He is currently an Assistant Professor with the School of Information Technology, Carleton University, Ottawa, ON. Dr. Gao’s research interests include machine learning for communications and networking, cloud and multiaccess edge computing, Internet of Things and industrial IoT solutions, and B5G/6G networks in general.
 
 

Email:

Address:1125 Colonel By Dr, , Ottawa, Ontario, Canada, K1S 5B6