Edge Intelligence-Empowered Integrated Sensing and Communication: Two Case Studies with Digital Twins
In this webinar, we explore edge intelligence (EI)-empowered integrated sensing and communication (ISAC) through two 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 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 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 resource reservation, continual model adaptation, and cost-efficient edge intelligence for ISAC.
Join us to explore how digital twins and edge intelligence can unlock more adaptive, proactive, and efficient ISAC systems in the next-generation communication networks.
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- Starts 17 August 2026 05:00 PM UTC
- Ends 18 September 2026 12:00 AM UTC
- No Admission Charge
Speakers
Jie Gao of Carleton University
Edge Intelligence-Empowered Integrated Sensing and Communication: Two Case Studies with Digital Twins
In this webinar, we explore edge intelligence (EI)-empowered integrated sensing and communication (ISAC) through two 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 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 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 resource reservation, continual model adaptation, and cost-efficient edge intelligence for ISAC.
Biography:
Dr. Jie Gao (IEEE Senior Member) is an Assistant Professor in the School of Information Technology at Carleton University. His research focuses on machine learning for communications and networking, digital twins, and other emerging technologies for 6G. Dr. Gao is the recipient of the 2025 IEEE VTS OJVT Best Paper Award and the 2024 IEEE Best Land Transportation Paper Award. He has served as Publicity Co-Chair for IEEE VTC2025-Fall and IEEE MetaCom 2023, Publication Co-Chair for ACM/IEEE IoTDI 2022, and Co-Chair for multiple tracks, symposia, and workshops at IEEE international conferences since 2021.
Address:Ottawa, Canada