Optimization Techniques for RISs - AI/ML webinar

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Special Presentation on “A Comprehensive Overview of Optimization Techniques for RISs

by Dr. Hao Zhou (MgGill University, Canada)

Hosted by Future Networks Artificial Intelligence & Machine Learning (AIML) Working Group

Date/Time: Thursday, February 15th, 2024 @ 12:00 UTC

Registration Link: https://bit.ly/FN-AIML-15Feb2024


Optimization Techniques for RISs


Reconfigurable intelligent surfaces (RISs) have received considerable attention as a key enabler for envisioned 6G networks, for the purpose of improving the network capacity, coverage, efficiency, and security with low energy consumption and low hardware cost. However, integrating RISs into the existing infrastructure greatly increases the network management complexity, especially for controlling a significant number of RIS elements. To unleash the full potential of RISs, efficient optimization approaches are of great importance. This talk will provide a comprehensive overview of state-of-the-art optimization techniques for RIS-aided 6G, e.g., model-based, heuristic, and especially machine learning (ML) algorithms. It will present in-depth analyses of the features, advantages, and difficulties of using various optimization techniques. In addition, it will also present some novel techniques for RIS-optimization, e.g., combining heuristic algorithms with machine learning techniques to enable higher flexibility.

  Date and Time




  • Date: 15 Feb 2024
  • Time: 12:00 PM to 01:00 PM
  • All times are (UTC+00:00) UTC
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  • Contact Event Host
  • Baw Chng baw@ieee.org
  • Co-sponsored by IEEE Future Networks
  • Starts 30 January 2024 11:00 PM
  • Ends 15 February 2024 01:00 PM
  • All times are (UTC+00:00) UTC
  • No Admission Charge


Dr. Hao Zhou of McGill University, Canada


Dr. Hao Zhou is currently a Postdoctoral Researcher at the Department of Computer Science, McGill University, Canada. He obtained his PhD degree in Electrical and Computer Engineering, University of Ottawa, Canada, in 2023. His research focuses on machine learning theory and applications to 5G/6G networks and Smart Grid, including reinforcement learning, transfer learning, large language models, etc. He has published more than 30 peer-reviewed publications in IEEE journals and flagship conferences. He received the 2023 IEEE ICC Conference Best Paper Award for contributions to O-RAN management, and the 2023 IEEE ComSoc CSIM TC Best Journal Paper Award for contributions to transfer learning-enabled network slicing. He also received the Chinese government award for "Outstanding Self-financed Chinese Students Abroad".