Tut7: Reinforcement Learning for Optimization of Wireless Systems: Methodology, Exploration and Optimization

Virtual Program Link: https://event.on24.com/wcc/r/2336363/74A4FC5B464937FE44FC3D25D51779EF

Instructor: Haris Gacanin, Nokia Bell Labs, Belgium

Abstract: This tutorial discusses technology and opportunities to embrace artificial intelligence (AI) in the design of autonomous wireless systems. We aim to provide readers with motivation and general AI methodology of autonomous agents in the context of self-organization in real time unifying sensing, perception, reasoning and learning. We discuss differences between training-based and training-free AI methodology for both matching and dynamic problems, respectively. Finally, we introduce the conceptual functions of autonomous agent with knowledge management. Finally, a practical case study is given to illustrate the application and potential gains.

Instructor Biography

Haris Gacanin
Bio: Haris Gacanin received his Dipl.-Ing. degree in Electrical engineering from University of Sarajevo, Bosnia and Herzegovina, in 2000. In 2005 and 2008, he received M.E.E. and Ph.D. from Tohoku University, Japan. He was with Tohoku University from April 2008 until May 2010 first as Japan Society for Promotion of Science postdoctoral fellow and then, as Assistant Professor. Since 2010, he is with Alcatel-Lucent (now Nokia), where he is currently Department Head at Nokia Bell Labs leading research activities related to application of artificial intelligence in network optimization with focus on physical (L1) and media access (L2) layer technologies and network architectures. He has more than 200+ publications (journals, conferences and patens) and invited/tutorial talks. He organized and hosted several tutorials and industry panels at IEEE conferences. He is VTS distinguished lecturer and senior member of the IEEE and IEICE.