Leveraging AI & Machine Learning to Optimize Today’s 5G Radio Access Network Systems and to Build the Foundation of Tomorrow’s 6G Wireless Systems

Machine Learning and data-driven models for signal processing have been an extremely exciting space over the past few years and hold enormous promise for state-of-the-art performance in communications systems in complex multi-user and multi-antenna environments and over difficult mediums.

Wireless applications have long struggled with the tradeoff between tractable optimization problems and model deficit, where our models for the propagation environment, hardware and antenna effects are not accurate enough to represent the stochastic nature of the environment to optimize to its full potential. Data-driven methods have up-ended many years’ worth of thinking in this domain, and allowed us to accomplish both, leveraging detailed and accurate data and distribution information in deployed wireless systems while also maintaining simple high-level problem definitions and efficient implementations.

This talk will highlight how AI and ML techniques are embracing data in order to better shape efficient radio access networks today and even more so in future cellular technology. We’ll focus on how the physical layer can be thought of as a data-driven machine learning problem, and what this means for better radio and gNB performance in today’s systems. We’ll also discuss how this could more deeply impact future, beyond 5G radio waveform and protocol design to further increase efficiency and density. We’ll provide an overview of work we’ve been doing at DeepSig and at Virginia Tech in order to help realize these visions and discuss other areas where AI/ML techniques hold enormous promise in helping to optimize future wireless systems. Overall, we’ll highlight how data, data driven competitions such as the ITU AI/ML 5G challenges, and data-driven optimization and validation of approaches are an important avenue for communications engineers to embrace at many levels of the stack as communications systems evolve.

Speakers, Panelists and Moderators

    Co-Founder/CTO of DeepSig
    Tim is the Co-Founder/CTO of DeepSig, a venture backed startup building machine learning driven wireless capabilities and optimizing 5G RAN and Open-RAN deployments, and also serves as a research assistant professor at Virginia Tech in Arlington, VA. He previously worked with a USG lab on software and cognitive radio applied research technologies and rapid prototyping, helping build and lead the GNU Radio project, is a Co-Chair of the IEEE Emerging Technology Area on Machine Learning for Communications, an editor for Transactions on Wireless Communications and TCCN, Co-Chair for IEEE GlobeCom and IEEE ICC ML4Comms workshops in 2020, Chair of the GNU Radio Conference Technical Proceedings, and Co-author of over 50 peer reviewed conference and journal papers with over 3000 citations, over 20 patents, primarily focusing on the intersection of wireless communications and machine learning. Previously he was a technical consultant for Hawkeye 360, Federated Wireless, O’Shea Research, and at Cisco Systems. He has served as a technical advisory board member for programs at NSF, DARPA, EU HORIZON 2020, and DOD programs. He completed his PhD from VT ECE in 2017 and his BS/MS in ECE at NC State in 2007.


27 Nov 2020


CET, Geneva
14:00 - 15:00



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