Announcements

Colloquium on Digital Transformation Science

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  October 1421, 3 pm CT Universal Laws and Architectures and Their Fragilities Resource Allocation through ML in Emerging Wireless Networks: 5G and Beyond to 6G Sanjay Shakkottai, Professor of Electrical and Computer Engineering, University of Texas at AustinJohn Doyle, Jean-Lou Chameau Professor of Control and Dynamical Systems, Electrical Engineering, and BioEngineering, California Institute of Technology REGISTER FOR ZOOM WEBINAR The past year unfortunately highlighted intrinsic and systemic unsustainability and fragilities in our society and technologies. While detailed mechanisms underlying “systemic fragilities” in immune, medical, computing, social, legal, energy, and transportation systems are incredibly diverse, all are enabled by shared universal features of their architectures, which are largely ad hoc historical artifacts. AI has many well-known fragilities, but outside social media has not so far contributed substantially to the catastrophes unfolding in these systems. This is poised to change dramatically. We need to more systematically design architectures that produce more robust and sustainable systems, including allowing higher layer learning and lower layer efficiencies to contribute. I’ll sketch the basic concepts of laws, layers, levels, and speed-efficiency-accuracy-flexibility trade-offs  (SEAFTs), diversity-enabled sweet spots (DeSS), how crucial hardware layer constraints on sparsity, locality, and delay limit system layer functionality, and how proper layering can mitigate this via DeSS. Examples include all our tech nets, layered brains (e.g., throwing and hitting 100 mph fastballs), layered immunity augmented by medicine and policy (and insights into the current pandemic), systemic legal fragilities and the 14th amendment, cascading failures in energy, climate change, language and its hijacking in social media, encouraging animal models for social architectures, and wildfire ecosystems. In this talk, we discuss learning-inspired algorithms for resource allocation in emerging wireless networks (5G and beyond to 6G). We begin with an overview of opportunities for wireless and ML at various time-scales in network resource allocation. We then present two specific instances to make the case that learning-assisted resource allocation algorithms can significantly improve performance in real wireless deployments. First, we study co-scheduling of ultra-low-latency traffic (URLLC) and broadband traffic (eMBB) in a 5G system, where we need to meet the dual objectives of maximizing utility for eMBB traffic while immediately satisfying URLLC demands. We study iterative online algorithms based on stochastic approximation to achieve these objectives. Next, we study online learning (through a bandit framework) of wireless capacity regions to assist in downlink scheduling, where these capacity regions are “maps” from each channel-state to the corresponding set of feasible transmission rates. In practice, these maps are hand-tuned by operators based on experiments, and these static maps are chosen such that they are good across several base-station deployment scenarios. Instead, we propose an epoch-greedy bandit algorithm for learning scenario-specific maps. We derive regret guarantees, and also empirically validate our approach on a high-fidelity 5G New Radio (NR) wireless simulator developed within AT&T Labs. This is based on joint work with Gustavo de Veciana, Arjun Anand, Isfar Tariq, Rajat Sen, Thomas Novlan, Salam Akoum, and Milap Majmundar. Sanjay Shakkottai received his PhD from the Department of Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign in 2002. He is with the University of Texas at Austin, where he is the Temple Foundation Endowed Professor No. 4, and a Professor in the Department of Electrical and Computer Engineering. He received the NSF CAREER award in 2004, was elected an IEEE Fellow in 2014, and was a co-recipient of the IEEE Communications Society William R. Bennett Prize in 2021. His research interests lie at the intersection of algorithms for resource allocation, statistical learning, and networks, with applications to wireless communication networks and online platformsJohn Doyle is the Jean-Lou Chameau Professor at the California Institute of Technology. He earned his BS and MS degrees in electrical engineering from MIT. He received his doctorate in mathematics from the University of California, Berkeley. His research interests are in integrated theory foundations and architectures for complex networks that enable efficiency and robustness, with applications to tech, bio, neuro, and social systems and an emphasis on the impact on control performance due to delays, sparsity, locality, and saturations in sensors, actuators, communications, and computing components, and how these arise in and challenge bio, neuro, tech, and social system design. He has few academic awards but when younger had many regional, national, and world records and championships in various sports, and he is known for fantastic students and colleagues.

  
  

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