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Colloquium on Digital Transformation Science

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  October 2128, 3 pm CT Resource Allocation through ML in Emerging Wireless Networks: 5G and Beyond to 6G Deep Learning to Replace, Improve, or Aid CFD analysis in Built Environment Applications Wei Liu, Assistant Professor of Civil and Architectural Engineering, KTH Royal Institute of TechnologySanjay Shakkottai, Professor of Electrical and Computer Engineering, University of Texas at Austin REGISTER FOR ZOOM WEBINAR 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. Fast and accurate airflow simulations in the built environment are critical to provide acceptable thermal comfort and air quality to occupants. Computational Fluid Dynamics (CFD) offers detailed analysis on airflow motion, heat transfer, and contaminant transport in indoor environments, as well as wind flow and pollution dispersion around buildings in urban environments. However, CFD still faces many challenges, mainly in terms of computational expense and accuracy. With the increasing availability of large amounts of data, data-driven models are starting to be investigated to either replace, improve, or aid CFD simulations. More specifically, the abilities of deep learning and Artificial Neural Networks (ANN) as universal non-linear approximators, handling of high dimensionality fields, and lower computational expense are very appealing. In built environment research, deep learning applications to airflow simulations show the ANN as a surrogate, replacement for expensive CFD analysis. Surrogate modeling enables fast or even real-time predictions, but usually at the cost of degraded accuracy. This talk presents the deep learning interactions with fluid mechanics simulations in general and proposes different techniques other than surrogate modeling for built environment applications. There are promising methods largely yet to be explored in the built environment scene. Wei Liu is an assistant professor at the Division of Sustainable Buildings, Department of Civil and Architectural Engineering, KTH Royal Institute of Technology in Stockholm, Sweden. Liu's current research topics include Indoor Air Quality and Air Distribution, Inverse Design and Control of indoor environments, and Data-Driven/AI-based Smart Buildings. He has published 47 journal papers and 30 conference papers. Liu is an Outstanding Winner and recipient of INFORMS Award from the Mathematical Contest in Modeling 2019, Best Paper Award from ROOMVENT 2018, Bilsland Dissertation Fellowship from Purdue University in 2016, and First Prize of the RP-1493-Shootout Contest from ASHRAE in 2012Sanjay 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 platforms.

  
  

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