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 a 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, Professor of Electrical and Computer Engineering, University of Texas at Austin. He 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.