Meta-Scheduling for the Wireless Downlink through Learning with Bandit Feedback

Abstract

In this talk, we study learning-assisted multi-user scheduling for the wireless downlink. There have been many scheduling algorithms developed that optimize for a plethora of performance metrics; however a systematic approach across diverse performance metrics and deployment scenarios is still lacking. We address this by developing a meta-scheduler given a diverse collection of schedulers, we develop a learning-based over- lay algorithm (meta-scheduler) that selects that “best” scheduler from amongst these for each deployment scenario. More formally, we develop a multi-armed bandit (MAB) framework for meta-scheduling that assigns and adapts a score for each scheduler to maximize reward (e.g., mean delay, timely throughput etc.). The meta-scheduler is based on a variant of the Upper Confidence Bound algorithm (UCB), but adapted to interrupt the queuing dynamics at the base-station so as to filter out schedulers that might render the system unstable. We show that the algorithm has a poly-logarithmic regret in the expected reward with respect to a genie that chooses the optimal scheduler for each scenario. Finally through simulation, we show that the meta-scheduler learns the choice of the scheduler to best adapt to the deployment scenario (e.g. load conditions, performance metrics).

Joint work with: Jianhan Song (UT Austin Ph.D) and Prof. Sanjay Shakkottai UT Austin.

Bio

Gustavo de Veciana received his Ph.D. in electrical engineering from the University of California at Berkeley in 1993, and joined the Department of Electrical and Computer Engineering where he is currently a Cullen Trust Professor of Engineering. He served as the Director and Associate Director of the Wireless Networking and Communications Group (WNCG) at the University of Texas at Austin, from 2003-2007. His research focuses on the analysis and design of communication and computing networks; data-driven decision-making in man-machine systems, and applied probability and queuing theory. Dr. de Veciana served as editor and is currently serving as editor-at-large for the IEEE/ACM Transactions on Networking. He was the recipient of a National Science Foundation CAREER Award 1996 and a co-recipient of five best paper awards. In 2009 he was designated IEEE Fellow for his contributions to the analysis and design of communication networks. He currently serves on the board of trustees of IMDEA Networks Madrid.

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