Wireless Scheduling with Partial Information - Low Delay and High Throughput Scheduling Algorithms

Modern wireless networks support increasing amounts of data traffic over a wide range of services, e.g. high-speed file transfer, real time audio/video and peer-to-peer applications. Effective scheduling is required in wireless systems to deliver high performance, with many users and limited system resources. Together with maximizing user throughput, scheduling must keep packet delays down to a minimum in order to reliably support delay-sensitive applications like audio/video streaming and gaming. The problem is especially compounded when, due to limited feedback resources in the wireless system, only partial network state information is available to the scheduler. In this talk, we consider wireless scheduling where channel state information can be obtained from only subsets of users. For this setting, we present joint subset-selection and user-scheduling algorithms that maximize throughput and minimize the likelihood of long queues. In particular, these scheduling algorithms provably achieve the best exponential decay rate (large-deviations rate function), among all scheduling algorithms using partial channel state, of the tail of the longest queue in the system. Time permitting, I will outline other recent work and new directions in scheduling with information delays/mismatches, the speed of controlled network information dissemination, and learning in dynamic environments.

Bio: Aditya Gopalan received the B.Tech. and M.Tech. degrees in Electrical Engineering from the Indian Institute of Technology (IIT) Madras, Chennai, in 2006, and the PhD in Electrical Engineering from The University of Texas at Austin in 2011. He was a summer intern at the Corporate Research and Development Center, Qualcomm Inc., in 2009. He is currently a Post Doctoral Research Fellow in the Faculty of Electrical Engineering at the Technion- Israel Institute of Technology, Haifa, Israel. His current research interests include network algorithms, stochastic control, and machine learning.