Wireless Scheduling with Limited Information

We consider the problem of scheduling with incomplete and/or local information in wireless environments. Wireless systems typically suffer from channel fading and interference, and good scheduling algorithms are necessary to adapt to and mitigate these effects. Classical studies on wireless scheduling investigate in much detail settings where the full state of the system is available to the scheduler while making scheduling decisions. In contrast, we focus on cases where valuable network state information is lacking at the scheduler, and study its resulting effect on system performance. Insights gained from the analysis are used to develop effective wireless scheduling algorithms that can operate in a limited information context, at the same time exploiting the full capabilities of the system to yield good performance - captured by metrics like throughput and delay. We present results on the throughput region and throughput-optimal algorithms for two representative wireless downlink scheduling problems which model restricted channel state information and limited coordination capabilities (joint work with Constantine Caramanis and Sanjay Shakkottai).

Bio: Aditya Gopalan is a fourth year PhD student at the Department of Electrical and Computer Engineering, The University of Texas at Austin. He received his Bachelors and Masters degrees in Electrical Engineering from the Indian Institute of Technology Madras in 2006. His research interests include wireless networks, scheduling and queueing theory.