清华大学|信息学院|国家实验室|English Version

【2017学术报告04】Timely Signal Updates

2017年清华大学信息技术研究院系列学术报告04

 

Title: Timely Signal Updates 

Speaker: Yin Sun,Research Associate , Ohio State University

Time: 10:00-12:00, 20th, July (Thu.) 

Place: 1-415, FIT Building 

Organizer: Research Institute of Information Technology (RIIT), Tsinghua University

 

BIOGRAPHY:

Yin Sun is a Research Associate at the Ohio State University and was a Postdoctoral Fellow at the Ohio State University until 2014. Since August 2017, he will be an Assistant Professor in the Department of Electrical and Computer Engineering at Auburn University. He received the B. Eng. and Ph.D. degrees in electronic engineering from Tsinghua University, Beijing, China, in 2006 and 2011, respectively. His research interests include wireless communications, communication networks, information status updating, and learning. Dr. Sun received the Excellent Doctoral Thesis Award of Tsinghua University in 2011. The paper he co-authored received the best student paper award at IEEE WiOpt 2013.


ABSTRACT:

In many networked control and monitoring systems (e.g., airplane/vehicular control, smart grid, stock trading, robotics, etc.), the system status state, usually in the form of a continuous-time signal, are critical for making decisions and should be reported to the supervisor or control center in a timely fashion. 

In this study, we investigate timely updates of signal measurements, where the signal is modeled as a Wiener process. Samples of the Wiener process are forwarded to a remote estimator via a channel with queueing and random delay. The estimator reconstructs an estimate of the real-time signal value from causally received samples. We obtain the jointly optimal sampling and estimation strategy that minimizes the mean-square estimation error subject to a maximum sampling rate constraint. We prove that a threshold-based sampler and a minimum mean-square error (MMSE) estimator are jointly optimal, and the optimal threshold is found exactly. Our jointly optimal solution exhibits an interesting coupling between the source and channel, which is different from the source-channel separation usually seen in classic information theory. If the sampling times are independent of the observed Wiener process, the joint sampling and estimation optimization problem reduces to an age-of-information optimization problem that has been recently solved. Our theoretical and numerical comparisons show that the estimation error of the optimal sampling policy can be much smaller than those of age-optimal sampling, zero-wait sampling, and classic periodic sampling.

2017信研院学术报告04(2)_副本_副本.jpg

【发布时间:2017-07-17】【浏览次数:417】