2020 IEEE International Symposium
on Information Theory
21-26 June 2020 • Los Angeles, California, USA
| Paper ID | O.5.3 | ||
| Paper Title | Tracking an Auto-Regressive Process with Limited Communication | ||
| Authors | Rooji Jinan, Parimal Parag, Himanshu Tyagi, Indian Institute of Science, Bengaluru, India | ||
| Session | O.5: Topics in Source Coding | ||
| Presentation | Lecture | ||
| Track | Source Coding | ||
| Manuscript | Click here to download the manuscript | ||
| Virtual Presentation | Click here to watch in the Virtual Symposium | ||
| Abstract | Samples from a high-dimensional AR[1] process are quantized and sent over a time-slotted communication channel of finite capacity. The receiver seeks to form an estimate of the process in real-time. We consider the slow-sampling regime where multiple communication slots occur between two sampling instants. We propose a successive update scheme which uses communication between sampling instants to update the estimates of the latest sample. We show that there exist quantizers that render the fast but loose version of this scheme, which updates estimates in every slot, universally optimal asymptotically. However, we provide evidence that most practical quantizers will require a judiciously chosen update frequency. | ||
Plan Ahead
2021 IEEE International Symposium on Information Theory
11-16 July 2021 | Melbourne, Victoria, Australia
