| Paper ID | SPCOM-1.4 |
| Paper Title |
A SAMPLE-EFFICIENT SCHEME FOR CHANNEL RESOURCE ALLOCATION IN NETWORKED ESTIMATION |
| Authors |
Marcos Vasconcelos, Virginia Tech, United States; Urbashi Mitra, University of Southern California, United States |
| Session | SPCOM-1: Signal Processing for Networks |
| Location | Gather.Town |
| Session Time: | Tuesday, 08 June, 16:30 - 17:15 |
| Presentation Time: | Tuesday, 08 June, 16:30 - 17:15 |
| Presentation |
Poster
|
| Topic |
Signal Processing for Communications and Networking: [SPCN-NETW] Networks and Network Resource allocation |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
Remote estimation over communication channels of limited capacity is an area of research with applications spanning many economically relevant areas, including cyber-physical systems and the Internet of Things. One popular choice of communication/scheduling policies used in remote estimation is the class of event-triggered policies. Typically, an event- triggering threshold is optimized, assuming complete knowledge of the system's underlying probabilistic model. However, this information is seldom available in real-world applications. This paper addresses the learning of an optimal threshold policy based on data samples collected at the sensor. Leveraging symmetry, quasi-convexity, and the method of Kernel density estimation, we propose a data-driven algorithm, which is guaranteed to converge to a globally optimal solution. Moreover, empirical evidence suggests that our algorithm is more sample-efficient than traditional learning approaches based on empirical risk minimization. |
