2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDSPCOM-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
SessionSPCOM-1: Signal Processing for Networks
LocationGather.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
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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.