Learning distributed channel access policies for networked estimation: data-driven optimization in the mean-field regime

• URL: http://arxiv.org/abs/2112.05837v1
• Date: Fri, 10 Dec 2021 21:27:45 GMT
• Title: Learning distributed channel access policies for networked estimation: data-driven optimization in the mean-field regime
• Authors: Marcos M. Vasconcelos
• Abstract summary: The problem of communicating sensor measurements over shared networks is prevalent in many modern large-scale distributed systems. We show that in the mean-field regime, this problem exhibits a structure that enables tractable optimization algorithms. We obtain a data-driven learning scheme that admits a finite sample-complexity guarantee on the performance of the resulting estimation system.
• Score: 1.52292571922932
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The problem of communicating sensor measurements over shared networks is prevalent in many modern large-scale distributed systems such as cyber-physical systems, wireless sensor networks, and the internet of things. Due to bandwidth constraints, the system designer must jointly design decentralized medium access transmission and estimation policies that accommodate a very large number of devices in extremely contested environments such that the collection of all observations is reproduced at the destination with the best possible fidelity. We formulate a remote estimation problem in the mean-field regime where a very large number of sensors communicate their observations to an access point, or base station, under a strict constraint on the maximum fraction of transmitting devices. We show that in the mean-field regime, this problem exhibits a structure that enables tractable optimization algorithms. More importantly, we obtain a data-driven learning scheme that admits a finite sample-complexity guarantee on the performance of the resulting estimation system under minimal assumptions on the data's probability density function.

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