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

TL;DR

This paper develops a data-driven approach to optimize distributed channel access policies in large-scale sensor networks, ensuring high-fidelity data collection under bandwidth constraints in the mean-field regime.

Contribution

It introduces a novel mean-field formulation for decentralized access and provides a finite-sample guarantee for the learning-based optimization scheme.

Findings

Tractable optimization algorithms for large-scale sensor networks.

Finite-sample performance guarantees for the learning scheme.

Effective data-driven policies for networked estimation in contested environments.

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.