Learning distributed channel access policies for networked estimation: data-driven optimization in the mean-field regime
This addresses bandwidth limitations in distributed systems like IoT and sensor networks, offering a scalable solution for remote estimation, though it is incremental as it builds on mean-field methods.
The paper tackles the problem of designing decentralized channel access and estimation policies for large-scale networked systems under bandwidth constraints, showing that in the mean-field regime, the problem becomes tractable and proposing a data-driven learning scheme with finite sample-complexity guarantees.
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.