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

arXiv: https://arxiv.org/abs/2112.05837 · HTML: https://mlsanity.com/paper/2112.05837/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 16 references that are in the map.
- First posted on arXiv 10 Dec 2021.

## Builds on (most cited first, 8 of 8)

- [The importance of better models in stochastic optimization](https://mlsanity.com/paper/1903.08619/) (2019)
- [Transporting Robotic Swarms via Mean-Field Feedback Control](https://mlsanity.com/paper/2006.11462/) (2020)
- [Optimal Solutions to Infinite-Player Stochastic Teams and Mean-Field Teams](https://mlsanity.com/paper/1808.02962/) (2018)
- [State Estimation for the Individual and the Population in Mean Field Control with Application to Demand Dispatch](https://mlsanity.com/paper/1504.00088/) (2015)
- [Thompson sampling for linear quadratic mean-field teams](https://mlsanity.com/paper/2011.04686/) (2020)
- [Minimizing a Sum of Clipped Convex Functions](https://mlsanity.com/paper/1910.12342/) (2019)
- [Distributed remote estimation over the collision channel with and without local communication](https://mlsanity.com/paper/2005.11438/) (2020)
- [Linear Regression over Networks with Communication Guarantees](https://mlsanity.com/paper/2103.04140/) (2021)

## Abstract (arXiv)

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.