Learning Distributed Channel Access Policies for Networked Estimation: Data-driven Optimization in the Mean-field Regime

Marcos Vasconcelos
Proceedings of The 4th Annual Learning for Dynamics and Control Conference, PMLR 168:702-712, 2022.

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 optimize 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 which enables tractable optimization algorithms. More importantly, we obtain a data-driven learning scheme and a characterization of its convergence rate.

Cite this Paper

BibTeX
@InProceedings{pmlr-v168-vasconcelos22a, title = {Learning Distributed Channel Access Policies for Networked Estimation: Data-driven Optimization in the Mean-field Regime}, author = {Vasconcelos, Marcos}, booktitle = {Proceedings of The 4th Annual Learning for Dynamics and Control Conference}, pages = {702--712}, year = {2022}, editor = {Firoozi, Roya and Mehr, Negar and Yel, Esen and Antonova, Rika and Bohg, Jeannette and Schwager, Mac and Kochenderfer, Mykel}, volume = {168}, series = {Proceedings of Machine Learning Research}, month = {23--24 Jun}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v168/vasconcelos22a/vasconcelos22a.pdf}, url = {https://proceedings.mlr.press/v168/vasconcelos22a.html}, 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 optimize 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 which enables tractable optimization algorithms. More importantly, we obtain a data-driven learning scheme and a characterization of its convergence rate.} }
Endnote
%0 Conference Paper %T Learning Distributed Channel Access Policies for Networked Estimation: Data-driven Optimization in the Mean-field Regime %A Marcos Vasconcelos %B Proceedings of The 4th Annual Learning for Dynamics and Control Conference %C Proceedings of Machine Learning Research %D 2022 %E Roya Firoozi %E Negar Mehr %E Esen Yel %E Rika Antonova %E Jeannette Bohg %E Mac Schwager %E Mykel Kochenderfer %F pmlr-v168-vasconcelos22a %I PMLR %P 702--712 %U https://proceedings.mlr.press/v168/vasconcelos22a.html %V 168 %X 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 optimize 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 which enables tractable optimization algorithms. More importantly, we obtain a data-driven learning scheme and a characterization of its convergence rate.
APA
Vasconcelos, M.. (2022). Learning Distributed Channel Access Policies for Networked Estimation: Data-driven Optimization in the Mean-field Regime. Proceedings of The 4th Annual Learning for Dynamics and Control Conference, in Proceedings of Machine Learning Research 168:702-712 Available from https://proceedings.mlr.press/v168/vasconcelos22a.html.

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