Reinforcement Learning for Minimizing Age of Information over Wireless Links

Date

2022-12-01

Author

Ceran Arslan, Elif Tuğçe
Gündüz, Deniz
Gyorgy, Andras

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

109
views

0
downloads

Cite This

In this chapter, we study the age of information (AoI) when status updates of an underlying process of interest, sampled and recorded by a source node, must be transmitted to one or more destination nodes over error-prone wireless channels. We consider the practical setting, in which the statistics of the system are not known a priori, and must be learned in an online fashion. This requires designing (RL) algorithms that can adapt their policy dynamically through interactions with the environment. Accordingly, the aim of this chapter is to design and analyze RL algorithms to minimize the average AoI at the destination nodes, taking into account retransmissions due to channel errors.

URI

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/85D7CE48241DB3E7FBBF5A855F6D92D1/stamped-9781108837873c13_327-363.pdf/reinforcement_learning_for_minimizing_age_of_information_over_wireless_links.pdf
https://hdl.handle.net/11511/105834

Collections

Department of Electrical and Electronics Engineering, Book / Book chapter