# Status Updates Through Multicast Networks

arXiv: https://arxiv.org/abs/1709.02427 · HTML: https://mlsanity.com/paper/1709.02427/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Sep 2017.

## Built on by (most cited first, 3 of 3)

- [A Reinforcement Learning Approach to Age of Information in Multi-User Networks with HARQ](https://mlsanity.com/paper/2102.09774/) (2021)
- [Timeliness-Fidelity Tradeoff in 3D Scene Representations](https://mlsanity.com/paper/2407.16575/) (2024)
- [Task-Oriented Communications for 3D Scene Representation: Balancing Timeliness and Fidelity](https://mlsanity.com/paper/2509.17282/) (2025)

## Abstract (arXiv)

Using age of information as the freshness metric, we examine a multicast network in which real-time status updates are generated by the source and sent to a group of n interested receivers. We show that in order to keep the information freshness at each receiver, the source should terminate the transmission of the current update and start sending a new update packet as soon as it receives the acknowledgements back from any k out of n nodes. As the source stopping threshold k increases, a node is more likely to get the latest generated update, but the age of the most recent update is more likely to become outdated. We derive the age minimized stopping threshold k that balances the likelihood of getting the latest update and the freshness of the latest update for shifted exponential link delay. Through numerical evaluations for different stopping strategies, we find that waiting for the acknowledgements from the earliest k out of n nodes leads to lower average age than waiting for a pre-selected group of k nodes. We also observe that a properly chosen threshold k can prevent information staleness for increasing number of nodes n in the multicast network.