# Minimizing the Age of Information in Wireless Networks with Stochastic Arrivals

arXiv: https://arxiv.org/abs/1905.07020 · HTML: https://mlsanity.com/paper/1905.07020/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 May 2019.

## Built on by (most cited first, 6 of 6)

- [Efficient Learning-based Scheduling for Information Freshness in Wireless Networks](https://mlsanity.com/paper/2101.00257/) (2021)
- [To Compute or not to Compute? Adaptive Smart Sensing in Resource-Constrained Edge Computing](https://mlsanity.com/paper/2209.02166/) (2022)
- [Transferable Graphical MARL for Real-Time Estimation in Dynamic Wireless Networks](https://mlsanity.com/paper/2404.03227/) (2024)
- [A Novel Switch-Type Policy Network for Resource Allocation Problems: Technical Report](https://mlsanity.com/paper/2501.11136/) (2025)
- [On the Regularity and Fairness of Combinatorial Multi-Armed Bandit](https://mlsanity.com/paper/2509.12457/) (2025)
- [Decentralized Learning Strategies for Estimation Error Minimization with Graph Neural Networks](https://mlsanity.com/paper/2601.12662/) (2026)

## Abstract (arXiv)

We consider a wireless network with a base station serving multiple traffic streams to different destinations. Packets from each stream arrive to the base station according to a stochastic process and are enqueued in a separate (per stream) queue. The queueing discipline controls which packet within each queue is available for transmission. The base station decides, at every time t, which stream to serve to the corresponding destination. The goal of scheduling decisions is to keep the information at the destinations fresh. Information freshness is captured by the Age of Information (AoI) metric. In this paper, we derive a lower bound on the AoI performance achievable by any given network operating under any queueing discipline. Then, we consider three common queueing disciplines and develop both an Optimal Stationary Randomized policy and a Max-Weight policy under each discipline. Our approach allows us to evaluate the combined impact of the stochastic arrivals, queueing discipline and scheduling policy on AoI. We evaluate the AoI performance both analytically and using simulations. Numerical results show that the performance of the Max-Weight policy is close to the analytical lower bound.