# Efficient Learning-based Scheduling for Information Freshness in Wireless Networks

arXiv: https://arxiv.org/abs/2101.00257 · HTML: https://mlsanity.com/paper/2101.00257/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 30 references that are in the map.
- First posted on arXiv 1 Jan 2021.

## Builds on (most cited first, 5 of 5)

- [Achieving Fairness in the Stochastic Multi-armed Bandit Problem](https://mlsanity.com/paper/1907.10516/) (2019)
- [Combinatorial Sleeping Bandits with Fairness Constraints](https://mlsanity.com/paper/1901.04891/) (2019)
- [Minimizing the Age of Information in Wireless Networks with Stochastic Arrivals](https://mlsanity.com/paper/1905.07020/) (2019)
- [Experimental Analysis of Safety Application Reliability in V2V Networks](https://mlsanity.com/paper/2005.13031/) (2020)
- [Throughput-Optimal Scheduling Design with Regular Service Guarantees in Wireless Networks](https://mlsanity.com/paper/1405.0625/) (2014)

## Built on by (most cited first, 4 of 4)

- [Decentralized Stochastic Multi-Player Multi-Armed Walking Bandits](https://mlsanity.com/paper/2212.06279/) (2022)
- [Learning-based Scheduling for Information Accuracy and Freshness in Wireless Networks](https://mlsanity.com/paper/2310.15705/) (2023)
- [On the Regularity and Fairness of Combinatorial Multi-Armed Bandit](https://mlsanity.com/paper/2509.12457/) (2025)
- [On the Robustness of Age for Learning-Based Wireless Scheduling in Unknown Environments](https://mlsanity.com/paper/2601.05956/) (2026)

## Abstract (arXiv)

Motivated by the recent trend of integrating artificial intelligence into the Internet-of-Things (IoT), we consider the problem of scheduling packets from multiple sensing sources to a central controller over a wireless network. Here, packets from different sensing sources have different values or degrees of importance to the central controller for intelligent decision making. In such a setup, it is critical to provide timely and valuable information for the central controller. In this paper, we develop a parameterized maximum-weight type scheduling policy that combines both the AoI metrics and Upper Confidence Bound (UCB) estimates in its weight measure with parameter η. Here, UCB estimates balance the tradeoff between exploration and exploitation in learning and are critical for yielding a small cumulative regret. We show that our proposed algorithm yields the running average total age at most by O(N2η). We also prove that our proposed algorithm achieves the cumulative regret over time horizon T at most by O(NT/η+√NTlog T). This reveals a tradeoff between the cumulative regret and the running average total age: when increasing η, the cumulative regret becomes smaller, but is at the cost of increasing running average total age. Simulation results are provided to evaluate the efficiency of our proposed algorithm.