# A Learning-based Distributed Algorithm for Scheduling in Multi-hop Wireless Networks

arXiv: https://arxiv.org/abs/2312.04754 · HTML: https://mlsanity.com/paper/2312.04754/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Dec 2023.

## Built on by (most cited first, 2 of 2)

- [On the Low-Complexity of Fair Learning for Combinatorial Multi-Armed Bandit](https://mlsanity.com/paper/2501.00924/) (2025)
- [Distributed No-Regret Learning for Multi-Stage Systems with End-to-End Bandit Feedback](https://mlsanity.com/paper/2404.04509/) (2024)

## Abstract (arXiv)

We address the joint problem of learning and scheduling in multi-hop wireless network without a prior knowledge on link rates. Previous scheduling algorithms need the link rate information, and learning algorithms often require a centralized entity and polynomial complexity. These become a major obstacle to develop an efficient learning-based distributed scheme for resource allocation in large-scale multi-hop networks. In this work, by incorporating with learning algorithm, we develop provably efficient scheduling scheme under packet arrival dynamics without a priori link rate information. We extend the results to distributed implementation and evaluation their performance through simulations.