# Multi-user Communication Networks: A Coordinated Multi-armed Bandit Approach

arXiv: https://arxiv.org/abs/1808.04875 · HTML: https://mlsanity.com/paper/1808.04875/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Aug 2018.

## Built on by (most cited first, 8 of 8)

- [Regret, stability & fairness in matching markets with bandit learners](https://mlsanity.com/paper/2102.06246/) (2021)
- [A survey on multi-player bandits](https://mlsanity.com/paper/2211.16275/) (2022)
- [Distributed Contextual Linear Bandits with Minimax Optimal Communication Cost](https://mlsanity.com/paper/2205.13170/) (2022)
- [UCB-based Algorithms for Multinomial Logistic Regression Bandits](https://mlsanity.com/paper/2103.11489/) (2021)
- [Medium Access Control protocol for Collaborative Spectrum Learning in Wireless Networks](https://mlsanity.com/paper/2111.12581/) (2021)
- [Online Network Source Optimization with Graph-Kernel MAB](https://mlsanity.com/paper/2307.03641/) (2023)
- [The Price of Decentralization in Top-K Arm Identification](https://mlsanity.com/paper/2608.22120/) (2026)
- [Approximating Optimal Policies in Heterogeneous Asynchronous Multi-player Bandits](https://mlsanity.com/paper/2610.18514/) (2026)

## Abstract (arXiv)

Communication networks shared by many users are a widespread challenge nowadays. In this paper we address several aspects of this challenge simultaneously: learning unknown stochastic network characteristics, sharing resources with other users while keeping coordination overhead to a minimum. The proposed solution combines Multi-Armed Bandit learning with a lightweight signalling-based coordination scheme, and ensures convergence to a stable allocation of resources. Our work considers single-user level algorithms for two scenarios: an unknown fixed number of users, and a dynamic number of users. Analytic performance guarantees, proving convergence to stable marriage configurations, are presented for both setups. The algorithms are designed based on a system-wide perspective, rather than focusing on single user welfare. Thus, maximal resource utilization is ensured. An extensive experimental analysis covers convergence to a stable configuration as well as reward maximization. Experiments are carried out over a wide range of setups, demonstrating the advantages of our approach over existing state-of-the-art methods.