# Dynamic Spectrum Access using Stochastic Multi-User Bandits

arXiv: https://arxiv.org/abs/2101.04388 · HTML: https://mlsanity.com/paper/2101.04388/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 16 references that are in the map.
- First posted on arXiv 12 Jan 2021.

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

- [A Practical Algorithm for Multiplayer Bandits when Arm Means Vary Among Players](https://mlsanity.com/paper/1902.01239/) (2019)
- [Concurrent bandits and cognitive radio networks](https://mlsanity.com/paper/1404.5421/) (2014)
- [Multi-User MABs with User Dependent Rewards for Uncoordinated Spectrum Access](https://mlsanity.com/paper/1910.09091/) (2019)
- [Multi-User Multi-Armed Bandits for Uncoordinated Spectrum Access](https://mlsanity.com/paper/1807.00867/) (2018)
- [Multi-user lax communications: a multi-armed bandit approach](https://mlsanity.com/paper/1504.08167/) (2015)

## Built on by (most cited first, 5 of 5)

- [A survey on multi-player bandits](https://mlsanity.com/paper/2211.16275/) (2022)
- [Distributed Bandits with Heterogeneous Agents](https://mlsanity.com/paper/2201.09353/) (2022)
- [Competing for Shareable Arms in Multi-Player Multi-Armed Bandits](https://mlsanity.com/paper/2305.19158/) (2023)
- [Anonymous Bandits for Multi-User Systems](https://mlsanity.com/paper/2210.12198/) (2022)
- [PPA-Game: Characterizing and Learning Competitive Dynamics Among Online Content Creators](https://mlsanity.com/paper/2403.15524/) (2024)

## Abstract (arXiv)

A stochastic multi-user multi-armed bandit framework is used to develop algorithms for uncoordinated spectrum access. In contrast to prior work, it is assumed that rewards can be non-zero even under collisions, thus allowing for the number of users to be greater than the number of channels. The proposed algorithm consists of an estimation phase and an allocation phase. It is shown that if every user adopts the algorithm, the system wide regret is order-optimal of order O(log T) over a time-horizon of duration T. The regret guarantees hold for both the cases where the number of users is greater than or less than the number of channels. The algorithm is extended to the dynamic case where the number of users in the system evolves over time, and is shown to lead to sub-linear regret.