# Joint AP Probing and Scheduling: A Contextual Bandit Approach

arXiv: https://arxiv.org/abs/2108.03297 · HTML: https://mlsanity.com/paper/2108.03297/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 22 references that are in the map.
- First posted on arXiv 6 Aug 2021.

## Builds on (most cited first, 1 of 1)

- [Combinatorial Sleeping Bandits with Fairness Constraints](https://mlsanity.com/paper/1901.04891/) (2019)

## Built on by (most cited first, 3 of 3)

- [Online Learning for Adaptive Probing and Scheduling in Dense WLANs](https://mlsanity.com/paper/2212.13585/) (2022)
- [Fair Algorithms with Probing for Multi-Agent Multi-Armed Bandits](https://mlsanity.com/paper/2506.14988/) (2025)
- [Online Learning with Probing for Sequential User-Centric Selection](https://mlsanity.com/paper/2507.20112/) (2025)

## Abstract (arXiv)

We consider a set of APs with unknown data rates that cooperatively serve a mobile client. The data rate of each link is i.i.d. sampled from a distribution that is unknown a priori. In contrast to traditional link scheduling problems under uncertainty, we assume that in each time step, the device can probe a subset of links before deciding which one to use. We model this problem as a contextual bandit problem with probing (CBwP) and present an efficient algorithm. We further establish the regret of our algorithm for links with Bernoulli data rates. Our CBwP model is a novel extension of the classic contextual bandit model and can potentially be applied to a large class of sequential decision-making problems that involve joint probing and play under uncertainty.