# Upper-Confidence Bound for Channel Selection in LPWA Networks with Retransmissions

## TL;DR

This paper investigates the use of UCB-based Multi-Arm Bandit algorithms for channel selection in LPWA IoT networks, demonstrating improved transmission success rates by leveraging retransmission data.

## Contribution

It introduces and evaluates UCB-based heuristics for IoT channel access, highlighting their effectiveness and simplicity compared to more complex strategies.

## Key findings

- UCB algorithms significantly improve successful transmission probabilities.
- Pure UCB channel access performs as well as more complex methods.
- Retransmission data enhances the contextual information for learning.

## Abstract

In this paper, we propose and evaluate different learning strategies based on Multi-Arm Bandit (MAB) algorithms. They allow Internet of Things (IoT) devices to improve their access to the network and their autonomy, while taking into account the impact of encountered radio collisions. For that end, several heuristics employing Upper-Confident Bound (UCB) algorithms are examined, to explore the contextual information provided by the number of retransmissions. Our results show that approaches based on UCB obtain a significant improvement in terms of successful transmission probabilities. Furthermore, it also reveals that a pure UCB channel access is as efficient as more sophisticated learning strategies.