# Parallel Contextual Bandits in Wireless Handover Optimization

## TL;DR

This paper introduces two parallel contextual bandit algorithms, based on UCB and Thompson sampling, for optimizing wireless handover parameters, demonstrating superior performance on real network data.

## Contribution

It develops and compares two novel parallel contextual bandit methods tailored for wireless network optimization, highlighting the advantages of Bayesian Thompson sampling.

## Key findings

- Thompson sampling outperforms UCB in toy experiments.
- Thompson sampling achieves better results than manual tuning.
- Both methods effectively optimize base station parameters.

## Abstract

As cellular networks become denser, a scalable and dynamic tuning of wireless base station parameters can only be achieved through automated optimization. Although the contextual bandit framework arises as a natural candidate for such a task, its extension to a parallel setting is not straightforward: one needs to carefully adapt existing methods to fully leverage the multi-agent structure of this problem. We propose two approaches: one derived from a deterministic UCB-like method and the other relying on Thompson sampling. Thanks to its bayesian nature, the latter is intuited to better preserve the exploration-exploitation balance in the bandit batch. This is verified on toy experiments, where Thompson sampling shows robustness to the variability of the contexts. Finally, we apply both methods on a real base station network dataset and evidence that Thompson sampling outperforms both manual tuning and contextual UCB.