  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.
