Parallel Contextual Bandits in Wireless Handover Optimization
This work addresses automated optimization for wireless handover in cellular networks, but it is incremental as it adapts existing bandit methods to a multi-agent setting.
The paper tackled the problem of optimizing wireless base station parameters in dense cellular networks using parallel contextual bandits, and found that Thompson sampling outperformed manual tuning and contextual UCB in real-world experiments.
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.