  Cellular network configuration plays a critical role in network performance.
In current practice, network configuration depends heavily on field experience
of engineers and often remains static for a long period of time. This practice
is far from optimal. To address this limitation, online-learning-based
approaches have great potentials to automate and optimize network
configuration. Learning-based approaches face the challenges of learning a
highly complex function for each base station and balancing the fundamental
exploration-exploitation tradeoff while minimizing the exploration cost.
Fortunately, in cellular networks, base stations (BSs) often have similarities
even though they are not identical. To leverage such similarities, we propose
kernel-based multi-BS contextual bandit algorithm based on multi-task learning.
In the algorithm, we leverage the similarity among different BSs defined by
conditional kernel embedding. We present theoretical analysis of the proposed
algorithm in terms of regret and multi-task-learning efficiency. We evaluate
the effectiveness of our algorithm based on a simulator built by real traces.
