Kernel-based Multi-Task Contextual Bandits in Cellular Network Configuration
This work addresses the inefficiency of static, experience-based network configuration for cellular operators, offering an incremental improvement through multi-task learning to optimize base station settings.
The paper tackles the problem of automating cellular network configuration by proposing a kernel-based multi-task contextual bandit algorithm that leverages similarities among base stations, achieving improved performance with theoretical regret guarantees and evaluation on real trace data.
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.