This paper proposes a scalable Bayesian optimization (BO) framework for dense base-station (BS) configuration design. BO can find an optimal BS configuration by iterating parameter search, channel simulation, and probabilistic modeling of the objective function. However, its performance is severely affected by the curse of dimensionality, thereby reducing its scalability. To overcome this limitation, the proposed method sequentially optimizes per-BS parameters based on block coordinate descent while fixing the remaining BS configurations, thereby reducing the effective dimensionality of each optimization step. Numerical results demonstrate that the proposed approach significantly outperforms naive optimization in dense deployment scenarios.

翻译：本文提出了一种用于密集基站配置设计的可扩展贝叶斯优化框架。贝叶斯优化可通过迭代参数搜索、信道仿真和目标函数的概率建模来寻找最优基站配置。然而，其性能受维度灾难影响严重，从而降低了可扩展性。为克服此限制，所提方法在固定其余基站配置的同时，基于块坐标下降依次优化每个基站的参数，从而降低每个优化步骤的有效维度。数值结果表明，在密集部署场景中，所提方法显著优于传统优化方法。