• Computer Science > Information Theory [Submitted on 18 Feb 2026] Title:Scalable Base Station Configuration via Bayesian Optimization with Block Coordinate Descent View PDF HTML (experimental)Abstract: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. • Submission history From: Koya Sato Prof.
Article Summaries:
- Scalable Base‑Station Design via Bayesian Optimization
A new framework combines Bayesian optimization (BO) with block coordinate descent to efficiently configure dense cellular base stations. Traditional BO struggles with high‑dimensional parameter spaces, limiting its scalability. The proposed method sequentially optimizes the settings of individual base stations while keeping others fixed, thereby reducing the effective dimensionality at each step. Numerical experiments show the approach markedly outperforms naive optimization in dense deployment scenarios, offering a practical solution for large‑scale network planning. The study, submitted to arXiv on 18 Feb 2026, highlights the potential of hybrid optimization techniques for next‑generation wireless infrastructure.
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