Scalable Base Station Configuration via Bayesian Optimization with Block Coordinate Descent

TL;DR

This paper introduces a scalable Bayesian optimization framework for dense base station configuration that employs block coordinate descent to efficiently optimize parameters, overcoming high-dimensional challenges.

Contribution

The paper presents a novel BO method that sequentially optimizes per-BS parameters using block coordinate descent, enhancing scalability in dense deployments.

Findings

Significantly outperforms naive optimization methods

Effectively handles high-dimensional configuration spaces

Demonstrates improved scalability in dense base station scenarios

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.