发表机构
The University of Electro-Communications（电气通信大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出物理引导的贝叶斯优化方法，联合优化MIMO基站选址与参数，利用物理代理和残差GP高效搜索，在城市场景中覆盖率提升最高约15个百分点。
AI 中文摘要
本文提出了一种物理引导的贝叶斯优化方法，用于高维混合变量多输入多输出（MIMO）基站（BS）设计。所考虑的问题联合选择候选站点子集进行基站部署，并优化基站的方位角、下倾角和发射功率谱密度，同时每个配置均使用计算成本高昂的站点特定射线追踪进行评估。为高效优化系统配置，所提方法利用预计算的传播信息构建低成本物理代理模型。代理估计的通信覆盖范围用作高斯过程（GP）先验均值，而具有三维物理特征的残差GP学习代理与完整评估之间的差异。在两个城市场景中基于射线追踪的评估表明，在相同评估预算下，所提方法相比传统和高维优化基线，覆盖率提升最高约15个百分点。
英文摘要
This paper proposes a physics-guided Bayesian optimization for high-dimensional mixed-variable multiple-input and multiple-output (MIMO) base station (BS) design. The considered problem jointly selects a subset of candidate sites for BS deployment and optimizes the azimuth angles, downtilt angles, and transmit power spectral densities of the BSs, while each configuration is evaluated using computationally expensive site-specific ray tracing. To efficiently optimize the system configuration, the proposed method constructs a low-cost physics-based proxy from precomputed propagation information. The proxy-estimated communication coverage is used as the Gaussian process (GP) prior mean, and a residual GP with three-dimensional physical features learns the discrepancy between the proxy and full evaluations. Ray-tracing-based evaluations in two urban scenarios show that the proposed method achieves up to approximately 15 percentage points higher coverage than conventional and high-dimensional optimization baselines under the same evaluation budget.
Comments6 pages, 4 figures. This work has been submitted to the IEEE for possible publication
