Physics-Guided Bayesian Optimization for High-Dimensional Mixed-Variable MIMO Base Station Design

Koki Kanzaki; Koya Sato

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arXiv · 2610.00974

Physics-Guided Bayesian Optimization for High-Dimensional Mixed-Variable MIMO Base Station Design

Koki Kanzaki·Koya Sato

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Abstract

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.

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Koki Kanzaki, Koya Sato. 2026-10-01. Physics-Guided Bayesian Optimization for High-Dimensional Mixed-Variable MIMO Base Station Design. https://arxiv.org/abs/2610.00974

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