Physics guided optimization improves coverage in complex 5g base station setups
Physics-Guided Bayesian Optimization for High-Dimensional Mixed-Variable MIMO Base Station Design
Networking and Internet Architecture
Summary
Designing 5G base stations that work well in cities is tricky because many settings must be adjusted, like where to put stations and how to aim their antennas. The authors present a new method that uses physics knowledge as a shortcut to help find better setups faster than usual techniques. Their approach mixes computer models with real-world physics to guess coverage quality before running expensive calculations. Tests in city environments show this method can find configurations that cover about 15% more area than other strategies with the same effort.
What this means in practice
- •For wireless network planners: Identify locations and antenna settings that maximize citywide 5G coverage using fewer costly simulations.
- •For wireless infrastructure engineers: Optimize multidimensional parameters of base stations, including angle and power, guided by physics-based models to reduce deployment cost.
Authors
Koki Kanzaki, Koya Sato
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.