Multiobjective Bayesian Optimization for Antenna Placement in In-Building Distributed Antenna System

Xilei Wu, Pei-Qiu Huang, Linqi Song, Hai-Lin Liu, Qingfu Zhang

Abstract: Optimizing antenna placement in in-building distributed antenna systems is critical for achieving comprehensive 5G coverage. Due to the utilization of high-frequency signal bands, the propagation of 5G signals is significantly influenced by distance and obstacles in indoor environments. Consequently, devising effective placement schemes faces challenges in complex indoor scenarios. This paper presents a multiobjective genetic algorithm for antenna placement optimization. It tracks the signal propagation by introducing the ray-tracing propagation model. Although the ray-tracing propagation model provides high simulation accuracy, it also incurs substantial computation costs, rendering the antenna placement problem expensive. To tackle this challenge, the algorithm introduces the idea of Bayesian optimization, where a surrogate model replaces certain calculations in the ray tracing propagation model, thereby reducing the computational burden. Experimental results demonstrate the superior performance of the proposed algorithm compared to other algorithms in two real-world scenarios, as evidenced by evaluations based on hypervolume and inverted generational distance metrics.

External IDs:dblp:conf/cec/WuHS0024