Abstract: Optimizing antenna placement in in-building distributed antenna systems is critical for achieving comprehensive 5G coverage.

Experimental results demonstrate the superior performance of the proposed algorithm compared to other algorithms in two real-world scenarios, as evidenced by.

Aug 11, 2024 · Optimization and parameter estimation techniques have been employed for many years as a method of improving and exploring designs in numerous ...

Bayesian optimization is a methodology for optimizing expensive objective functions that has proven success in the sciences, engineering, and beyond.

A multiobjective genetic algorithm for antenna placement optimization that introduces the idea of Bayesian optimization, where a surrogate model replaces ...

May 1, 2024 · In this paper, we study the multiobjective network planning problem, aiming to minimize both construction costs and average power loss. The main ...

Multiobjective Bayesian Optimization for Antenna Placement in In-Building Distributed Antenna System. CEC 2024: 1-8; 2022. [d2]. view. electronic edition via ...

Multiobjective Bayesian Optimization for Antenna Placement in In-Building Distributed Antenna System. Wu, Xilei;Huang, Pei-Qiu;Song ...

This paper studies the multiobjective network planning problem, aiming to minimize both construction costs and average power loss, and proposes a ...

Highlights · We devise a multi-objective optimization (MOO) for sensor placement. · Our MOO integrates reduced order model and lazy greedy combinatorial approach.