论文标题
机器学习辅助设计的稀薄天线阵列，以优化网络级别性能
Machine Learning-aided Design of Thinned Antenna Arrays for Optimized Network Level Performance
论文作者
论文摘要
随着毫米波（MMWave）通信的出现，需要详细的5G网络模拟器与精确天线辐射模型的组合来分析复杂蜂窝场景的现实性能。但是，由于电磁模型和网络模型的复杂性，由于所需的计算资源和仿真时间，天线阵列的设计和优化通常是不可行的。在本文中，我们提出了一个机器学习框架，该框架能够基于模拟天线设计的优化。我们展示了学习方法如何通过从中获得的适度数据集模拟复杂的模拟器，从而在合理的时间内实现了在庞大的多维参数空间上进行全局数值优化。总体而言，我们的结果表明，所提出的方法可以成功地应用于稀薄天线阵列的优化。
With the advent of millimeter wave (mmWave) communications, the combination of a detailed 5G network simulator with an accurate antenna radiation model is required to analyze the realistic performance of complex cellular scenarios. However, due to the complexity of both electromagnetic and network models, the design and optimization of antenna arrays is generally infeasible due to the required computational resources and simulation time. In this paper, we propose a Machine Learning framework that enables a simulation-based optimization of the antenna design. We show how learning methods are able to emulate a complex simulator with a modest dataset obtained from it, enabling a global numerical optimization over a vast multi-dimensional parameter space in a reasonable amount of time. Overall, our results show that the proposed methodology can be successfully applied to the optimization of thinned antenna arrays.
