Machine-Learning-Assisted Optimization and Its Application to Antenna Designs: Opportunities and Challenges  

作　　者：Qi Wu Yi Cao Haiming Wang Wei Hong 

机构地区：[1]State Key Laboratory of Millimeter Waves,Southeast University,Nanjing 211111,China [2]Purple Mountain Laboratories,Nanjing 211111,China

出　　处：《China Communications》2020年第4期152-164,共13页中国通信（英文版）

基　　金：supported in part by the National Key R&D Program of China under grant 2018YFB1801101;the National Natural Science Foundation of China under grants 61671145 and 61960206006;the Key R&D Program of Jiangsu Province of China under grant BE2018121.

摘　　要：With the rapid development of modern wireless communications and radar, antennas and arrays are becoming more complex, therein having, e.g., more degrees of design freedom, integration and fabrication constraints and design objectives. While fullwave electromagnetic simulation can be very accurate and therefore essential to the design process, it is also very time consuming, which leads to many challenges for antenna design, optimization and sensitivity analysis(SA). Recently, machine-learning-assisted optimization(MLAO) has been widely introduced to accelerate the design process of antennas and arrays. Machine learning(ML) methods, including Gaussian process regression, support vector machine(SVM) and artificial neural networks(ANNs), have been applied to build surrogate models of antennas to achieve fast response prediction. With the help of these ML methods, various MLAO algorithms have been proposed for different applications. A comprehensive survey of recent advances in ML methods for antenna modeling is first presented. Then, algorithms for ML-assisted antenna design, including optimization and SA, are reviewed. Finally, some challenges facing future MLAO for antenna design are discussed.

关 键 词：ANTENNA DESIGNS MACHINE learning OPTIMIZATION sensitivity analysis surrogate models 

分 类 号：TN820[电子电信—信息与通信工程] TP181[自动化与计算机技术—控制理论与控制工程]