中文版 | English
Title

天线与微波器件的高效优化方法研究

Alternative Title
RESEARCH ON EFFICIENT OPTIMIZATION ALGORITHMS FOR ANTENNAS AND MICROWAVE COMPONENTS
Author
Name pinyin
ZHANG Zhen
School number
11849553
Degree
博士
Discipline
0810 信息与通信工程
Subject category of dissertation
08 工学
Supervisor
程庆沙
Mentor unit
电子与电气工程系
Publication Years
2022-03-05
Submission date
2022-10-14
University
哈尔滨工业大学
Place of Publication
哈尔滨
Abstract

随着5G通信技术发展进入新阶段,天线和微波器件数量的需求大幅度提升,其市场空间有望突破千亿元。新一代器件构造更复杂、参数更多、设计指标要求更严苛,导致天线与微波器件的传统设计优化设计方法已经无法满足要求,迫切需要高效的优化算法。基于自适应差分进化的天线阵列优化算法、局部替代模型辅助的天线设计优化算法、全局替代模型辅助的微波滤波器设计优化算法、全局替代模型辅助的微波滤波器良品率优化算法、天线优化算法性能评估的测试集等,具体研究内容包括以下几个方面:

(1)研究了基于自适应差分进化的天线阵列优化算法。随着通信系统不断的发展,大规模天线阵列需求越来越大,急切需要高性能的天线阵列优化算法。本文针对复杂高维的天线阵列综合问题提出了搜索能力更强的多阶段差分进化算法。该算法将差分进化算法的进化阶段分为三个阶段,为每个阶段设计合适的变异策略,提高了算法的搜索能力。另一方面,本文针对计算代价高的天线阵列设计问题提出能快速收敛的多分组差分进化算法。该算法利用K-均值聚类方法将群体分为三组,根据每组的平均适应度值为其分配不同的变异策略,提高了算法的收敛速度。两种算法均经过了性能分析和实验验证。

(2)研究了局部替代模型辅助的天线设计优化算法。天线设计优化需要利用电磁仿真模型,存在优化能力差和优化效率低等问题。为了提高基于电磁仿真模型的天线设计优化效率,本文提出了一种局部替代模型辅助的快速优化算法。该算法利用高效的采样方法提高替代模型的建模效率,利用拟牛顿增强型差分进化算法提高优化能力,利用基于空间映射技术的多保真度优化框架减少算法的计算代价。该算法的性能经过了两个天线实例验证。

(3)研究了全局替代模型辅助的微波滤波器设计优化算法。为了提高微波滤波器设计效率,本文提出了一种全局替代模型辅助的优化算法,利用贪婪采样方法增加了替代模型中重要的局部信息,利用矢量拟合方法获得传递函数的极点和留数作为特征,降低建模的复杂度,利用径向基神经网络建立替代模型,利用全局优化算法优化替代模型获得最优设计。该算法的性能经过了两个微波滤波器实例验证。

(4)进行了全局替代模型辅助的微波滤波器良品率优化算法研究。在微波滤波器大规模制造加工过程中,制造公差不可避免。制造公差的存在大幅降低了成品的良品率和提高了生产成本。现有的良品率优化算法需要大量EM仿真,优化代价高,而且仅考虑到少数影响良品率的优化目标。本文提出了一种基于多项式混沌的全局替代模型辅助良品率优化算法。首先,利用多项式混沌替代模型建立目标函数的替代模型,其次,将目标函数分为良品率目标函数和设计目标函数,利用良品率目标函数作为约束条件,优化设计目标函数,能够获得兼顾良品率和设计目标的设计变量。该算法的性能经过了三个微波滤波器实例验证。

5)研究了天线优化算法性能评估的测试函数集。天线设计工程师缺乏选择优化算法的依据,无法快速选择合适的优化算法。本文提出了一个用于天线优化算法性能评估的测试函数集,实现了天线设计优化算法快速的性能评估。该测试函数集从四种典型的天线结构等出发,利用四种常用目标函数,获得多种天线特征。根据天线特征,设计了包括八个测试函数(四个单峰函数、两个多峰函数和两个组合函数)的测试函数集。该测试函数集的有效性经过了两个天线电磁仿真实例验证。

Other Abstract

With the development of 5G communication technology entering a new era, the demand for high performance antennas and microwave components is greatly increased. The market is expected to exceed 100 billion RMB. The development of miniaturized, lightweight, and highly integrated antennas and microwave components is highly desirable. Efficient optimization algorithms for antennas and microwave components are the key to the fast design of new generation RF microwave device. Taking the design of new generation antenna and microwave devices as the application background, this thesis systematically studies direct optimization algorithm, surrogate-assisted optimization algorithm, surrogate-assisted yield optimization algorithm and test suite of optimization algorithm evaluation. The specific contents include the following aspects. The contents of this thesis include:

(1) Two adaptive differential evolution algorithms are studied for antenna arrays. Firstly, a three-phase adaptive differential evolution (TADE) algorithm is proposed for antenna array synthesis problems. In the TADE algorithm, the evolution process is divided into three phases (namely, the initial phase, the stable phase, and the precise phase) according to the population fitness distribution and iterative information. Based on the statistical information of population fitness values, a suitable mutation strategy for each phase is designed to enhance the search ability. Secondly, we propose a novel differential evolution optimization method named K-means-based multi-group differential evolution (KMGDE) algorithm for antenna array design. The KMGDE algorithm divides the population into three groups using the K-means clustering method in each iteration. We develop a mutation strategy plan that assigns different mutation strategies for each group according to its average fitness value. The fitness function considering the characteristics of the antenna array is proposed to speed up optimization. The performance of the KMGDE and the TADE is verified and analyzed using test functions and antenna array synthesis and design examples.

(2) A novel surrogate-assisted Quasi-Newton enhanced optimization algorithm is proposed for antenna design. In this proposed method, the heuristic hypersphere sampling method is used to obtain representative samples. The surrogate model is built based on the low-fidelity model. The Quasi-Newton enhanced differential evolution method is designed to optimize the surrogate model. Finally, the optimal design of a high-fidelity model is obtained through a space mapping procedure. The proposed algorithm is verified through two antenna design examples including a dipole antenna with balun and a SIW cavity-backed slot antenna. The results show that the proposed algorithm finds a more accurate minimum value with less computational time than direct optimization using differential evolution.

(3) A novel global surrogate-assisted optimization is proposed for filter design. Because microwave filters use electromagnetic simulation for design optimization. There are some problems, such as high optimization cost, low design efficiency and long design cycle. In this thesis, a new global alternative model aided optimization algorithm is proposed. The greedy sampling method is used to obtain high-quality samples as the input of modeling data, and the vector fitting method is used to obtain the poles and residues of transfer function as the output of modeling data, The radial basis function neural network is used to establish the input and output surrogate model, and the efficient optimization algorithm is used to optimize the surrogate model to obtain the optimal design parameters. The results of two band-pass filter examples verify the performance of the algorithm.

(4) An efficient yield-constrained optimization using a polynomial chaos surrogate considering multiple objectives is proposed for microwave filter design. Yield optimization aims at finding microwave filter designs with maximal yield satisfying a certain performance objective under fabrication tolerance. The EM-based yield optimization methods are very expensive because a large number of EM simulations are required. Moreover, microwave filter design usually requires several performance objectives to be met at the same time, which is not considered by the current method. Two contributions of the proposed method are (a) the cost and accuracy of the polynomial chaos model are improved by a sampling reduction strategy and an adaptive order determination strategy; (b) An efficient yield-constrained design framework is implemented to obtain the optimal design solutions under yield constraints. The results of three microwave filter design examples verify the effectiveness and efficiency of the proposed algorithm.

(5) We propose a method for designing a test suite for antenna design problems. Performance evaluation of antenna optimization algorithms is essential. It is desirable to develop an efficient test suite with characteristics of typical antennas to replace the high-computational-cost EM-based evaluation. Four typical antennas with respect to four typical objectives based on EM-simulated S-parameters are systematically investigated. Based on the investigation results, we propose a versatile performance-evaluation test suite allowing efficient benchmarking for antenna S-parameter optimization algorithms. The test suite consists of eight analytical functions. Each function matches a typical landscape of the EM-simulated antenna problems. The performance of six widely used global optimization algorithms are evaluated using the test suite. The results using the proposed test suite for all six optimization algorithms are consistent with those of the real EM-based antenna design problems. The time cost for performance evaluation using the test suite is only a fraction of the cost using the real EM-based antenna design problems.

Keywords
Language
Chinese
Training classes
联合培养
Enrollment Year
2018
Year of Degree Awarded
2022-07
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Academic Degree Assessment Sub committee
电子与电气工程系
Domestic book classification number
TN911.7
Data Source
人工提交
Document TypeThesis
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/405998
DepartmentDepartment of Electrical and Electronic Engineering
Recommended Citation
GB/T 7714
张珍. 天线与微波器件的高效优化方法研究[D]. 哈尔滨. 哈尔滨工业大学,2022.
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