Machine Learning and Additive Manufacturing Based Antenna Design Techniques

This dissertation investigates the application of machine learning (ML) techniques to additive manufacturing (AM) technology with the ultimate goal of tackling the universal antenna design challenges and achieving automated antenna design for a broad range of applications. First, we investigate the implementation and accuracy of few modern machine learning techniques including, least absolute shrinkage and selection operator (lasso), artificial neural networks (ANN) and k-nearest neighbor (kNN) methods, for antenna design optimization for antennas. The automated techniques provide an efficient, flexible, and reliable framework to identify optimal design parameters for a reference dual-band double T-shaped monopole antenna to achieve favorite performance in terms of its dual bandwidth. We first provide a brief background for these techniques and then explain how these techniques can be used to optimize the performance of the referenced antenna. Then the accuracy of these techniques is tested by doing a comparative analysis with HFSS simulations as well. After obtaining encouraging results from the primitive work mentioned above, we implement ML techniques for the optimization of a more complex 3D-printed slotted waveguide antenna. The design has more design parameters that are be tuned and, also multiple performance parameters, including bandwidth, realized gain, sidelobe level, and back lobe level, are optimized. This is a higher-dimensional and non-linear problem. Hence, we use an artificial neural network for this work. Next, we demonstrate the advantages and challenges of using ML techniques compared to heuristic optimization techniques. We apply ML techniques first for ‘modeling’ that refers to prediction of the performance curve (e.g., reflection coefficient w.r.t. frequency, gain plots in a given plane, etc.) for a given design of antenna with particular set of design parameters and then use it for obtaining ‘optimization’ results that refers to searching the value of the design parameters that can give optimized results for a particular goal (e.g., specific frequency band of operation, maximum gain, minimum sidelobe level, etc.). To explain modeling using ML-techniques, we use two antenna examples in this work, first is the modeling of the reflection coefficient curve with respect to frequency for a planar patch antenna when its shape changes from square to circular and second is the modeling of gain response of a monopole antenna when it is loaded with 3D-printed dielectric material. To explain the optimization process, we use the behavioral model obtained in the second antenna example, and find the design parameter values that are capable of providing single-beam, and multiple-beam radiation. The performance of ML is compared with a heuristic technique like genetic algorithm for this work and the benefits of using ML over GA are mentioned in this work. One of the prototypes that can provide a 3-beam radiation pattern is manufactured and its fabrication process and measurement results are also presented in this work. The ultimate goal of this research work is to overcome universal antenna design challenges and achieving automated antenna design for a broad range of applications. With this work, ML models are built to find the relationship between design parameters and antenna performance parameters analytically, thus requiring only analytical calculations instead of time-consuming numerical simulations for different design goals. This is useful for applications such as IoT, which involve a large number of antenna designs with different goals and constraints. ML techniques help build such behavioral models for antennas automatically from data which is beneficial for fully exploring the vast design degrees of freedom offered by AM.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

doctoral

Discipline thesis:degree_discipline

Graduate College

Grantor dc:publisher

The University of Arizona.

Year dc:date.issued

2020

Author dc:creator

• Sharma, Yashika

Advisor dc:contributor.advisor

• Xin, Hao

Committee members dc:contributor.committeemember

• Dvorak, Steven L.
• Roveda, Janet Meiling
• Zhang, Hao Helen

• Antenna
• Gaussian Process
• lasso
• Machine Learning
• Optimization

Statement dc:rights

• Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction, presentation (such as public display or performance) of protected items is prohibited except with permission of the author.

Language dc:language.iso

en

Handle dc:identifier.uri

http://hdl.handle.net/10150/637723

OAI identifier oai:identifier

oai:repository.arizona.edu:10150/637723