A Machine Learning Generative Method for Automating Antenna Design and Optimization Article Swipe
Yang Zhong
,
Peter Renner
,
Weiping Dou
,
Ye Geng
,
Jiang Zhu
,
Qing Liu
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2203.11698
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2203.11698
To facilitate the antenna design with the aid of computer, one of the practices in consumer electronic industry is to model and optimize antenna performances with a simplified antenna geometric scheme. Traditional antenna modeling requires profound prior knowledge of electromagnetics in order to achieve a good design which satisfies the performance specifications from both antenna and product designs. The ease of handling multidimensional optimization problems and the less dependence on domain knowledge and experience are the key to achieve the popularity of simulation driven antenna design and optimization for the industry. In this paper, we introduce a flexible geometric scheme with the concept of mesh network that can form any arbitrary shape by connecting different nodes. For such problems with high dimensional parameters, we propose a machine learning based generative method to assist the searching of optimal solutions. It consists of discriminators and generators. The discriminators are used to predict the performance of geometric models, and the generators to create new candidates that will pass the discriminators. Moreover, an evolutionary criterion approach is proposed for further improving the efficiency of our method. Finally, not only optimal solutions can be found, but also the well trained generators can be used to automate future antenna design and optimization. For a dual resonance antenna design with wide bandwidth, our proposed method is in par with Trust Region Framework and much better than the other mature machine learning algorithms including the widely used Genetic Algorithm and Particle Swarm Optimization. When there is no wide bandwidth requirement, it is better than Trust Region Framework.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2203.11698
- https://arxiv.org/pdf/2203.11698
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4221139597
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4221139597Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2203.11698Digital Object Identifier
- Title
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A Machine Learning Generative Method for Automating Antenna Design and OptimizationWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-02-28Full publication date if available
- Authors
-
Yang Zhong, Peter Renner, Weiping Dou, Ye Geng, Jiang Zhu, Qing LiuList of authors in order
- Landing page
-
https://arxiv.org/abs/2203.11698Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2203.11698Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2203.11698Direct OA link when available
- Concepts
-
Computer science, Particle swarm optimization, Bandwidth (computing), Genetic algorithm, Key (lock), Computer engineering, Evolutionary algorithm, Antenna (radio), Electronic engineering, Artificial intelligence, Machine learning, Engineering, Telecommunications, Computer securityTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.consists | 139 |
| abstract_inverted_index.consumer | 15 |
| abstract_inverted_index.designs. | 57 |
| abstract_inverted_index.flexible | 97 |
| abstract_inverted_index.handling | 61 |
| abstract_inverted_index.industry | 17 |
| abstract_inverted_index.learning | 127, 233 |
| abstract_inverted_index.modeling | 33 |
| abstract_inverted_index.optimize | 22 |
| abstract_inverted_index.problems | 64, 118 |
| abstract_inverted_index.profound | 35 |
| abstract_inverted_index.proposed | 173, 216 |
| abstract_inverted_index.requires | 34 |
| abstract_inverted_index.Algorithm | 240 |
| abstract_inverted_index.Framework | 224 |
| abstract_inverted_index.Moreover, | 167 |
| abstract_inverted_index.arbitrary | 110 |
| abstract_inverted_index.bandwidth | 250 |
| abstract_inverted_index.computer, | 9 |
| abstract_inverted_index.criterion | 170 |
| abstract_inverted_index.different | 114 |
| abstract_inverted_index.geometric | 29, 98, 153 |
| abstract_inverted_index.improving | 176 |
| abstract_inverted_index.including | 235 |
| abstract_inverted_index.industry. | 90 |
| abstract_inverted_index.introduce | 95 |
| abstract_inverted_index.knowledge | 37, 71 |
| abstract_inverted_index.practices | 13 |
| abstract_inverted_index.resonance | 209 |
| abstract_inverted_index.satisfies | 48 |
| abstract_inverted_index.searching | 134 |
| abstract_inverted_index.solutions | 186 |
| abstract_inverted_index.Framework. | 258 |
| abstract_inverted_index.algorithms | 234 |
| abstract_inverted_index.bandwidth, | 214 |
| abstract_inverted_index.candidates | 161 |
| abstract_inverted_index.connecting | 113 |
| abstract_inverted_index.dependence | 68 |
| abstract_inverted_index.efficiency | 178 |
| abstract_inverted_index.electronic | 16 |
| abstract_inverted_index.experience | 73 |
| abstract_inverted_index.facilitate | 1 |
| abstract_inverted_index.generative | 129 |
| abstract_inverted_index.generators | 157, 195 |
| abstract_inverted_index.popularity | 80 |
| abstract_inverted_index.simplified | 27 |
| abstract_inverted_index.simulation | 82 |
| abstract_inverted_index.solutions. | 137 |
| abstract_inverted_index.Traditional | 31 |
| abstract_inverted_index.dimensional | 121 |
| abstract_inverted_index.generators. | 143 |
| abstract_inverted_index.parameters, | 122 |
| abstract_inverted_index.performance | 50, 151 |
| abstract_inverted_index.evolutionary | 169 |
| abstract_inverted_index.optimization | 63, 87 |
| abstract_inverted_index.performances | 24 |
| abstract_inverted_index.requirement, | 251 |
| abstract_inverted_index.Optimization. | 244 |
| abstract_inverted_index.optimization. | 205 |
| abstract_inverted_index.discriminators | 141, 145 |
| abstract_inverted_index.specifications | 51 |
| abstract_inverted_index.discriminators. | 166 |
| abstract_inverted_index.electromagnetics | 39 |
| abstract_inverted_index.multidimensional | 62 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/10 |
| sustainable_development_goals[0].score | 0.41999998688697815 |
| sustainable_development_goals[0].display_name | Reduced inequalities |
| citation_normalized_percentile |