Globalized Knowledge-Based, Simulation-Driven Antenna Miniaturization Using Domain-Confined Surrogates and Dimensionality Reduction
The design of contemporary antenna systems encounters multifold challenges, one of which is a limited size. Compact antennas are indispensable for new fields of application such as the Internet of Things or 5G/6G mobile communication. Still, miniaturization generally undermines electrical and field...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/14/8144 |
| _version_ | 1827734075328167936 |
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| author | Slawomir Koziel Anna Pietrenko-Dabrowska Lukasz Golunski |
| author_facet | Slawomir Koziel Anna Pietrenko-Dabrowska Lukasz Golunski |
| author_sort | Slawomir Koziel |
| collection | DOAJ |
| description | The design of contemporary antenna systems encounters multifold challenges, one of which is a limited size. Compact antennas are indispensable for new fields of application such as the Internet of Things or 5G/6G mobile communication. Still, miniaturization generally undermines electrical and field performance. When attempted using numerical optimization, it turns into a constrained problem with costly constraints requiring electromagnetic (EM) simulations. At the same time, due to the parameter redundancy of compact antennas, size reduction poses a multimodal task. In particular, the achievable miniaturization rate heavily depends on the starting point, while identifying a suitable starting point is a challenge on its own. These issues indicate that miniaturization should be addressed using global optimization methods. Unfortunately, the most popular nature-inspired algorithms cannot be applied for solving size reduction tasks because of their inferior computational efficacy and difficulties in handling constraints. This work proposes a novel methodology for the globalized size reduction of antenna structures. Our methodology is a multi-stage knowledge-based procedure, initialized with the detection of the approximate location of the feasible region boundary, followed by the construction of a dimensionality-reduced metamodel and global optimization thereof; the last stage is the miniaturization-oriented local refinement of geometry parameters. For cost reduction, the first stages of the procedure are realized with the use of a low-fidelity EM antenna model. Our approach is verified using four broadband microstrip antennas and benchmarked against multi-start local search as well as nature-inspired methods. Superior size reduction rates are demonstrated for all considered cases while maintaining reasonably low computational costs. |
| first_indexed | 2024-03-11T01:20:25Z |
| format | Article |
| id | doaj.art-345de13dcdf64517888618c6805fc122 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T01:20:25Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-345de13dcdf64517888618c6805fc1222023-11-18T18:08:54ZengMDPI AGApplied Sciences2076-34172023-07-011314814410.3390/app13148144Globalized Knowledge-Based, Simulation-Driven Antenna Miniaturization Using Domain-Confined Surrogates and Dimensionality ReductionSlawomir Koziel0Anna Pietrenko-Dabrowska1Lukasz Golunski2Engineering Optimization & Modeling Center, Reykjavik University, 102 Reykjavik, IcelandEngineering Optimization & Modeling Center, Reykjavik University, 102 Reykjavik, IcelandFaculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, 80-233 Gdansk, PolandThe design of contemporary antenna systems encounters multifold challenges, one of which is a limited size. Compact antennas are indispensable for new fields of application such as the Internet of Things or 5G/6G mobile communication. Still, miniaturization generally undermines electrical and field performance. When attempted using numerical optimization, it turns into a constrained problem with costly constraints requiring electromagnetic (EM) simulations. At the same time, due to the parameter redundancy of compact antennas, size reduction poses a multimodal task. In particular, the achievable miniaturization rate heavily depends on the starting point, while identifying a suitable starting point is a challenge on its own. These issues indicate that miniaturization should be addressed using global optimization methods. Unfortunately, the most popular nature-inspired algorithms cannot be applied for solving size reduction tasks because of their inferior computational efficacy and difficulties in handling constraints. This work proposes a novel methodology for the globalized size reduction of antenna structures. Our methodology is a multi-stage knowledge-based procedure, initialized with the detection of the approximate location of the feasible region boundary, followed by the construction of a dimensionality-reduced metamodel and global optimization thereof; the last stage is the miniaturization-oriented local refinement of geometry parameters. For cost reduction, the first stages of the procedure are realized with the use of a low-fidelity EM antenna model. Our approach is verified using four broadband microstrip antennas and benchmarked against multi-start local search as well as nature-inspired methods. Superior size reduction rates are demonstrated for all considered cases while maintaining reasonably low computational costs.https://www.mdpi.com/2076-3417/13/14/8144miniaturized antennassimulation-based designfootprint reductionglobal optimizationsurrogate modelingdimensionality reduction |
| spellingShingle | Slawomir Koziel Anna Pietrenko-Dabrowska Lukasz Golunski Globalized Knowledge-Based, Simulation-Driven Antenna Miniaturization Using Domain-Confined Surrogates and Dimensionality Reduction Applied Sciences miniaturized antennas simulation-based design footprint reduction global optimization surrogate modeling dimensionality reduction |
| title | Globalized Knowledge-Based, Simulation-Driven Antenna Miniaturization Using Domain-Confined Surrogates and Dimensionality Reduction |
| title_full | Globalized Knowledge-Based, Simulation-Driven Antenna Miniaturization Using Domain-Confined Surrogates and Dimensionality Reduction |
| title_fullStr | Globalized Knowledge-Based, Simulation-Driven Antenna Miniaturization Using Domain-Confined Surrogates and Dimensionality Reduction |
| title_full_unstemmed | Globalized Knowledge-Based, Simulation-Driven Antenna Miniaturization Using Domain-Confined Surrogates and Dimensionality Reduction |
| title_short | Globalized Knowledge-Based, Simulation-Driven Antenna Miniaturization Using Domain-Confined Surrogates and Dimensionality Reduction |
| title_sort | globalized knowledge based simulation driven antenna miniaturization using domain confined surrogates and dimensionality reduction |
| topic | miniaturized antennas simulation-based design footprint reduction global optimization surrogate modeling dimensionality reduction |
| url | https://www.mdpi.com/2076-3417/13/14/8144 |
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