Differential Evolution-Based End-Fire Realized Gain Optimization of Active and Parasitic Arrays
We propose a novel approach for boosting the realized gain of arrays with enhanced directivity, utilizing both active and parasitic dipoles. The optimization process first maximizes the end-fire gain in the active array by selecting the optimal current excitation vector. For the parasitic arrays, th...
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| Language: | English |
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2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10829613/ |
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| author | Rozita Konstantinou Ihsan Kanbaz Okan Yurduseven Michail Matthaiou |
| author_facet | Rozita Konstantinou Ihsan Kanbaz Okan Yurduseven Michail Matthaiou |
| author_sort | Rozita Konstantinou |
| collection | DOAJ |
| description | We propose a novel approach for boosting the realized gain of arrays with enhanced directivity, utilizing both active and parasitic dipoles. The optimization process first maximizes the end-fire gain in the active array by selecting the optimal current excitation vector. For the parasitic arrays, the dipoles are reactively loaded based on the input impedances of the active dipoles, after which the optimization focuses on the inter-element distance to achieve a balance between the gain and the reflection efficiency. This multi-objective optimization, underpinned by the differential evolution (DE) algorithm, uses a simple wire dipole as the unit element. Full-wave simulations validate our theoretical results, showing that our two- and three-element parasitic arrays achieve realized gain comparable to state-of-the-art designs without relying on intricate unit elements or resource-intensive simulations, while our four- and five-element parasitic arrays yield the highest realized gain values reported in the literature. The simplicity of our approach allows optimizations to run significantly faster than full-wave simulations, whilst the sensitivity analysis showcases the robustness of the design under small deviations in loads and element positioning. Compact and power-efficient, the proposed parasitic arrays are well-suited for base stations, aligning with modern communication system requirements while minimizing hardware complexity. |
| format | Article |
| id | doaj-art-34fd6dad61174f8ca2afa79f2f9e5ff9 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-34fd6dad61174f8ca2afa79f2f9e5ff92025-01-21T00:01:47ZengIEEEIEEE Access2169-35362025-01-01139857986710.1109/ACCESS.2025.352650110829613Differential Evolution-Based End-Fire Realized Gain Optimization of Active and Parasitic ArraysRozita Konstantinou0https://orcid.org/0009-0009-4840-0533Ihsan Kanbaz1https://orcid.org/0000-0002-0333-7464Okan Yurduseven2https://orcid.org/0000-0002-0242-3029Michail Matthaiou3https://orcid.org/0000-0001-9235-7741Centre for Wireless Innovation (CWI), Queen’s University Belfast, Belfast, U.K.Centre for Wireless Innovation (CWI), Queen’s University Belfast, Belfast, U.K.Centre for Wireless Innovation (CWI), Queen’s University Belfast, Belfast, U.K.Centre for Wireless Innovation (CWI), Queen’s University Belfast, Belfast, U.K.We propose a novel approach for boosting the realized gain of arrays with enhanced directivity, utilizing both active and parasitic dipoles. The optimization process first maximizes the end-fire gain in the active array by selecting the optimal current excitation vector. For the parasitic arrays, the dipoles are reactively loaded based on the input impedances of the active dipoles, after which the optimization focuses on the inter-element distance to achieve a balance between the gain and the reflection efficiency. This multi-objective optimization, underpinned by the differential evolution (DE) algorithm, uses a simple wire dipole as the unit element. Full-wave simulations validate our theoretical results, showing that our two- and three-element parasitic arrays achieve realized gain comparable to state-of-the-art designs without relying on intricate unit elements or resource-intensive simulations, while our four- and five-element parasitic arrays yield the highest realized gain values reported in the literature. The simplicity of our approach allows optimizations to run significantly faster than full-wave simulations, whilst the sensitivity analysis showcases the robustness of the design under small deviations in loads and element positioning. Compact and power-efficient, the proposed parasitic arrays are well-suited for base stations, aligning with modern communication system requirements while minimizing hardware complexity.https://ieeexplore.ieee.org/document/10829613/Active antenna arraysdifferential evolutionend-fire arrayshigh efficiencyhigh realized gainparasitic antenna arrays |
| spellingShingle | Rozita Konstantinou Ihsan Kanbaz Okan Yurduseven Michail Matthaiou Differential Evolution-Based End-Fire Realized Gain Optimization of Active and Parasitic Arrays IEEE Access Active antenna arrays differential evolution end-fire arrays high efficiency high realized gain parasitic antenna arrays |
| title | Differential Evolution-Based End-Fire Realized Gain Optimization of Active and Parasitic Arrays |
| title_full | Differential Evolution-Based End-Fire Realized Gain Optimization of Active and Parasitic Arrays |
| title_fullStr | Differential Evolution-Based End-Fire Realized Gain Optimization of Active and Parasitic Arrays |
| title_full_unstemmed | Differential Evolution-Based End-Fire Realized Gain Optimization of Active and Parasitic Arrays |
| title_short | Differential Evolution-Based End-Fire Realized Gain Optimization of Active and Parasitic Arrays |
| title_sort | differential evolution based end fire realized gain optimization of active and parasitic arrays |
| topic | Active antenna arrays differential evolution end-fire arrays high efficiency high realized gain parasitic antenna arrays |
| url | https://ieeexplore.ieee.org/document/10829613/ |
| work_keys_str_mv | AT rozitakonstantinou differentialevolutionbasedendfirerealizedgainoptimizationofactiveandparasiticarrays AT ihsankanbaz differentialevolutionbasedendfirerealizedgainoptimizationofactiveandparasiticarrays AT okanyurduseven differentialevolutionbasedendfirerealizedgainoptimizationofactiveandparasiticarrays AT michailmatthaiou differentialevolutionbasedendfirerealizedgainoptimizationofactiveandparasiticarrays |