A Further Realization of Binary Genetic Algorithm to Design a Dual Frequency Band Rectenna for Energy Harvesting in 5G Networks
Energy-autonomous systems have evolved in response to the quick deployment of 5G networks and growing presence of Internet of Things (IoT) devices. One reasonable approach is hybrid RF energy collecting using rectenna designs. Optimizing antenna performance for multi-band operation is a signifi...
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| Format: | Article |
| Language: | English |
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University of Diyala
2025-06-01
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| Series: | Diyala Journal of Engineering Sciences |
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| Online Access: | https://djes.info/index.php/djes/article/view/1863 |
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| author | Yahiea Al Naiemy Aqeel N. Abdulateef Ahmed Rifaat Hamad Mohammed Saadi Ismael Balachandran Ruthramurthy Taha A. Elwi Lajos Nagy Thomas Zwick |
| author_facet | Yahiea Al Naiemy Aqeel N. Abdulateef Ahmed Rifaat Hamad Mohammed Saadi Ismael Balachandran Ruthramurthy Taha A. Elwi Lajos Nagy Thomas Zwick |
| author_sort | Yahiea Al Naiemy |
| collection | DOAJ |
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Energy-autonomous systems have evolved in response to the quick deployment of 5G networks and growing presence of Internet of Things (IoT) devices. One reasonable approach is hybrid RF energy collecting using rectenna designs. Optimizing antenna performance for multi-band operation is a significant design difficulty. This article presents a complex rectenna design targeted toward hybrid energy harvesting in 5G networks. The design maximizes the geometry of a microstrip patch antenna running at 2.4GHz and 5.8GHz using a Binary Genetic Algorithm (BGA) based on Artificial Intelligence (AI). By use of binary representation of the antenna's patch form, the problem becomes combinatorial optimization. Using Schottky diodes from the Skyworks SMS7630 and Avago HSMS 285B families, the optimized antenna combines a commercial RF rectifier with nine-stage voltage doubler branches. After 250 generations with a 50,000-population count, the BGA produced antenna designs with widths of 810 MHz (5.14–5.95 GHz) and 200 MHz (2.38–2.58 GHz). Obtained were return losses of –41 dB at 2.4 GHz and –38 dB at 5.8 GHz along with matching gains of 6.2 dBi and 7.12 dBi. With input power levels kept at 6 dBm, the rectifier showed maximum conversion efficiencies of 70% at 2.4 GHz and 42% at 5.8 GHz. Using a 1 kΩ load resistor to provide impedance matching and preserve a power conversion efficiency of 40%, outside testing generated DC output voltages of 92.6 mV and 64 mV. For ambient RF energy collecting in 5G and IoT devices, the suggested AI-optimized rectenna design shows great efficiency and dependability.
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| format | Article |
| id | doaj-art-b91c26e5f57149bca80f1f78c8344b84 |
| institution | Kabale University |
| issn | 1999-8716 2616-6909 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | University of Diyala |
| record_format | Article |
| series | Diyala Journal of Engineering Sciences |
| spelling | doaj-art-b91c26e5f57149bca80f1f78c8344b842025-08-20T03:27:40ZengUniversity of DiyalaDiyala Journal of Engineering Sciences1999-87162616-69092025-06-0118210.24237/djes.2024.18213A Further Realization of Binary Genetic Algorithm to Design a Dual Frequency Band Rectenna for Energy Harvesting in 5G NetworksYahiea Al Naiemy0Aqeel N. Abdulateef1Ahmed Rifaat Hamad2Mohammed Saadi Ismael3Balachandran Ruthramurthy4Taha A. Elwi5Lajos Nagy6Thomas Zwick 7Computer Science Department, College of Science, University of Diyala, IraqMedical Instruments Techniques Engineering Department, Technical College of Engineering, Albayan University, Baghdad, Iraq.Electric Department, Institution of Technology, Middle Technical University, Baghdad, Iraq.Department of Medical Instruments, Engineering Techniques, Al-Farahidi University, Baghdad, Iraq.Department of ECE, College of Electrical Engineering and Computing, Adama Science and Technology University, P.O.Box No. 1888, EthiopiaDepartment of Automation and Artificial Intelligence Engineering, College of Information Engineering at Al-Nahrain University, Baghdad, Iraq.Department of Broadband Infocommunicatons and Electromagnetic Theory, Budapest University of Technology and Economics, Budapest,HungaryInstitute of Radio Frequency Engineering and Electronics (IHE), Karlsruhe Institute of Technology (KIT), Germany. Energy-autonomous systems have evolved in response to the quick deployment of 5G networks and growing presence of Internet of Things (IoT) devices. One reasonable approach is hybrid RF energy collecting using rectenna designs. Optimizing antenna performance for multi-band operation is a significant design difficulty. This article presents a complex rectenna design targeted toward hybrid energy harvesting in 5G networks. The design maximizes the geometry of a microstrip patch antenna running at 2.4GHz and 5.8GHz using a Binary Genetic Algorithm (BGA) based on Artificial Intelligence (AI). By use of binary representation of the antenna's patch form, the problem becomes combinatorial optimization. Using Schottky diodes from the Skyworks SMS7630 and Avago HSMS 285B families, the optimized antenna combines a commercial RF rectifier with nine-stage voltage doubler branches. After 250 generations with a 50,000-population count, the BGA produced antenna designs with widths of 810 MHz (5.14–5.95 GHz) and 200 MHz (2.38–2.58 GHz). Obtained were return losses of –41 dB at 2.4 GHz and –38 dB at 5.8 GHz along with matching gains of 6.2 dBi and 7.12 dBi. With input power levels kept at 6 dBm, the rectifier showed maximum conversion efficiencies of 70% at 2.4 GHz and 42% at 5.8 GHz. Using a 1 kΩ load resistor to provide impedance matching and preserve a power conversion efficiency of 40%, outside testing generated DC output voltages of 92.6 mV and 64 mV. For ambient RF energy collecting in 5G and IoT devices, the suggested AI-optimized rectenna design shows great efficiency and dependability. https://djes.info/index.php/djes/article/view/1863BGAEnergy HarvestingMicrostrip AntennaRectifier5Gsub-6GHz |
| spellingShingle | Yahiea Al Naiemy Aqeel N. Abdulateef Ahmed Rifaat Hamad Mohammed Saadi Ismael Balachandran Ruthramurthy Taha A. Elwi Lajos Nagy Thomas Zwick A Further Realization of Binary Genetic Algorithm to Design a Dual Frequency Band Rectenna for Energy Harvesting in 5G Networks Diyala Journal of Engineering Sciences BGA Energy Harvesting Microstrip Antenna Rectifier 5G sub-6GHz |
| title | A Further Realization of Binary Genetic Algorithm to Design a Dual Frequency Band Rectenna for Energy Harvesting in 5G Networks |
| title_full | A Further Realization of Binary Genetic Algorithm to Design a Dual Frequency Band Rectenna for Energy Harvesting in 5G Networks |
| title_fullStr | A Further Realization of Binary Genetic Algorithm to Design a Dual Frequency Band Rectenna for Energy Harvesting in 5G Networks |
| title_full_unstemmed | A Further Realization of Binary Genetic Algorithm to Design a Dual Frequency Band Rectenna for Energy Harvesting in 5G Networks |
| title_short | A Further Realization of Binary Genetic Algorithm to Design a Dual Frequency Band Rectenna for Energy Harvesting in 5G Networks |
| title_sort | further realization of binary genetic algorithm to design a dual frequency band rectenna for energy harvesting in 5g networks |
| topic | BGA Energy Harvesting Microstrip Antenna Rectifier 5G sub-6GHz |
| url | https://djes.info/index.php/djes/article/view/1863 |
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