Phased Antenna-Array Synthesis Using Taylor-Series Expansion and Neural Networks
This paper presents a novel approach to synthesizing phased antenna arrays (PAAs) by combining Taylor-series expansion with neural networks (NNs), enhancing the PAA synthesis process for modern communication and radar systems. Synthesizing PAAs is crucial for these systems, offering versatile beamfo...
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| Format: | Article |
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MDPI AG
2025-06-01
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| Series: | Telecom |
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| Online Access: | https://www.mdpi.com/2673-4001/6/2/37 |
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| author | Adel Kouki Ramzi Kheder Ridha Ghayoula Issam El Gmati Lassaad Latrach Wided Amara Leila Ben Ayed Jaouhar Fattahi |
| author_facet | Adel Kouki Ramzi Kheder Ridha Ghayoula Issam El Gmati Lassaad Latrach Wided Amara Leila Ben Ayed Jaouhar Fattahi |
| author_sort | Adel Kouki |
| collection | DOAJ |
| description | This paper presents a novel approach to synthesizing phased antenna arrays (PAAs) by combining Taylor-series expansion with neural networks (NNs), enhancing the PAA synthesis process for modern communication and radar systems. Synthesizing PAAs is crucial for these systems, offering versatile beamforming capabilities. Traditional methods often rely on complex analytical formulations or numerical optimizations, leading to suboptimal solutions or high computational costs. The proposed method uses Taylor-series expansion to derive analytical expressions for PAA radiation patterns and beamforming characteristics, simplifying the optimization process. Additionally, neural networks are employed to model the intricate relationships between PAA parameters and desired performance metrics, providing adaptive learning and real-time adjustments. A validation of the proposed method is performed on a dual-band 5G antenna, which exhibits marked resonances at 28.14 GHz and 37.88 GHz, with reflection coefficients of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>S</mi><mn>11</mn></msub></semantics></math></inline-formula> = −19 dB and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>S</mi><mn>11</mn></msub></semantics></math></inline-formula> = −19.33 dB, respectively. The integration of Taylor expansion with NNs offers improved efficiency, reduced computational complexity, and the ability to explore a broader design space. Simulation results and case studies demonstrate the effectiveness and applicability of the approach in practical scenarios. This work represents a significant advancement in PAA synthesis, showcasing the synergistic integration of mathematical modeling and artificial intelligence for optimized antenna design in modern communication and radar systems. |
| format | Article |
| id | doaj-art-352f644311dc4a17aa2ce0b48085a0a1 |
| institution | OA Journals |
| issn | 2673-4001 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Telecom |
| spelling | doaj-art-352f644311dc4a17aa2ce0b48085a0a12025-08-20T02:21:58ZengMDPI AGTelecom2673-40012025-06-01623710.3390/telecom6020037Phased Antenna-Array Synthesis Using Taylor-Series Expansion and Neural NetworksAdel Kouki0Ramzi Kheder1Ridha Ghayoula2Issam El Gmati3Lassaad Latrach4Wided Amara5Leila Ben Ayed6Jaouhar Fattahi7Heterogeneous Advanced Networking & Applications (HANALab), National School of Computer Science ENSI, University of Manouba, Manouba 2010, TunisiaHeterogeneous Advanced Networking & Applications (HANALab), National School of Computer Science ENSI, University of Manouba, Manouba 2010, TunisiaHeterogeneous Advanced Networking & Applications (HANALab), National School of Computer Science ENSI, University of Manouba, Manouba 2010, TunisiaCollege of Engineering, Umm Al Qura University, KSA, Al Gunfudha 28821, Saudi ArabiaHeterogeneous Advanced Networking & Applications (HANALab), National School of Computer Science ENSI, University of Manouba, Manouba 2010, TunisiaSysCom Laboratory, ENIT, University of Tunis El Manar, Tunis 1068, TunisiaHeterogeneous Advanced Networking & Applications (HANALab), National School of Computer Science ENSI, University of Manouba, Manouba 2010, TunisiaDepartment of Computer Science and Software Engineering, Laval University, Quebec, QC G1V 0A6, CanadaThis paper presents a novel approach to synthesizing phased antenna arrays (PAAs) by combining Taylor-series expansion with neural networks (NNs), enhancing the PAA synthesis process for modern communication and radar systems. Synthesizing PAAs is crucial for these systems, offering versatile beamforming capabilities. Traditional methods often rely on complex analytical formulations or numerical optimizations, leading to suboptimal solutions or high computational costs. The proposed method uses Taylor-series expansion to derive analytical expressions for PAA radiation patterns and beamforming characteristics, simplifying the optimization process. Additionally, neural networks are employed to model the intricate relationships between PAA parameters and desired performance metrics, providing adaptive learning and real-time adjustments. A validation of the proposed method is performed on a dual-band 5G antenna, which exhibits marked resonances at 28.14 GHz and 37.88 GHz, with reflection coefficients of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>S</mi><mn>11</mn></msub></semantics></math></inline-formula> = −19 dB and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>S</mi><mn>11</mn></msub></semantics></math></inline-formula> = −19.33 dB, respectively. The integration of Taylor expansion with NNs offers improved efficiency, reduced computational complexity, and the ability to explore a broader design space. Simulation results and case studies demonstrate the effectiveness and applicability of the approach in practical scenarios. This work represents a significant advancement in PAA synthesis, showcasing the synergistic integration of mathematical modeling and artificial intelligence for optimized antenna design in modern communication and radar systems.https://www.mdpi.com/2673-4001/6/2/37Taylorneural networksradiation patternphased antenna arraybeamforming5G |
| spellingShingle | Adel Kouki Ramzi Kheder Ridha Ghayoula Issam El Gmati Lassaad Latrach Wided Amara Leila Ben Ayed Jaouhar Fattahi Phased Antenna-Array Synthesis Using Taylor-Series Expansion and Neural Networks Telecom Taylor neural networks radiation pattern phased antenna array beamforming 5G |
| title | Phased Antenna-Array Synthesis Using Taylor-Series Expansion and Neural Networks |
| title_full | Phased Antenna-Array Synthesis Using Taylor-Series Expansion and Neural Networks |
| title_fullStr | Phased Antenna-Array Synthesis Using Taylor-Series Expansion and Neural Networks |
| title_full_unstemmed | Phased Antenna-Array Synthesis Using Taylor-Series Expansion and Neural Networks |
| title_short | Phased Antenna-Array Synthesis Using Taylor-Series Expansion and Neural Networks |
| title_sort | phased antenna array synthesis using taylor series expansion and neural networks |
| topic | Taylor neural networks radiation pattern phased antenna array beamforming 5G |
| url | https://www.mdpi.com/2673-4001/6/2/37 |
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