Newton Raphson based optimizer for optimal integration of FAS and RIS in wireless systems
In modern wireless networks, the integration of Reconfigurable Intelligent Surface (RIS) and Fluid Antenna System (FAS) technologies offers a promising solution to the critical challenges such as signal attenuation, interference management, and security enhancement. This paper presents an applicatio...
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| Language: | English |
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Elsevier
2025-03-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123024020656 |
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| author | Ahmed S. Alwakeel Ali M. El-Rifaie Ghareeb Moustafa Abdullah M. Shaheen |
| author_facet | Ahmed S. Alwakeel Ali M. El-Rifaie Ghareeb Moustafa Abdullah M. Shaheen |
| author_sort | Ahmed S. Alwakeel |
| collection | DOAJ |
| description | In modern wireless networks, the integration of Reconfigurable Intelligent Surface (RIS) and Fluid Antenna System (FAS) technologies offers a promising solution to the critical challenges such as signal attenuation, interference management, and security enhancement. This paper presents an application of a novel population-based metaheuristic algorithm of Newton Raphson Based Optimizer (NRBO) to efficiently integrating of FAS and RISs in wireless systems. The designed NRBO combines gradient-based search with adaptive population dynamics and leverages the Newton-Raphson Search Rule (NRSR) and Trap Avoidance Strategy (TAS) to balance exploration and exploitation, ensuring faster convergence and avoiding local optima. NRBO dynamically configures RIS elements and fluid-based antennas, adapting to environmental changes and specific communication requirements to enhance wireless performance. Multiple objective models are designed and addressed using the NRBO: maximizing the average attainable rate for each participant, minimizing the average number of FAS in all Users, and jointly optimizing both objectives with weighted considerations. Simulation results show that the NRBO successfully save 62.5% of the FAS ports to be utilized in the jointly optimized model of both objectives. Also, it achieves more saving with 72.9% when considering minimizing the average number of FAS in all Users as a single objective. |
| format | Article |
| id | doaj-art-d64c01dff89d4597ab9e3b6fae5c2761 |
| institution | Kabale University |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-d64c01dff89d4597ab9e3b6fae5c27612025-01-09T06:14:29ZengElsevierResults in Engineering2590-12302025-03-0125103822Newton Raphson based optimizer for optimal integration of FAS and RIS in wireless systemsAhmed S. Alwakeel0Ali M. El-Rifaie1Ghareeb Moustafa2Abdullah M. Shaheen3Department of Electrical Engineering, Faculty of Engineering, Suez University, P.O. Box: 43221, Suez, Egypt; Corresponding authors.College of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait; Corresponding authors.Department of Electrical and Electronic Engineering, College of Engineering and Computer Science, Jazan University, P.O. Box 114, Jazan 45142, Saudi ArabiaDepartment of Electrical Engineering, Faculty of Engineering, Suez University, P.O. Box: 43221, Suez, EgyptIn modern wireless networks, the integration of Reconfigurable Intelligent Surface (RIS) and Fluid Antenna System (FAS) technologies offers a promising solution to the critical challenges such as signal attenuation, interference management, and security enhancement. This paper presents an application of a novel population-based metaheuristic algorithm of Newton Raphson Based Optimizer (NRBO) to efficiently integrating of FAS and RISs in wireless systems. The designed NRBO combines gradient-based search with adaptive population dynamics and leverages the Newton-Raphson Search Rule (NRSR) and Trap Avoidance Strategy (TAS) to balance exploration and exploitation, ensuring faster convergence and avoiding local optima. NRBO dynamically configures RIS elements and fluid-based antennas, adapting to environmental changes and specific communication requirements to enhance wireless performance. Multiple objective models are designed and addressed using the NRBO: maximizing the average attainable rate for each participant, minimizing the average number of FAS in all Users, and jointly optimizing both objectives with weighted considerations. Simulation results show that the NRBO successfully save 62.5% of the FAS ports to be utilized in the jointly optimized model of both objectives. Also, it achieves more saving with 72.9% when considering minimizing the average number of FAS in all Users as a single objective.http://www.sciencedirect.com/science/article/pii/S2590123024020656Reconfigurable intelligent surfacesFluid antenna systemsWireless communicationNewton Raphson based optimization algorithmAchievable rate limitation |
| spellingShingle | Ahmed S. Alwakeel Ali M. El-Rifaie Ghareeb Moustafa Abdullah M. Shaheen Newton Raphson based optimizer for optimal integration of FAS and RIS in wireless systems Results in Engineering Reconfigurable intelligent surfaces Fluid antenna systems Wireless communication Newton Raphson based optimization algorithm Achievable rate limitation |
| title | Newton Raphson based optimizer for optimal integration of FAS and RIS in wireless systems |
| title_full | Newton Raphson based optimizer for optimal integration of FAS and RIS in wireless systems |
| title_fullStr | Newton Raphson based optimizer for optimal integration of FAS and RIS in wireless systems |
| title_full_unstemmed | Newton Raphson based optimizer for optimal integration of FAS and RIS in wireless systems |
| title_short | Newton Raphson based optimizer for optimal integration of FAS and RIS in wireless systems |
| title_sort | newton raphson based optimizer for optimal integration of fas and ris in wireless systems |
| topic | Reconfigurable intelligent surfaces Fluid antenna systems Wireless communication Newton Raphson based optimization algorithm Achievable rate limitation |
| url | http://www.sciencedirect.com/science/article/pii/S2590123024020656 |
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