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Integrated fluid antenna systems with reconfigurable intelligent surfaces via quadratic interpolation optimization.

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Title: Integrated fluid antenna systems with reconfigurable intelligent surfaces via quadratic interpolation optimization.
Authors: Alwakeel, Ahmed S.1 (AUTHOR) ahmed.alwakeel@eng.suezuni.edu.eg, Shaheen, Abdullah M.1 (AUTHOR) abdullah.mohamed.eng19@suezuni.edu.eg, Moustafa, Ghareeb2 (AUTHOR) gmoustafa@jazanu.edu.sa, Faiya, Badr Al2 (AUTHOR) Balfaiya@jazanu.edu.sa, Smaili, Idris H.2 (AUTHOR) ihsmaili@jazanu.edu.sa
Source: Cluster Computing. Oct2025, Vol. 28 Issue 9, p1-19. 19p.
Subject Terms: *OPTIMIZATION algorithms, *QUADRATIC programming, *ATTENUATION (Physics), *INTERFERENCE suppression, *WIRELESS communications, *COMPUTER security vulnerabilities, *ANTENNAS (Electronics)
Abstract: The integration of Reconfigurable Intelligent Surface (RIS) and Fluid Antenna System (FAS) technologies offers a promising solution to the problems facing contemporary wireless networks, such as signal attenuation, interference reduction, and security vulnerabilities. In this paper, an innovative Quadratic Interpolation Optimization Algorithm (QIOA) is developed to improve wireless communication performance and reliability. The designed QIOA, also, seeks for maintaining the security requirements through the simultaneous deployment of FAS and RIS. Rooted in the Generalized Quadratic Interpolation Method (GQIM), the QIOA effectively balances exploration and exploitation within the search space. By leveraging the GQIM, the developed QIOA activates adaptive weight adjustments to dynamically configure RIS elements and fluid-based antennas, optimizing their deployment based on environmental conditions and communication requirements. By optimal deployment of both technologies using the proposed QIOA, the wireless coverage is effectively maximized, the signal strength is improved, and the system reliability is enhanced. Through simulations, QIOA outperformed different algorithms like Differential Evolution (DE), Honey Formation Optimization (HFO), Kepler Optimization Algorithm (KOA), and War Strategy Optimization (WSO) by retaining lower infeasibility rates and obtaining more consistency in optimization outcomes. The proposed QIOA had superior performance as evidenced by a 100% successfulness in finding feasible solution. On the other side, KOA, HFO, WSO, and DE fail in achieving feasible solutions with 15%, 30%, 85% and 100%, respectively, of the whole experimental runs. [ABSTRACT FROM AUTHOR]
Database: Academic Search Index
Description
Abstract:The integration of Reconfigurable Intelligent Surface (RIS) and Fluid Antenna System (FAS) technologies offers a promising solution to the problems facing contemporary wireless networks, such as signal attenuation, interference reduction, and security vulnerabilities. In this paper, an innovative Quadratic Interpolation Optimization Algorithm (QIOA) is developed to improve wireless communication performance and reliability. The designed QIOA, also, seeks for maintaining the security requirements through the simultaneous deployment of FAS and RIS. Rooted in the Generalized Quadratic Interpolation Method (GQIM), the QIOA effectively balances exploration and exploitation within the search space. By leveraging the GQIM, the developed QIOA activates adaptive weight adjustments to dynamically configure RIS elements and fluid-based antennas, optimizing their deployment based on environmental conditions and communication requirements. By optimal deployment of both technologies using the proposed QIOA, the wireless coverage is effectively maximized, the signal strength is improved, and the system reliability is enhanced. Through simulations, QIOA outperformed different algorithms like Differential Evolution (DE), Honey Formation Optimization (HFO), Kepler Optimization Algorithm (KOA), and War Strategy Optimization (WSO) by retaining lower infeasibility rates and obtaining more consistency in optimization outcomes. The proposed QIOA had superior performance as evidenced by a 100% successfulness in finding feasible solution. On the other side, KOA, HFO, WSO, and DE fail in achieving feasible solutions with 15%, 30%, 85% and 100%, respectively, of the whole experimental runs. [ABSTRACT FROM AUTHOR]
ISSN:13867857
DOI:10.1007/s10586-025-05249-5