volume | PIER Journals

Abstract

Integrating Reconfigurable Intelligent Surfaces (RIS) with wideband systems, such as millimeter-wave (mm-wave) and terahertz (THz) systems, has shown great potential for improving communication system performance. However, accurate circuit-level modeling for RIS unit cell remains to date a significant challenge. This is because the unit cell in wideband systems faces a strongly coupled electromagnetic behavior that cannot be accurately captured using conventional circuit models. To address this challenge, this study introduces a novel hybrid modeling framework that combines Artificial Neural Networks (ANN) with discrete transfer functions H(z) for accurately modeling the unit cell in wideband systems. Specifically, the proposed framework allows a direct prediction of the H(z) coefficients from the S₁₂ data obtained from a full-wave Computer Simulation Technology (CST) simulation. The proposed framework aims to bridge the electromagnetic theory and circuit theory, which are considered to be complex, by representing the unit cell behavior using computationally efficient H(z) modeling. The results show that the proposed framework can accurately capture the sharp resonant characteristics of the RIS unit cell. The proposed hybrid framework achieves a performance improvement of 6 dB in Root Mean Square Error (RMSE) in comparison with the basic fitting model over all the Ka-band frequencies (30-40 GHz).

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Dr. Marwah Abdulrazzaq Naser

Dr. Jit Singh Mandeep

Prof. Taha Ahmed Elwi

Prof. Mohammad Tariqul Islam