Adaptive FSS – Based Electromagnetic Shielding using AI Decision Control for Implantable Biotelemetry
DOI:
https://doi.org/10.15662/IJEETR.2026.0802391Keywords:
Frequency Selective Surface (FSS), Electromagnetic Shielding, Artificial Intelligence (AI), Adaptive Filtering, Implantable Biotelemetry, S-Parameters, Wireless Medical DevicesAbstract
This paper presents the design and analysis of an AI-based adaptive Frequency Selective Surface (FSS) for electromagnetic shielding in implantable biotelemetry applications. Implantable medical devices such as pacemakers and biosensors require reliable wireless communication while being protected from unwanted electromagnetic interference. Conventional FSS structures provide fixed frequency filtering characteristics, which may not be sufficient in dynamic electromagnetic environments. To overcome this limitation, the proposed system integrates an artificial intelligence (AI)-based decision control mechanism with the FSS structure to enable adaptive filtering behavior.
The proposed FSS is designed using a square conductive patch printed on a dielectric substrate and is analyzed using Ansys HFSS over a wide frequency range. The structure exhibits both transmission and filtering characteristics, allowing selective control of electromagnetic wave propagation. The AI module is responsible for analyzing incoming electromagnetic signals and determining whether to allow or suppress specific frequency components based on predefined decision logic. This enables dynamic control of the shielding performance depending on the operating conditions.
Simulation results based on S-parameter analysis demonstrate that the proposed FSS provides effective transmission in the desired frequency bands and strong attenuation at unwanted frequencies. Furthermore, the adaptive mechanism enhances the flexibility of the design by allowing real-time tuning of the response. The integration of AI with FSS improves the overall reliability and safety of implantable biotelemetry systems by minimizing the impact of electromagnetic interference. The proposed approach can be extended to advanced applications such as smart healthcare devices, wireless body area networks, and next-generation communication systems. Overall, the developed system offers an efficient and intelligent solution for adaptive electromagnetic shielding in sensitive biomedical environments.
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