Optimized Graph Sample and Aggregate‐Attention Network‐Based High Gain Meta Surface Antenna Design for IoT Application

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ABSTRACTThis paper presents a High Gain Metasurface (HGM) antenna design optimized for IoT applications. The antenna operates at a 5.8 GHz frequency and utilizes a quarter‐wavelength unit cell structure. The design employs a Graph Sample and Aggregate‐Attention Network (GSAAN) to enhance the transmission characteristics of the metasurface. To optimize the performance of GSAAN, the Giza Pyramids Construction Algorithm (GPCN) is applied to adjust the weight parameters, leading to significant improvements in radiation efficiency, bandwidth, gain, directivity, and return loss. The proposed HGM antenna is simulated in MATLAB 2023b, where it demonstrates substantial performance gains over existing methods. Specifically, the proposed design achieves 28.34% higher gain compared to the MSA‐RHCOA method, 24.47% improvement over HGM‐AD‐MATCS, 26.34% better gain than BR‐HGA‐PGM, and a 32.12% gain increase relative to MSA‐Hyb‐ADSA‐HMSOA. These enhancements make the proposed antenna design a competitive solution for high‐gain applications in wireless communication, satellite communication, and radar systems. The integration of GSAAN with GPCN optimization in the HGM antenna design enables the creation of highly directive radiation patterns, making it well‐suited for IoT applications that require high performance and reliability. The results of this study demonstrate the effectiveness of the proposed approach in achieving superior antenna performance, positioning it as a strong alternative to existing metasurface antenna designs.