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The paper presents a machine learning-driven state selection method for a reconfigurable cylindrical conformal antenna designed for millimeter-wave (mmWave) communication, enhancing 5G network performance. The antenna features 360° beam scanning capabilities with eight distinct states, optimized through a low-complexity algorithm that adapts to dynamic environments. Simulation results demonstrate the effectiveness of the antenna design and the state selection process in various communication scenarios, incl

Author

Aditya Gupta

Language

EN