Dataset Dependency of Data-Driven ML Techniques in Pattern Prediction Under Mutual Coupling
N.B. Onat (TU Delft - Microwave Sensing, Signals & Systems)
Francesco Fioranelli (TU Delft - Microwave Sensing, Signals & Systems)
Marco Spirito Alexander Yarovoy (TU Delft - Microwave Sensing, Signals & Systems)
Yanki Aslant (TU Delft - Microwave Sensing, Signals & Systems)
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Abstract
This paper examines how training data affects machine learning-assisted antenna pattern prediction under mutual coupling. For demonstration, a neural network-based approach is used to predict the embedded pattern of a central patch antenna element near randomly distributed patches. It is shown that when the full-wave simulated dataset size is excessively reduced, the high prediction error in the validation set may become a critical issue. Maintaining sufficient accuracy in pattern prediction with a relatively small dataset remains an open challenge.