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Description
This study introduces a Deep Learning (DL) framework for the efficient evaluation of mobile phone antenna performance , addressing the time-consuming nature of traditional full-wave numerical simulations. The DL model, built on convolutional neural networks, uses the Near-field Electromagnetic Field (NEMF) distribution of a mobile phone antenna in free space to predict the Effective Isotropic Radiated Power (EIRP), Total Radiated Power (TRP), and Specific Absorption Rate (SAR) across various configurations. By converting antenna features and internal mobile phone components into near-field EMF distributions within a Huygens' box, the model simplifies its input. A dataset of 7000 mobile phone models was used for training and evaluation. The model's accuracy is validated using the Wilcoxon Signed Rank Test (WSR) for SAR and TRP, and the Feature Selection Validation Method (FSV) for EIRP. E-field distribution can also be super-resolution reconstructed by a specially desinged Generative Adversarial Networks informed with physical knowledge, which enables for deriving dosimetric values at even higher frequencies. The proposed model achieves remarkable computational efficiency, approximately 2000-fold faster than full-wave simulations, and demonstrates generalization capabilities for different antenna types, various frequencies, and antenna positions. This makes it a valuable tool for practical research and development , offering a promising alternative to traditional electromagnetic field simulations. 1/2 part of the work has been published recently in Sensors (10.3390/s24175646)
