Accurate assessment of human exposure to millimeter-wave (mmWave) electric fields (E-fields) has recently become critical for public health and safety. High-spatial-resolution E-field distribution is required for assessment of mmWave electromagnetic exposure according to the International Electrotechnical Commission (IEC) and the Institute of Electrical and Electronics Engineers (IEEE) (IEC/IEEE 63195-2 standard). This study proposes a generative adversarial network (GAN) integrated with field gradient loss, termed EFGraGAN, for superresolution reconstruction of mmWave E-fields. The incorporation of E-field gradient loss enables the network to learn both local field magnitudes and spatial structures, thereby enhancing the accuracy and fine structural details of reconstructed E-field maps. To improve generalization across antenna types, the training dataset is generated using plane-wave integral representation (PWIR) and randomized parametric incidence, simulating diverse field distributions. Combined with bilinear interpolation, the method achieves high-resolution reconstruction at 30 GHz and 60 GHz, meeting the requirements of the IEC/IEEE 63195-2 standard for exposure assessment. Numerical simulations show that EFGraGAN reconstructs E-field distributions in a skin phantom with a maximum mean relative error (MRE) of <9% up to 60 GHz in a 4×4 dipole array scenario, outperforming conventional interpolation and traditional GAN methods. The approach also demonstrates strong robustness to noise, enabling current measurement systems to achieve accurate and efficient evaluation of mmWave exposure.
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