Propagation- and gradient-guided GAN for E-field super-resolution in millimeter-wave exposure assessment

Abstract

The rapid deployment of the new generation of mobile networks and terminals has significantly intensified public concern over potential health risks associated with electromagnetic field (EMF) exposure, particularly in the context of millimeter-wave frequency bands. The current measurement system based on point-wise sampling of electric field (E-field) strength in the given volume is inefficient at the frequency band. Therefore, super-resolution construction of the E-field from the low-resolution values is in great need. This study introduces a generative adversarial network (GAN) integrated with a field gradient branch and loss function to achieve super-resolution reconstruction of electric fields (E-fields), for the purpose of evaluating millimeter-wave (mmW) exposure. Utilizing a dataset created based on plane wave integral representation (PWIR) and randomized parameter incidence, the model effectively captures the wave propagation characteristics of diverse antennas. The incorporation of gradient information sharpens the E-field distribution details. When combined with cubic interpolation, this approach is validated for frequencies of 30 GHz and 60 GHz. Results by numerical validation show that this method achieves a maximum mean relative error (MRE) below 8% at up to 60 GHz, surpassing both interpolation techniques and conventional GAN-based approaches. In conclusion, this method demonstrates a physics-guided, standard-aligned framework for E-field super-resolution, enabling high-fidelity exposure assessment from sparsely sampled data and offering a potential pathway to support high-frequency electromagnetic exposure evaluation in the compliance testing of fifth-generation (5 G) millimeter-wave (mmW) communication devices.