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Electromagnetic (EM) body models predict the impact of human presence and motions on the Radio-Frequency (RF) field originated from wireless devices nearby. Despite their accuracy, EM models are time-consuming methods which prevent their adoption in strict real-time computational imaging and estimation problems, such as passive localization, RF tomography, and holography. Physicsinformed Generative Neural Network (GNN) models have recently attracted a lot of attention thanks to their potential to reproduce a process by incorporating relevant physical laws and constraints. They can be used to simulate or reconstruct missing data or samples, reproduce EM propagation effects, approximated EM fields, and learn a physics-informed data distribution, i.e., the Bayesian prior. Generative machine learning represents a multidisciplinary research area weaving together physical/EM modelling, signal processing, and Artificial Intelligence (AI). The paper discusses two popular techniques, namely Variational Auto-Encoders (VAEs) and Generative Adversarial Networks (GANs), and their adaptations to incorporate relevant EM body diffraction methods. The proposed EM-informed GNN models are verified against classical EM tools driven by diffraction theory, and validated on real data. The paper explores emerging opportunities of GNN tools targeting real-time passive RF sensing in communication systems with dense antenna arrays. Proposed tools are also designed, implemented, and verified on resource constrained wireless devices. Simulated and experimental analysis reveal that GNNs can limit the use of time-consuming and privacy-sensitive training stages as well as intensive EM computations. On the other hand, they require hyper-parameter tuning to achieve a good compromise between accuracy and generalization. |
