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Electromagnetic-informed generative models for passive RF sensing and perception of body motions
  • +2
  • Stefano Savazzi,
  • Federica Fieramosca,
  • Sanaz Kianoush,
  • Michele D'Amico,
  • Vittorio Rampa
Stefano Savazzi
Consiglio Nazionale delle Ricerche, IEIIT institute

Corresponding Author:

Federica Fieramosca
DEIB, Politecnico di Milano

Corresponding Author:[email protected]

Author Profile
Sanaz Kianoush
Consiglio Nazionale delle Ricerche, IEIIT institute
Michele D'Amico
DEIB, Politecnico di Milano
Vittorio Rampa
Consiglio Nazionale delle Ricerche, IEIIT institute


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 problems and Bayesian estimation, such as passive localization, RF tomography, and holography. Physics-informed 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, reproducing EM propagation effects, approximated EM fields and learn a physics-informed data distribution, i.e., a Bayesian prior. 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 concepts. Proposed EM-informed generative models are verified against classical EM tools driven by diffraction theory and validated on real data. Physics-informed generative machine learning represents a multidisciplinary research area weaving together physical/EM modelling, signal processing and artificial intelligence (AI): the paper explores emerging opportunities of GNN tools targeting real-time passive RF sensing in multiple-input multiple-output (MIMO) communication systems. Proposed generative tools are designed, implemented and verified on resource constrained wireless devices being members of a Wireless Local Area Network (WLAN).
19 Apr 2024Submitted to TechRxiv
26 Apr 2024Published in TechRxiv