EM Informed Holographic Imaging via Unrolled Deep Networks

Abstract

Smart Radio Environments (SREs) are a foundational paradigm for the Internet of Everything (IoE), in which dense, phase-coherent antenna arrays form a shared infrastructure for integrated sensing and communication (ISAC). Radio-Frequency (RF) holography is a core sensing building block for SREs: it reconstructs a volumetric map of the electromagnetic (EM) scattering scene from phase-sensitive field measurements, an infrastructural snapshot from stray RF radiation, without dedicated sensors. To make reconstructions accurate and rapidly adaptable, this paper adopts algorithm unrolling, in which the iterations of a classical holographic solver become the layers of a compact, trainable deep network that preserves the EM physical interpretation and learns only a few parameters from limited data. Building on the unrolled Iterative Shrinkage-Thresholding Algorithm (ISTA), namely Learned ISTA (LISTA), this paper first proposes a Weighted LISTA (W-LISTA) that preserves the EM forward model and learns a spatially-varying regularization that steers the sparsity prior towards target shapes consistent with the deployment. Second, the Low-Rank Weighted LISTA (LoRaW-LISTA) applies a low-rank adaptation (LoRa) of the holographic operator to compensate for model mismatch from linearized EM approximations. Both methods are validated on full-wave EM simulations and on a 2.45 GHz indoor campaign with human-body phantoms, improving accuracy and resolution over baselines. Combining the spatially-varying regularization of W-LISTA with the LoRa adaptation of the EM model yields superior reconstruction where classical iterative solvers fail. The proposed tools are rapidly adaptable building blocks for the SRE sensing layer, whose reconstructions, up to body-shape imaging, remain privacy-preserving by design.