3D Environment Sensing with Channel State Information Based on Computational Imaging

As a promising realization of integrated sensing and communication (ISAC), the environment information can be extracted from the channel state information (CSI) obtained in the communication processes, without major modification to the existing communication systems. In this paper, we aim to achieve 3D environment sensing with CSI obtained from the communication signals of multiple users. To this end, we propose a unique computational imaging approach by appropriately considering both the occlusion effect and the perturbation effect caused by the complicated behavior of the electromagnetic waves propagating across the environment scatterers. By exploiting the intrinsic sparsity of the environment, we formulate the problem as a generalized compressed sensing optimization problem and then propose a novel iterative algorithm to reconstruct the images of the environment scatterers, called expectation-maximization generalized approximate message passing with modified mea-surement matrix (EM-GAMP-3M) algorithm. The simulation results validate its high accuracy and effectiveness.