Electromagnetic Property Sensing in ISAC With Multiple Base Stations: Algorithm, Pilot Design, and Performance Analysis

Integrated sensing and communication (ISAC) has opened up numerous game-changing opportunities for future wireless systems. In this paper, we develop a novel scheme that utilizes orthogonal frequency division multiplexing (OFDM) pilot signals to sense the electromagnetic (EM) property of the target and thus identify the materials of the target. Specifically, we first establish an EM wave propagation model with Maxwell equations, where the EM property of the target is captured by a closed-form expression of the channel. We then build the mathematical model for the relative permittivity and conductivity distribution (RPCD) within a predetermined region of interest shared by multiple base stations (BSs). By leveraging the Lippmann-Schwinger equation, we propose an EM property sensing method that reconstructs the RPCD using compressive sensing techniques. This approach exploits the joint sparsity of the EM property vector, which enables the proposed method to effectively handle the high dimensionality and ill-posed nature of the inverse scattering problem. We then develop a fusion algorithm to combine data from multiple BSs, which can enhance the reconstruction accuracy of EM property by efficiently integrating diverse measurements. Moreover, the fusion is performed at the feature level of RPCD and features low transmission overhead. We further design the pilot signals that can minimize the mutual coherence of the equivalent channels and enhance the diversity of incident EM wave patterns. Simulation results demonstrate the efficacy of the proposed method in achieving high-quality RPCD reconstruction and accurate material classification.