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Polarization Characterization of THz Scattering from Rough Surfaces Based on Deep Learning

Xu Zhao, Ben Chen,Ke Guan, Bin Lu, Yao Wei, Mingyang Dong

2024 4TH URSI ATLANTIC RADIO SCIENCE MEETING, AT-RASC 2024(2024)

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Abstract
With the increasing demand for data transmission, researchers are getting interested in the topic of terahertz (THz) communication systems. As an important parameter of electromagnetic wave propagation, polarization is critical for THz channel modeling. In the previous work, a deep learning (DL)-based THz scattering model has been proposed to reconstruct the scattered electric field on the rough Perfect electric conductor (PEC) surface in the far-field region of the upper hemisphere space. For further refinement, the model is retrained in this paper, and the polarization characteristics of the scattered electric field for both Transverse Magnetic (TM)- and Transverse Electric (TE)-polarized incidence are analyzed in terms of the cross-polarization power ratio (XPR) and the Poincare sphere. Finally, a scattering model covering multiple dimensions such as amplitude and polarization is expected to be realized.
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Key words
Deep Learning,Far-field,Cross-polarization,Polarization Characteristics,Perfect Electric Conductor,Terahertz Communications,Simulation Results,Fitting Parameters,Incident Angle,Surface Current,Incident Wave,Modulation Of Processes,Error Parameters,Finite-difference Time-domain,Equatorial Plane,Generation Module,Electric Vector,Logistic Distribution,Elliptically Polarized,Transverse Magnetic Polarization,Transverse Electric Polarization,DL-based Models,Incident Angle Increases
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