This paper addresses a near-field imaging problem utilizing extremely large-scale multiple-input multiple-output (XL-MIMO) antennas and reconfigurable intelligent surfaces (RISs) already in place for wireless communications. To this end, we consider a system with a fixed transmitting antenna array illuminating a region of interest (ROI) and a fixed receiving antenna array inferring the ROI’s scattering coefficients. Leveraging XL-MIMO and high frequencies, the ROI is situated in the radiative near-field region of both antenna arrays, thus enhancing the degrees of freedom (DoF) (i.e., the channel matrix rank) of the illuminating and sensing channels available for imaging, here referred to as holographic imaging. To further boost the imaging performance, we optimize the illuminating waveform by solving a min-max optimization problem having the upper bound of the mean squared error (MSE) of the image estimate as the objective function. Additionally, we address the challenge of non-line-of-sight (NLOS) scenarios by considering the presence of a RIS and deriving its optimal reflection coefficients. Numerical results investigate the interplay between illumination optimization, geometric configuration (monostatic and bistatic), the DoF of the illuminating and sensing channels, image estimation accuracy, and image complexity.

Holographic Imaging With XL-MIMO and RIS: Illumination and Reflection Design

Guidi, F.;
2024

Abstract

This paper addresses a near-field imaging problem utilizing extremely large-scale multiple-input multiple-output (XL-MIMO) antennas and reconfigurable intelligent surfaces (RISs) already in place for wireless communications. To this end, we consider a system with a fixed transmitting antenna array illuminating a region of interest (ROI) and a fixed receiving antenna array inferring the ROI’s scattering coefficients. Leveraging XL-MIMO and high frequencies, the ROI is situated in the radiative near-field region of both antenna arrays, thus enhancing the degrees of freedom (DoF) (i.e., the channel matrix rank) of the illuminating and sensing channels available for imaging, here referred to as holographic imaging. To further boost the imaging performance, we optimize the illuminating waveform by solving a min-max optimization problem having the upper bound of the mean squared error (MSE) of the image estimate as the objective function. Additionally, we address the challenge of non-line-of-sight (NLOS) scenarios by considering the presence of a RIS and deriving its optimal reflection coefficients. Numerical results investigate the interplay between illumination optimization, geometric configuration (monostatic and bistatic), the DoF of the illuminating and sensing channels, image estimation accuracy, and image complexity.
2024
Istituto di Elettronica e di Ingegneria dell'Informazione e delle Telecomunicazioni - IEIIT
Holographic imaging
illumination design
near-field
reconfigurable intelligent surfaces
XL-MIMO
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/515431
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