Distributed Computational Imaging with Reconfigurable Intelligent Surface

Abstract

As a promising imaging technique, computational imaging is able to reconstruct the target by transmitting temporal-spatial incoherent signals. With the reconstructed image, a deterministic model of the scene can be established. However, in previous computational imaging systems, the transceiver is an antenna array and each antenna element needs to be modulated respectively, which requires many radio-frequency (RF) chains and complicated signal processing techniques. In addition, the traditional method uses colocated transmitting and receiving antenna which is not conducive to the flexibility of the system. Reconfigurable intelligent surface (RIS) is a revolutionary technology for achieving spectrum and energy efficient in future wireless communication systems. In this paper, we propose distributed computational imaging with reconfigurable intelligent surface. The transmitting antenna, receiving antenna and RIS are distributed in different spatial positions. By deploying reconfigurable intelligent surface, the phase and amplitude of the detecting signals are stochastically modulated by reconfigurable passive elements of RIS, which achieves high spectrum and energy efficiency with low hardware cost. Furthermore, we analyze four models of the detecting signal in detail and subdivide region of interest (ROI) into a collection of grids to formulate the reconstruction process as a sparse recovery problem. Simulation results demonstrate that the proposed distributed computational imaging with RIS can achieve outstanding imaging performance and reconstruct the target effectively.