Radio imaging is rapidly gaining prominence in the design of future communication systems, with the potential to utilize reconfigurable intelligent surfaces (RISs) as imaging apertures. Although the sparsity of targets in three-dimensional (3D) space has led most research to adopt compressed sensing (CS)-based imaging algorithms, these often require substantial computational and memory burdens. Drawing inspiration from conventional Fourier transform (FT)-based imaging methods, our research seeks to accelerate radio imaging in RIS-aided communication systems. To begin, we introduce a two-stage wavenumber domain 3D imaging technique: first, we modify RIS phase shifts to recover the equivalent channel response from the user equipment to the RIS array, subsequently employing traditional FT-based wavenumber domain methods to produce target images. We also determine the diffraction resolution limits of the system through k-space analysis, taking into account factors including system bandwidth, transmission direction, operating frequency, and the angle subtended by the RIS. Addressing the challenge of limited pilots in communication systems, we unveil an innovative algorithm that merges the strengths of both FT- and CS-based techniques by substituting the expansive sensing matrix with FT-based operators. Our simulation outcomes confirm that our proposed FT-based methods achieve high-quality images while demanding few time, memory, and communication resources.

翻译：无线电成像在未来的通信系统设计中正迅速崭露头角，其有潜力利用可重构智能表面（RIS）作为成像孔径。尽管三维空间中目标的稀疏性使得大多数研究采用基于压缩感知（CS）的成像算法，但这些算法往往需要巨大的计算和内存负担。借鉴传统基于傅里叶变换（FT）的成像方法，我们的研究旨在加速RIS辅助通信系统中的无线电成像。首先，我们引入一种两阶段波数域三维成像技术：第一步，通过调整RIS相位偏移来恢复从用户设备到RIS阵列的等效信道响应，随后利用传统基于FT的波数域方法生成目标图像。我们还通过k空间分析确定了系统的衍射分辨率极限，考虑了系统带宽、传输方向、工作频率以及RIS所张角度等因素。针对通信系统中导频受限的挑战，我们提出一种创新算法，该算法通过用基于FT的算子替换庞大的感知矩阵，融合了FT和CS两种方法的优势。我们的仿真结果证实，所提出的基于FT的方法能够在消耗较少时间、内存和通信资源的同时，实现高质量的图像。