Near Field Sparse Representation and Dictionary Design Using Discrete Fresnel Transform

作者：Ziyi Xu, Shuoyao Wang, Ying–Jun Angela Zhang · 发表于：IEEE Transactions on Wireless Communications · 年份：2025 · DOI：10.1109/twc.2025.3626538 · 被引用次数：1 · 研究领域：Millimeter-Wave Propagation and Modeling、Advanced MIMO Systems Optimization、Radio Astronomy Observations and Technology

Extremely large-scale antenna arrays (ELAAs) and millimeter wave (mmWave) communication are prominent enablers in advanced 6G networks. To accommodate the unique characteristics introduced by these trends in communication system design, near-field spherical-wave propagation modeling is imperative, departing from the classical far-field planar-wave model. In the millimeter wave band, where wavelengths are small compared to the array aperture, many propagation phenomena resemble those in optics. Inspired by Fresnel diffraction analysis in classical optics, we explore the utilization of the Fresnel Transform for channel modeling, especially in the near-field region. In this paper, we propose a Discrete Fresnel Transform (DFnT)-based dictionary towards effectively characterizing and sparsely representing the channel vector in both near and far-field scenarios. Specifically, we bridge the gap between the continuous Fresnel Transform and the near-field DFnT dictionary by discretizing and parameterizing the transform, adapting it to ELAA codewords. We assess the near-field representation capability of the dictionary by employing it for channel estimation using Orthogonal Matching Pursuit, a compressive channel estimation algorithm whose performance relies heavily on the sparsity of the representation. Simulation results underscore the superiority of the DFnT dictionary over the existing far-field and the Polar dictionaries.