The structured sparsity can be leveraged in traditional far-field channels, greatly facilitating efficient sparse channel recovery by compressing the complexity of overheads to the level of the scatterer number. However, when experiencing a fundamental shift from planar-wave-based far-field modeling to spherical-wave-based near-field modeling, whether these benefits persist in the near-field regime remains an open issue. To answer this question, this article delves into structured sparsity in the near-field realm, examining its peculiarities and challenges. In particular, we present the key features of near-field structured sparsity in contrast to the far-field counterpart, drawing from both physical and mathematical perspectives. Upon unmasking the theoretical bottlenecks, we resort to bypassing them by decoupling the geometric parameters of the scatterers, termed the triple parametric decomposition (TPD) framework. It is demonstrated that our novel TPD framework can achieve robust recovery of near-field sparse channels by applying the potential structured sparsity and avoiding the curse of complexity and overhead.

翻译：结构化稀疏性可在传统远场信道中得到有效利用，通过将开销复杂度压缩至散射体数量级，极大促进了稀疏信道的高效恢复。然而，当信道建模经历从基于平面波的远场模型到基于球面波的近场模型这一根本性转变时，这些优势在近场区域是否依然存在仍是一个悬而未决的问题。为回答这一问题，本文深入探究近场领域中的结构化稀疏性，剖析其特性与挑战。特别地，我们从物理与数学双重角度出发，系统阐述了近场结构化稀疏性相较于远场情形的关键特征。在揭示理论瓶颈后，我们通过解耦散射体的几何参数来规避这些障碍，提出了三重参数分解框架。研究表明，该新型TPD框架能够通过挖掘潜在的结构化稀疏性，同时规避复杂度与开销的指数级增长，从而实现近场稀疏信道的鲁棒恢复。