Exploiting Structured Sparsity in Near Field: From the Perspective of Decomposition

TL;DR

This paper investigates whether structured sparsity benefits in far-field channels extend to near-field scenarios, proposing a novel decomposition framework to enable robust sparse channel recovery in near-field conditions.

Contribution

It introduces the triple parametric decomposition (TPD) framework, addressing near-field structured sparsity challenges and enabling efficient channel recovery.

Findings

TPD effectively decouples scatterer parameters.

Near-field structured sparsity can be exploited similarly to far-field.

The framework avoids complexity and overhead issues.

Abstract

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.