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Wideband Near-Field Sensing in ISAC: Unified Algorithm Design and Decoupled Effect Analysis

Ruiyun Zhang, Zhaolin Wang, Zhiqing Wei, Yuanwei Liu, Zehui Xiong, Zhiyong Feng

Abstract

To advance integrated sensing and communications (ISAC) in sixth-generation (6G) extremely large-scale multiple-input multiple-output (XL-MIMO) networks, a low-complexity compressed sensing (CS)-based dictionary design is proposed for wideband near-field (WB-NF) target localization. Currently, the massive signal dimensions in the WB-NF regime impose severe computational burdens and high spatial-frequency coherence on conventional grid-based algorithms. Furthermore, a unified framework exploiting both wideband (WB) and near-field (NF) effects is lacking, and the analytical conditions for simplifying this model into decoupled approximations remain uncharacterized. To address these challenges, the proposed algorithm mathematically decouples the mutual coherence function and introduces a novel angle-distance sampling grid with customized distance adjustments, drastically reducing dictionary dimensions while ensuring low coherence. To isolate the individual WB and NF impacts, two coherence-based metrics are formulated to establish the effective boundaries of the narrowband near-field (NB-NF) and wideband far-field (WB-FF) regions, where respective multiple signal classification (MUSIC) algorithms are utilized. Simulations demonstrate that the CS-based method achieves robust performance across the entire regime, and the established boundaries provide crucial theoretical guidelines for WB and NF effect decoupling.

Wideband Near-Field Sensing in ISAC: Unified Algorithm Design and Decoupled Effect Analysis

Abstract

To advance integrated sensing and communications (ISAC) in sixth-generation (6G) extremely large-scale multiple-input multiple-output (XL-MIMO) networks, a low-complexity compressed sensing (CS)-based dictionary design is proposed for wideband near-field (WB-NF) target localization. Currently, the massive signal dimensions in the WB-NF regime impose severe computational burdens and high spatial-frequency coherence on conventional grid-based algorithms. Furthermore, a unified framework exploiting both wideband (WB) and near-field (NF) effects is lacking, and the analytical conditions for simplifying this model into decoupled approximations remain uncharacterized. To address these challenges, the proposed algorithm mathematically decouples the mutual coherence function and introduces a novel angle-distance sampling grid with customized distance adjustments, drastically reducing dictionary dimensions while ensuring low coherence. To isolate the individual WB and NF impacts, two coherence-based metrics are formulated to establish the effective boundaries of the narrowband near-field (NB-NF) and wideband far-field (WB-FF) regions, where respective multiple signal classification (MUSIC) algorithms are utilized. Simulations demonstrate that the CS-based method achieves robust performance across the entire regime, and the established boundaries provide crucial theoretical guidelines for WB and NF effect decoupling.

Paper Structure

This paper contains 34 sections, 53 equations, 8 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model
  3. Transmit Signal Model
  4. Received Signal Model
  5. Problem Statement
  6. Sparse Matrix Formulation and Localization Algorithm Design
  7. Sparse Matrix Representation for WB-NF Sensing
  8. Angular Sampling Design
  9. Design of Spatial Sampling Grid
  10. Angular Sampling Criterion
  11. Distance Sampling Design
  12. Fresnel-Based Spatial Coherence Analysis
  13. Bandwidth Constraint and Grid Alignment
  14. Dimensionality Reduction and Complexity Analysis
  15. The Proposed Algorithm for WB-NF Parameter Estimation
  16. ...and 19 more sections

Figures (8)

  • Figure 1: Illustration of the considered NF system model.
  • Figure 2: Coherence function magnitude $|F(\zeta)|$ as a function of the normalized distance $\zeta$.
  • Figure 3: Visualization of the proposed parameter algorithm.
  • Figure 4: The maximum threshold distance $r_{\mathrm{NB-NF}}$ versus system bandwidth $B$ for various correlation thresholds $\rho_0$.
  • Figure 5: Spatial spectrum estimation results of the NB-NF MUSIC algorithm.
  • ...and 3 more figures