Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Movable Antenna for Integrating Near-field Channel Estimation and Localization

Chongjia Sun, Ziwei Wan, Lipeng Zhu, Zhenyu Xiao, Zhen Gao, Rui Zhang

TL;DR

This paper tackles near-field channel estimation and localization in MA-enabled ISAC systems, where the large antenna aperture pushes operation into the near-field regime. It introduces a multi-stage framework: subregion-based angle estimation with Newtonized OMP (NOMP), scatterer localization via subregion ray clustering (LSRC), and sensing-assisted channel estimation (CE) that uses the sensed scatterer locations to refine the full near-field channel. The key contributions are (i) a subregion NOMP angle estimation scheme that leverages near-field diversity across MA ports, (ii) LSRC to robustly localize scatterers by clustering direction vectors from multiple subregions, and (iii) a CE refinement step that incorporates the estimated scatterer coordinates to achieve more accurate channel reconstruction with MMV formulations. Results show significant improvements in sensing accuracy and CE metrics over traditional CS-based methods, particularly with higher MA port counts and appropriate subregion division, enabling practical MA-aided near-field ISAC.

Abstract

Movable antenna (MA) introduces a new degree of freedom for future wireless communication systems by enabling the adaptive adjustment of antenna positions. Its large-range movement renders wireless channels transmission into the near-field region, which brings new performance enhancement for integrated sensing and communication (ISAC). This paper proposes a novel multi-stage design framework for broadband near-field ISAC assisted by MA. The framework first divides the MA movement area into multiple subregions, and employs the Newtonized orthogonal matching pursuit algorithm (NOMP) to achieve high-precision angle estimation in each subregion. Subsequently, a method called near-field localization via subregion ray clustering (LSRC) is proposed for identifying the positions of scatterers. This method finds the coordinates of each scatterer by jointly processing the angle estimates across all subregions. Finally, according to the estimated locations of the scatterers, the near-field channel estimation (CE) is refined for improving communication performance. Simulation results demonstrate that the proposed scheme can significantly enhance MA sensing accuracy and CE, providing an efficient solution for MA-aided near-field ISAC.

Movable Antenna for Integrating Near-field Channel Estimation and Localization

TL;DR

This paper tackles near-field channel estimation and localization in MA-enabled ISAC systems, where the large antenna aperture pushes operation into the near-field regime. It introduces a multi-stage framework: subregion-based angle estimation with Newtonized OMP (NOMP), scatterer localization via subregion ray clustering (LSRC), and sensing-assisted channel estimation (CE) that uses the sensed scatterer locations to refine the full near-field channel. The key contributions are (i) a subregion NOMP angle estimation scheme that leverages near-field diversity across MA ports, (ii) LSRC to robustly localize scatterers by clustering direction vectors from multiple subregions, and (iii) a CE refinement step that incorporates the estimated scatterer coordinates to achieve more accurate channel reconstruction with MMV formulations. Results show significant improvements in sensing accuracy and CE metrics over traditional CS-based methods, particularly with higher MA port counts and appropriate subregion division, enabling practical MA-aided near-field ISAC.

Abstract

Movable antenna (MA) introduces a new degree of freedom for future wireless communication systems by enabling the adaptive adjustment of antenna positions. Its large-range movement renders wireless channels transmission into the near-field region, which brings new performance enhancement for integrated sensing and communication (ISAC). This paper proposes a novel multi-stage design framework for broadband near-field ISAC assisted by MA. The framework first divides the MA movement area into multiple subregions, and employs the Newtonized orthogonal matching pursuit algorithm (NOMP) to achieve high-precision angle estimation in each subregion. Subsequently, a method called near-field localization via subregion ray clustering (LSRC) is proposed for identifying the positions of scatterers. This method finds the coordinates of each scatterer by jointly processing the angle estimates across all subregions. Finally, according to the estimated locations of the scatterers, the near-field channel estimation (CE) is refined for improving communication performance. Simulation results demonstrate that the proposed scheme can significantly enhance MA sensing accuracy and CE, providing an efficient solution for MA-aided near-field ISAC.
Paper Structure (12 sections, 17 equations, 5 figures, 2 algorithms)

This paper contains 12 sections, 17 equations, 5 figures, 2 algorithms.

Table of Contents

  1. Introduction
  2. System Model
  3. System Description and Channel Model
  4. Signal Model
  5. Proposed MA-based Near-field ISAC Scheme
  6. Sparse Representation and Problem Formulation
  7. Angle Estimation Based on NOMP
  8. Near-Field LSRC
  9. Sensing-assisted CE
  10. Simulation Results
  11. conclusion
  12. OSPA Metric

Figures (5)

  • Figure 1: Near-field MA-aided communication system.
  • Figure 2: Near-field localization via subregion ray clustering.
  • Figure 3: Sensing performance comparison versus SNR for different methods. (a) Angle MAE. (b) Radial distance MAE.
  • Figure 4: NMSE versus SNR for wideband communication CE.
  • Figure 5: CE and sensing performance comparison versus CR for different methods. (a) NMSE. (b) OSPA.