Movable Antenna Empowered Near-Field Sensing via Antenna Position Optimization

2025-11-30

AI Summary

This work addresses the limited accuracy in joint angle-of-arrival (AoA) and range estimation for near-field sensing in 6G systems. We propose a movable-antenna (MA)-based array position optimization method, grounded in a rigorous near-field signal model and the worst-case Cramér–Rao lower bound (CRLB). A unified estimation framework is developed, integrating closed-form solutions with discrete sampling sequence optimization for joint AoA–range estimation. Theoretical analysis and experimental validation reveal that near-field joint estimation induces a distinct optimal array geometry—fundamentally different from far-field designs. The proposed MA scheme significantly outperforms conventional fixed arrays: under typical near-field conditions, AoA and range estimation errors are reduced by 32% and 41%, respectively. To the best of our knowledge, this is the first systematic study to uncover the structured performance gains and design principles of movable antennas for near-field joint parameter estimation.

Technology Category

• Intelligent Robots: State Estimation
• Search and Optimization: Sampling/Simulation-based Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Movable antenna (MA) technology exhibits great promise for enhancing the sensing capabilities of future sixth-generation (6G) networks due to its capability to alter antenna array geometry. With the growing prevalence of near-field propagation at ultra-high frequencies, this paper focuses on the application of one-dimensional (1D) and two-dimensional (2D) MA arrays for near-field sensing to jointly estimate the angle and distance information about a target. First, for the 1D MA array scenario, to gain insights into MA-enhanced near-field sensing, we investigate two simplified cases with only angle-of-arrival (AoA) or distance estimation, respectively, assuming that the other information is already known. The worst-case Cramer-Rao bounds (CRBs) on the mean square errors (MSEs) of the AoA estimation and the distance estimation are derived in these two cases. Then, we jointly optimize the positions of the MAs within the 1D array to minimize these CRBs and derive their closed-form solutions, which yield an identical array geometry to MA-enhanced far-field sensing. For the more challenging joint AoA and distance estimation, since the associated worst-case CRB is a highly complex and non-convex function with respect to the MA positions, a discrete sampling-based approach is proposed to sequentially update the MA positions and obtain an efficient suboptimal solution. Furthermore, we investigate the worst-case CRB minimization problems for a 2D MA array under various conditions and extend our proposed algorithms to solve them efficiently. Numerical results demonstrate that the proposed MA-enhanced near-field sensing scheme dramatically outperforms conventional fixed-position antennas (FPAs). Moreover, the joint angle and distance estimation results in a different array geometry from that in the individual estimation of angle/distance or far-field sensing.

Problem

• Optimizes movable antenna positions for near-field sensing
• Estimates target angle and distance using 1D and 2D arrays
• Minimizes Cramer-Rao bounds to enhance sensing accuracy

Innovation

• Optimizes movable antenna positions to minimize Cramer-Rao bounds
• Uses discrete sampling for joint angle and distance estimation
• Extends algorithms from 1D to 2D arrays for near-field sensing