Movable Antenna Empowered Near-Field Sensing via Antenna Position Optimization
Yushen Wang, Weidong Mei, Xin Wei, Ya Fei Wu, Zhi Chen, Boyu Ning
TL;DR
The paper introduces movable antenna arrays to dramatically improve near-field sensing performance by jointly estimating target angle and distance in 1D and 2D geometries. It derives worst-case CRBs for AoA and distance, provides closed-form optimal geometries in the 1D single-parameter cases, and develops discrete-sampling algorithms to obtain suboptimal APVs for joint estimation. Extending to 2D, it analyzes AoA-only, distance-only, and joint estimation with corresponding CRBs and uses a similar discrete optimization to cope with non-convexity, revealing symmetric or corner-centered array layouts that maximize aperture and suppress ambiguities. Numerical results show substantial CRB reductions and improved steering properties over fixed-position antennas, validating MA’s potential to achieve high-resolution near-field sensing with fewer elements. The findings highlight how MA geometry and positioning can reshape sensing performance in 6G-era near-field environments.
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.