Movable Antenna Enhanced Wireless Sensing Via Antenna Position Optimization

TL;DR

This paper introduces a movable-antenna array system that optimizes antenna positions to significantly improve wireless sensing accuracy, especially in angle estimation, by minimizing the Cramer-Rao bound compared to fixed arrays.

Contribution

It derives optimal antenna positioning solutions for 1D and 2D movable-antenna arrays to enhance angle estimation performance in wireless sensing.

Findings

Movable antennas reduce the CRB of AoA estimation compared to fixed arrays.

Optimal position solutions are derived for circular and arbitrary movement regions.

Numerical results show significant MSE improvements with the proposed arrays.

Abstract

In this paper, we propose a new wireless sensing system equipped with the movable-antenna (MA) array, which can flexibly adjust the positions of antenna elements for improving the sensing performance over conventional antenna arrays with fixed-position antennas (FPAs). First, we show that the angle estimation performance in wireless sensing is fundamentally determined by the array geometry, where the Cramer-Rao bound (CRB) of the mean square error (MSE) for angle of arrival (AoA) estimation is derived as a function of the antennas' positions for both one-dimensional (1D) and two-dimensional (2D) MA arrays. Then, for the case of 1D MA array, we obtain a globally optimal solution for the MAs' positions in closed form to minimize the CRB of AoA estimation MSE. While in the case of 2D MA array, we aim to achieve the minimum of maximum (min-max) CRBs of estimation MSE for the two AoAs with respect to the horizontal and vertical axes, respectively. In particular, for the special case of circular antenna movement region, an optimal solution for the MAs' positions is derived under certain numbers of MAs and circle radii. Thereby, both the lower- and upper-bounds of the min-max CRB are obtained for the antenna movement region with arbitrary shapes. Moreover, we develop an efficient alternating optimization algorithm to obtain a locally optimal solution for MAs' positions by iteratively optimizing one between their horizontal and vertical coordinates with the other being fixed. Numerical results demonstrate that our proposed 1D/2D MA arrays can significantly decrease the CRB of AoA estimation MSE as well as the actual MSE compared to conventional uniform linear arrays (ULAs)/uniform planar arrays (UPAs) with different values of uniform inter-antenna spacing.