Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Movable Antenna-Enhanced Near-Field Flexible Beamforming: Performance Analysis and Optimization

Shun Yang, Xin Wei, Nianbing Su, Weidong Mei, Zhi Chen, Boyu Ning

TL;DR

This work introduces Movable Antennas (MAs) to enable flexible near-field beamforming, formulating non-convex joint optimization problems for beam nulling and multi-beam forming. It develops discrete-sampling and alternating-optimization strategies to obtain high-quality APV and AWV solutions under near-field conditions, and provides a Taylor-series based analysis of antenna-position errors to quantify robustness. Special-case results under ALMR reveal that MRT toward a target can achieve nulls or full gains at other directions, with constructive results like Theorem 1 and Algorithm 1 guiding APV design. Numerical simulations validate the proposed methods, show superiority over baselines, and quantify how frequency, geometry, and movement range affect robustness and performance.

Abstract

As an emerging wireless communication technology, movable antennas (MAs) offer the ability to adjust the spatial correlation of steering vectors, enabling more flexible beamforming compared to fixed-position antennas (FPAs). In this paper, we investigate the use of MAs for two typical near-field beamforming scenarios: beam nulling and multi-beam forming. In the first scenario, we aim to jointly optimize the positions of multiple MAs and the beamforming vector to maximize the beam gain toward a desired direction while nulling interference toward multiple undesired directions. In the second scenario, the objective is to maximize the minimum beam gain among all the above directions. However, both problems are non-convex and challenging to solve optimally. To gain insights, we first analyze several special cases and show that, with proper positioning of the MAs, directing the beam toward a specific direction can lead to nulls or full gains in other directions in the two scenarios, respectively. For the general cases, we propose a discrete sampling method and an alternating optimization algorithm to obtain high-quality suboptimal solutions to the two formulated problems. Furthermore, considering the practical limitations in antenna positioning accuracy, we analyze the impact of position errors on the performance of the optimized beamforming and MA positions, by introducing a Taylor series approximation for the near-field beam gain at each target. Numerical results validate our theoretical findings and demonstrate the effectiveness of our proposed algorithms.

Movable Antenna-Enhanced Near-Field Flexible Beamforming: Performance Analysis and Optimization

TL;DR

This work introduces Movable Antennas (MAs) to enable flexible near-field beamforming, formulating non-convex joint optimization problems for beam nulling and multi-beam forming. It develops discrete-sampling and alternating-optimization strategies to obtain high-quality APV and AWV solutions under near-field conditions, and provides a Taylor-series based analysis of antenna-position errors to quantify robustness. Special-case results under ALMR reveal that MRT toward a target can achieve nulls or full gains at other directions, with constructive results like Theorem 1 and Algorithm 1 guiding APV design. Numerical simulations validate the proposed methods, show superiority over baselines, and quantify how frequency, geometry, and movement range affect robustness and performance.

Abstract

As an emerging wireless communication technology, movable antennas (MAs) offer the ability to adjust the spatial correlation of steering vectors, enabling more flexible beamforming compared to fixed-position antennas (FPAs). In this paper, we investigate the use of MAs for two typical near-field beamforming scenarios: beam nulling and multi-beam forming. In the first scenario, we aim to jointly optimize the positions of multiple MAs and the beamforming vector to maximize the beam gain toward a desired direction while nulling interference toward multiple undesired directions. In the second scenario, the objective is to maximize the minimum beam gain among all the above directions. However, both problems are non-convex and challenging to solve optimally. To gain insights, we first analyze several special cases and show that, with proper positioning of the MAs, directing the beam toward a specific direction can lead to nulls or full gains in other directions in the two scenarios, respectively. For the general cases, we propose a discrete sampling method and an alternating optimization algorithm to obtain high-quality suboptimal solutions to the two formulated problems. Furthermore, considering the practical limitations in antenna positioning accuracy, we analyze the impact of position errors on the performance of the optimized beamforming and MA positions, by introducing a Taylor series approximation for the near-field beam gain at each target. Numerical results validate our theoretical findings and demonstrate the effectiveness of our proposed algorithms.
Paper Structure (18 sections, 1 theorem, 61 equations, 17 figures, 3 algorithms)

This paper contains 18 sections, 1 theorem, 61 equations, 17 figures, 3 algorithms.

Table of Contents

  1. Introduction
  2. System Model And Problem Formulation
  3. Scenario 1: Beam Nulling
  4. Scenario 2: Multi-Beam Forming
  5. Special Case Analysis
  6. Scenario 1: Beam Nulling
  7. Scenario 2: Multi-Beam Forming
  8. Proposed Solutions to (P2) and (P3)
  9. Proposed Solution to (P2)
  10. Proposed Solution to (P3)
  11. Antenna Position Errors Analysis
  12. Worst-Case Performance in Scenario 1
  13. Worst-Case Performance in Scenario 2
  14. Numerical Results
  15. Scenario 1: Beam Nulling
  16. ...and 3 more sections

Key Result

Proposition 1

If $K=1$, there must exist an APV $\boldsymbol{x}$ that satisfies $f_1(\boldsymbol{x})=0$ for any given $N\ge 2$.

Figures (17)

  • Figure 1: MA-enhanced near-field beam nulling (Scenario 1).
  • Figure 2: MA-enhanced near-field multi-beam forming (Scenario 2).
  • Figure 3: Distribution of beam gain in Scenario 1.
  • Figure 4: Beam gain at user 0 versus number of MAs in Scenario 1.
  • Figure 5: Beam gain at user 0 versus size of the movement region in Scenario 1.
  • ...and 12 more figures

Theorems & Definitions (1)

  • Proposition 1