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Movable Antenna Enabled Near-Field Communications: Channel Modeling and Performance Optimization

Lipeng Zhu, Wenyan Ma, Zhenyu Xiao, Rui Zhang

TL;DR

This paper extends the field response channel model for MA systems to the near-field propagation scenario, and examines MA-aided multiuser communication systems under both digital and analog beamforming architectures.

Abstract

Movable antenna (MA) technology offers promising potential to enhance wireless communication by allowing flexible antenna movement. To maximize spatial degrees of freedom (DoFs), larger movable regions are required, which may render the conventional far-field assumption for channels between transceivers invalid. In light of it, we investigate in this paper MA-enabled near-field communications, where a base station (BS) with multiple movable subarrays serves multiple users, each equipped with a fixed-position antenna (FPA). First, we extend the field response channel model for MA systems to the near-field propagation scenario. Next, we examine MA-aided multiuser communication systems under both digital and analog beamforming architectures. For digital beamforming, spatial division multiple access (SDMA) is utilized, where an upper bound on the minimum signal-to-interference-plus-noise ratio (SINR) across users is derived in closed form. A low-complexity algorithm based on zero-forcing (ZF) is then proposed to jointly optimize the antenna position vector (APV) and digital beamforming matrix (DBFM) to approach this bound. For analog beamforming, orthogonal frequency division multiple access (OFDMA) is employed, and an upper bound on the minimum signal-to-noise ratio (SNR) among users is derived. An alternating optimization (AO) algorithm is proposed to iteratively optimize the APV, analog beamforming vector (ABFV), and power allocation until convergence. For both architectures, we further explore MA design strategies based on statistical channel state information (CSI), with the APV updated less frequently to reduce the antenna movement overhead. Simulation results demonstrate that our proposed algorithms achieve performance close to the derived bounds and also outperform the benchmark schemes using dense or sparse arrays with FPAs.

Movable Antenna Enabled Near-Field Communications: Channel Modeling and Performance Optimization

TL;DR

This paper extends the field response channel model for MA systems to the near-field propagation scenario, and examines MA-aided multiuser communication systems under both digital and analog beamforming architectures.

Abstract

Movable antenna (MA) technology offers promising potential to enhance wireless communication by allowing flexible antenna movement. To maximize spatial degrees of freedom (DoFs), larger movable regions are required, which may render the conventional far-field assumption for channels between transceivers invalid. In light of it, we investigate in this paper MA-enabled near-field communications, where a base station (BS) with multiple movable subarrays serves multiple users, each equipped with a fixed-position antenna (FPA). First, we extend the field response channel model for MA systems to the near-field propagation scenario. Next, we examine MA-aided multiuser communication systems under both digital and analog beamforming architectures. For digital beamforming, spatial division multiple access (SDMA) is utilized, where an upper bound on the minimum signal-to-interference-plus-noise ratio (SINR) across users is derived in closed form. A low-complexity algorithm based on zero-forcing (ZF) is then proposed to jointly optimize the antenna position vector (APV) and digital beamforming matrix (DBFM) to approach this bound. For analog beamforming, orthogonal frequency division multiple access (OFDMA) is employed, and an upper bound on the minimum signal-to-noise ratio (SNR) among users is derived. An alternating optimization (AO) algorithm is proposed to iteratively optimize the APV, analog beamforming vector (ABFV), and power allocation until convergence. For both architectures, we further explore MA design strategies based on statistical channel state information (CSI), with the APV updated less frequently to reduce the antenna movement overhead. Simulation results demonstrate that our proposed algorithms achieve performance close to the derived bounds and also outperform the benchmark schemes using dense or sparse arrays with FPAs.
Paper Structure (22 sections, 2 theorems, 47 equations, 12 figures, 2 algorithms)

This paper contains 22 sections, 2 theorems, 47 equations, 12 figures, 2 algorithms.

Table of Contents

  1. Introduction
  2. System and Channel Model
  3. MA Systems with Digital Beamforming
  4. Problem Formulation
  5. Performance Analysis
  6. Optimization Algorithm
  7. Statistical CSI Based Antenna Position Optimization
  8. MA Systems With Analog Beamforming
  9. Problem Formulation
  10. Performance Analysis
  11. Optimization Algorithm
  12. Statistical CSI Based Antenna Position Optimization
  13. Performance Evaluation
  14. Simulation Setup and Benchmark Schemes
  15. Numerical Results of Digital Beamforming System
  16. ...and 7 more sections

Key Result

Theorem 1

Under the condition of a single channel path for each user and $N=1$, the upper bound on the minimum SINR in eq_minSINR_bound is achieved if and only if the 3D position of each MA subarray belongs to the following set: with $\sum_{m=1}^{M} e^{\mathrm{j}2\pi\phi_{k,\hat{k},m}}=0$.

Figures (12)

  • Figure 1: Illustration of the considered MA-enabled near-field communication system.
  • Figure 2: Convergence evaluation of Algorithm \ref{['alg_DBF']}.
  • Figure 3: Performance comparison of the minimum SINR achieved by the proposed and benchmark schemes versus the number of users.
  • Figure 4: Comparison of the minimum SINR achieved by the proposed and benchmark schemes versus the size of the antenna moving region.
  • Figure 5: Comparison of the minimum SINR achieved by the proposed and benchmark schemes under nonuniform user distribution.
  • ...and 7 more figures

Theorems & Definitions (4)

  • Theorem 1
  • proof
  • Theorem 2
  • proof