6D Movable Antenna Enhanced Cell-free MIMO: Two-timescale Decentralized Beamforming and Antenna Movement Optimization

TL;DR

The paper tackles the challenge of scalable, low-latency beamforming in 6DMA-enabled cell-free MIMO under high mobility. It introduces a two-timescale decentralized framework where short-timescale APs compute LMMSE-like receive beamformers using local instantaneous CSI and global statistics via FP-based UatF bounds, while a central unit optimizes antenna positions and orientations over the long timescale using CSSCA with a relaxed auxiliary parameter c. The proposed approach decouples the optimization, enabling parallel per-AP processing and iterative long-term reconfiguration, and is shown to outperform fixed-movement schemes and approach centralized benchmarks in favorable conditions. Results indicate that decentralized 6DMA with statistical CSI sharing yields notable ergodic sum-rate gains, especially with sparse user distributions, and offers a practical balance between performance and complexity for scalable cell-free networks.

Abstract

This paper investigates a six-dimensional movable antenna (6DMA)-aided cell-free multi-user multiple-input multiple-output (MIMO) communication system. In this system, each distributed access point (AP) can flexibly adjust its array orientation and antenna positions to adapt to spatial channel variations and enhance communication performance. However, frequent antenna movements and centralized beamforming based on global instantaneous channel state information (CSI) sharing among APs entail extremely high signal processing delay and system overhead, which is difficult to be practically implemented in high-mobility scenarios with short channel coherence time. To address these practical implementation challenges and improve scalability, a two-timescale decentralized optimization framework is proposed in this paper to jointly design the beamformer, antenna positions, and array orientations. In the short timescale, each AP updates its receive beamformer based on local instantaneous CSI and global statistical CSI. In the long timescale, the central processing unit optimizes the antenna positions and array orientations at all APs based on global statistical CSI to maximize the ergodic sum rate of all users. The resulting optimization problem is non-convex and involves highly coupled variables, thus posing significant challenges for obtaining efficient solutions. To address this problem, a constrained stochastic successive convex approximation algorithm is developed. Numerical results demonstrate that the proposed 6DMA-aided cell-free system with decentralized beamforming significantly outperforms other antenna movement schemes with less flexibility and even achieves a performance comparable to that of the centralized beamforming benchmark.

Figures (7)

• Figure 1: Illustration of the 6DMA-aided cell-free MU-MIMO system.
• Figure 2: Illustration of the cosine radiation pattern antennapattern.
• Figure 3: Illustration of the two-timescale decentralized design framework, including the short-timescale workflow at each ap and the long-timescale workflow at the cpu.
• Figure 4: The simulation setups under different user distributions. (a) Users are uniformly distributed within serving area. (b) Users are densely concentrated around three hotspots.
• Figure 5: Convergence of Algorithm \ref{['CSSCA']} under uniform user distribution.

Theorems & Definitions (3)

• Remark 1
• Remark 2
• Remark 3