Distributed Channel Estimation and Optimization for 6D Movable Antenna: Unveiling Directional Sparsity

TL;DR

This work tackles CSI acquisition for a distributed 6DMA system by revealing a directional sparsity property: each user has nonzero gains only toward a small subset of the $M$ candidate position-rotation pairs. It introduces a distributed 6DMA processing architecture with LPUs and a three-stage protocol for statistical CSI, position/rotation optimization, and instantaneous CSI, leveraging sparsity to reduce pilot overhead. A two-stage estimation framework is developed: (i) JDC for joint directional sparsity detection and channel power estimation across groups of candidate pairs, and (ii) directional sparsity-aided LS for instantaneous CSI, followed by a channel-power-based PSO to optimize 6DMA positions/rotations; results show higher estimation accuracy with fewer pilots and sum-rate gains even when compared to 2D-only optimization with perfect CSI. Overall, the proposed approach enables scalable, cost-efficient deployment of 6DMA by decoupling statistical and instantaneous CSI and exploiting directional sparsity to drive optimization and adaptive transmission.

Abstract

Six-dimensional movable antenna (6DMA) is an innovative technology to improve wireless network capacity by adjusting 3D positions and 3D rotations of antenna surfaces based on channel spatial distribution. However, the existing works on 6DMA have assumed a central processing unit (CPU) to jointly process the signals of all 6DMA surfaces to execute various tasks. This inevitably incurs prohibitively high processing cost for channel estimation. Therefore, we propose a distributed 6DMA processing architecture to reduce processing complexity of CPU by equipping each 6DMA surface with a local processing unit (LPU). In particular, we unveil for the first time a new \textbf{\textit{directional sparsity}} property of 6DMA channels, where each user has significant channel gains only for a (small) subset of 6DMA position-rotation pairs, which can receive direct/reflected signals from users. In addition, we propose a practical three-stage protocol for the 6DMA-equipped base station (BS) to conduct statistical CSI acquisition for all 6DMA candidate positions/rotations, 6DMA position/rotation optimization, and instantaneous channel estimation for user data transmission with optimized 6DMA positions/rotations. Specifically, the directional sparsity is leveraged to develop distributed algorithms for joint sparsity detection and channel power estimation, as well as for directional sparsity-aided instantaneous channel estimation. Using the estimated channel power, we develop a channel power-based optimization algorithm to maximize the ergodic sum rate of the users by optimizing the antenna positions/rotations. Simulation results show that our channel estimation algorithms are more accurate than benchmarks with lower pilot overhead, and our optimization outperforms fluid/movable antennas optimized only in two dimensions (2D), even when the latter have perfect instantaneous CSI.

Figures (12)

• Figure 1: 6DMA-equipped BS and different processing architectures.
• Figure 2: Illustration of the geometry of the 6DMA surface at the $m$-th candidate position-rotation pair.
• Figure 3: Illustration of the directional sparsity of 6DMA channels and the 6DMA candidate position-rotation pair grouping.
• Figure 4: An illustration of the sparse pattern in $\mathbf{H}$.
• Figure 5: The proposed three-stage protocol for 6DMA system.