Directional Sparsity Based Statistical Channel Estimation for 6D Movable Antenna Communications

2025-05-21

AI Summary

For six-degree-of-freedom movable antennas (6DMA), acquiring accurate statistical channel state information (CSI) incurs prohibitively high overhead and suffers from low estimation accuracy. This work first reveals a pronounced directional sparsity of the 6DMA channel in the joint position–orientation domain. Leveraging this insight, we propose a covariance-driven statistical CSI estimation framework that reconstructs the full-region average channel power distribution with high fidelity using only a small number of position–orientation samples. Our method jointly estimates multipath average power and directions of arrival (DOAs), circumventing explicit geometric propagation modeling. Experimental results demonstrate that, compared to baseline schemes, the proposed approach reduces pilot overhead by over 80% and decreases the mean squared error of channel power estimation by up to 45%, validating both the effectiveness of directional sparsity modeling and its practical feasibility for 6DMA systems.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Six-dimensional movable antenna (6DMA) is an innovative and transformative technology to improve wireless network capacity by adjusting the 3D positions and 3D rotations of antennas/surfaces (sub-arrays) based on the channel spatial distribution. For optimization of the antenna positions and rotations, the acquisition of statistical channel state information (CSI) is essential for 6DMA systems. In this paper, we unveil for the first time a new extbf{ extit{directional sparsity}} property of the 6DMA channels between the base station (BS) and the distributed users, where each user has significant channel gains only with a (small) subset of 6DMA position-rotation pairs, which can receive direct/reflected signals from the user. By exploiting this property, a covariance-based algorithm is proposed for estimating the statistical CSI in terms of the average channel power at a small number of 6DMA positions and rotations. Based on such limited channel power estimation, the average channel powers for all possible 6DMA positions and rotations in the BS movement region are reconstructed by further estimating the multi-path average power and direction-of-arrival (DOA) vectors of all users. Simulation results show that the proposed directional sparsity-based algorithm can achieve higher channel power estimation accuracy than existing benchmark schemes, while requiring a lower pilot overhead.

Problem

• Estimating statistical CSI for 6DMA systems efficiently
• Exploiting directional sparsity in 6DMA channels for optimization
• Reducing pilot overhead while improving channel power estimation accuracy

Innovation

• Directional sparsity property in 6DMA channels
• Covariance-based statistical CSI estimation algorithm
• Reconstruction using multi-path power and DOA vectors