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Hybrid-Field 6D Movable Antenna for Terahertz Communications: Channel Modeling and Estimation

Xiaodan Shao, Yixiao Zhang, Shisheng Hu, Zhixuan Tang, Mingcheng He, Xinyu Huang, Weihua Zhuang, Xuemin Shen

TL;DR

This work tackles THz communications with six-dimensional movable antennas (6DMA) by introducing a hybrid-field channel model that captures planar propagation within each 6DMA surface and spherical propagation across surfaces. It proposes a low-complexity, directional sparsity–driven channel estimation algorithm that first computes surface-wise coarse estimates and then refines them via multi-surface clustering, enabling reconstruction of channels for all candidate position-rotation pairs with limited RF chains. Simulations show the hybrid-field model achieves sum-rate close to ground-truth near-field channels and that the proposed estimation method yields significantly lower MSE than LS, especially when more surface configurations are available. The results suggest a practical path to efficient, flexible THz networks using 6DMA with reduced pilot overhead and computational burden, and future work on optimizing surface configurations for further gains.

Abstract

In this work, we study a six-dimensional movable antenna (6DMA)-enhanced Terahertz (THz) network that supports a large number of users with a few antennas by controlling the three-dimensional (3D) positions and 3D rotations of antenna surfaces/subarrays at the base station (BS). However, the short wavelength of THz signals combined with a large 6DMA movement range extends the near-field region. As a result, a user can be in the far-field region relative to the antennas on one 6DMA surface, while simultaneously residing in the near-field region relative to other 6DMA surfaces. Moreover, 6DMA THz channel estimation suffers from increased computational complexity and pilot overhead due to uneven power distribution across the large number of candidate position-rotation pairs, as well as the limited number of radio frequency (RF) chains in THz bands. To address these issues, we propose an efficient hybrid-field generalized 6DMA THz channel model, which accounts for planar wave propagation within individual 6DMA surfaces and spherical waves among different 6DMA surfaces. Furthermore, we propose a low-overhead channel estimation algorithm that leverages directional sparsity to construct a complete channel map for all potential antenna position-rotation pairs. Numerical results show that the proposed hybrid-field channel model achieves a sum rate close to that of the ground-truth near-field channel model and confirm that the channel estimation method yields accurate results with low complexity.

Hybrid-Field 6D Movable Antenna for Terahertz Communications: Channel Modeling and Estimation

TL;DR

This work tackles THz communications with six-dimensional movable antennas (6DMA) by introducing a hybrid-field channel model that captures planar propagation within each 6DMA surface and spherical propagation across surfaces. It proposes a low-complexity, directional sparsity–driven channel estimation algorithm that first computes surface-wise coarse estimates and then refines them via multi-surface clustering, enabling reconstruction of channels for all candidate position-rotation pairs with limited RF chains. Simulations show the hybrid-field model achieves sum-rate close to ground-truth near-field channels and that the proposed estimation method yields significantly lower MSE than LS, especially when more surface configurations are available. The results suggest a practical path to efficient, flexible THz networks using 6DMA with reduced pilot overhead and computational burden, and future work on optimizing surface configurations for further gains.

Abstract

In this work, we study a six-dimensional movable antenna (6DMA)-enhanced Terahertz (THz) network that supports a large number of users with a few antennas by controlling the three-dimensional (3D) positions and 3D rotations of antenna surfaces/subarrays at the base station (BS). However, the short wavelength of THz signals combined with a large 6DMA movement range extends the near-field region. As a result, a user can be in the far-field region relative to the antennas on one 6DMA surface, while simultaneously residing in the near-field region relative to other 6DMA surfaces. Moreover, 6DMA THz channel estimation suffers from increased computational complexity and pilot overhead due to uneven power distribution across the large number of candidate position-rotation pairs, as well as the limited number of radio frequency (RF) chains in THz bands. To address these issues, we propose an efficient hybrid-field generalized 6DMA THz channel model, which accounts for planar wave propagation within individual 6DMA surfaces and spherical waves among different 6DMA surfaces. Furthermore, we propose a low-overhead channel estimation algorithm that leverages directional sparsity to construct a complete channel map for all potential antenna position-rotation pairs. Numerical results show that the proposed hybrid-field channel model achieves a sum rate close to that of the ground-truth near-field channel model and confirm that the channel estimation method yields accurate results with low complexity.
Paper Structure (14 sections, 28 equations, 4 figures, 1 algorithm)

This paper contains 14 sections, 28 equations, 4 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model
  3. 6DMA System Model
  4. 6DMA THz Channel Model
  5. Far-Field 6DMA Channel Model
  6. Near-Field 6DMA Channel Model
  7. Hybrid-Field 6DMA Channel Model
  8. Signal Model
  9. Channel Estimation
  10. Unequal Channel Power from Directional Sparsity
  11. 6DMA Surface-Wise Estimation
  12. Multi-Surface Estimation Refinement
  13. Simulation Results
  14. Conclusion

Figures (4)

  • Figure 1: (a) A typical hybrid-field propagation environment in a 6DMA THz system; (b) The geometry of hybrid-field channel model.
  • Figure 2: An illustration of the directional sparsity in the LoS 6DMA channel.
  • Figure 3: Channel capacity with various communication distances.
  • Figure 4: The MSE of 6DMA THz channel estimation versus SNR.