Hybrid Near-Far Field 6D Movable Antenna Design Exploiting Directional Sparsity and Deep Learning

Shao X, Hu L, Sun Y, Li X, Zhang Y, Ding J, Shi X, Chen F, Ng DWK, Schober R (2025)

Publication Type: Journal article

Publication year: 2025

Journal

IEEE Transactions on Wireless Communications Institute of Electrical and Electronics Engineers (IEEE)

DOI: 10.1109/TWC.2025.3605550

Abstract

Six-dimensional movable antenna (6DMA) has been identified as a new disruptive technology for future wireless systems to support a large number of users with only a few antennas. However, the intricate relationships between the signal carrier wavelength and the transceiver region size lead to inaccuracies in traditional far-field 6DMA channel model, causing discrepancies between the model predictions and the hybrid-field channel characteristics in practical 6DMA systems, where users might be in the far-field region relative to the antennas on the same 6DMA surface, while simultaneously being in the near-field region relative to different 6DMA surfaces. Moreover, due to the high-dimensional channel and the coupled position and rotation constraints, the estimation of the 6DMA channel and the joint design of the 6DMA positions and rotations and the transmit beamforming at the base station (BS) incur extremely high computational complexity. To address these issues, we propose an efficient hybrid-field generalized 6DMA channel model, which accounts for planar-wave propagation within individual 6DMA surfaces and spherical-wave propagation among different 6DMA surfaces. Furthermore, by leveraging directional sparsity, we propose a low-overhead channel estimation algorithm that efficiently constructs a complete channel map for all potential antenna position-rotation pairs while limiting the training overhead incurred by antenna movement. In addition, we propose a low-complexity design leveraging deep reinforcement learning (DRL), which facilitates the joint design of the 6DMA positions, rotations, and beamforming in a unified manner. Numerical results demonstrate the superiority of the proposed hybrid-field channel model, which achieves sum rates closely approaching that of the near-field channel model. The results also show that the proposed channel estimation algorithm can accurately recover the channel with lower computational complexity than traditional channel estimation algorithm. Moreover, the 6DMA system enhanced by the proposed DRL algorithm significantly outperforms existing flexible antenna systems, especially in the near-field region.

Authors with CRIS profile

Xiaodan Shao Lehrstuhl für Digitale Übertragung (IDC) Robert Schober Lehrstuhl für Digitale Übertragung (IDC)

Involved external institutions

Southwest University (SWU) / 西南大学 CN China (CN) University of Waterloo (UW) CA Canada (CA) Peking University (PKU) / 北京大学 CN China (CN) The Chinese University of Hong Kong (CUHK) CN China (CN) University of New South Wales (UNSW) AU Australia (AU)

How to cite

APA:

Shao, X., Hu, L., Sun, Y., Li, X., Zhang, Y., Ding, J.,... Schober, R. (2025). Hybrid Near-Far Field 6D Movable Antenna Design Exploiting Directional Sparsity and Deep Learning. IEEE Transactions on Wireless Communications. https://doi.org/10.1109/TWC.2025.3605550

MLA:

Shao, Xiaodan, et al. "Hybrid Near-Far Field 6D Movable Antenna Design Exploiting Directional Sparsity and Deep Learning." IEEE Transactions on Wireless Communications (2025).

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