利用方向稀疏性和深度学习的混合近-远场6维可移动天线设计
摘要
六维可移动天线（6DMA）被识别为未来无线系统中一种具有颠覆性的新技术，可通过少量天线数目支持大量用户。然而，信号载波波长与收发器区域尺寸之间的复杂关系导致传统远场6DMA信道模型存在偏差，使得模型预测与实际6DMA系统中混合场信道特性之间存在差异。在实际6DMA系统中，用户相对于同一6DMA表面的天线可能处于远场，而相对于不同6DMA表面的天线则处于近场。此外，由于信道维数高且位置与旋转约束耦合，6DMA信道估计以及6DMA位置、旋转与基站（BS）的发射波束联合设计计算复杂度极高。为解决上述问题，我们提出一种高效的混合场广义6DMA信道模型，考虑单个6DMA表面内的平面波传播以及不同6DMA表面间的球面波传播。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 that the proposed hybrid-field channel model and channel estimation algorithm outperform existing approaches and that the DRL-enhanced 6DMA system significantly surpasses flexible antenna systems.
引用
@article{arxiv.2506.15808,
title = {Hybrid Near-Far Field 6D Movable Antenna Design Exploiting Directional Sparsity and Deep Learning},
author = {Xiaodan Shao and Limei Hu and Yulong Sun and Xing Li and Yixiao Zhang and Jingze Ding and Xiaoming Shi and Feng Chen and Derrick Wing Kwan Ng and Robert Schober},
journal= {arXiv preprint arXiv:2506.15808},
year = {2025}
}
备注
13 pages