6D Movable Antenna Based on User Distribution: Modeling and Optimization
<|reference_start|>6D Movable Antenna Based on User Distribution: Modeling and Optimization: In this paper, we propose a new six-dimensional (6D) movable antenna (6DMA) system for future wireless networks to improve the communication performance. Unlike the traditional fixed-position antenna (FPA) and existing fluid antenna/two-dimensional (2D) movable antenna (FA/2DMA) systems that adjust the positions of antennas only, the proposed 6DMA system consists of distributed antenna surfaces with independently adjustable three-dimensional (3D) positions as well as 3D rotations within a given space. In particular, this paper applies the 6DMA to the base station (BS) in wireless networks to provide full degrees of freedom (DoFs) for the BS to adapt to the dynamic user spatial distribution in the network. However, a challenging new problem arises on how to optimally control the 6D positions and rotations of all 6DMA surfaces at the BS to maximize the network capacity based on the user spatial distribution, subject to the practical constraints on 6D antennas' movement. To tackle this problem, we first model the 6DMA-enabled BS and the user channels with the BS in terms of 6D positions and rotations of all 6DMA surfaces. Next, we propose an efficient alternating optimization algorithm to search for the best 6D positions and rotations of all 6DMA surfaces by leveraging the Monte Carlo simulation technique. Specifically, we sequentially optimize the 3D position/3D rotation of each 6DMA surface with those of the other surfaces fixed in an iterative manner. Numerical results show that our proposed 6DMA-BS can significantly improve the network capacity as compared to the benchmark BS architectures with FPAs or 6DMAs with limited/partial movability, especially when the user distribution is more spatially non-uniform.<|reference_end|>
@article{shao20246d,
    title={6D Movable Antenna Based on User Distribution: Modeling and Optimization},
    author={Xiaodan Shao, Qijun Jiang, and Rui Zhang},
    journal={arXiv preprint arXiv:2403.08123},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.08123},
    primaryClass={cs.IT eess.SP math.IT}
}