6D Movable Antenna Enhanced Wireless Network Via Discrete Position and Rotation Optimization
<|reference_start|>6D Movable Antenna Enhanced Wireless Network Via Discrete Position and Rotation Optimization: Six-dimensional movable antenna (6DMA) is an effective approach to improve wireless network capacity by adjusting the 3D positions and 3D rotations of distributed antenna surfaces based on the users' spatial distribution and statistical channel information. Although continuously positioning/rotating 6DMA surfaces can achieve the greatest flexibility and thus the highest capacity improvement, it is difficult to implement due to the discrete movement constraints of practical stepper motors. Thus, in this paper, we consider a 6DMA-aided base station (BS) with only a finite number of possible discrete positions and rotations for the 6DMA surfaces. We aim to maximize the average network capacity for random numbers of users at random locations by jointly optimizing the 3D positions and 3D rotations of multiple 6DMA surfaces at the BS subject to discrete movement constraints. In particular, we consider the practical cases with and without statistical channel knowledge of the users, and propose corresponding offline and online optimization algorithms, by leveraging the Monte Carlo and conditional sample mean (CSM) methods, respectively. Simulation results verify the effectiveness of our proposed offline and online algorithms for discrete position/rotation optimization of 6DMA surfaces as compared to various benchmark schemes with fixed-position antennas (FPAs) and 6DMAs with limited movability. It is shown that 6DMA-BS can significantly enhance wireless network capacity, even under discrete position/rotation constraints, by exploiting the spatial distribution characteristics of the users.<|reference_end|>
@article{shao20246d,
    title={6D Movable Antenna Enhanced Wireless Network Via Discrete Position and
  Rotation Optimization},
    author={Xiaodan Shao, Rui Zhang, Qijun Jiang, and Robert Schober},
    journal={arXiv preprint arXiv:2403.17122},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.17122},
    primaryClass={cs.IT eess.SP math.IT}
}