A physics-based perspective for understanding and utilizing spatial resources of wireless channels
<|reference_start|>A physics-based perspective for understanding and utilizing spatial resources of wireless channels: To satisfy the increasing demands for transmission rates of wireless communications, it is necessary to use spatial resources of electromagnetic (EM) waves. In this context, EM information theory (EIT) has become a hot topic by integrating the theoretical framework of deterministic mathematics and stochastic statistics to explore the transmission mechanisms of continuous EM waves. However, the previous studies were primarily focused on frame analysis, with limited exploration of practical applications and a comprehensive understanding of its essential physical characteristics. In this paper, we present a three-dimensional (3-D) line-of-sight channel capacity formula that captures the vector EM physics and accommodates both near- and far-field scenes. Based on the rigorous mathematical equation and the physical mechanism of fast multipole expansion, a channel model is established, and the finite angular spectral bandwidth feature of scattered waves is revealed. To adapt to the feature of the channel, an optimization problem is formulated for determining the mode currents on the transmitter, aiming to obtain the optimal design of the precoder and combiner. We make comprehensive analyses to investigate the relationship among the spatial degree of freedom, noise, and transmitted power, thereby establishing a rigorous upper bound of channel capacity. A series of simulations are conducted to validate the theoretical model and numerical method. This work offers a novel perspective and methodology for understanding and leveraging EIT, and provides a theoretical foundation for the design and optimization of future wireless communications.<|reference_end|>
@article{xu2024a,
    title={A physics-based perspective for understanding and utilizing spatial
  resources of wireless channels},
    author={Hui Xu, Jun Wei Wu, Zhen Jie Qi, Hao Tian Wu, Rui Wen Shao, Qiang
  Cheng, Jieao Zhu, Linglong Dai, and Tie Jun Cui},
    journal={arXiv preprint arXiv:2410.06115},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.06115},
    primaryClass={cs.IT eess.SP math.IT}
}