Spatial-spectral Cell-free Networks: A Large-scale Case Study
<|reference_start|>Spatial-spectral Cell-free Networks: A Large-scale Case Study: This paper studies the large-scale cell-free networks where dense distributed access points (APs) serve many users. As a promising next-generation network architecture, cell-free networks enable ultra-reliable connections and minimal fading/blockage, which are much favorable to the millimeter wave and Terahertz transmissions. However, conventional beam management with large phased arrays in a cell is very time-consuming in the higher-frequencies, and could be worsened when deploying a large number of coordinated APs in the cell-free systems. To tackle this challenge, the spatial-spectral cell-free networks with the leaky-wave antennas are established by coupling the propagation angles with frequencies. The beam training overhead in this direction can be significantly reduced through exploiting such spatial-spectral coupling effects. In the considered large-scale spatial-spectral cell-free networks, a novel subchannel allocation solution at sub-terahertz bands is proposed by leveraging the relationship between cross-entropy method and mixture model. Since initial access and AP clustering play a key role in achieving scalable large-scale cell-free networks, a hierarchical AP clustering solution is proposed to make the joint initial access and cluster formation, which is adaptive and has no need to initialize the number of AP clusters. After AP clustering, a subchannel allocation solution is devised to manage the interference between AP clusters. Numerical results are presented to confirm the efficiency of the proposed solutions and indicate that besides subchannel allocation, AP clustering can also have a big impact on the large-scale cell-free network performance at sub-terahertz bands.<|reference_end|>
@article{zhu2024spatial-spectral,
    title={Spatial-spectral Cell-free Networks: A Large-scale Case Study},
    author={Zesheng Zhu, Lifeng Wang, Xin Wang, Dongming Wang and Kai-Kit Wong},
    journal={arXiv preprint arXiv:2407.11389},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.11389},
    primaryClass={cs.NI eess.SP}
}