Computationally Efficient Unsupervised Deep Learning for Robust Joint AP Clustering and Beamforming Design in Cell-Free Systems
<|reference_start|>Computationally Efficient Unsupervised Deep Learning for Robust Joint AP Clustering and Beamforming Design in Cell-Free Systems: In this paper, we consider robust joint access point (AP) clustering and beamforming design with imperfect channel state information (CSI) in cell-free systems. Specifically, we jointly optimize AP clustering and beamforming with imperfect CSI to simultaneously maximize the worst-case sum rate and minimize the number of AP clustering under power constraint and the sparsity constraint of AP clustering. By transformations, the semi-infinite constraints caused by the imperfect CSI are converted into more tractable forms for facilitating a computationally efficient unsupervised deep learning algorithm. In addition, to further reduce the computational complexity, a computationally effective unsupervised deep learning algorithm is proposed to implement robust joint AP clustering and beamforming design with imperfect CSI in cell-free systems. Numerical results demonstrate that the proposed unsupervised deep learning algorithm achieves a higher worst-case sum rate under a smaller number of AP clustering with computational efficiency.<|reference_end|>
@article{chen2024computationally,
    title={Computationally Efficient Unsupervised Deep Learning for Robust Joint AP
  Clustering and Beamforming Design in Cell-Free Systems},
    author={Guanghui Chen, Zheng Wang, Hongxin Lin, Yongming Huang, Luxi Yang},
    journal={arXiv preprint arXiv:2404.02531},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.02531},
    primaryClass={cs.IT eess.SP math.IT}
}