A Deep Learning Method: QoS-Aware Joint AP Clustering and Beamforming Design for Cell-Free Networks
Guanghui Chen, Shiwen He, Zhenyu An, Yongming Huang, Lüxi Yang
Abstract
Joint access point (AP) clustering and beamforming design is an effective way to improve system performance and reduce signaling overhead for cell-free networks. However, conventional optimization methods usually solved the joint AP clustering and beamforming design by separately handling them, at the cost of high computing resources, especially when quality of service (QoS) constraint is also considered. To this end, this paper proposes a low-complexity unsupervised deep learning method to jointly optimize AP clustering and beamforming design, called as joint clustering and beamforming network (JcbNet). The JcbNet also designs a neural network to handle the QoS constraint to reduce the hyperparameters of loss function, and it introduces a learnable safety distance parameter in the loss function to reduce the violation rate of QoS constraint. In addition, the JcbNet is scalable since the dimensions of parameters and output beamforming vary with the dimension of input channel state information (CSI). The experimental results show that the JcbNet is low-complexity, and achieves a higher sum rate under a smaller number of AP clustering compared to traditional and deep learning algorithms such as weighted minimum mean square error (WMMSE), sparse WMMSE (S-WMMSE) and convolutional neural network (CNN).