Federated Learning for Precoding Design in Cell-Free Massive MIMO Systems

Wang, Dong; Tao, Meixia; Zeng, Xing; Liang, Jing

doi:10.1109/ojcoms.2023.3293591

Cited by 3 publications

(2 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By connecting all access points (APs) to a central processing unit (CPU) via backhaul links, cell-free systems allow multiple APs to simultaneously collaborate to serve users within the network coverage area, which could overcome many of the interference issues that appear in cellular networks [3], [4]. Nevertheless, popular beamforming design in cell-free systems generally assumes that all APs in the network coverage area serve users simultaneously [5], [6]. This appears to be impractical as long-range APs serving users consume precious power and bandwidth resources while contributing little useful power due to high path losses [7].…”

Section: Introductionmentioning

confidence: 99%

“…It is worth noting that beamforming design or AP clustering in most of these references, e.g., [3], [5], [6], [9], [16], [17], optimistically assumes the availability of perfect CSI, which leads to system performance degradation in practice. To this end, it is highly desired to take the CSI estimation errors into account, and there have been some studies on robust beamforming design, especially for multicellular networks, e.g., in [18], [19], [20].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Deep Learning Method: QoS-Aware Joint AP Clustering and Beamforming Design for Cell-Free Networks

Chen,

He,

et al. 2023

IEEE Trans. Commun.

View full text Add to dashboard Cite

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.

show abstract