Full-spectrum cell-free RAN for 6G systems: system design and experimental results

Wang, Dongming; You, Xiaohu; Huang, Yongming; Xu, Wei; Li, Jiamin; Zhu, Pengcheng; Jiang, Yanxiang; Cao, Yang; Xia, Xinjiang; Zhang, Ziyang; Jiang, Qingji; Wang, Pan; Liu, Dongjie; Zheng, Kun; Lou, Mengting; Jin, Jing; Wang, Xinbo; Wang, Jiangzhou

doi:10.1007/s11432-022-3664-x

Cited by 23 publications

(2 citation statements)

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“…if and only if there exist real numbers µ ≥ 0 such that 1) , C5 is first equivalently converted into a matrix inequality by utilizing the Schur's complement lemma [25], i.e., min…”

Section: Problem Transformationmentioning

confidence: 99%

“…R ECENTLY, cell-free systems have received significant attention [1], [2]. 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].…”

Section: Introductionmentioning

confidence: 99%

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

Chen,

He,

et al. 2023

IEEE Trans. Commun.

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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.

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“…if and only if there exist real numbers µ ≥ 0 such that 1) , C5 is first equivalently converted into a matrix inequality by utilizing the Schur's complement lemma [25], i.e., min…”

Section: Problem Transformationmentioning

confidence: 99%