Dynamic AP Clustering and Precoding for User-Centric Virtual Cell Networks

Shi, Jianfeng; Chen, Ming; Zhang, Wence; Yang, Zhaohui; Xu, Hao

doi:10.1109/tcomm.2018.2883290

Cited by 13 publications

(2 citation statements)

References 43 publications

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“…The basic idea of the Affinity Propagation (AP) algorithm [23,24] is consider all samples as nodes of the network, and then calculate the clustering center of each sample through the message passing of each edge in the network. By continuously iterating the attractiveness ( , ) r i j and attribution ( , ) a i j produce m high-quality prime centers, while the remaining data points are assigned to the corresponding clusters, shown in formula ( 2…”

Section:    mentioning

confidence: 99%

Wafer Defect Detection Technology Based on CTM-IYOLOv5 Network

Yang,

Peng,

et al. 2023

Preprint

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With the development of semiconductor processing technology, the components within the wafers are highly integrated and complex, which can increase the number of surface defects. Traditional methods of wafer defect detection which were done manually by experienced engineers using computer-aided tools have some disadvantages, such as small defective area miss detection, low efficiency and tend to cause secondary contamination. Therefore, it is essential to identify dies defects quickly and locate defects timely, which can efficiently improve the yield of wafers. A novel wafer defect detection method based on clustering-template matching combined with improved YOLOv5 network (CTM-IYOLOv5) has been proposed in this paper. An inspection platform is used to acquire images of wafers in real-time. And the pre-processing operations are used for the preliminary processing of the acquired grain maps, which can reduce the computation of image processing and improve the efficiency of inspection. To solve the phenomenon of false detection and missed detection caused by multiple inter-grain channels in the field of view, clustering-template matching is used to segment the grains in the field of view. In addition, the convolutional layer structure, network structure, and data enhancement of YOLOv5 have been improved to become lighter, more accurate, and more suitable for small-target detection, with the model size been effectively reduced by 16%. The experimental results show that the accuracy of the proposed method is up to 99%, and the efficiency improvement of 44%, which can realize the need for real-time detection of wafer defects.

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Section:    mentioning

confidence: 99%

Wafer Defect Detection Technology Based on CTM-IYOLOv5 Network

Yang,

Peng,

et al. 2023

Preprint

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“…Though static clustering is simple enough and does not rely on fast backhaul, MSs at the edge of clusters still suffer from serious intercluster influence. Several studies on dynamic clustering have investigated to overcome the above mentioned problems, Shi et al proposed a dynamic user-centric cell clustering algorithm [10], which can not only cancel the joint intracluster interference but also effectively alleviate the overall and per-BS cooperation cost. However, it can only count on short-term CSI.…”

Section: Introductionmentioning

confidence: 99%

A Semidynamic Bidirectional Clustering Algorithm for Downlink Cell-Free Massive Distributed Antenna System

Qian

Zhao

Zhu

et al. 2021

Wireless Communications and Mobile Computing

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Cell-free massive distributed antenna system (CF-MDAS) can further reduce the access distance between mobile stations (MSs) and remote access points (RAPs), which brings a lower propagation loss and higher multiplexing gain. However, the interference caused by the overlapping coverage areas of distributed RAPs will severely degrade the system performance in terms of the sum-rate. Since that clustering RAPs can mitigate the interference, in this paper, we investigate a novel clustering algorithm for a downlink CF-MDAS with the limited-capacity backhaul. To reduce the backhaul burden and mitigate interference effectively, a semidynamic bidirectional clustering algorithm based on the long-term channel state information (CSI) is proposed, which has a low computational complexity. Simulation results show that the proposed algorithm can efficiently achieve a higher sum-rate than that of the static clustering one, which is close to the curve obtained by dynamic clustering algorithm using the short-term CSI. Furthermore, the proposed algorithm always reveals a significant performance gain regardless of the size of the networks.

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