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西北工业大学 国内
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Joint Admission and Power Control for Massive Connections via Graph Neural Network
Mengke Yang,
Daosen Zhai
,
Ruonan Zhang
, Bin Li, Lin Cai, F. Richard Yu
电子信息学院
Northwestern Polytechnical University Xian
Southeast University, Nanjing
University of Victoria BC
Carleton University
科研成果
:
期刊稿件
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引用 （Scopus）
综述
指纹
指纹
探究 'Joint Admission and Power Control for Massive Connections via Graph Neural Network' 的科研主题。它们共同构成独一无二的指纹。
分类
加权
按字母排序
Engineering
Joints (Structural Components)
100%
Power Control
100%
Admission Control
100%
Computation Time
33%
Q-Learning
33%
Signal-to-Interference-Plus-Noise Ratio
33%
Service Requirement
33%
Quality of Service
33%
Mobile Communication Systems
33%
Computer Science
Graph Neural Network
100%
Interference Model
66%
Attention (Machine Learning)
33%
Performance Gain
33%
Quality of Service
33%
Computation Time
33%
Signal-to-Noise plus Interference Ratio
33%
Service Requirement
33%
Graph Convolutional Network
33%
Admission Control
33%
Independent Set
33%
Connection Density
33%