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Deep Neural Network Based Active User Detection for Grant-Free Multiple Access
Zhen Shuo Lien,
Chia Han Lee
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電機工程學系
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Keyphrases
Neural Network
100%
Deep Neural Network
100%
Grant-free Access
100%
Active User Detection
100%
Sparsity
22%
Internet of Things Devices
22%
Interference Cancellation
22%
Detection Problem
22%
Deep Learning Architectures
22%
Activation Probability
22%
Grant-free Non-orthogonal multiple Access
22%
Deep Learning Methods
11%
Frequency-selective Fading Channel
11%
Wireless Communication
11%
User Equipment
11%
Challenging Problems
11%
Signaling Overhead
11%
Compressed Sensing
11%
Deep Learning
11%
Base Station
11%
Channel State Information
11%
Multiple Access Systems
11%
Promising Solutions
11%
Error Rate
11%
Latency Constraint
11%
Uplink Transmission
11%
Signal Structure
11%
Compressive Sensing Technique
11%
Single Neural Network
11%
Sensing Algorithm
11%
Frequency Resource
11%
Deep Learning Model
11%
Transfer Learning Model
11%
Sporadic Transmission
11%
Grant-based
11%
Sparsity Estimation
11%
Computer Science
Deep Neural Network
100%
Multiple Access
100%
Deep Learning Method
100%
Neural Network
75%
Sparsity
75%
Internet of Things Device
50%
Compressive Sensing
50%
Noma
50%
frequency selective fading
25%
Wireless Communication
25%
Channel State Information
25%
Uplink Transmission
25%
Transfer Learning
25%
Frequency Resource
25%
Signal Structure
25%
Engineering
Active User
100%
Multiple Access
100%
Deep Neural Network
100%
Deep Learning Method
44%
Sparsity
33%
Compressive Sensing
22%
Interference Cancellation
22%
Internet of Things Device
22%
Noma
22%
Simulation Result
11%
Selective Fading
11%
Fading Channel
11%
Wireless Communication
11%
Signaling Overhead
11%
Error Rate
11%
Basestation
11%
Channel State Information
11%
Sensing Method
11%
Signal Structure
11%
Sensing Algorithm
11%
Frequency Resource
11%
Transfer Learning
11%