Adaptive Multi-Dimensional Shrinkage Block for Automatic Modulation Recognition
Author:
Affiliation:
1. State Key Laboratory of Integrated Services Networks, Xidian University, Xi’an, China
2. Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong
Funder
National Key Research and Development Program of China
National Natural Science Foundation for Distinguished Young Scholar
Innovative Research Groups of the National Natural Science Foundation of China
Key Research and Development Projects of Shaanxi Province
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Computer Science Applications,Modeling and Simulation
Link
http://xplorestaging.ieee.org/ielx7/4234/10314840/10247564.pdf?arnumber=10247564
Reference15 articles.
1. Real-Time Radio Technology and Modulation Classification via an LSTM Auto-Encoder
2. MCNet: An Efficient CNN Architecture for Robust Automatic Modulation Classification
3. Deep neural network architectures for modulation classification
4. Complex-Valued Convolution and Frequency Global Filter for Automatic Modulation Recognition
5. Deep Residual Shrinkage Networks for Fault Diagnosis
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1. Modulation Recognition based on One-Dimensional Complex Convolutional Networks under Low SNR;2024 IEEE 6th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC);2024-05-24
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