Data-and-Knowledge Dual-Driven Automatic Modulation Recognition for Wireless Communication Networks
Author:
Affiliation:
1. Nanjing University of Aeronautics and Astronautics,China
2. University of Houston,USA
Funder
National Natural Science Foundation of China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9837954/9838246/09838977.pdf?arnumber=9838977
Reference19 articles.
1. Automatic Modulation Classification Using CNN-LSTM Based Dual-Stream Structure
2. Deep Residual Learning for Image Recognition
3. LightAMC: Lightweight Automatic Modulation Classification via Deep Learning and Compressive Sensing
4. The rfml ecosystem: A look at the unique challenges of applying deep learning to radio frequency applications;wong;arXiv preprint arXiv 2010 00170,2020
5. Developing RFML Intuition: An Automatic Modulation Classification Architecture Case Study
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1. ClST: A Convolutional Transformer Framework for Automatic Modulation Recognition by Knowledge Distillation;IEEE Transactions on Wireless Communications;2024-07
2. KGAMC: A Novel Knowledge Graph Driven Automatic Modulation Classification Scheme;ICC 2024 - IEEE International Conference on Communications;2024-06-09
3. OATGA: Optimizing Adversarial Training via Genetic Algorithm for Automatic Modulation Classification;GLOBECOM 2023 - 2023 IEEE Global Communications Conference;2023-12-04
4. Modulation Recognition of Communication Signal with Class-Imbalance Sample Based on CNN-LSTM Dual Channel Model;2023 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC);2023-11-14
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