Deep learning based automatic modulation recognition: Models, datasets, and challenges
F Zhang, C Luo, J Xu, Y Luo, FC Zheng - Digital Signal Processing, 2022 - Elsevier
Automatic modulation recognition (AMR) detects the modulation scheme of the received
signals for further signal processing without needing prior information, and provides the …
signals for further signal processing without needing prior information, and provides the …
Adversarial machine learning in wireless communications using RF data: A review
D Adesina, CC Hsieh, YE Sagduyu… - … Surveys & Tutorials, 2022 - ieeexplore.ieee.org
Machine learning (ML) provides effective means to learn from spectrum data and solve
complex tasks involved in wireless communications. Supported by recent advances in …
complex tasks involved in wireless communications. Supported by recent advances in …
A survey of modulation classification using deep learning: Signal representation and data preprocessing
Modulation classification is one of the key tasks for communications systems monitoring,
management, and control for addressing technical issues, including spectrum awareness …
management, and control for addressing technical issues, including spectrum awareness …
Multitask-learning-based deep neural network for automatic modulation classification
Automatic modulation classification (AMC) is to identify the modulation type of a received
signal, which plays a vital role to ensure the physical-layer security for Internet of Things …
signal, which plays a vital role to ensure the physical-layer security for Internet of Things …
Lightweight automatic modulation classification via progressive differentiable architecture search
Automatic modulation classification (AMC) is a key step of signal demodulation that
determines whether the receiver can correctly receive the transmitted signal without prior …
determines whether the receiver can correctly receive the transmitted signal without prior …
A data preprocessing method for automatic modulation classification based on CNN
H Zhang, M Huang, J Yang… - IEEE Communications …, 2020 - ieeexplore.ieee.org
As a backbone of deep learning models, convolutional neural networks (CNNs) are widely
used in the field of automatic modulation classification. Nevertheless, we speculate that the …
used in the field of automatic modulation classification. Nevertheless, we speculate that the …
Learning constellation map with deep CNN for accurate modulation recognition
Modulation classification, recognized as the intermediate step between signal detection and
demodulation, is widely deployed in several modern wireless communication systems …
demodulation, is widely deployed in several modern wireless communication systems …
A review on orthogonal time–frequency space modulation: State-of-art, hotspots and challenges
M Li, W Liu, J Lei - Computer Networks, 2023 - Elsevier
High-speed mobility communication is one of the challenging scenarios because of the
rapidly changing channel involved. In light of this, the orthogonal time–frequency space …
rapidly changing channel involved. In light of this, the orthogonal time–frequency space …
ConvLSTMAE: A spatiotemporal parallel autoencoders for automatic modulation classification
S Yunhao, X Hua, J Lei, Q Zisen - IEEE Communications …, 2022 - ieeexplore.ieee.org
Automatic modulation classification (AMC) is the key technique in both military and civilian
wireless communication. However, the performance is unsatisfactory, even several deep …
wireless communication. However, the performance is unsatisfactory, even several deep …
A hybrid deep learning model for automatic modulation classification
Automatic modulation classification (AMC) is one of the major challenges for cognitive radio
(CR), which can enhance the spectrum utilization efficiency. In this study, a hybrid signal and …
(CR), which can enhance the spectrum utilization efficiency. In this study, a hybrid signal and …