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 …
[HTML][HTML] Adversarial attacks and defenses for digital communication signals identification
As modern communication technology advances apace, the digital communication signals
identification plays an important role in cognitive radio networks, the communication …
identification plays an important role in cognitive radio networks, the communication …
Multimodal fusion convolutional neural network with cross-attention mechanism for internal defect detection of magnetic tile
H Lu, Y Zhu, M Yin, G Yin, L Xie - IEEE Access, 2022 - ieeexplore.ieee.org
The internal defect detection of magnetic tile is extremely significant before mounting.
Currently, this task is completely realized by manual operation in the magnetic tile …
Currently, this task is completely realized by manual operation in the magnetic tile …
Fine-grained modulation classification using multi-scale radio transformer with dual-channel representation
Q Zheng, P Zhao, H Wang, A Elhanashi… - IEEE …, 2022 - ieeexplore.ieee.org
Automatic modulation classification (AMC) plays a critical role in both civilian and military
applications. In this letter, we propose a multi-scale radio transformer (Ms-RaT) with dual …
applications. In this letter, we propose a multi-scale radio transformer (Ms-RaT) with dual …
Real-time radio technology and modulation classification via an LSTM auto-encoder
Identification of the type of communication technology and/or modulation scheme based on
detected radio signal are challenging problems encountered in a variety of applications …
detected radio signal are challenging problems encountered in a variety of applications …
Toward next-generation signal intelligence: A hybrid knowledge and data-driven deep learning framework for radio signal classification
S Zheng, X Zhou, L Zhang, P Qi, K Qiu… - IEEE Transactions …, 2023 - ieeexplore.ieee.org
Automatic modulation classification (AMC) can generally be divided into knowledge-based
methods and data-driven methods. In this paper, we explore combining the knowledge …
methods and data-driven methods. In this paper, we explore combining the knowledge …
Signal modulation classification based on the transformer network
In this work, the Transformer Network (TRN) is applied to the automatic modulation
classification (AMC) problem for the first time. Different from the other deep networks, the …
classification (AMC) problem for the first time. Different from the other deep networks, the …
Federated learning for automatic modulation classification under class imbalance and varying noise condition
Automatic modulation classification (AMC) is a promising technology for identifying
modulation types, and deep learning (DL)-based AMC is one of its main research directions …
modulation types, and deep learning (DL)-based AMC is one of its main research directions …
Automatic modulation classification based on decentralized learning and ensemble learning
To deal with the deep learning-based automatic modulation classification (AMC) in the
scenario that the training dataset are distributed over a network without gathering the data at …
scenario that the training dataset are distributed over a network without gathering the data at …
Automatic modulation classification using ResNeXt-GRU with deep feature fusion
With the integrated design and application of radar, communication, and electronic
reconnaissance, automatic modulation classification (AMC) is becoming increasingly …
reconnaissance, automatic modulation classification (AMC) is becoming increasingly …