Holistic network virtualization and pervasive network intelligence for 6G
In this tutorial paper, we look into the evolution and prospect of network architecture and
propose a novel conceptual architecture for the 6th generation (6G) networks. The proposed …
propose a novel conceptual architecture for the 6th generation (6G) networks. The proposed …
A comprehensive survey on radio frequency (RF) fingerprinting: Traditional approaches, deep learning, and open challenges
Fifth generation (5G) network and beyond envision massive Internet of Things (IoT) rollout to
support disruptive applications such as extended reality (XR), augmented/virtual reality …
support disruptive applications such as extended reality (XR), augmented/virtual reality …
[HTML][HTML] Large-scale real-world radio signal recognition with deep learning
TU Ya, LIN Yun, ZHA Haoran, J Zhang, W Yu… - Chinese Journal of …, 2022 - Elsevier
In the past ten years, many high-quality datasets have been released to support the rapid
development of deep learning in the fields of computer vision, voice, and natural language …
development of deep learning in the fields of computer vision, voice, and natural language …
Contour stella image and deep learning for signal recognition in the physical layer
The rapid development of communication systems poses unprecedented challenges, eg,
handling exploding wireless signals in a real-time and fine-grained manner. Recent …
handling exploding wireless signals in a real-time and fine-grained manner. Recent …
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 …
Spectrum interference-based two-level data augmentation method in deep learning for automatic modulation classification
Q Zheng, P Zhao, Y Li, H Wang, Y Yang - Neural Computing and …, 2021 - Springer
Automatic modulation classification is an essential and challenging topic in the development
of cognitive radios, and it is the cornerstone of adaptive modulation and demodulation …
of cognitive radios, and it is the cornerstone of adaptive modulation and demodulation …
Automatic modulation classification using CNN-LSTM based dual-stream structure
Z Zhang, H Luo, C Wang, C Gan… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Deep learning (DL) has recently aroused substantial concern due to its successful
implementations in many fields. Currently, there are few studies on the applications of DL in …
implementations in many fields. Currently, there are few studies on the applications of DL in …
Complex-valued networks for automatic modulation classification
Deep learning (DL) has been recognized as an effective solution for automatic modulation
classification (AMC). However, most recent DL based AMC works are based on real-valued …
classification (AMC). However, most recent DL based AMC works are based on real-valued …
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 …
An improved neural network pruning technology for automatic modulation classification in edge devices
Automatic modulation classification (AMC) plays an important role in both civilian and
military applications. Today, increasingly more researchers apply a deep learning …
military applications. Today, increasingly more researchers apply a deep learning …