Deep learning in mobile and wireless networking: A survey
The rapid uptake of mobile devices and the rising popularity of mobile applications and
services pose unprecedented demands on mobile and wireless networking infrastructure …
services pose unprecedented demands on mobile and wireless networking infrastructure …
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 …
Over-the-air deep learning based radio signal classification
We conduct an in depth study on the performance of deep learning based radio signal
classification for radio communications signals. We consider a rigorous baseline method …
classification for radio communications signals. We consider a rigorous baseline method …
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 …
Deep learning models for wireless signal classification with distributed low-cost spectrum sensors
This paper looks into the modulation classification problem for a distributed wireless
spectrum sensing network. First, a new data-driven model for automatic modulation …
spectrum sensing network. First, a new data-driven model for automatic modulation …
Learning to optimize: Training deep neural networks for interference management
Numerical optimization has played a central role in addressing key signal processing (SP)
problems. Highly effective methods have been developed for a large variety of SP …
problems. Highly effective methods have been developed for a large variety of SP …
Machine learning in beyond 5G/6G networks—State-of-the-art and future trends
Artificial Intelligence (AI) and especially Machine Learning (ML) can play a very important
role in realizing and optimizing 6G network applications. In this paper, we present a brief …
role in realizing and optimizing 6G network applications. In this paper, we present a brief …
Machine learning for wireless communications in the Internet of Things: A comprehensive survey
Abstract The Internet of Things (IoT) is expected to require more effective and efficient
wireless communications than ever before. For this reason, techniques such as spectrum …
wireless communications than ever before. For this reason, techniques such as spectrum …
Learning to optimize: Training deep neural networks for wireless resource management
For decades, optimization has played a central role in addressing wireless resource
management problems such as power control and beamformer design. However, these …
management problems such as power control and beamformer design. However, these …
Towards artificial intelligence enabled 6G: State of the art, challenges, and opportunities
S Zhang, D Zhu - Computer Networks, 2020 - Elsevier
Abstract 6G is expected to support the unprecedented Internet of everything scenarios with
extremely diverse and challenging requirements. To fulfill such diverse requirements …
extremely diverse and challenging requirements. To fulfill such diverse requirements …