A deep convolutional network demodulator for mixed signals with different modulation types
X Lin, R Liu, W Hu, Y Li, X Zhou… - … , 3rd Intl Conf on Big Data …, 2017 - ieeexplore.ieee.org
In recent years, deep learning is becoming more and more popular. It has been widely
applied to fields including image recognition, automatic speech recognition and natural …
applied to fields including image recognition, automatic speech recognition and natural …
CNN and RNN-based deep learning methods for digital signal demodulation
T Wu - Proceedings of the 2019 International Conference on …, 2019 - dl.acm.org
In this paper we presented a neural network-based method to demodulate digital signals.
After training with different modulation schemes, the learning-based receiver can perform …
After training with different modulation schemes, the learning-based receiver can perform …
Demodulator based on deep belief networks in communication system
M Fan, L Wu - 2017 International Conference on …, 2017 - ieeexplore.ieee.org
Deep belief network (DBN) has been successfully applied in variety areas such as image
recognition and natural language processing. In this paper, we investigate the signal …
recognition and natural language processing. In this paper, we investigate the signal …
An End-to-End Demodulation System Based on Convolutional Neural Networks
R Zhao, J Wang, J Li - Journal of Physics: Conference Series, 2021 - iopscience.iop.org
The demodulation of digital signal plays a key role in the communication system. The
traditional demodulator is usually realized by special hardware platform, which has the …
traditional demodulator is usually realized by special hardware platform, which has the …
Demodulation of faded wireless signals using deep convolutional neural networks
AS Mohammad, N Reddy, F James… - 2018 IEEE 8th Annual …, 2018 - ieeexplore.ieee.org
This paper demonstrates exceptional performance of approximately 10.0 dB learning-based
gain using the Deep Convolutional Neural Network (DCNN) for demodulation of a Rayleigh …
gain using the Deep Convolutional Neural Network (DCNN) for demodulation of a Rayleigh …
A deep learning method based on convolution neural network for blind demodulation of mixed signals with different modulation types
H Zhu, Z Wang, D Li, Q Guo, Z Wang - … 12–13, 2019, Proceedings, Part I …, 2019 - Springer
In recent years, deep learning is becoming more and more popular. It has been widely used
in image recognition, automatic speech recognition and natural language processing. In the …
in image recognition, automatic speech recognition and natural language processing. In the …
A signal demodulation algorithm based on generative adversarial networks
W Lijun, Z Minghong, H Yu, S Lei - 2021 IEEE 3rd International …, 2021 - ieeexplore.ieee.org
In recent years, extensive research has been done to obtain better demodulation
performance by combining signal demodulation with deep learning algorithms, such as …
performance by combining signal demodulation with deep learning algorithms, such as …
Intelligent demodulation method for communication signals based on multi-layer deep belief network
Q Miao, Y Zhang, X Zhang - 2019 International Conference on …, 2019 - ieeexplore.ieee.org
Aiming at the problem of signal demodulation under noise interference channel, a signal
recognition method using deep learning is proposed. The signal demodulation is completed …
recognition method using deep learning is proposed. The signal demodulation is completed …
Deep learning for signal demodulation in physical layer wireless communications: Prototype platform, open dataset, and analytics
In this paper, we investigate deep learning (DL)-enabled signal demodulation methods and
establish the first open dataset of real modulated signals for wireless communication …
establish the first open dataset of real modulated signals for wireless communication …
A deep learning approach for modulation recognition
Y Zhang, LIU Tong, L Zhang… - 2018 IEEE 23rd …, 2018 - ieeexplore.ieee.org
Communication signal modulation recognition refers to a process of automatically
processing a received signal and determining its modulation type. As an intermediate part of …
processing a received signal and determining its modulation type. As an intermediate part of …