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Found 12 papers, 0 papers with code
Deep Learning-Based Frequency Offset Estimation
In wireless communication systems, the asynchronization of the oscillators in the transmitter and the receiver along with the Doppler shift due to relative movement may lead to the presence of carrier frequency offset (CFO) in the received signals.
Augmenting Radio Signals with Wavelet Transform for Deep Learning-Based Modulation Recognition
The use of deep learning for radio modulation recognition has become prevalent in recent years.
AIR: Threats of Adversarial Attacks on Deep Learning-Based Information Recovery
It can also be found that the DeepReceiver is vulnerable to adversarial perturbations even with very low power and limited PAPR.
Mixing Signals: Data Augmentation Approach for Deep Learning Based Modulation Recognition
Data augmentation, as the strategy of expanding dataset, can improve the generalization of the deep learning models and thus improve the accuracy of the models to a certain extent.
Deep Transfer Clustering of Radio Signals
In this letter, we turn to the more challenging problem: can we cluster the modulation types just based on a large number of unlabeled radio signals?
Adaptive Visibility Graph Neural Network and It's Application in Modulation Classification
In this paper, we propose an Adaptive Visibility Graph (AVG) algorithm that can adaptively map time series into graphs, based on which we further establish an end-to-end classification framework AVGNet, by utilizing GNN model DiffPool as the classifier.
CLPVG: Circular limited penetrable visibility graph as a new network model for time series
Visibility Graph (VG) transforms time series into graphs, facilitating signal processing by advanced graph data mining algorithms.
DemodNet: Learning Soft Demodulation from Hard Information Using Convolutional Neural Network
The simulation results show that under the AWGN channel, the performance of both hard demodulation and soft demodulation of DemodNet is very close to the traditional methods.
SigNet: A Novel Deep Learning Framework for Radio Signal Classification
More interestingly, our proposed models behave extremely well in small-sample learning when only a small training dataset is provided.
DeepReceiver: A Deep Learning-Based Intelligent Receiver for Wireless Communications in the Physical Layer
In this paper, we propose a new receiver model, namely DeepReceiver, that uses a deep neural network to replace the traditional receiver's entire information recovery process.
Spectrum Sensing Based on Deep Learning Classification for Cognitive Radios
We present spectrum sensing as a classification problem and propose a sensing method based on deep learning classification.
Deep Learning for Large-Scale Real-World ACARS and ADS-B Radio Signal Classification
In order to verify the performance of the deep learning-based radio signal classification on real-world radio signal data, in this paper we conduct experiments on large-scale real-world ACARS and ADS-B signal data with sample sizes of 900, 000 and 13, 000, 000, respectively, and with categories of 3, 143 and 5, 157 respectively.
