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Found 23 papers, 4 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.
Deep Image Semantic Communication Model for Artificial Intelligent Internet of Things
Particularly, at the transmitter side, a high-precision image semantic segmentation algorithm is proposed to extract the semantic information of the image to achieve significant compression of the image data.
Can pre-trained models assist in dataset distillation?
Dataset Distillation (DD) is a prominent technique that encapsulates knowledge from a large-scale original dataset into a small synthetic dataset for efficient training.
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.
PathMLP: Smooth Path Towards High-order Homophily
However, common practices in GNNs to acquire high-order information mainly through increasing model depth and altering message-passing mechanisms, which, albeit effective to a certain extent, suffer from three shortcomings: 1) over-smoothing due to excessive model depth and propagation times; 2) high-order information is not fully utilized; 3) low computational efficiency.
Subgraph Networks Based Contrastive Learning
In addition, we also investigate the impact of the second-order subgraph augmentation on mining graph structure interactions, and further, propose a contrastive objective that fuses the first-order and second-order subgraph information.
Clarify Confused Nodes via Separated Learning
A minority of studies attempt to train different node groups separately but suffer from inappropriate separation metrics and low efficiency.
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.
Understanding the Dynamics of DNNs Using Graph Modularity
Specifically, we model the process of class separation of intermediate representations in pre-trained DNNs as the evolution of communities in dynamic graphs.
Graph-Based Similarity of Neural Network Representations
Understanding the black-box representations in Deep Neural Networks (DNN) is an essential problem in deep learning.
RGP: Neural Network Pruning through Its Regular Graph Structure
In this paper, we study the graph structure of the neural network, and propose regular graph based pruning (RGP) to perform a one-shot neural network pruning.
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?
GGT: Graph-Guided Testing for Adversarial Sample Detection of Deep Neural Network
Deep Neural Networks (DNN) are known to be vulnerable to adversarial samples, the detection of which is crucial for the wide application of these DNN models.
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.
Open DNN Box by Power Side-Channel Attack
However, recent studies indicate that they are also vulnerable to adversarial attacks.
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.
GA Based Q-Attack on Community Detection
In particular, we first give two heuristic attack strategies, i. e., Community Detection Attack (CDA) and Degree Based Attack (DBA), as baselines, utilizing the information of detected community structure and node degree, respectively.
Social and Information Networks
