A Multi-level Complex Feature Mining Method Based on Deep Learning for Automatic Modulation Recognition

Huang, Chenzhao; Ji, Mingrui; Zhang, Hang; Luo, Ruisen

doi:10.1109/ispds56360.2022.9874223

Cited by 2 publications

(5 citation statements)

References 9 publications

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“…As mentioned above, time-domain and frequency-domain representations of signals convey different features; meanwhile, RVC and CVC can extract different features from time-domain and frequency-domain signals. In particular, RVC can extract independent features of the real and imaginary parts of signals, while CVC can extract interaction features between both parts [19,24]. In this section, we propose an intelligent modulation sensing algorithm by first extracting multiple features of time-domain and frequencydomain signals and then fusing the extracted features with the attention mechanism.…”

Section: Proposed Modulation Sensing Algorithmmentioning

confidence: 99%

“…Four feature flows in Figure 1 have distinct effects on the sensing accuracy, but it is challenging to quantify these effects and obtain the optimal weights with closed forms. So, motivated by the successful application in multi-modal feature fusion [24], we also adopt the attention mechanism to automatically learn the optimal weights to enhance the performance.…”

Section: Feature Fusionmentioning

confidence: 99%

“…We optimize the number of residual blocks [29], the number of FC layers, and the number of neurons in each layer [24] through extensive cross-validations. In particular, the number of residual blocks is one, while the numbers of neurons in the FC layers are 128 and 64, respectively.…”

Section: Fc Layermentioning

confidence: 99%

“…To demonstrate the advantages of the proposed algorithm, we compare the sensing accuracy of the proposed algorithm with benchmarks, which are widely adopted in modulation sensing [7,24], including dual-stream CNN-LSTM [9], TFA CNN [20], CNN-LSTM [32], and improved CNN [33]. In particular, dual-stream CNN-LSTM takes IQ and amplitudephase sequences as inputs and utilizes CNN and LSTM layers to sense the modulation modes.…”

Section: Algorithms Comparisonmentioning

confidence: 99%

See 3 more Smart Citations

Deep Learning-Based Multi-Feature Fusion for Communication and Radar Signal Sensing

Li,

Liu,

Song

et al. 2024

Electronics

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Recent years witness the rapid development of communication and radar technologies, and many transmitters are equipped with both communication and radar functionalities. To keep track of the working state of a target dual-functional transmitter, it is crucial to sense the modulation mode of the emitted signals. In this paper, we propose a deep learning-based intelligent modulation sensing technique for dual-functional transmitters. Different from existing modulation sensing methods, which usually focus on communication signals, we take both communication and radar signals into consideration. Typically, the dominant features of communication signals lie in the time domain, while those of radar signals lie in both time and frequency domains. To enhance the sensing accuracy, we first exploit real and complex value convolution operations to extract both time-domain and frequency-domain features of emitted signals from the target transmitter. Then, we fuse the extracted features by assigning proper weights with the attention mechanism. Simulation results reveal that the proposed technique can improve the sensing accuracy by up to 4% on average compared with benchmarks.

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Section: Proposed Modulation Sensing Algorithmmentioning

confidence: 99%

Section: Feature Fusionmentioning

confidence: 99%

Section: Fc Layermentioning

confidence: 99%

Section: Algorithms Comparisonmentioning

confidence: 99%

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Deep Learning-Based Multi-Feature Fusion for Communication and Radar Signal Sensing

Li,

Liu,

Song

et al. 2024

Electronics

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“…The ResNeXt model achieved the highest recognition accuracy of 96.10% on the RadioML2016.10B dataset and 99.70% for the 10 modulation modes of the RadioML2018.01A dataset with a high signal-to-noise ratio (SNR). In the same year, Zhang [15] extracted both temporal and spatial features of modulation signals using a CNN and a bidirectional long short-term memory (Bi-LSTM) network. Combined with the correlation between the radio signal channels, they managed to improve the recognition accuracy to 92.68% with a high SNR.…”

Section: Introductionmentioning

confidence: 99%

A Modulation Classification Algorithm Based on Feature-Embedding Graph Convolutional Network

Zhu,

Xu,

Shi

et al. 2024

IEEE Access

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Deep-learning is widely used in modulation classification to reduce labor and improve the efficiency. Graph convolutional network (GCN) is a type of feature extraction network for graph data. Considering the signals as graph nodes and the similarity of each signal as an edge, the GCN propagates node information to similar nodes along the edges. GCN extracts more features and achieves better classification results, particularly for characterless examples. In this paper, we propose a modulation classification algorithm based on a feature-embedding GCN (FE-GCN). It comprises three parts: feature-embedding network (FEN), similarity adjacent matrix calculation network (SAMCN), and graph convolutional classification network (GCCN). The FEN embeds the signal data into a one-dimensional feature vector. The SAMCN calculates the similarity of all signal feature vectors to a matrix using a single convolutional neural network (CNN). The GCCN is used to extract the final features and classify the signals in a graph. Simulation results on the public dataset RML2016.10A show that the FE-GCN performs effectively and outperforms a series of advanced deep-learning methods.

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A Multi-level Complex Feature Mining Method Based on Deep Learning for Automatic Modulation Recognition

Cited by 2 publications

References 9 publications

Deep Learning-Based Multi-Feature Fusion for Communication and Radar Signal Sensing

Deep Learning-Based Multi-Feature Fusion for Communication and Radar Signal Sensing

A Modulation Classification Algorithm Based on Feature-Embedding Graph Convolutional Network

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