Automatic Modulation Classification with Deep Neural Networks

Harper, Clayton A.; Thornton, Mitchell A.; Larson, Eric C.

doi:10.3390/electronics12183962

Cited by 4 publications

(2 citation statements)

References 48 publications

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“…• CNN Model Proposed in [40]: Inspired by the architecture of the X-Vector [41] and the reproduced ResNet architecture from [18].…”

Section: Experimental Results and Analysismentioning

confidence: 99%

SDR implementation of a light deep learning model based CNN for joint spectrum sensing and AMC

Nesraoui,

Teguig,

Sadoudi

2024

Phys. Scr.

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Automatic modulation classification (AMC) aims to blindly recognize themodulation type of a received signal in wireless systems. It is also a critical componentof non-cooperative communication systems after the detection of the presence of asignal. In this paper, we introduce a robust approach, termed DET-AMC (jointDetection and Automatic Modulation Classification), employing Convolutional NeuralNetworks (CNNs) trained via transfer learning methodology. The main advantage ofour approach is its ability to handle a wide range of modulation types, including 10different schemes generated in Gnuradio and their detection using the same model.Through extensive experimentation, we evaluate the performance of our light CNNbased DET-AMC method across varying signal-to-noise ratio (SNR) levels, as well asin the presence of phase noise and frequency offset. We find that the CNN’s learnedfeatures, obtained through transfer learning, exhibit robustness, particularly in lowSNR and various challenging conditions, leading to accurate modulation classification.In general, our approach outperforms existing methods by using the effectivenessof deep learning in capturing relevant discriminative features. Additionally, ourmodel offers a robust solution for join detection and AMC by achieving an accurateprobability of detection and modulation classification without the need for manualfeature engineering or the consideration of frequency offset, phase noise or noiseestimation. Our model achieves 100% accuracy for synthetic and real data at anSNR equal to -10 dB for detection, and 100% and 98% for classification of syntheticand real signals at -4 dB, respectively.

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“…• CNN Model Proposed in [40]: Inspired by the architecture of the X-Vector [41] and the reproduced ResNet architecture from [18].…”

Section: Experimental Results and Analysismentioning

confidence: 99%

SDR implementation of a light deep learning model based CNN for joint spectrum sensing and AMC

Nesraoui,

Teguig,

Sadoudi

2024

Phys. Scr.

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“…It is illustrated that radio features extracted by deep learning models are similar to the knowledge of human experts [13]. Therefore, DL has now become the mainstream pipeline for AMC [14].…”

Section: Introductionmentioning

confidence: 98%

Diffusion-Based Radio Signal Augmentation for Automatic Modulation Classification

Xu,

Huang,

Zhang

et al. 2024

Electronics

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Deep learning has become a powerful tool for automatically classifying modulations in received radio signals, a task traditionally reliant on manual expertise. However, the effectiveness of deep learning models hinges on the availability of substantial data. Limited training data often results in overfitting, which significantly impacts classification accuracy. Traditional signal augmentation methods like rotation and flipping have been employed to mitigate this issue, but their effectiveness in enriching datasets is somewhat limited. This paper introduces the Diffusion-based Radio Signal Augmentation algorithm (DiRSA), a novel signal augmentation method that significantly enhances dataset scale without compromising signal integrity. Utilizing prompt words for precise signal generation, DiRSA allows for flexible modulation control and significantly expands the training dataset beyond the original scale. Extensive evaluations demonstrate that DiRSA outperforms traditional signal augmentation techniques such as rotation and flipping. Specifically, when applied with the LSTM model in small dataset scenarios, DiRSA enhances modulation classification performance at SNRs above 0 dB by 6%.

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