Fusion Methods for CNN-Based Automatic Modulation Classification

Zheng, Shilian; Qi, Peihan; Chen, Shichuan; Yang, Xiaoniu

doi:10.1109/access.2019.2918136

Cited by 116 publications

(79 citation statements)

References 45 publications

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“…The feature-based fusion scheme (FFS) is proposed in [11] for automatic modulation classification and uses a CNNbased architecture for feature extraction. Fig.…”

Section: B Feature-based Fusion Schemementioning

confidence: 99%

“…The proposed DRC employs a CNN with a relatively small structure, compared to the CNN structure normally exploited for automatic modulation classification or image classification [11], [29]- [31]. The proposed DRC achieves a high sensing accuracy because it only needs to classify two classes, unlike image classification, where typically a number of classes have to be distinguished.…”

Section: Cnn Structurementioning

confidence: 99%

“…To evaluate the performance of the proposed scheme by comparison, two conventional schemes are implemented: a feature-based fusion scheme with CNN models [11] and an ED scheme with thresholds satisfying the target false alarm probability, P F A = 0.05.…”

Section: A Simulation Environmentmentioning

confidence: 99%

“…[10] proposed a CNN-based modulation recognition scheme with two CNNs trained on different data sets: in-phase and quadrature-phase (IQ) components and an image of a constellation diagram. [11] introduced a CNN-based featurebased fusion scheme for automatic modulation classification, where a parallel multi-CNN model was adopted to combine the different features. The authors of [12] discussed a cooperative spectrum sensing (CSS) based on the CNN scheme for CRNs, which is constructed using a sensing matrix that takes into account the spatial and spectral correlations of the channels [13].…”

Section: Introductionmentioning

confidence: 99%

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Deep RP-CNN for Burst Signal Detection in Cognitive Radios

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2020

IEEE Access

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This paper proposes a convolutional neural network (CNN)-based signal detection scheme using image encoding techniques for burst signals in wireless networks. The conventional signal detection approach based on energy measurement performs poorly when detecting burst signals owing to the short signal length and relatively long sensing duration. To detect the presence of a burst signal, the proposed scheme encodes the received time-series signal into an image that is further fed to a CNN model. For image encoding techniques, recurrence plot algorithms are adopted in the proposed scheme with a CNN. In particular, the proposed scheme achieves the correct detection probability of 99% even in the presence of a short burst signal at SNR= −10dB.

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“…The feature-based fusion scheme (FFS) is proposed in [11] for automatic modulation classification and uses a CNNbased architecture for feature extraction. Fig.…”

Section: B Feature-based Fusion Schemementioning

confidence: 99%

Section: Cnn Structurementioning

confidence: 99%

Section: A Simulation Environmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deep RP-CNN for Burst Signal Detection in Cognitive Radios

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2020

IEEE Access

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“…In most cases, the input size is fixed CNN-based modulation classification. The authors [27] proposed three fusion methods to improve the classification accuracy when the signal length is greater than the CNN input length. The above studies are all based on the classification of raw in-phase and quadrature (IQ) data.…”

Section: A Related Workmentioning

confidence: 99%

Deep Learning for Large-Scale Real-World ACARS and ADS-B Radio Signal Classification

et al. 2019

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Radio signal classification has a very wide range of applications in the field of wireless communications and electromagnetic spectrum management. In recent years, deep learning has been used to solve the problem of radio signal classification and has achieved good results. However, the radio signal data currently used is very limited in scale. 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 largescale 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. We use the same Inception-Residual neural network model structure for ACARS signal classification and ADS-B signal classification to verify the ability of a single basic deep neural network model structure to process different types of radio signals, i.e., communication bursts in ACARS and pulse bursts in ADS-B. We build an experimental system for radio signal deep learning experiments. Experimental results show that the signal classification accuracy of ACARS and ADS-B is 98.1% and 96.3%, respectively. When the signal-to-noise ratio (with injected additive white Gaussian noise) is greater than 9 dB, the classification accuracy is greater than 92%. These experimental results validate the ability of deep learning to classify large-scale real-world radio signals. The results of the transfer learning experiment show that the model trained on large-scale ADS-B datasets is more conducive to the learning and training of new tasks than the model trained on small-scale datasets.INDEX TERMS Radio signal classification, deep learning, convolutional neural network, residual network, Inception, cognitive radio, ACARS, ADS-B.

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Deep Reinforcement Learning and Its Applications

2023

Deep Reinforcement Learning for Wireless Communications and Networking

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Fusion Methods for CNN-Based Automatic Modulation Classification

Cited by 116 publications

References 45 publications

Deep RP-CNN for Burst Signal Detection in Cognitive Radios

Deep RP-CNN for Burst Signal Detection in Cognitive Radios

Deep Learning for Large-Scale Real-World ACARS and ADS-B Radio Signal Classification

Deep Reinforcement Learning and Its Applications

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