Automatic Modulation Recognition Based on Adaptive Attention Mechanism and ResNeXt WSL Model

Liang, Zhi Jie; Tao, Mingliang; Wang, Ling; Su, Jia; Yang, Xin

doi:10.1109/lcomm.2021.3093485

Cited by 41 publications

(20 citation statements)

References 13 publications

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“…Liang proposes a two-dimensional attention mechanism module that can effectively extract channel and spatial features. [18] This approach verifies the positive impact of the attention mechanism module on feature extraction. Therefore, this paper designs a TDIA module to help the network extract the collaborative interaction features of signals from different antennas.…”

Section: Two-dimensional Interactive Attention Mechanismmentioning

confidence: 63%

“…The sampled data in the modulation recognition includes mainly the in-phase/quadrature (I/Q) information of the signal and the constellation map information of the amplitude-phase mapping. In [15]- [18], Long Short-Term Memory (LSTM) and Convolutional Long Short-Term Deep Neural Network (CLDNN) are proposed to take signal I/Q information as input. Y.Mao proposed a graph neural network based on constellation map information that possesses excellent performance for phase modulation signal recognition in literature [19].…”

Section: Introductionmentioning

confidence: 99%

“…The input samples of the lightweight network are the I/Q information of the signals received by all antennas. Additionally, it is found in [18]- [23] that adding an attention module to a deep learning network can significantly improve classification accuracy. The attention module can effectively guide the network to extract the network features needed for classification, just as humans consciously learn essential knowledge.…”

Section: Introductionmentioning

confidence: 99%

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Modulation Recognition of MIMO Systems Based on Dimensional Interactive Lightweight Network

2022

KSII TIIS

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Automatic modulation recognition is the core algorithm in the field of modulation classification in communication systems. Our investigations show that deep learning (DL) based modulation recognition techniques have achieved effective progress for multiple-input multiple-output (MIMO) systems. However, network complexity is always an additional burden for high-accuracy classifications, which makes it impractical. Therefore, in this paper, we propose a low-complexity dimensional interactive lightweight network (DilNet) for MIMO systems. Specifically, the signals received by different antennas are cooperatively input into the network, and the network calculation amount is reduced through the depth-wise separable convolution. A two-dimensional interactive attention (TDIA) module is designed to extract interactive information of different dimensions, and improve the effectiveness of the cooperation features. In addition, the TDIA module ensures low complexity through compressing the convolution dimension, and the computational burden after inserting TDIA is also acceptable. Finally, the network is trained with a penalized statistical entropy loss function. Simulation results show that compared to existing modulation recognition methods, the proposed DilNet dramatically reduces the model complexity. The dimensional interactive lightweight network trained by penalized statistical entropy also performs better for recognition accuracy in MIMO systems.

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Section: Two-dimensional Interactive Attention Mechanismmentioning

confidence: 63%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modulation Recognition of MIMO Systems Based on Dimensional Interactive Lightweight Network

2022

KSII TIIS

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“…Novel deep learning models specialised for modulation inference: According to the existing research, CNN-based spatial feature extraction and RNN-based temporal feature extraction have led to the breakthroughs on AMR. Other types of neural network structures such as generative adversarial networks (GAN) [63], attention mechanism [6,23,39,41,40], and transformer, which have been shown to achieve good performance in specific fields, could be further exploited for AMR. For example, GAN can be used as a generator to expand the training set which can be regarded as a data augmentation method.…”

Section: Designing Novel Dl-amr Modelsmentioning

confidence: 99%

Deep learning based automatic modulation recognition: Models, datasets, and challenges

Zhang

Luo

et al. 2022

Digital Signal Processing

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“…Semi-supervised label knowledge distillation (SSLD) algorithm was also used in the proposed approach, which used prediction information of ResNeXt101 [32] as a label to supervise the learning of MobileNetV3 model. The specific process is shown in Figure 4.…”

Section: Knowledge Distillationmentioning

confidence: 99%

Recognition of grape leaf diseases using MobileNetV3 and deep transfer learning

Yin¹,

Li²,

Li³

et al. 2022

International Journal of Agricultural and Biological Engineering

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Timely diagnosis and accurate identification of grape leaf diseases are decisive for controlling the spread of disease and ensuring the healthy development of the grape industry. The objective of this research was to propose a simple and efficient approach to improve grape leaf disease identification accuracy with limited computing resources and scale of training image dataset based on deep transfer learning and an improved MobileNetV3 model (GLD-DTL). A pre-training model was obtained by training MobileNetV3 using the ImageNet dataset to extract common features of the grape leaves. And the last convolution layer of the pre-training model was modified by adding a batch normalization function. A dropout layer followed by a fully connected layer was used to improve the generalization ability of the pre-training model and realize a weight matrix to quantify the scores of six diseases, according to which the Softmax method was added as the top layer of the modified networks to give probability distribution of six diseases. Finally, the grape leaf diseases dataset, which was constructed by processing the image with data augmentation and image annotation technologies, was input into the modified networks to retrain the networks to obtain the grape leaf diseases recognition (GLDR) model. Results showed that the proposed GLD-DTL approach had better performance than some recent approaches. The identification accuracy was as high as 99.84% while the model size was as small as 30 MB.

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Automatic Modulation Recognition Based on Adaptive Attention Mechanism and ResNeXt WSL Model

Cited by 41 publications

References 13 publications

Modulation Recognition of MIMO Systems Based on Dimensional Interactive Lightweight Network

Modulation Recognition of MIMO Systems Based on Dimensional Interactive Lightweight Network

Deep learning based automatic modulation recognition: Models, datasets, and challenges

Recognition of grape leaf diseases using MobileNetV3 and deep transfer learning

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