Dual-Stream CNN-BiLSTM Model with Attention Layer for Automatic Modulation Classification

Parmar, Ashok; Divya, K A; Chouhan, Ankit; Captain, Kamal M.

doi:10.1109/comsnets56262.2023.10041403

Cited by 7 publications

(2 citation statements)

References 17 publications

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“…RadioML2016.10a was updated to RadioML2016.10b, which is used in [32],The writers train a proposed model based on CNN and Bidirectional Long Short-Term Memory (BiLSTM), using the attention layer to make use of temporal correlation. The main advantage of attention mechanisms is to enable the network to focus on specific parts of the input sequence.…”

Section: Related Workmentioning

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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Section: Related Workmentioning

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) methods based on deep learning [11][12] [13]. These methods automatically learn and extract features and classify through deep neural network layers, which can realize end-to-end modulation classification [14][15] [16] [17].…”

Section: Introductionmentioning

confidence: 99%

DeepSIG: A Hybrid Heterogeneous Deep Learning Framework for Radio Signal Classification

Qiu

Zheng

Zhang

et al. 2024

IEEE Trans. Wireless Commun.

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Deep learning has been widely used in automatic modulation classification (AMC) recently. Most of deep learningbased AMC uses a single network model to deal with radio signals with a single input format. In this paper, we propose a hybrid heterogeneous modulation classification architecture named DeepSIG, which integrates Recurrent Neural Network (RNN), Convolutional Neural Network (CNN) and Graph Neural Network (GNN) models in a single framework to process radio signals with heterogeneous input formats, i.e., in-phase (I) and quadrature (Q) sequences, images mapped from IQ signals and graphs converted from IQ signals, to extract and integrate the features from different perspectives. A fusion training mechanism is presented to train DeepSIG. We use three different radio signal datasets for simulations. Results show that our proposed DeepSIG performs the best in terms of classification accuracy compared with the three methods with single input, i.e., sequence, image or graph. The performance gain is larger in few-shot scenarios.

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MAE‐SigNet: An effective network for automatic modulation recognition

Zhang,

Song,

Wang

2024

IET Communications

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The rapid development of the Internet of Things has exacerbated issues such as spectrum resource scarcity, poor communication quality, and high communication energy consumption. Automatic modulation recognition (AMR), a key technology in cognitive radio, has emerged as a crucial solution to these challenges. Deep neural networks have been recently applied in AMR tasks and have achieved remarkable success. However, existing deep learning‐based AMR methods often need to consider the sensitivity of models to noise fully. This study proposes a masked autoencoder multi‐scale attention feature fusion model (MAE‐SigNet). This model integrates a MAE, multi‐scale feature extraction module, bidirectional long short‐term memory module, and MAM to accomplish the AMR task under low signal‐to‐noise ratio. Additionally, we optimize the cross‐entropy loss of the MAE‐SigNet model by introducing MAE decoder reconstruction error, which enhances the model's sensitivity to noise while achieving more accurate feature representation. Experimental results demonstrate that the MAE‐SigNet model achieves average recognition rates of 63.77%, 65.28%, and 75.26% on the RML2016.10a, RML2016.10b, and RML2016.04c datasets. Mainly, MAE‐SigNet exhibits outstanding performance at various levels of low signal‐to‐noise ratios from −6 to 4 dB.

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Dual-Stream CNN-BiLSTM Model with Attention Layer for Automatic Modulation Classification

Cited by 7 publications

References 17 publications

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

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

DeepSIG: A Hybrid Heterogeneous Deep Learning Framework for Radio Signal Classification

MAE‐SigNet: An effective network for automatic modulation recognition

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