Automatic Modulation Classification Research Articles

Distributed Cooperative Automatic Modulation Classification Using DWA-ADMM in Wireless Communication Networks

Automatic modulation classification (AMC) is an important component in non-cooperative wireless communication networks to identify the modulation schemes of the received signals. In this paper, considering the multipath effect in practical propagation environments, a distributed cooperative AMC (Co-AMC) network based on machine learning is proposed to identify the modulation scheme in non-cooperative wireless communication networks. Specifically, feature vectors are first obtained by applying a cyclic spectrum to facilitate the feature extraction of the received signal. Then, a classifier based on the K-nearest neighbor (KNN) method is designed to obtain the local decision for modulation classification at each distributed node. Meanwhile, the reliability of the local decision is estimated by applying two loss functions to assess the quality of the local decision. Finally, the unified classification result is obtained to fuse the local decisions according to their reliabilities by applying a designed decision fusion algorithm based on the distributed weighted average alternating direction method of multipliers (DWA-ADMM). The simulation results demonstrate that the proposed Co-AMC network achieves superior classification accuracy compared to existing AMC methods across a range of modulation schemes and SNRs. More importantly, the proposed Co-AMC exhibits great flexibility and practicability since it is adaptive to wireless networks with various scales and topologies.

AL-aided AMC in a multi-user white-light OFDMA VLC system over a light-diffusing fiber loop.

To develop an adaptive modulation scheme for flexible high-speed multi-user visible light communication (VLC), automatic modulation classification (AMC) is adopted for monitoring the modulation formats of different subcarrier groups. An AMC scheme based on a joint convolutional neural network (CNN), active learning (AL), and data augmentation (DA) is demonstrated over an orthogonal frequency division multiplexing access (OFDMA) VLC system. The configuration of the diffuse white-light VLC system is combined with a pair integrated transceiver module, a light-diffusing fiber (LDF), and a wireless channel, which can provide white-light illumination and ubiquitous access. Within the forward error correction (FEC) threshold, the data rates of the white-light VLC links can reach 325.5 Mbps with a bit error rate (BER) of 2.163 × 10-3. An experiment with two-user access via the proposed VLC link with an unequal bandwidth allocation was demonstrated. The performance of the AL-aided CNN AMC scheme also shows a classification accuracy rate of 95.48% for the constellation diagrams of different subcarriers of the OFDMA signal over 240 training samples and faster convergence than a CNN-based AMC.

High-Order Convolutional Attention Networks for Automatic Modulation Classification in Communication

Automatic modulation classification is a challenging and critical task in the field of communication. Deep convolutional networks (ConvNets) have been recently applied in cognitive radio and achieved remarkable performance. However, existing ConvNet-based methods mainly focus on the first-order architecture design, while fail to explore feature correlations of radio signal, which are particularly significant for useful information extraction in low signal-to-noise ratios (SNRs). In this paper, we propose high-order convolutional attention networks (HoCANs) for radio signal expression and feature correlation learning, based on a novel high-order attention mechanism to rescale the convolutional features along channel and sequence dimensions. High-order convolutional layer and covariance matrix after nonlinear transformation are led for tenser filtering with more discriminative representations of radio signals. Experiments have been conducted to validate the superiority of HoCANs which achieve state-of-the-art accuracy for automatic modulation classification on RADIOML 2018.01A dataset.

AUTOMATIC MODULATION CLASSIFICATION USING DEEP LEARNING POLAR FEATURE

The automatic modulation classification of signals is of great importance in modern communications, especially on cognitive radio. Several methods have been used in this field, the most important of which is the classification of modulation automatically using Deep Learning, where the methods depend on the convolution neural network, which is one of the Deep Learning networks, achieved high accuracy in classifying the modulation, so the proposed network depends on the type of deep learning CNN consisting of four blocks, each block contains a set of symmetric and asymmetric filters. The network also contains Max Pool. In this paper, the features extracted in phase-squaring and polar have been combined for the input, which helps in extending the input, that is, an increase in the features inside the network. It also contributes to improving the accuracy of classifying the higher-order modulation through the Polar plane. The dataset RadioML 2018.01A was adopted, which is used in the most recent research, where 11 types of modulation normal-class: (FM, GMSK, QPSK, BPSK, 0QPSK, AM-SSB-SC, 4ASK, AM-DSB-SC, 16QAM, 8PSK,00K) were taken. A simulation of which can be found in Matlab 2021. The proposed network achieved 100% classification accuracy when the signal-to-noise ratio is greater or equal to 2 dB for 11 types of modulation. The results of the paper were compared with modern networks Baseline network, Visual Geometry Group network, and Residual Neural network. The comparison showed the superiority of the proposed network over these networks, as the proposed network achieved an accuracy equal to 100% at SNR 2 dB while BL achieved an accuracy equal to 72% at SNR 2 dB, RN, and VGG almost reach 93% at SNR 2 dB.

Method for the automatic modulation classification based on linear regression and feature selection

Method for the automatic modulation classification based on linear regression and feature selection

A Novel Self-Learning Filters for Automatic Modulation Classification Based on Deep Residual Shrinking Networks

A Novel Self-Learning Filters for Automatic Modulation Classification Based on Deep Residual Shrinking Networks

Robust automatic modulation classification under noise mismatch

Automatic modulation classification plays a critical role in the intelligent reception of unknown wireless signals. In practice, the dynamic wireless environment brings a great challenge, and the actual test model is inconsistent with the training model. Therefore, aiming at the problem of noise mismatch, this paper proposes a new modulation classification method based on KD-GoogLeNet and Squeeze-Excitation (KD-GSENet). Using the k-dimensional tree, the complex wireless signals are converted into color images rather than normal constellations, which can enhance the classification features. Considering the attention block has the inherent advantage of assigning more weights to important features, this paper further uses it to improve the GoogLeNet. Finally, extensive experiments are presented including Gaussian noise, non-Gaussian noise, and the scenarios of noise mismatch. Numerical results verify the superior classification performance of the proposed KD-GSENet under different scenarios.

Automatic Modulation Classification Using Deep Residual Neural Network with Masked Modeling for Wireless Communications

Automatic modulation classification (AMC) is a signal processing technology used to identify the modulation type of unknown signals without prior information such as modulation parameters for drone communications. In recent years, deep learning (DL) has been widely used in AMC methods due to its powerful feature extraction ability. The significant performance of DL-based AMC methods is highly dependent on large amount of data. However, with the increasingly complex signal environment and the emergence of new signals, several recognition tasks have difficulty obtaining sufficient high-quality signals. To address this problem, we propose an AMC method based on a deep residual neural network with masked modeling (DRMM). Specifically, masked modeling is adopted to improve the performance of a deep neural network with limited signal samples. Both complex-valued and real-valued residual neural networks (ResNet) play an important role in extracting signal features for identification. Several typical experiments are conducted to evaluate our proposed DRMM-based AMC method on the RadioML 2016.10A dataset and a simulated dataset, and comparison experiments with existing AMC methods are also conducted. The simulation results illustrate that our proposed DRMM-based AMC method achieves better performance in the case of limited signal samples with low signal-to-noise ratio (SNR) than other existing methods.

Decentralized Learning and Model Averaging Based Automatic Modulation Classification in Drone Communication Systems

Automatic modulation classification (AMC) is a promising technology to identify the modulation mode of the received signal in drone communication systems. Recently, benefiting from the outstanding classification performance of deep learning (DL), various deep neural networks (DNNs) have been introduced into AMC methods. Most current AMC methods are based on a local framework (LocalAMC) where there is only one device, or a centralized framework (CentAMC) where multiple local devices (LDs) upload their data to only one central server (CS). LocalAMC may not achieve ideal results due to insufficient data and finite computational power. CentAMC carries a significant risk of privacy leakage and the final data for training model in CS are quite massive. In this paper, we propose a practical and light AMC method based on decentralized learning with residual network (ResNet) in drone communication systems. Simulation results show that the ResNet-based decentralized AMC (DecentAMC) method achieves similar classification performance to CentAMC while improving training efficiency and protecting data privacy.

Toward the Automatic Modulation Classification With Adaptive Wavelet Network

With the evolutionary development of modern communications technology, automatic modulation classification (AMC) has played an increasing role in the complex wireless communication environment. Existing AMC schemes based on deep learning use a neural network to extract features and calculate feature maps, then feed them into fully connected layers for classification. However, existing schemes still are insufficient in utilizing feature maps. To overcome this limitation, a novel adaptive wavelet network (AWN) is proposed, which combines adaptive wavelet decomposition based on the lifting scheme and channel attention mechanism.In contrast to the previous models, the multi-level decomposition of AWN explicitly extracts the features of multiple frequency bands. The channel attention mechanism further selects the optimal frequencies from the candidate frequencies. AWN explores a novel AMC paradigm that efficiently integrates the inherent properties of the signal by introducing prior knowledge in the frequency domain. Simulation results demonstrate that our proposed AMC scheme outperforms the benchmark scheme and has rather low computational complexity.

Toward Next-Generation Signal Intelligence: A Hybrid Knowledge and Data-Driven Deep Learning Framework for Radio Signal Classification

Automatic modulation classification (AMC) can generally be divided into knowledge-based methods and data-driven methods. In this paper, we explore combining the knowledge-based method and data-driven technology to take full advantage of both and propose a hybrid knowledge and data-driven deep learning framework (HKDD) for AMC. To make the handcrafted features more discriminative, various traditional features are adopted, including instantaneous features, statistical features, and spectral features. In the HKDD framework, a feature fusion mechanism is proposed to integrate the features learned from the original signal with those processed by a fully connected network from the handcrafted features. Besides, an attention mechanism is implemented on the fused features to neglect immature features and highlight important features. To evaluate the performance of the proposed method, we construct two modulation classification datasets containing both traditional features and raw IQ data. The bigger one contains 36 modulation categories, which is greater than the number of categories of any AMC dataset currently available. Simulation results show that our proposed method has significant performance gain in both adequate-sample classification scenario and few-shot classification scenario.

Automatic Modulation Classification: Convolutional Deep Learning Neural Networks Approaches

Automatic Modulation Classification: Convolutional Deep Learning Neural Networks Approaches

A Highly Controllable Cooperative Automatic Modulation Classification

A Highly Controllable Cooperative Automatic Modulation Classification

Deep Learning-Enabled Deceptive Jammer Detection for Low Probability of Intercept Communications

The detection of deceptive jamming in low probability of intercept/low probability of detection (LPI/LPD) tactical communications has been receiving increasing attention, especially in the context of radar communications. In this article, we propose a novel deep-learning-based algorithm, named two-stage deceptive jammer detection (TDJD), for pseudonoise (PN) code-integrated linear frequency modulation chirp jamming signals in radar communications. Specifically, the proposed algorithm uses the time–frequency distribution (TFD) and in-phase and quadrature (I/Q) representations of the received signals to obtain more discriminating features for the detection of unknown deceptive jammers under the realistic assumption that information about the deceptive jammer, such as the PN-code signal, power, and range, is not available. In particular, the received radar signals are converted into two kinds of TFD representations using the Choi–William distribution (CWD) and I/Q representations. Next, a two-stage approach based on deep learning-enabled outlier detection and automatic modulation classification techniques is applied to the CWD and I/Q representations to extract high-level features for jammer detection. Simulation results show that the TDJD algorithm outperforms other relevant deep learning algorithms in terms of the probability of detection and the probability of false alarm. In addition, the TDJD algorithm provides accurate detection at a low signal-to-jammer-plus-noise ratio.

Hybrid Data Augmentation and Dual-Stream Spatiotemporal Fusion Neural Network for Automatic Modulation Classification in Drone Communications

Automatic modulation classification (AMC) is one of the most important technologies in various communication systems, including drone communications. It can be applied to confirm the legitimacy of access devices, help drone systems better identify and track signals from other communication devices, and prevent drone interference to ensure the safety and reliability of communication. However, the classification performance of previously proposed AMC approaches still needs to be improved. In this study, a dual-stream spatiotemporal fusion neural network (DSSFNN)-based AMC approach is proposed to enhance the classification accuracy for the purpose of aiding drone communication because SDDFNN can effectively mine spatiotemporal features from modulation signals through residual modules, long-short term memory (LSTM) modules, and attention mechanisms. In addition, a novel hybrid data augmentation method based on phase shift and self-perturbation is introduced to further improve performance and avoid overfitting. The experimental results demonstrate that the proposed AMC approach can achieve an average classification accuracy of 63.44%, and the maximum accuracy can reach 95.01% at SNR = 10 dB, which outperforms the previously proposed methods.

A convolutional and transformer based deep neural network for automatic modulation classification

Automatic modulation classification (AMC) aims at identifying the modulation of the received signals, which is a significant approach to identifying the target in military and civil applications. In this paper, a novel data-driven framework named con-volutional and transformer-based deep neural network (CTDNN) is proposed to improve the classification performance. CTDNN can be divided into four modules, i.e., convolutional neural network (CNN) backbone, transition module, transformer module, and final classifier. In the CNN backbone, a wide and deep convolution structure is designed, which consists of 1 × 15 convolution kernels and intensive cross-layer connections instead of traditional 1×3 kernels and sequential connections. In the transition module, a 1 × 1 convolution layer is utilized to compress the channels of the previous multi-scale CNN features. In the transformer module, three self-attention layers are designed for extracting global features and generating the classification vector. In the classifier, the final decision is made based on the maximum a posterior probability. Extensive simulations are conducted, and the result shows that our proposed CTDNN can achieve superior classification performance than traditional deep models.

Automatic Modulation Classification Using Hybrid Data Augmentation and Lightweight Neural Network.

Automatic modulation classification (AMC) plays an important role in intelligent wireless communications. With the rapid development of deep learning in recent years, neural network-based automatic modulation classification methods have become increasingly mature. However, the high complexity and large number of parameters of neural networks make them difficult to deploy in scenarios and receiver devices with strict requirements for low latency and storage. Therefore, this paper proposes a lightweight neural network-based AMC framework. To improve classification performance, the framework combines complex convolution with residual networks. To achieve a lightweight design, depthwise separable convolution is used. To compensate for any performance loss resulting from a lightweight design, a hybrid data augmentation scheme is proposed. The simulation results demonstrate that the lightweight AMC framework reduces the number of parameters by approximately 83.34% and the FLOPs by approximately 83.77%, without a degradation in performance.

A Lightweight Deep Learning Model for Automatic Modulation Classification Using Residual Learning and Squeeze–Excitation Blocks

Automatic modulation classification (AMC) is a vital process in wireless communication systems that is fundamentally a classification problem. It is employed to automatically determine the type of modulation of a received signal. Deep learning (DL) methods have gained popularity in addressing the problem of modulation classification, as they automatically learn the features without needing technical expertise. However, their efficacy depends on the complexity of the algorithm, which can be characterized by the number of parameters. In this research, we presented a deep learning algorithm for AMC, inspired by residual learning, which has remarkable accuracy and great representational ability. We also employed a squeeze-and-excitation network that is capable of exploiting modeling interconnections between channels and adaptively re-calibrates the channel-wise feature response to improve performance. The proposed network was designed to meet the accuracy requirements with a reduced number of parameters for efficiency. The proposed model was evaluated on two benchmark datasets and compared with existing methods. The results show that the proposed model outperforms existing methods in terms of accuracy and has up to 72.5% fewer parameters than convolutional neural network designs.

Voting-Based Deep Convolutional Neural Networks (VB-DCNNs) for M-QAM and M-PSK Signals Classification

Automatic modulation classification (AMC) using convolutional neural networks (CNNs) is an active area of research that has the potential to improve the efficiency and reliability of wireless communication systems significantly. AMC is the approach used in a communication system to detect the type of modulation format at the receiver end. This paper proposes a voting-based deep convolutional neural network (VB-DCNN) for classifying M-QAM and M-PSK signals. M-QAM and M-PSK signal waveforms are generated and passed through the fading channel in the presence of additive white Gaussian noise (AWGN). The VB-DCNN extracts features from the input signal through convolutional layers, and classification is performed on these features. Multiple network instances are trained on different subsets of training data in the VB-DCNN. A network instance predicts the input signal during testing. Based on the votes, the final prediction is made. Different simulation experiments are carried out to analyze the performance of the trained network, and the DCNN is designed with the Deep Neural Network Toolbox in MATLAB. The generated frames are divided into training, validation, and test datasets. Lastly, the classification accuracy of the trained network is determined using test frames. The proposed model’s accuracy is near to 100% at lower SNRs. The simulation results show the superiority of the proposed VB-DCNN compared to existing state-of-the-art techniques.

A Family of Automatic Modulation Classification Models Based on Domain Knowledge for Various Platforms

Identifying the modulation type of radio signals is challenging in both military and civilian applications such as radio monitoring and spectrum allocation. This has become more difficult as the number of signal types increases and the channel environment becomes more complex. Deep learning-based automatic modulation classification (AMC) methods have recently achieved state-of-the-art performance with massive amounts of data. However, existing models struggle to achieve the required level of accuracy, guarantee real-time performance at edge devices, and achieve higher classification performance on high-performance computing platforms when deployed on various platforms. In this paper, we present a family of AMC models based on communication domain knowledge for various computing platforms. The higher-order statistical properties of signals, customized data augmentation methods, and narrowband convolution kernels are the domain knowledge that is specifically employed to the AMC task and neural network backbone. We used separable convolution and depth-wise convolution with very few residual connections to create our lightweight model, which has only 4.61k parameters while maintaining accuracy. On the four different platforms, the classification accuracy and inference time outperformed those of the existing lightweight models. Meanwhile, we use the squeeze-and-excitation attention mechanism, channel shuffle module, and expert feature parallel branch to improve the classification accuracy. On the three most frequently used benchmark datasets, the high-accuracy models achieved state-of-the-art average accuracies of 64.63%, 67.22%, and 65.03%, respectively. Furthermore, we propose a generic framework for evaluating the complexity of deep learning models and use it to comprehensively assess the complexity strengths of the proposed models.