Robust Automatic Modulation Classification Research Articles

Binarized ResNet: Enabling Robust Automatic Modulation Classification at the Resource-Constrained Edge

Binarized ResNet: Enabling Robust Automatic Modulation Classification at the Resource-Constrained Edge

Prototypical Network with Residual Attention for Modulation Classification of Wireless Communication Signals

Automatic modulation classification (AMC) based on data-driven deep learning (DL) can achieve excellent classification performance. However, in the field of electronic countermeasures, it is difficult to extract salient features from wireless communication signals under scarce samples. Aiming at the problem of modulation classification under scarce samples, this paper proposes a few-shot learning method using prototypical network (PN) with residual attention (RA), namely PNRA, to achieve the AMC. Firstly, the RA is utilized to extract the feature vector of wireless communication signals. Subsequently, the feature vector is mapped to a new feature space. Finally, the PN is utilized to measure the Euclidean distance between the feature vector of the query point and each prototype in this space, determining the type of the signals. In comparison to mainstream few-shot learning (FSL) methods, the proposed PNRA can achieve effective and robust AMC under the data-hungry condition.

Deep Learning-Based Automatic Modulation Classification Using Robust CNN Architecture for Cognitive Radio Networks

Automatic modulation classification (AMC) is an essential technique in intelligent receivers of non-cooperative communication systems such as cognitive radio networks and military applications. This article proposes a robust automatic modulation classification model based on a new architecture of a convolutional neural network (CNN). The basic building convolutional blocks of the proposed model include asymmetric kernels organized in parallel combinations to extract more meaningful and powerful features from the raw I/Q sequences of the received signals. These blocks are connected via skip connection to avoid vanishing gradient problems. The experimental results reveal that the proposed model performs well in classifying nine different modulation schemes simulated with different real wireless channel impairments, including AWGN, Rician multipath fading, and clock offset. The performance of the proposed system systems shows that it outperforms its best rivals from the literature in recognizing the modulation type. The proposed CNN architecture remarkably improves classification accuracy at low SNRs, which is appropriate in realistic scenarios. It achieves 86.1% accuracy at −2 dB SNR. Furthermore, it reaches an accuracy of 96.5% at 0 dB SNR and 99.8% at 10 dB SNR. The proposed architecture has strong feature extraction abilities that can effectively recognize 16QAM and 64QAM signals, the challenging modulation schemes of the same modulation family, with an overall average accuracy of 81.02%.

Robust Automatic Modulation Classification in Low Signal to Noise Ratio

In a non-cooperative communication environment, automatic modulation classification (AMC) is an essential technology for analyzing signals and classifying different kinds of signal modulation before they are demodulated. Deep learning (DL)-based AMC has been proposed as an efficient method of achieving high classification performance. However, most current DL-AMC methods have limited generalization capabilities under varying noise conditions, especially at low signal-to-noise ratios (SNRs). Therefore, these methods can not be directly applied to practical systems. In this paper, we propose a threshold autoencoder denoiser convolutional neural network (TADCNN), which consists of a threshold autoencoder denoiser (TAD) and a convolutional neural network (CNN). TADs reduce noise power and clean input signals, which are then passed on to CNN for classification. The TAD network generally consists of three components: the batch normalization layer, the autoencoder, and the threshold denoise. The threshold denoise component uses an auto-learning threshold sub-network to compute thresholds automatically. According to experiments, AMC with TAD improved classification accuracy by 70% at low SNR compared with a model without a denoiser. Additionally, our model achieves an average accuracy of 66.64% on the RML2016.10A dataset, which is 6% to 18% higher than the current AMC model.

A Deep Learning-Based Robust Automatic Modulation Classification Scheme for Next-Generation Networks

Due to stochastic wireless environment, the process of modulation classification has become a challenging task. Because of its powerful feature extraction ability and promising performance over the conventional schemes, deep learning (DL) models are employed to automatic modulation classification (AMC) problems. Most of the conventional models proposed are tested for the limited set of modulation schemes transmitted over additive white Gaussian noise (AWGN) channels without considering the effect of multipath fading and Doppler shift. The next-generation networks use adaptive and higher-order quadrature amplitude modulation (QAM) schemes for higher spectral efficiency. The classification accuracy of conventional DL-based AMC schemes drastically reduces, when different order QAM modulation schemes are accommodated. In this work, different scaling factors are selected for the generation of [Formula: see text]-QAM frames. The combination of scaling factors, which maximize the classification accuracy is chosen. A convolutional neural network (CNN) with six stages is employed for AMC. The simulation results show that the classification accuracy of proposed scheme is higher than the conventional DL-based schemes under various signal-to-noise ratio (SNR) conditions. The proposed scheme shows at least 4% improvement in classification accuracy over the other DL-based schemes.

Deep cascading network architecture for robust automatic modulation classification

BACKGROUND: Automatic modulation classification (AMC) plays a crucial role in cognitive radio, such as industrial automation, transmitter identification, and spectrum resource allocation. Recently, deep learning (DL) as a new machine learning (ML) methodology has achieved considerable implementation in AMC missions. However, few studies have examined the robustness of DL models under varying signal-to-noise ratio (SNR) environments.OBJECTIVE: The primary objective of this paper is to design a robust DL-based AMC model to adapt to noise changes.METHODS: The AMC task is divided into two sub-problems: SNR environment perception and modulation classification in sub-environments. A deep cascading network architecture (DCNA) is proposed to solve these two problems. DCNA is composed of an SNR estimator network (SEN) and a modulation recognition cluster network (MRCN). SEN is designed to identify the SNR levels of samples, and MRCN is composed of several subnetworks for further modulation recognition under diverse SNR settings. In addition, a label-smoothing method is proposed to promote the integration between SEN and MRCN. An auxiliary data-segmenting method is also presented to deal with the contrasting data requirements of DCNA. Note that DCNA does not utilize a specific network structure and can be generalized to various deep learning models with advanced improvements.RESULTS: Experimental results on dataset RML2016.10b show that our proposed DCNA can enhance the recognition performance of different network structures on AMC tasks. In particular, a combination of DCNA and convolutional long short-term deep neural network (CLDNN) can achieve a classification accuracy of 91.0%, outperforming the previous research.CONCLUSION: The performance of the cascading network demonstrates the significant performance advantage and application feasibility of DCNA.

Deep Learning-Based Robust Automatic Modulation Classification for Cognitive Radio Networks

In this paper, a novel deep learning-based robust automatic modulation classification (AMC) method is proposed for cognitive radio networks. Generally, as network input of AMC convolutional neural networks (CNNs) images or complex signals are utilized in time domain or frequency domain. In terms of the image that contains RGB(Red, Green, Blue) levels the input size may be larger than the complex signal, which represents the increase of computational complexity. In terms of the complex signal it is normally used as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2 \times N$ </tex-math></inline-formula> size for the input, which is divided into in-phase and quadrature-phase (IQ) components. In this paper, the input size is extended as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4 \times N$ </tex-math></inline-formula> size by copying IQ components and concatenating in reverse order to improve the classification accuracy. Since the increase in the amount of computation complexity due to the extended input size, the proposed CNN archiecture is designed to reduce the size from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4 \times N$ </tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2 \times N$ </tex-math></inline-formula> by an average pooling layer, which can enhence the classification accuracy as well. The simulation results show that the classification accuracy of the proposed model is higher than the conventional models in the almost signal-to-noise ratio (SNR) range.

Corrections to “Deep Learning-Based Robust Automatic Modulation Classification for Cognitive Radio Networks”

In the above article [1] , in Table 4 , the operator * was used to represent frame sizes, which are 2 * 128 and 4 * 128. The correct operator should be a multiplication operator $\times $ for $2 \times 128$ and $4 \times 128$ .

MCNet: An Efficient CNN Architecture for Robust Automatic Modulation Classification

This letter proposes a cost-efficient convolutional neural network (CNN) for robust automatic modulation classification (AMC) deployed for cognitive radio services of modern communication systems. The network architecture is designed with several specific convolutional blocks to concurrently learn the spatiotemporal signal correlations via different asymmetric convolution kernels. Additionally, these blocks are associated with skip connections to preserve more initially residual information at multi-scale feature maps and prevent the vanishing gradient problem. In the experiments, MCNet reaches the overall 24-modulation classification rate of 93.59% at 20 dB SNR on the well-known DeepSig dataset.

Introducing an Efficient Statistical Model for Automatic Modulation Classification

Nowadays, Automatic Modulation Classification (AMC) plays an important role in many applications of cooperative and non-cooperative communication such as spectrum management, cognitive radio, intelligent modems, and interference identification. In this paper, a new robust AMC algorithm based on Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model is proposed. Primarily, multi-level wavelet transform is applied on the received data samples. To select the efficient statistical model for wavelet coefficient description, the statistical characteristics of these coefficients are surveyed. The proposed analysis precisely illustrates that these coefficients have heteroscedasticity property which has not been mentioned before. Subsequently, to describe the wavelet coefficients, the heteroscedastic GARCH model is employed and its parameters are extracted as the features. Finally, the obtained features are applied to support vector machine (SVM) classifier to simultaneously determine the modulation type and constellation size. Eleven different types and sizes of the digital modulation schemes in various channels such as AWGN, flat fading and multipath fading in presence of common channel impairments are examined. The experimental results reveal the superior performance of the proposed method in comparison with the previously introduced approaches.

Robust Automatic Modulation Classification Technique for Fading Channels via Deep Neural Network

In this paper, we propose a deep neural network (DNN)-based automatic modulation classification (AMC) for digital communications. While conventional AMC techniques perform well for additive white Gaussian noise (AWGN) channels, classification accuracy degrades for fading channels where the amplitude and phase of channel gain change in time. The key contributions of this paper are in two phases. First, we analyze the effectiveness of a variety of statistical features for AMC task in fading channels. We reveal that the features that are shown to be effective for fading channels are different from those known to be good for AWGN channels. Second, we introduce a new enhanced AMC technique based on DNN method. We use the extensive and diverse set of statistical features found in our study for the DNN-based classifier. The fully connected feedforward network with four hidden layers are trained to classify the modulation class for several fading scenarios. Numerical evaluation shows that the proposed technique offers significant performance gain over the existing AMC methods in fading channels.

Robust Automatic Modulation Classification Under Varying Noise Conditions

Automatic modulation classification (AMC) plays a key role in non-cooperative communication systems. Feature-based (FB) methods have been widely studied in particular. Most existing FB methods are deployed at a fixed SNR level, and the pre-trained classifiers may no longer be effective when the SNR level changes. The classifiers may also need to be re-trained to be suitable for the varying channel environment. To address these problems, a robust AMC method under varying noise conditions is proposed in this paper. The method attempts to select noise-insensitive features from a large feature set to ensure that the trained classifiers will be robust to SNR variations. First, a feature set consisting of 25 types of features is extracted, and 4 features that are insensitive to noise are chosen through a feature selection method based on rough set theory. The generalizability of an SVM classifier trained on the 4 chosen features is evaluated based on numerical results. The classification accuracy remains reasonable when the SNR varies between 5 and 20 dB, indicating that the proposed method can be deployed under varying noise conditions.

Robust automatic modulation classification and blind equalization: novel cognitive receivers

The automatic modulation classifier (AMC) is an important signal processing component that helps the cognitive radio (CR) better utilize the spectrum. In a typical CR scenario, training sequence or channel knowledge is not available, and hence blind equalizers are widely used. The multipath fading channel not only affects symbol detection performance but also affects the performance of the AMC. In a conventional single input single output blind equalizer, the weights of the equalizer are adapted by minimizing cost functions that are non quadratic (multimodal). Convergence of the blind equalizer to a local minimum affects both symbol detection performance and AMC performance. In a CR scenario, it is preferable to consider the performance of the AMC also while adapting the blind equalizer weights. In this article, we propose novel CR receivers where the performance of the AMC is also considered while adapting the parameters of the blind equalizer. Computer simulations are given to illustrate the concept and yield promising results.

Novel Automatic Modulation Classification Using Cumulant Features for Communications via Multipath Channels

Nowadays, automatic modulation classification (AMC) plays an important role in both cooperative and non-cooperative communication applications. Very often, multipath fading channels result in the severe AMC performance degradation or induce large classification errors. The negative impacts of multipath fading channels on AMC have been discussed in the existing literature but no solution has ever been proposed so far to the best of our knowledge. In this paper, we propose a new robust AMC algorithm, which applies higher-order statistics (HOS) in a generic framework for blind channel estimation and pattern recognition. We also derive the Cramer-Rao lower bound for the fourth-order cumulant estimator when the AMC candidates are BPSK and QPSK over the additive white Gaussian noise channel, and it is a nearly minimum-variance estimator leading to robust AMC features in a wide variety of signal-to-noise ratios. The advantage of our new algorithm is that, by carefully designing the essential features needed for AMC, we do not really have to acquire the complete channel information and therefore it can be feasible without any a priori information in practice. The Monte Carlo simulation results show that our new AMC algorithm can achieve the much better classification accuracy than the existing AMC techniques.