Modulation Classification Method Research Articles

SOAMC: A Semi-Supervised Open-Set Recognition Algorithm for Automatic Modulation Classification

Traditional automatic modulation classification methods operate under the closed-set assumption, which proves to be impractical in real-world scenarios due to the diverse nature of wireless technologies and the dynamic characteristics of wireless propagation environments. Open-set environments introduce substantial technical challenges, particularly in terms of detection effectiveness and computational complexity. To address the limitations of modulation classification and recognition in open-set scenarios, this paper proposes a semi-supervised open-set recognition approach, termed SOAMC (Semi-Supervised Open-Set Automatic Modulation Classification). The primary objective of SOAMC is to accurately classify unknown modulation types, even when only a limited subset of samples is manually labeled. The proposed method consists of three key stages: (1) A signal recognition pre-training model is constructed using data augmentation and adaptive techniques to enhance robustness. (2) Feature extraction and embedding are performed via a specialized extraction network. (3) Label propagation is executed using a graph convolutional neural network (GCN) to efficiently annotate the unlabeled signal samples. Experimental results demonstrate that SOAMC significantly improves classification accuracy, particularly in challenging scenarios with limited amounts of labeled data and high signal similarity. These findings are critical for the practical identification of complex and diverse modulation signals in real-world wireless communication systems.

A lightweight deep learning architecture for automatic modulation classification of wireless internet of things

AbstractThe wireless Internet of Things (IoT) is widely used for data transmission in power systems. Wireless communication is an important part of the IoT. The existing modulation classification algorithms have low classification accuracy when facing strong electromagnetic interference, which causes decoding error link interruption and wastes wireless channel resources. Therefore, it is necessary to study signal modulation classification methods in a low signal‐to‐noise ratio (SNR) environment. In this paper, a lightweight Deep Neural Networks (DNNs) modulation classification method based on the Informer architecture classifier and two‐dimensional (2‐D) curves input of the spectral correlation function (SCF) is proposed, which uses in‐phase and quadrature (I/Q) signals to generate 2‐D cross‐section SCF curve first and then feeds the feature curve into the Informer network to classify the modulation method. This model can better learn the robustness characteristics in a long sequence. Through testing, the classification accuracy of the modulation signal is not much lower than that of the current good classification method when the SNR is 10 dB, and this method can still show higher accuracy when hardware resources are limited. It is a compact design of a modulation classification model and easy to deploy on low‐cost embedded platforms.

A few-shot learning-based automatic modulation classification method for Internet of Things

A few-shot learning-based automatic modulation classification method for Internet of Things

GAF-MAE: A Self-Supervised Automatic Modulation Classification Method Based on Gramian Angular Field and Masked Autoencoder

GAF-MAE: A Self-Supervised Automatic Modulation Classification Method Based on Gramian Angular Field and Masked Autoencoder

Deep learning-based modulation recognition with constellation diagram: A case study

Automatic modulation recognition is a promising solution for identifying and classifying signals received in heterogeneous wireless networks. In dynamic and autonomous environments, receivers must extract the relevant signal from various modulated signals to enable further communication procedures. Machine learning, including its sub-branches for classification problems, offers promising operational capabilities. This study utilized the ResNet-50 deep learning method for modulation classification. A dataset consisting of eight digital modulation techniques was generated, with constellation diagrams created as image data over the additive white Gaussian noise (AWGN) channel at signal-to-noise ratios (SNR) of 5 dB, 10 dB, and 20 dB. The deep learning algorithm's performance metrics were evaluated using a confusion matrix, and F1 scores were compared to those of the AlexNet deep learning algorithm. The simulation results clearly indicate the superior performance of ResNet-50 over AlexNet. In terms of average F1 scores, ResNet-50 exhibits a significant advantage, surpassing AlexNet by approximately 67%, 29%, and 10% at SNR values of 5 dB, 10 dB, and 20 dB, respectively.

An efficient Automatic Modulation Classification method based on the Convolution Adaptive Noise Reduction network

Due to the influence of noise in the received signal in non-cooperative communication, it is difficult for existing Automatic modulation classification methods to balance classification accuracy and model complexity. This paper proposes a novel Convolutional Adaptive Noise Reduction (CANR) network, which consists of an Adaptive Noise Reduction (ANR) module and a Convolutional Feature Extraction (CFE) module. The ANR and CFE modules denoise the combined input and capture the spatiotemporal features in the time series. Experiments on benchmark datasets show that the proposed network has the fewest training parameters and state-of-the-art recognition accuracy under the same conditions.

MobileRaT: A Lightweight Radio Transformer Method for Automatic Modulation Classification in Drone Communication Systems

Nowadays, automatic modulation classification (AMC) has become a key component of next-generation drone communication systems, which are crucial for improving communication efficiency in non-cooperative environments. The contradiction between the accuracy and efficiency of current methods hinders the practical application of AMC in drone communication systems. In this paper, we propose a real-time AMC method based on the lightweight mobile radio transformer (MobileRaT). The constructed radio transformer is trained iteratively, accompanied by pruning redundant weights based on information entropy, so it can learn robust modulation knowledge from multimodal signal representations for the AMC task. To the best of our knowledge, this is the first attempt in which the pruning technique and a lightweight transformer model are integrated and applied to processing temporal signals, ensuring AMC accuracy while also improving its inference efficiency. Finally, the experimental results—by comparing MobileRaT with a series of state-of-the-art methods based on two public datasets—have verified its superiority. Two models, MobileRaT-A and MobileRaT-B, were used to process RadioML 2018.01A and RadioML 2016.10A to achieve average AMC accuracies of 65.9% and 62.3% and the highest AMC accuracies of 98.4% and 99.2% at +18 dB and +14 dB, respectively. Ablation studies were conducted to demonstrate the robustness of MobileRaT to hyper-parameters and signal representations. All the experimental results indicate the adaptability of MobileRaT to communication conditions and that MobileRaT can be deployed on the receivers of drones to achieve air-to-air and air-to-ground cognitive communication in less demanding communication scenarios.

Feature Fusion Based on Graph Convolution Network for Modulation Classification in Underwater Communication.

Automatic modulation classification (AMC) of underwater acoustic communication signals is of great significance in national defense and marine military. Accurate modulation classification methods can make great contributions to accurately grasping the parameters and characteristics of enemy communication systems. While a poor underwater acoustic channel makes it difficult to classify the modulation types correctly. Feature extraction and deep learning methods have proven to be effective methods for the modulation classification of underwater acoustic communication signals, but their performance is still limited by the complex underwater communication environment. Graph convolution networks (GCN) can learn the graph structured information of the data, making it an effective method for processing structured data. To improve the stability and robustness of AMC in underwater channels, we combined the feature extraction and deep learning methods by fusing the multi-domain features and deep features using GCN. The proposed method takes the relationships among the different multi-domain features and deep features into account. Firstly, a feature graph was built using the properties of the features. Secondly, multi-domain features were extracted from the received signals and deep features were extracted from the signals using a deep neural network. Thirdly, we constructed the input of GCN using these features and the graph. Then, the multi-domain features and deep features were fused by the GCN. Finally, we classified the modulation types using the output of GCN by way of a softmax layer. We conducted the experiments on a simulated dataset and a real-world dataset, respectively. The results show that the AMC based on GCN can achieve a significant improvement in performance compared to the current state-of-the-art methods. Our approach is robust in underwater acoustic channels.

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

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 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 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.

DL-PR: Generalized automatic modulation classification method based on deep learning with priori regularization

Automatic modulation classification (AMC) is an essential and indispensable topic in the development of cognitive radios. It is the cornerstone of adaptive modulation and demodulation capabilities to perceive and understand surrounding environments and make corresponding decisions. In this paper, we propose a priori regularization method in deep learning (DL-PR) for guiding loss optimization during model training process. The regularization factor designed by the combination of inter-class confrontation factor, global and dimensional divergence can help increase the inter-class distance and reduce the intra-class distance of samples. While preserving the original information of received signals as much as possible, it makes full use of the prior knowledge in the signal transmission process and ultimately helps deep learning models to be well generalized on signals with various signal-to-noise ratios (SNRs). As far as we know, this is the first attempt to regularize deep learning models based on SNR distribution of samples to improve AMC accuracy. Moreover, it can be proved that priori regularization can be interpreted as implicit data augmentation and model ensemble methods. By comparing with a series of state-of-the-art AMC methods and different regularization techniques on the public dataset RadioML 2016.10a, experimental results of multiple deep learning models illustrate the superiority of DL-PR, including CNN with accuracy of 62.6% and inference time of 0.82 ms per signal, LSTM with 61.8% and 0.87 ms, and hybrid CNN–LSTM with 64.2% and 0.94 ms. In practical applications, DL-PR can be also easily applied to complex environments due to its robustness to hyper-parameters and SNR estimation.

Efficient Convolutional Networks for Robust Automatic Modulation Classification in OFDM-Based Wireless Systems

Orthogonal frequency-division multiplexing (OFDM) is commonly deployed in Internet of Things (IoT) systems to achieve high data rates with reasonable complexity, where noncooperative protocols encode signals with different modulations for multiple users. However, conventional modulation recognition is limited to single-carrier communication systems, and there is an urgent need to design an effective method to blindly identify the unknown modulation format of received signals. In this article, we propose an automatic modulation classification method for the OFDM systems with the presence of channel deterioration. Our method first leverages a data reconstruction mechanism to arrange signals into high-dimensional data arrays and then exploits an efficient convolutional network, namely OFDMsym-Net, to learn underlying radio characteristics. OFDMsym-Net is designed by two kinds of processing modules, which manipulate one-dimensional asymmetric convolution filters to extract the intracorrelation within an OFDM symbol and the intercorrelation between different symbols. Moreover, a sophisticated structure with addition and concatenation layers is developed inside every module to improve learning efficiency. Based on simulation results achieved on a synthetic dataset of OFDM signals, our proposed method shows the classification robustness under various channel impairments. It reveals to have an overall accuracy of 95.41% at 10-dB signal-to-noise ratio, being superior to several state-of-the-art approaches.

Correction to: The modulation classification methods in PPM–VLC systems

Correction to: The modulation classification methods in PPM–VLC systems

The modulation classification methods in PPM–VLC systems

Intelligent methods have been applied to many fields for a long time. Recently, Visible Light Communication (VLC) systems widely include learning and classification models to improve their performances. The classification of L-Pulse Position Modulation (L-PPM) formats is crucial for VLC systems since the modulation order L is very effective for providing energy efficiency and increasing the transmission capacity. In this paper, therefore, it is reported for the first time, the classification of L-PPM schemes in VLC systems by using Decision Tree, K-nearest neighbor (KNN), Support Vector Machine, and a Direct Decision-based Linear Model technique. A novel feature extraction model is derived to be able to classify the type of L-PPM modulation schemes. A comparison has been given to observe the performance of classification schemes by taking into account the level of Signal to Noise Ratio and the transmission distance between receiver and transmitter. The 2-PPM scheme has the highest success rate against other schemes in terms of performance metrics at the overall distance. Additionally, the KNN method gives the best accuracy performance at the distance of 2.32 m and more, while it has been obtained the best accuracy of 97.85% by the Decision Tree Model at the distance of 2.20 m.

A Lightweight Decentralized-Learning-Based Automatic Modulation Classification Method for Resource-Constrained Edge Devices

Due to the computing capability and memory limitations, it is difficult to apply the traditional deep learning (DL) models to the edge devices (EDs) for realizing lightweight automatic modulation classification (AMC). Recently, many works attempt to use different ways to realize lightweight AMC methods for EDs. However, the lightweight seems to be a contradiction with the classification performance in these lightweight networks. In this article, we propose an efficient lightweight decentralized-learning-based AMC (DecentAMC) method using spatiotemporal hybrid deep neural network based on multichannels and multifunction blocks (MCMBNN). Specifically, the lightweight network is designed from the perspectives of comprehensive consideration of lightweight and classification performance, which is composed of three parts to extract different features for realizing high classification performance and they are phase estimator and transformer (PET) block, spatial feature extraction block and temporal feature extraction & Softmax block. In addition, we use a multichannel input to extract complementary features of different channels for a better classification performance. The proposed DecentAMC method is an efficient training method, which is achieved by the cooperation in which multiple EDs update and upload the model weight to a central device (CD) for model aggregation to avoid the data privacy disclosure and reduce the computing power and storage pressure of CD. Experimental results show that the proposed MCMBNN can obtain an improved classification accuracy while reducing model complexity with the contributions of three blocks. Moreover, the proposed DecentAMC method can be deployed on EDs efficiently. Thus, the method has the advantages of avoiding data leakage on EDs and relieving the computing pressure of CD with relatively lower communication overhead. The simulation code and datasets are shared on GitHub.

Deep Learning-Based Modulation Recognition for Low Signal-to-Noise Ratio Environments

Automatic modulation classification (AMC), which plays a significant role in wireless communication, can recognize the modulation type of the received signal without large amounts of transmitted data and parameter information. Supported by deep learning, which is a powerful tool for functional expression and feature extraction, the development of AMC can be greatly promoted. In this paper, we propose a deep learning-based modulation classification method with 2D time-frequency signal representation. In our proposed method, signals which have been received are first analyzed by time-frequency based on continuous wavelet transform (CWT). Then, CWT images of received signals are obtained and input to the deep learning model for classifying. We create a new CWT image dataset including 12 modulation types of signals under various signal-to-noise ratio (SNR) environment to verify the effectiveness of the proposed method. The experimental results demonstrate that our proposed method can reach to a high classification accuracy over the SNR of −11 dB.

Accurate modulation classification under impaired wireless channels via shallow convolutional neural networks

Classifying the modulation type of radio signals plays an important role in current and future wireless communication systems. We present a modulation classification method based on convolutional neural networks that reaches high accuracy in face of various channel characteristics and signal conditions without requiring the network to have a very large depth. Experiment results show that the proposed method reaches accurate classification under different system impairment settings that include sampling rate offset, carrier frequency offset, multi-path fading, and additive white Gaussian noise. For instance, compared to a state-of-the-art method, accuracy is improved up to 25% in classifying difficult modulation types under system impairments. Source code of the proposed method is available online.

Blind Modulation Classification Under Uncertain Noise Conditions: A Multitask Learning Approach

Blind modulation classification is widely used in various military and civilian applications. Characterized by its exemption from likelihood calculation and the ability of using well-trained models, feature-based (FB) classifiers are popular with their high computational efficiency. In recent years, Deep Neural Networks based methods have shown significant improvement in the classification accuracy. However, the performance of FB methods decreases rapidly under low signal-to-noise ratios. To overcome this shortcoming, this letter proposes a joint denoising and modulation classification method based on Multitask Learning, where a denoising network and a classification network are simultaneoulsy trained in an end-to-end manner. In addition, the focal loss function is adopted to highlight the importance of hard-to-classify samples during training. Our numerical experiments show that the proposed method can effectively improve the classification accuracy, and outperform state-of-the-art methods. For example, under the 0dB SNR condition, the performance of our proposed Multitask CNN method is 20% higher than that of the traditional CNN method.