Automatic Modulation Recognition Method Research Articles

Automatic Modulation Recognition Method Based on Phase Transformation and Deep Residual Shrinkage Network

Automatic Modulation Recognition Method Based on Phase Transformation and Deep Residual Shrinkage Network

Automatic modulation recognition of radiation source signals based on two-dimensional data matrix and improved residual neural network

Automatic modulation recognition (AMR) of radiation source signals is a research focus in the field of cognitive radio. However, the AMR of radiation source signals at low SNRs still faces a great challenge. Therefore, the AMR method of radiation source signals based on two-dimensional data matrix and improved residual neural network is proposed in this paper. First, the time series of the radiation source signals are reconstructed into two-dimensional data matrix, which greatly simplifies the signal preprocessing process. Second, the depthwise convolution and large-size convolutional kernels based residual neural network (DLRNet) is proposed to improve the feature extraction capability of the AMR model. Finally, the model performs feature extraction and classification on the two-dimensional data matrix to obtain the recognition vector that represents the signal modulation type. Theoretical analysis and simulation results show that the AMR method based on two-dimensional data matrix and improved residual network can significantly improve the accuracy of the AMR method. The recognition accuracy of the proposed method maintains a high level greater than 90% even at −14 dB SNR.

Complex-Valued Convolution and Frequency Global Filter for Automatic Modulation Recognition

Automatic modulation recognition (AMR) plays an important role in cognitive radio and dynamic spectrum access, which has been widely applied in military and civilian applications. Due to the breakthroughs in deep learning (DL), DL-based AMR methods are becoming extremely popular. However, most existing DL-based methods are unable to deal with complex format data, and learning the mappings from the time series or its transformed representation to the true modulation type directly is difficult. To address these difficulties, this letter presents a complex-valued convolution and frequency global filter unit (CGFU), and proposes a hybrid neural network, namely CGF-HNN, which can efficiently exploit features from different domains. We evaluate the recognition performance of the proposed model on two well-known datasets, i.e., RML2016.10a and RML2018.01a. Simulation results show that the proposed model outperforms the existing state-of-the-art models.

Deep Learning-Aided Modulation Recognition for Non-Orthogonal Signals.

Automatic Modulation Recognition (AMR) can obtain the modulation mode of the received signal for subsequent processing without the assistance of the transmitter. Although the existing AMR methods have been mature for the orthogonal signals, these methods face challenges when deployed in non-orthogonal transmission systems due to the superimposed signals. In this paper, we aim to develop efficient AMR methods for both downlink and uplink non-orthogonal transmission signals using deep learning-based data-driven classification methodology. Specifically, for downlink non-orthogonal signals, we propose a Bi-directional Long Short-Term Memory (BiLSTM)-based AMR method that exploits long-term data dependence to automatically learn irregular signal constellation shapes. Transfer learning is further incorporated to improve recognition accuracy and robustness under varying transmission conditions. For uplink non-orthogonal signals, the combinatorial number of classification types explodes exponentially with the number of signal layers, which becomes the major obstacle to AMR. We develop a spatio-temporal fusion network based on the attention mechanism to efficiently extract spatio-temporal features, and network details are optimized according to the superposition characteristics of non-orthogonal signals. Experiments show that the proposed deep learning-based methods outperform their conventional counterparts in both downlink and uplink non-orthogonal systems. In a typical uplink scenario with three non-orthogonal signal layers, the recognition accuracy can approach 96.6% in the Gaussian channel, which is 19% higher than the vanilla Convolution Neural Network.

Modulation Classifier: A Few-Shot Learning Semi-Supervised Method Based on Multimodal Information and Domain Adversarial Network

This letter addresses the issue of underfitting or failure of deep learning models caused by insufficient training samples. Unlike previous supervised methods, a new few-shot learning semi-supervised automatic modulation recognition method based on multimodal information and domain adversarial network is proposed herein. The fusion input of multimodal information realizes the joint utilization of modulated signal modal features in the time and frequency domains. Domain adversarial training mines the potential knowledge information of a large number of unlabeled target domain data and introduces the convolutional block attention module (CBAM) to enhance the ability of the network to represent the key features of data. Numerical results validate the high-average classification accuracy of the proposed scheme compared to that of state-of-the-art schemes using fewer samples, particularly in high-order modulation classification. Alternative network structures are compared to confirm the applicability of multimodal information, domain adversarial training, and CBAM.

A Survey of Applications of Deep Learning in Radio Signal Modulation Recognition

With the continuous development of communication technology, the wireless communication environment becomes more and more complex with various intentional and unintentional signals. Radio signals are modulated in different ways. The traditional radio modulation recognition technology cannot recognize the modulation modes accurately. Consequently, the communication system has embraced Deep Learning (DL) models as they can automatically recognize the modulation modes and have better accuracy. This paper systematically summarizes the related contents of radio Automatic Modulation Recognition (AMR) based on DL over the last seven years. First, we summarize the current research status of modulation recognition and the necessity of AMR research based on DL. Then, we review current radio AMR methods based on DL. In addition, we also propose a network model of AMR based on Convolutional Neural Network (CNN) and prove its effectiveness. Finally, we highlight existing challenges and research directions of radio AMR based on DL.

NAS-AMR: Neural Architecture Search-Based Automatic Modulation Recognition for Integrated Sensing and Communication Systems

Automatic modulation recognition (AMR) technique plays an important role in the identification of modulation types of unknown signal of integrated sensing and communication (ISAC) systems. Deep neural network (DNN) based AMR is considered as a promising method. Considering the complexity of a typical ISAC system, devising the DNN manually with limited knowledge of its various classifications will be very tasking. This paper proposes a neural architecture search (NAS) based AMR method to automatically adjust the structure and parameters of DNN and find the optimal structure under the combination of training and constraints. The proposed NAS-AMR method will improve the flexibility of model search and overcome the difficulty of gradient propagation caused by the non-differentiable quantization function in the process of back propagation. Simulation results are provided to confirm that the proposed NAS-AMR method can identify the modulation types in various ISAC electromagnetic environments. Furthermore, compared with other fixed structure networks, our proposed method delivers the highest recognition accuracy, under the condition of low parameters and floating-point operations (FLOPs).

A Review of Research on Signal Modulation Recognition Based on Deep Learning

Since the emergence of 5G technology, the wireless communication system has had a huge data throughput, so the joint development of artificial intelligence technology and wireless communication technology is one of the current mainstream development directions. In particular the combination of deep learning technology and communication physical layer technology is the future research hotspot. The purpose of this research paper is to summarize the related algorithms of the combination of Automatic Modulation Recognition (AMR) technology and deep learning technology in the communication physical layer. In order to elicit the advantages of the modulation recognition algorithm based on deep learning, this paper firstly introduces the traditional AMR method, and then summarizes the advantages and disadvantages of the traditional algorithm. Then, the application of the deep learning algorithm in AMR is described, and the identification method based on a typical deep learning network is emphatically described. Afterwards, the existing Deep Learning (DL) modulation identification algorithm in a small sample environment is summarized. Finally, DL modulation is discussed, identifying field challenges, and future research directions.

An Advancing Temporal Convolutional Network for 5G Latency Services via Automatic Modulation Recognition

Automatic modulation recognition (AMR) has received significant attention since its decisive factor for modern non-cooperative communication systems. Meanwhile, the existing works on deep learning technique achieve exceptional accuracy; however, these works dissatisfy real-time requirements for 5G low-latency services. To remedy this flaw, this brief proposes a low-latency AMR method by applying temporal convolutional network (TCN). Furthermore, the principal component analysis (PCA)-based TCN and uniform subsampling-based TCN methods are leveraged to further alleviate the computation complexity and render real-time TCN viable. Experimental results demonstrate that the proposed method can achieve lower complexity and superior recognition accuracy than existing works and pave the way for 5G low-latency services.

OAE-EEKNN: An Accurate and Efficient Automatic Modulation Recognition Method for Underwater Acoustic Signals

The automatic modulation recognition (AMR) enables the receiver to automatically recognize the modulation type of the received signal for achieving correct demodulation. However, the noise and interference in the underwater acoustic channel greatly influence the features extracted from the signals. In this letter, we proposed the optimizing autoencoder (OAE) and the evaluation enhanced K-nearest neighbors (EEKNN) algorithms. The combination of OAE and EEKNN not only improves the feature discrimination but also avoids the misjudgment caused by abnormal samples and realizes accurate and efficient AMR. The experimental results of the data measured in the South China sea show that the proposed method successfully recognizes eight modulation types. The recognition accuracy is up to 99.25%, and the recognition time is only 3.48 ms.

Automatic Modulation Recognition Method Based on Hybrid Model of Convolutional Neural Networks and Gated Recurrent Units

Automatic Modulation Recognition Method Based on Hybrid Model of Convolutional Neural Networks and Gated Recurrent Units

Domain Adaptation-Based Automatic Modulation Recognition

Deep learning-based Automatic Modulation Recognition (AMR) can improve the recognition rate compared with traditional AMR methods. However, in practical applications, as training samples and real scenario signal samples have different distributions in practical applications, the recognition rate for target domain samples can deteriorate significantly. This paper proposed an unsupervised domain adaptation based AMR method, which can enhance the recognition performance by adopting labeled samples from the source domain and unlabeled samples from the target domain. The proposed method is validated through signal samples generated from the open-sourced Software Defined Radio (SDR) GNU Radio. The training dataset is composed of labeled samples in the source domain and unlabeled samples in the target domain. In the testing dataset, the samples are from the target domain to simulate the real scenario. Through the experiment, the proposed method has a recognition rate increase of about 88% under the CNN network structure and 91% under the ResNet network structure.

Generalized automatic modulation recognition method based on distributed learning in the presence of data mismatch problem

Deep learning-based automatic modulation recognition (DL-AMR) methods are mainly based on centralized learning and decentralized learning. These methods have been developed for many applications in heterogeneous wireless networks (HWNs). However, the centralized DL-AMR methods involve high communications cost and computation cost and the decentralized DL-AMR methods are not robust since these methods are based on the assumption of uniform data distribution. In the practical HWNs, most sub-networks are independent and their heterogeneous datasets often do not match. In order to solve these problems, we propose a generalized AMR (GAMR) method based on distributed learning by considering the data mismatch scenario. First, each sub-network trains its own model by means of initialization model download from fusion center and formative modulated datasets. Second, sub-networks upload model weight of all sub-networks to the fusion center for re-training a generalized model, which will be immediately distributed to sub-networks for updating local model. Repeat these two steps until the global model converges successfully. Finally, simulation results are given to confirm advantages of the proposed GAMR method in different scenarios of HWNs.

Robust Automatic Modulation Recognition Through Joint Contribution of Hand-Crafted and Contextual Features

Automatic modulation recognition (AMR) has become increasingly important in the field of signal processing, especially with the advancements of intelligent communication systems. Deep Learning (DL) technologies have been incorporated into the AMR field and they have shown outstanding performances against conventional AMR methods. The robustness of DL-based AMR methods under varying noise regimes is one of major concerns for the widespread utilization of this technology. Furthermore, most existing works have neglected the contributions of hand-crafted features (HCFs) in boosting the classification performances of DL-based AMR methods. In order to address the aforementioned technical challenges, a novel and robust DL-AMR method is proposed by leveraging the benefits of both contextual features (CFs) and HCFs for a specific range of signal-to-noise ratio (SNR). A novel feature selection algorithm is also proposed to search for the optimal sets of HCFs in order to reduce the dimensions of feature vectors without losing any important and relevant features. Simulation studies are performed to investigate the feasibility of proposed method in classifying 11 types of modulation schemes. Extensive performance analyses revealed the superiority of proposed method over baseline method in terms of the classification performance as well as the excellent capability of proposed feature selection algorithm in determining an optimal subset of HCFs.

Machine Learning Based Automatic Modulation Recognition for Wireless Communications: A Comprehensive Survey

The rapid development of information and wireless communication technologies together with the large increase in the number of end-users have made the radio spectrum more crowded than ever. Besides, providing a stable and reliable service is challenging, as electromagnetic environments are evolving and becoming more sophisticated. Accordingly, there is an urgent need for more reliable and intelligent communication systems that can improve the spectrum efficiency and the quality of service to provide agile management of network resources, so as to better meet the needs of future wireless users. Specifically, Automatic Modulation Recognition (AMR) plays an essential role in most intelligent communication systems especially with the emergence of Software Defined Radio (SDR). AMR is an indispensable task while performing spectrum sensing in Cognitive Radio (CR). Thanks to the significant advancements in Deep Learning (DL) applications, new and powerful tools have been provided which can tackle problems in this space. Thus, today, integrating DL models into AMR has gained the attention of many researchers. This work aims to provide a comprehensive state-of-the-art review of the most recent Machine Learning (ML) based AMR methods for Single-Input Single-Output (SISO) and Multiple-Input Multiple-Output (MIMO) systems. Furthermore, the architecture of each model will be identified along with a detailed comparison in terms of specifications and performance. Finally, an outline of the open problems, challenges, and potential research directions is provided along with discussion and conclusion.

Deep Learning-Based Automatic Modulation Recognition Method in the Presence of Phase Offset

Automatic modulation recognition (AMR) plays an important role in various communications systems. It has the ability of adaptive modulation and can adapt to various complex environments. Automatic modulation recognition is also widely used in orthogonal frequency division multiplexing (OFDM) systems. However, because the recognition accuracy of traditional methods to extract the features of OFDM signals is very limited. In order to solve these problems, many deep learning based AMR methods have been proposed to improve the recognition performance. However, most of these AMR methods neglect the harmful effect by carrier phase offset (PO) which often appears in real communications systems. Hence it is required to consider the PO effect for designing the OFDM system. Unlike conventional methods, we propose a convolutional neural network (CNN) based AMR method for considering PO in the OFDM system. The proposed method is used to eliminate the PO to achieve the high classification accuracy. Experiment results are provided to confirm the proposed method when comparing to conventional methods.

An Automatic Modulation Recognition Method with Low Parameter Estimation Dependence Based on Spatial Transformer Networks

Recently, automatic modulation recognition has been an important research topic in wireless communication. Due to the application of deep learning, it is prospective of using convolution neural networks on raw in-phase and quadrature signals in developing automatic modulation recognition methods. However, the errors introduced during signal reception and processing will greatly deteriorate the classification performance, which affects the practical application of such methods. Therefore, we first analyze and quantify the errors introduced by signal detection and isolation in noncooperative communication through a baseline convolution neural network. In response to these errors, we then design a signal spatial transformer module based on the attention model to eliminate errors by a priori learning of signal structure. By cascading a signal spatial transformer module in front of the baseline classification network, we propose a method that can adaptively resample the signal capture to adjust time drift, symbol rate, and clock recovery. Besides, it can also automatically add a perturbation on the signal carrier to correct frequency offset. By applying this improved model to automatic modulation recognition, we obtain a significant improvement in classification performance compared with several existing methods. Our method significantly improves the prospect of the application of automatic modulation recognition based on deep learning under nonideal synchronization.

Blind Channel Identification Aided Generalized Automatic Modulation Recognition Based on Deep Learning

Automatic modulation recognition (AMR) plays an important role in cognitive radio (CR), which relies on AMR responding to changes in the surrounding environment and then adjust strategies simultaneously. Deep learning based reliable AMR method have been developed in recent years. However, all of their AMR training models are considered in a specialized channel rather than generalized channel. Hence, these AMR methods are hard to be applied in general scenarios. In this paper, we propose a blind channel identification (BCI) aided generalized AMR (GenAMR) method based on deep learning which is conducted by two independent convolutional neural networks (CNNs). The first CNN is trained on in-phase and quadrature (IQ) sampling signals, which is utilized to distinguish channel categories like BCI behaviors. The second CNN is trained by line of sight (LOS) model and non-line of sight (NLOS) model, respectively. Simulation results confirm that our proposed generalized AMR method is significantly better than conventional method.

A New Method of Cognitive Signal Recognition Based on Hybrid Information Entropy and D-S Evidence Theory

The automatic modulation recognition of communication signal has been widely used in many fields. However, it is very difficult to recognize the modulation in low SNR. Based on information entropy features and Dempster-Shafer evidence theory, a novel automatic modulation recognition methods is proposed in this paper. Firstly, Renyi entropy and singular entropy is used to obtain the modulation feature. Secondly, based on the normal test theory, a novel basic probability assignment function(BPAF) is presented. Finally, Dempster-Shafer evidence theory is used as a classifier. Experiment results indicate that the new approach can obtain a higher recognition result in low SNR.

Modulation recognition in the 868 MHz band using classification trees and random forests

Automatic modulation recognition (AMR) enables the detection of different data-transmission formats sharing the same frequency band. One such band is the European 868MHz band that is dedicated for short-range devices. Recently, an AMR method that applies a feature-based tree has been proposed for this band. In this paper, we present alternative feature-based classifiers that enable a more accurate AMR. In particular, we propose the use of classification tree and random forest classifiers, and we devise an extended set of features for the modulation classification problem at hand. Through simulation experiments we demonstrate a significant improvement in recognition success rate for typical transmission types in the 868MHz band.