Modulation Recognition Of Signals Research Articles

Improving Modulation Recognition Using Time Series Data Augmentation via a Spatiotemporal Multi-Channel Framework

Automatic modulation recognition technology with deep learning has a broad prospective owing to big data and computing power. However, the accuracy of modulation recognition largely depends on the massive volume of data and the applicability of the model. Here, to eliminate the difficulties of manual feature extraction, a low accuracy, and a small sample dataset, we propose an effective recognition method that combines time series data augmentation with a spatiotemporal multi-channel learning framework. Compared with other advanced network models, the results showed that the method gave a positive index in the order of 93.5% for ten modulation signal types, which was increased by at least 15%. Especially for QAM16 and QAM64 signals, the average recognition accuracy was improved by nearly 50% at SNRs as low as −2 dB, showing a significant recognition performance. The proposed method provides an attractive method for signal modulation recognition in wireless or wired communication fields.

Multi-label Deep Forest: Towards Automatic Modulation Recognition of Compound Wireless Signals at Low-SNR Environment

Multi-label Deep Forest: Towards Automatic Modulation Recognition of Compound Wireless Signals at Low-SNR Environment

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.

Intra-pulse modulation recognition of radar signals based on multi-feature random matching fusion network

Intra-pulse modulation recognition of radar signals based on multi-feature random matching fusion network

Modulation Recognition of Digital Signal Using Graph Feature and Improved K-Means

Automatic modulation recognition (AMR) has been wildly used in both military and civilian fields. Since the recognition of digital signal at low signal-to-noise (SNR) ratio is difficult and complex, in this paper, a clustering analysis algorithm is proposed for its recognition. Firstly, the digital signal constellation is extracted from the received waveform (digital signal + noise) by using the orthogonal decomposition and then, it is denoised by using an algorithm referred to as auto density-based spatial clustering technique in noise (ADBSCAN). The combination of density peak clustering (DPC) algorithm and improved K-means clustering is used to extract the constellation’s graph features, the eigenvalues are input into cascade support vector machine (SVM) multi-classifiers, and the signal modulation mode is obtained. BPSK, QPSK, 8PSK, 16QAM and 32QAM five kinds of digital signals are trained and classified by our proposed method. Compared with the classical machine learning algorithm, the proposed algorithm has higher recognition accuracy at low SNR (less than 4dB), which confirmed that the proposed modulation recognition method is effective in noncooperation communication systems.

Multisignal Modulation Classification Using Sliding Window Detection and Complex Convolutional Network in Frequency Domain

With the development of the Internet of Things (IoT), the IoT devices are increasing day by day, resulting in increasingly scarce spectrum resources. At the same time, many IoT devices are facing inevitable malicious attacks. The cognitive Radio-enabled IoT (CR-IoT) is proposed as an effective method for spectrum resource allocation and risk monitoring in the IoT. The signal detection and modulation recognition are the key technologies for CR-IoT, addressing the problem of multisignal detection and automatic modulation classification (AMC) is one of the prerequisites for realizing secure dynamic spectrum access. Based on sliding window and deep learning (DL), this study proposes a multisignal frequency domain detection and recognition method. The frequency spectrum of the time-domain overlapping signal is obtained through the fast Fourier transform (FFT), and the frequency spectrum is segmented based on the signal energy detection method. Finally a complex convolutional neural network (CNN) is constructed for the identification of signal spectrum information. The proposed method can recognize 264 time-domain aliasing and frequency-closed signals with an accuracy of 97.3% under the influence of −2 dB corresponding to the noise of the calibration signal. In addition, the proposed method eliminates the influence of bandwidth, which can effectively detect and recognize the signal types of each component in the frequency band. This method has wide applicability and provides an effective scheme for the IoT cognitive technology.

Radio Signal Modulation Recognition Method Based on Deep Learning Model Pruning

With the development of communication technology and the increasingly complex wireless communication channel environment, the requirements for radio modulation recognition are also increased to avoid interference and improve the efficiency of radio spectrum resources. To achieve high recognition accuracy with less computational overload, we propose a radio signal modulation recognition method based on deep learning, which uses a pruning strategy to reduce computational overload, based on the original model, CNN-LSTM-DNN (CLDNN), and the double-layer long short-term memory (LSTM). Effect factors are analyzed in terms of recognition accuracy by adjusting the parameters of each network layer. The results of the experiments show that the model not only has a greater precision improvement than some existing models, but also reduces the computational resources necessary.

A Radio Signal Recognition Approach Based on Complex-Valued CNN and Self-Attention Mechanism

Automatic modulation recognition (AMR) of radio signals is becoming increasingly important due to its key role in wireless communication system management, monitoring, and control. In this paper, we propose an end-to-end AMR framework based on deep learning (DL), named CCNN-Atten. First, the complex-valued convolutional neural network (CCNN) extracts the features of the radio signal, and the feature calibration (FC) module selectively enhances the important features and suppresses irrelevant features. Then, a temporal context capture (TCC) module uses a modified multi-head attention mechanism (MHA) to capture the temporal dependence in the extracted features. The improved MHA mechanism, as a kind of self-attention mechanism, deploys causal convolutions to encode the temporal information of the input features and captures their local temporal relationship. In addition, due to the limited hardware resources in the real scenarios, we also considered a good compromise between recognition accuracy and computational complexity. Experiments were performed with the RadioML2016.10B, RadioML2016.10A, and RadioML2018.01A datasets, demonstrating the ability of the proposed CCNN-Atten to learn more robust features than other state-of-the-art (SOTA) techniques with 1 ~ 10% higher accuracy and a lower computational complexity than the SOTA models. The experimental results also show that CCNN-Atten achieves outstanding performance in dealing with radio signals with a lower sampling rate and small signal observation window.

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.

Modulation Recognition of Communication Signals Based on Multimodal Feature Fusion.

Modulation recognition is the indispensable part of signal interception analysis, which has always been the research hotspot in the field of radio communication. With the increasing complexity of the electromagnetic spectrum environment, interference in signal propagation becomes more and more serious. This paper proposes a modulation recognition scheme based on multimodal feature fusion, which attempts to improve the performance of modulation recognition under different channels. Firstly, different time- and frequency-domain features are extracted as the network input in the signal preprocessing stage. The residual shrinkage building unit with channel-wise thresholds (RSBU-CW) was used to construct deep convolutional neural networks to extract spatial features, which interact with time features extracted by LSTM in pairs to increase the diversity of the features. Finally, the PNN model was adapted to make the features extracted from the network cross-fused to enhance the complementarity between features. The simulation results indicated that the proposed scheme has better recognition performance than the existing feature fusion schemes, and it can also achieve good recognition performance in multipath fading channels. The test results of the public dataset, RadioML2018.01A, showed that recognition accuracy exceeds 95% when the signal-to-noise ratio (SNR) reaches 8dB.

Open Set Recognition of Communication Signal Modulation Based on Deep Learning

This letter proposes a deep learning-based method for wireless communication signal modulation recognition. Modified generalized end-to-end (GE2E) loss is used to train the designed neural network, which can increase the similarity of the feature vectors of the same modulation type and reduce that of different types. The similarities between training samples and centroid vectors are calculated to set the adaptive thresholds for each known modulation type. The experimental results show that the proposed method can reject unknown signals while remaining the recognition quality of known signals, which has higher efficiency and lower complexity than other open set recognition (OSR) algorithms in modulation recognition.

Research on Modulation Signal Recognition Based on CLDNN Network

Modulated signal recognition and classification occupies an important position in electronic information warfare, intelligent wireless communication, and fast modulation and demodulation. To address the shortcomings of existing recognition methods, such as high manual involvement, few recognition types, and a low recognition rate under a low signal-to-noise ratio, we propose an attention mechanism short-link convolution long short-term memory deep neural networks (ASCLDNN) recognition model. The network is optimized for modulated signal recognition and incorporates an attention mechanism to achieve higher accuracy by adding weights to important signals. The experimental results show that ASCLDNN can recognize 11 signal modulations with high accuracy at a low signal-to-noise ratio and no confusion for specific signals.

Mobile Signal Modulation Recognition Based on Multimodal Feature Fusion

Mobile Signal Modulation Recognition Based on Multimodal Feature Fusion

Few-Shot Learning for Fine-Grained Signal Modulation Recognition Based on Foreground Segmentation

Automotive radars have faced to various jamming signals with different types and parameters. One counter measure is to identify the fine-grained modulation types of these jamming signals, which can provide supplementary information for intercepting. Most existing signal modulation recognition methods attempt to establish a machine-learning mechanism by training with a large number of annotated samples, which is hardly applicable in real-world electronic reconnaissance scenarios where only a few samples can be intercepted in advance. Few-Shot Learning aims to learn from base classes with many samples and infuse this knowledge to support classes with only a few samples, thus realizing model generalization. In this paper, inspired by the fact that the energy distribution of the signal of interest is always concentrated in time-frequency images, a novel few-shot learning framework based on foreground segmentation is proposed, which benefits from its powerful filtering to remove the noise and clutter from background. The experimental results show that the proposed method can achieve excellent performance for fine-grained signal modulation recognition even with only one support sample and is robust under different signal-to-noise-ratio conditions.

Automatic modulation classification: Cauchy-Score-function-based cyclic correlation spectrum and FC-MLP under mixed noise and fading channels

Automatic modulation classification (AMC), also termed blind signal modulation recognition, plays a critical role in various civilian and military applications. Although existing approaches have made substantial contributions in this area, most fail to simultaneously consider mixed noise, different channel modes and low-power scenarios. In this paper, a novel cyclic correlation spectrum based on the Cauchy Score function is first proposed as the robust pattern for the outliers in the Gaussian and non-Gaussian mixed noise. Besides, its properties are studied and proved for further potential applications in wireless signal processing. Furthermore, a modified lightweight neural network, termed feature-coupling multi-layer perceptron (FC-MLP), is designed to avoid the potential risk of overfitting and meet the needs when applied in low-power chips. In addition, a novel AMC method is proposed under a dual-layer decision tree framework, and different patterns are adopted in different layers to make full use of the robustness of the proposed pattern and the information embedded in the time sequence. Meanwhile, different classifiers are also chosen according to the characteristics of the patterns. In the simulations, state-of-the-art machine learning techniques, neural networks and patterns are employed as comparison candidates to verify the superiority of the proposed pattern, the lightweight classifier and the novel AMC approach in the scenario of Rayleigh channel or Rician channel with additive mixed noise.

Towards recurrent neural network with multi-path feature fusion for signal modulation recognition

Towards recurrent neural network with multi-path feature fusion for signal modulation recognition

Pattern Recognition of Modulation Signal Classification Using Deep Neural Networks

In recent times, pattern recognition of communication modulation signals has gained significant attention in several application areas such as military, civilian field, etc. It becomes essential to design a safe and robust feature extraction (FE) approach to efficiently identify the various signal modulation types in a complex platform. Several works have derived new techniques to extract the feature parameters namely instant features, fractal features, and so on. In addition, machine learning (ML) and deep learning (DL) approaches can be commonly employed for modulation signal classification. In this view, this paper designs pattern recognition of communication signal modulation using fractal features with deep neural networks (CSM-FFDNN). The goal of the CSM-FFDNN model is to classify the different types of digitally modulated signals. The proposed CSM-FFDNN model involves two major processes namely FE and classification. The proposed model uses Sevcik Fractal Dimension (SFD) technique to extract the fractal features from the digital modulated signals. Besides, the extracted features are fed into the DNN model for modulation signal classification. To improve the classification performance of the DNN model, a barnacles mating optimizer (BMO) is used for the hyperparameter tuning of the DNN model in such a way that the DNN performance can be raised. A wide range of simulations takes place to highlight the enhanced performance of the CSM-FFDNN model. The experimental outcomes pointed out the superior recognition rate of the CSM-FFDNN model over the recent state of art methods interms of different evaluation parameters.

Fully Complex Deep Learning Classifiers for Signal Modulation Recognition in Non-Cooperative Environment

Deep learning (DL) classifiers have significantly outperformed traditional likelihood-based or feature-based classifiers for signal modulation recognition in non-cooperative environments. However, despite these recent improvements, the conventional DL classifiers still have an unintended problem in handling the received signal in which the in-phase and quadrature components are separated. Even though the two components seem to be individually uncorrelated to each other, they are definitely the theoretical real and imaginary parts of a signal sample in the complex domain. Thus, it may be helpful for a classifier to regard and treat the modulated signal as a complex data array representing beneficial mutual information between the two real data arrays. In this paper, we propose two types of fully complex convolutional neural network (CNN) and residual neural network (ResNet) classifiers that deal with the complex data instead of the two separated data. First, we organize and define the core complex operations for implementing the complex DL classifiers. Next, the architectures of the proposed classifiers are realized by applying the structural optimizations and the regularization technique. Then, the various aspects of the classifiers’ performance are analyzed and explained for providing comprehensive and deep understandings: (a) the effectiveness of the complex signal handling, (b) the in-depth evaluation of classification accuracy, (c) the impact of the data size on performance, and (d) the computational complexity. The experimental results show that the proposed classifiers can provide the faster learning speed, the higher optimization, and the better generalization with sacrificing acceptable learning and classification costs. Especially, our approaches remarkably improve the performance on the mutually correlated modulation types (i.e., the phase-related modulations: PSK, APSK, and QAM) even in the less-scale datasets.

Radio signal modulation recognition algorithm based on convolutional neural network

Radio signal modulation recognition algorithm based on convolutional neural network

Radio signal modulation recognition algorithm based on convolutional neural network

In modern communications, it is often necessary to correctly identify the modulation of a signal with almost zero a priori knowledge as a non-collaborator for subsequent work such as demodulation and analysis. This paper is based on the convolutional neural network algorithm to study the modulation recognition algorithm of radio signals. Firstly, this paper studies the format, parameters and channel model of the radio signal dataset, and constructs a dataset in the format of I/Q data. Then, this article uses Convolutional Neural Network (CNN) algorithm to identify and classify the signal. Finally, to address the defects and training time problems in the classical convolutional neural network recognition method, this paper proposes an improved convolutional neural network named X-CNN. The X-CNN is optimised in terms of connection structure, enhancement mechanism and pooling method to improve the recognition rate and the overall fitting ability of the network.