Digital Modulation Recognition Research Articles

Dendrogram-based Heterogeneous Learners for Automatic Modulation Classification in DSTBC-OFDM Systems

Software-Defined Radios (SDRs) have a crucial role in dynamic spectrum sensing and sharing as they can quickly adapt to changes in their environment. Digital Modulation Recognition (DMR) enables this process by giving SDR the ability to recognize the received signal modulation format to make intelligent and autonomous decisions to switch on the optimal modulation scheme. However, with the deployment of the Distributed Space-Time Block Coding Orthogonal Frequency Division Multiplexing (DSTBC-OFDM) signals that support MIMO systems, that provides spatio-temporel multiplexing, new and challenging signal identification problems have emerged.This paper proposes a robust DMR method for relaying Space-Time Block Coding (STBC) Orthogonal Frequency Division Multiplexing (OFDM) systems. The proposed classifier is built using heterogeneous base classifiers namely, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest (RFC), based on a Dendrogram or decision tree structure.The Dendrogram-based Heterogeneous Learners (DHL) classifier were obtained via Ascendant Hierarchical Clustering (AHC) and by employing Higher-Order Moment (HOMs) and Higher-Order Cumulants (HOCs) as features. Moreover, we propose a grid search-based hyperparameter tuning for DHL to produce the best structure and optimize the performance.

Intelligent Modulation Recognition of Communication Signal for Next-Generation 6G Networks

In recent years, the need for a fast, efficient and a reliable wireless network has increased dramatically. Numerous 5G networks have already been tested while a few are in the early stages of deployment. In non-cooperative communication scenarios, the recognition of digital signal modulations assists people in identifying the communication targets and ensures an effective management over them. The recent advancements in both Machine Learning (ML) and Deep Learning (DL) models demand the development of effective modulation recognition models with self-learning capability. In this background, the current research article designs a Deep Learning enabled Intelligent Modulation Recognition of Communication Signal (DLIMR-CS) technique for next-generation networks. The aim of the proposed DLIMR-CS technique is to classify different kinds of digitally-modulated signals. In addition, the fractal feature extraction process is applied with the help of the Sevcik Fractal Dimension (SFD) approach. Then, the extracted features are fed into the Deep Variational Autoencoder (DVAE) model for the classification of the modulated signals. In order to improve the classification performance ofthe DVAE model, the Tunicate Swarm Algorithm (TSA) is used to fine-tune the hyperparameters involved in DVAE model. A wide range of simulations was conducted to establish the enhanced performance of the proposed DLIMR-CS model. The experimental outcomes confirmed the superior recognition rate of the DLIMR-CS model over recent state-of-the-art methods under different evaluation parameters.

Research on Distributed Multisensor Spectrum Semantic Sensing and Recognition in Internet of Things Environment

The development of 5G technology has brought about a new era of Internet of Thing (IoT), and at the same time, electromagnetic spectrum monitoring and sensing have also ushered in huge challenges. Digital modulation recognition technology is an important content of electromagnetic spectrum sensing. In the increasingly complex wireless communication transmission environment, especially in noncooperative communication, it becomes more and more difficult to receive target signals and accurately extract effective semantic information from diverse modulation signals of the electromagnetic spectrum. At this stage, with the rapid development of network information and wireless communication technology, within a prescribed distance, the IoT built by many sensors has attracted wide attention from people in related fields. This paper proposes a distributed collaborative sensing spectrum semantic recognition architecture for communication signals based on feature fusion. Perform wireless communication and transmission between multiple sensors to form a self-organizing network to cooperatively sense signal semantic information, and extract the signal features of each sensor in the distributed network structure. Finally, the extracted sensor features are semantically analyzed and modeled, and the effective features are fused to complete the entire perception and recognition process. Even if the channel environment of a small number of receiving nodes deteriorates in a complex transmission environment, the signal quality features can still be accurately extracted, the classification and recognition effect like or higher than the best channel state performance can be achieved, and the fault tolerance of the system can be effectively improved. It can also enhance the performance of spectral semantic information sensing and recognition in the IoT environment.

Automatic Digital Modulation Recognition Based on Genetic-Algorithm-Optimized Machine Learning Models

Recognition of the modulation scheme is the intermediate step between signal detection and demodulation of the received signal in communication networks. Automatic modulation recognition (AMR) plays a central role in many applications, especially in the military and security sectors. In general, several properties of the received signal are extracted and employed for AMR. Selecting the appropriate features has a significant impact on increasing the efficiency of AMR. In this paper, we implement and compare digital modulation recognition via multi-layer perceptrons (MLP), radial basis function (RBF), adaptive neuro-fuzzy inference system (ANFIS), decision tree (DT), and naïve Bayes (NB) algorithms. In addition, the optimal parameters of each model are obtained by utilizing a genetic algorithm (GA). A series of studies are conducted in this work in order to determine the efficiency of different algorithms in identifying modulated signals with commonly used digital modulations. Numerous computer simulations are performed in the presence of additive white Gaussian noise (AWGN) with a signal-to-noise ratio (SNR) ranging from -10 dB to 30 dB. The simulation results and comparisons with previous studies demonstrate that applying the proposed algorithms along with the selected features leads to a significant enhancement in the accuracy and speed of the automatic determination of the digital modulation types at low SNRs. In addition, the convergence rates of the models are enhanced.

Modulation Recognition of Communication Signal Based on Convolutional Neural Network

In the noncooperation communication scenario, digital signal modulation recognition will help people to identify the communication targets and have better management over them. To solve problems such as high complexity, low accuracy and cumbersome manual extraction of features by traditional machine learning algorithms, a kind of communication signal modulation recognition model based on convolution neural network (CNN) is proposed. In this paper, a convolution neural network combines bidirectional long short-term memory (BiLSTM) with a symmetrical structure to successively extract the frequency domain features and timing features of signals and then assigns importance weights based on the attention mechanism to complete the recognition task. Seven typical digital modulation schemes including 2ASK, 4ASK, 4FSK, BPSK, QPSK, 8PSK and 64QAM are used in the simulation test, and the results show that, compared with the classical machine learning algorithm, the proposed algorithm has higher recognition accuracy at low SNR, which confirmed that the proposed modulation recognition method is effective in noncooperation communication systems.

Deep Learning for Recognition of Digital Modulations: A Detailed Study

The automatic modulation recognition of the received signal is very attractive in both military and civilian applications. In recent years, deep learning techniques have received much attention since their excellent performance in signal, audio, image and video processing. This paper examines the feasibility of using deep learning algorithms on automatic recognition of the received radio signals' modulation schemes. Modulation recognition has been performed on eight digital modulation types with a signal-to-noise ratio (SNR) from -20dB to 20dB. First, a vanilla neural network is used to classify the type of modulation. Then, convolutional neural networks (CNN) and recurrent neural networks are applied for modulation recognition. These neural networks are widely used in image and signal processing applications. This is followed by designing the other architectures, including densely connected neural network (DenseNet), inception network, recurrent neural network (RNN), long-short term memory network (LSTM), and convolutional long short-term memory deep neural network (CLDNN) for modulation recognition problem, and their results are presented. During this investigation, a basic model is initially considered for each architecture, and then the network performance is studied by adjusting its parameters. The simulation results show that the proposed modified CLDNN model can provide an accuracy of 98% in high SNRs.

Digital Modulation Classification Based On Chicken Swarm Optimization and J48 Algorithm

Automatic Modulation Recognition (AMR) has a significant impact in the military as well as civil applications. Recognizing the modulation of the received signal has been considered as an intermediate step between the detection and demodulation of the signal. Which is why, in many military and communication systems, the AMR is considered as part of the system. Presently, due to increasing digital modulations in military and civil applications. Digital modulation recognition is especially important. Usually for the AMR a small number of the received signal features are obtained and utilized. The choice of the suitable feature plays an important part in the increase of AMR efficiency. The presented paper indicates hybrid intelligent system for the recognitions of digital signal types, consisting of 3 major modules: classifier module, feature extraction module and J48 Classifier that was used for the first time in our research in the field of classification of modulated signals and optimization module by Chicken Swarm Optimization (CSO). To get better results of the system suggested optimization the features to discard weak or irrelevant features in the system and keep only strong relevant features Chicken Swarm Optimization. The results of simulation confirm the high accuracy of recognition that is related to the suggested system even at low SNR.

Automatic digital modulation recognition in the presence of alpha-stable noise

In this paper, we introduce a novel modulation recognition method of digital communication signals with alpha-stable noise to solve the problem of low recognition performance in mixed signal-to-noise-ratio environment. The received signals are separated into signals and alpha-stable noise by employing fractional lower order fast independent component analysis (FLO-Fast-ICA), and then the separated signals are interpolated based on the global average local mean decomposition. With these processing, two classification features that are the segmented instantaneous frequency standard deviation and the segmental instantaneous amplitude standard deviation are extracted. Thereafter, a decision tree classifier is developed to recognize the digital communication signals including minimum shift keying (MSK), 2 amplitude shift keying (2ASK), quadrature phase shift keying (QPSK) and 16 quadrature amplitude modulation (16QAM). Simulation results show that the proposed method has a promising performance without prior information.

A Novel Digital Modulation Recognition Algorithm Based on Deep Convolutional Neural Network

The modulation recognition of digital signals under non-cooperative conditions is one of the important research contents here. With the rapid development of artificial intelligence technology, deep learning theory is also increasingly being applied to the field of modulation recognition. In this paper, a novel digital signal modulation recognition algorithm is proposed, which has combined the InceptionResNetV2 network with transfer adaptation, called InceptionResnetV2-TA. Firstly, the received signal is preprocessed and generated the constellation diagram. Then, the constellation diagram is used as the input of the InceptionResNetV2 network to identify different kinds of signals. Transfer adaptation is used for feature extraction and SVM classifier is used to identify the modulation mode of digital signal. The constellation diagram of three typical signals, including Binary Phase Shift Keying(BPSK), Quadrature Phase Shift Keying(QPSK) and 8 Phase Shift Keying(8PSK), was made for the experiments. When the signal-to-noise ratio(SNR) is 4dB, the recognition rates of BPSK, QPSK and 8PSK are respectively 1.0, 0.9966 and 0.9633 obtained by InceptionResnetV2-TA, and at the same time, the recognition rate can be 3% higher than other algorithms. Compared with the traditional modulation recognition algorithms, the experimental results show that the proposed algorithm in this paper has a higher accuracy rate for digital signal modulation recognition at low SNR.

Performance of Feature-Based Techniques for Automatic Digital Modulation Recognition and Classification—A Review

The demand for bandwidth-critical applications has stimulated the research community not only to develop new ways of communication, but also to use the existing spectrum efficiently. Networks have become dynamic and heterogeneous. Receivers have received various signals that can be modulated differently. Automatic modulation classification (AMC) is a key procedure for present and next-generation communication networks, and facilitates the demodulation process at the receiver side. Under the presence of noise from the channel, the transmitter and receiver with its unknown parameters, such as carrier frequency, phase offset, signal power, and timing information, have become cumbersome because detecting the modulation scheme of the received signal is a complicated procedure. Two main methods, namely maximum likelihood functions and the signal statistical feature-based (FB) approach, are used for the automatic classification of modulated signals. In this study, a comprehensive survey of various modulation techniques based on FB approach is conducted. In this research, a number of basic features that are usually used in determining and discriminating modulation types were investigated. The classifier that was used in the discrimination process is studied in detail and compared to other types of classifiers to help the reader determine the limitations associated with the FB approach. Both classifiers and basic features were compared, and their advantages and disadvantages were investigated based on previous researches to determine the best type of classifier and the set of features in relation to each discrimination environment. This work serves as a guide for researchers of AMC to determine the suitable features and algorithms.

An Algorithm for Automatic Recogniton of Digital QAM Modulations

Abstract This paper proposes a new Matlab-developed algorithm for automatic recognition of digital modulations using the constellation of states. Using this technique the automatic distinction between four digital modulation schemes (8-QAM, 16-QAM, 32-QAM and 64-QAM) was made. It has been seen that the efficiency of the algorithm is influenced by the type of modulation, the value of the signal-to-noise ratio and the number of samples. In the case of an AWGN noise channel the simulation results indicated that the value of SNR (signal-to-noise ratio) has a small influence on the recognition rate for lower-order QAM (8-QAM and 16-QAM). The length of the signal may change essentially the recognition rate of this algorithm especially for modulations with a high number of bits per symbol. Consequently, for the 64-QAM modulation in a case of 25dB signal-to-noise ratio the recognition rate is doubled if the sample rate is incresed from 5400 to 80640.

Deep Neural Network Compression Technique Towards Efficient Digital Signal Modulation Recognition in Edge Device

Digital signal modulation recognition is meaningful for military application and civilian application. In the non-cooperation communication scenario, digital signal modulation recognition will help people identify communication target and have better management over them. In order to the classification accuracy, deep learning is widely used to complete this task. However, current papers have not considered the deployment of deep learning in compute capability and storage limited edge equipment. In this paper, we utilize neural network pruning techniques to reduce the convolution parameters and floating point operations per second (FLOPs), which will pave a wide way to deploy signal classification convolution neural network (CNN) in edge equipment. We set the Average Percentage of Zeros (APoZ) criterion for convolution layers. Compared to original CNN, the experiment result shows that light CNN convolution layer could use only 1.5%~5% parameter and 33%~35% time without losing significant accuracy.

Deep Learning in Digital Modulation Recognition Using High Order Cumulants

By considering the different cumulant combinations of the 2FSK, 4FSK, 2PSK, 4PSK, 2ASK, and 4ASK, this paper established new identification parameters to achieve the recognition of those digital modulations. The deep neural network (DNN) was also employed to improve the recognition rate, which was designed to classify the signal based on the distinct feature of each signal type that was extracted with high order cumulants. The extensive simulations demonstrated the exceptional classification performance for new key features based on high order cumulants. The overall success rate of the proposed algorithm was over 99% at the signal to noise ratio (SNR) of -5 dB and 100% at the SNR of -2 dB. The results of the experiments also showed the robustness of the proposed method for a variety of conditions, such as frequency offset, multi-path, and so on.

Automatic Modulation Classification Architectures Based on Cyclostationary Features in Impulsive Environments

Cyclostationary analysis has several applications in communications, e.g., spectral sensing, signal parameter estimation, and modulation classification. Most of them consider the additive white Gaussian noise (AWGN) channel model, although wireless communication systems may also be subject to non-Gaussian interference and impulsive noise. In this context, the communication channel can be better modeled by heavy-tailed distributions, such as the non-Gaussian alpha-stable one. Some applications of the cyclostationary approach based on the spatial sign cyclic correlation function (SSCCF), fractional lower-order cyclic autocorrelation function (FLOCAF), and cyclic correntropy function (CCF) demonstrate that these are promising solutions for the analysis of signals in the presence of impulsive non-Gaussian noise. However, the investigation of functions above applied to digital modulation recognition in impulsive environments, and the comparison among them are topics that did not adequately explore yet. This work demonstrates that SSCCF is a particular case of the FLOCAF. Besides, a detailed analysis of the use of the FLOCAF and CCF is presented to obtain cyclostationary descriptors for the recognition of digital modulations BPSK, QPSK, 8-QAM, 16-QAM, and 32-QAM. Automatic modulation classification (AMC) architectures, based on the functions mentioned above, are also proposed. Besides, another contribution showed is that both the FLOCAF and CCF allow the symbol rate parameter estimation. The performances of AMC architectures were evaluated in the scenario with modulated signals contaminated with additive non-Gaussian alpha-stable noise. The results demonstrate that both architectures can classify signals in different contamination scenarios. However, the architecture based on the CCF is more efficient than the FLOCAF-based one.

Particle Swarm Optimization-Based Deep Neural Network for Digital Modulation Recognition

Modulation recognition is a major task in many wireless communication systems including cognitive radio and signal reconnaissance. The diversification of modulation schemes and the increased complexity of the channel environment put higher requirements on the correct identification of modulated signals. Deep learning (DL) is considered as a potential solution to solve these problems due to the superior big data processing and classification capabilities. This paper proposes an efficient digital modulation recognition method based on deep neural network (DNN) model. Furthermore, we present the particle swarm optimization (PSO) algorithm to optimize the number of hidden layer nodes of the DNN so as to solve the problem that the traditional DNN is trapped in local minimum values and the number of hidden layer nodes needs selecting manually. In this paper, we utilize the proposed PSO-DNN method to learn characteristics extracted from the modulated signal added by additive white Gaussian noise (AWGN) and to train the network, which can improve the performance of recognition under the condition of low signal-to-noise ratio (SNR). The experimental results demonstrate that the recognition rate on this algorithm has improved by 9.4% and 8.8% compared with methods that adopt conventional DNN and support vector machine (SVM) when SNR equals 0 and 1 dB, respectively. Besides, another experiment compared with the genetic algorithm (GA) also proves that our proposed algorithm is more effective in optimizing the DNN. The proposed method is easy to be implemented so that it has a broad development prospect in modulation recognition.

Study on Digital Modulation Recognition in Different Environments

For the using of the different types of modulation, the precondition of receiving and demodulating signal is to determine the type of the modulation, so automatic recognition of modulation signal has significant influence on the analysis of the signals. In this paper, digital modulation recognition is studied respectively in different environment of White Gaussian Noise (WGN), stationary interference and multipath interference. The simulation results show that the recognition success rate is the highest in stationary interference environment and the lowest in multipath interference environment with the same signal to noise ratio (SNR).

Development and Comparative Study of Effects of Training Algorithms on Performance of Artificial Neural Network Based Analog and Digital Automatic Modulation Recognition

This paper proposes two new classifiers that automatically recognise twelve combined analog and digital modulated signals without any a priori knowledge of the modulation schemes and the modulation parameters. The classifiers are developed using pattern recognition approach. Feature keys extracted from the instantaneous amplitude, instantaneous phase and the spectrum symmetry of the simulated signals are used as inputs to the artificial neural network employed in developing the classifiers. The two developed classifiers are trained using scaled conjugate gradient (SCG) and conjugate gradient (CONJGRAD) training algorithms. Sample results of the two classifiers show good success recognition performance with an average overall recognition rate above 99.50% at signal-to-noise ratio (SNR) value from 0 dB and above with the two training algorithms employed and an average overall recognition rate slightly above 99.00% and 96.40% respectively at - 5 dB SNR value for SCG and CONJGRAD training algorithms. The comparative performance evaluation of the two developed classifiers using the two training algorithms shows that the two training algorithms have different effects on both the response rate and efficiency of the two developed artificial neural networks classifiers. In addition, the result of the performance evaluation carried out on the overall success recognition rates between the two developed classifiers in this study using pattern recognition approach with the two training algorithms and one reported classifier in surveyed literature using decision-theoretic approach shows that the classifiers developed in this study perform favourably with regard to accuracy and performance probability as compared to classifier presented in previous study.

Automatic Modulation Recognition in Wireless Multi-carrier Wireless Systems with Cepstral Features

Automatic digital modulation recognition (ADMR) has become an interesting problem in wireless communication systems with various civil and military applications. In this paper, an ADMR algorithm is proposed for both orthogonal frequency division multiplexing and multi-carrier code division multiple access systems using discrete transforms and mel-frequency cepstral coefficients (MFCCs). The proposed algorithm uses one of the discrete cosine transform, discrete sine transform, and discrete wavelet transform with MFCCs to extract the modulated signal coefficients, and uses also either a support vector machine (SVM) or an artificial neural network (ANN) for modulation classification. Simulation results show that the proposed algorithm provides higher recognition rates than those obtained in previous studies, in addition to a superiority of SVM performance compared to ANN performance at low signal-to-noise ratios.

Digital Modulation Recognition in Different Environments

For the using of multi-modulation, the precondition of receiving and demodulating signal is to determine the type of the modulation, so automatic recognition of modulation signal has significant influence on the analysis of the signals. In this paper, digital modulation recognition is studied respectively in different environment of White Gaussian Noise (WGN), stationary interference and multipath interference. The simulation results show that the recognition success rate is the highest in stationary interference environment and the lowest in multipath interference environment with the same signal to noise ratio (SNR).

Digital Signal Modulation with Recognition Algorithm to Identify the Ability to Optimize the Wavelet

Large errors in the digital signal modulation recognition algorithm for the general wavelet transform, we propose a discrete optimization algorithm based on wavelet transform, which is generally improved algorithm based on wavelet transform algorithm, the original algorithm in order to avoid redundancy Yu phenomenon, discrete variable domain transform operation, the wavelet transform and then use the algorithm to improve the amplitude modulation of digital signals, quadrature amplitude modulated signal, a digital frequency-modulated signal and the digital phase modulated signal is identified. Simulation results show that the discrete wavelet transform algorithm optimization wavelet transform algorithm than the digital signal modulation recognition applications to identify errors in the proposed smaller, more accuracy.