Automatic modulation recognition of communication signal based on wavelet transform combined with singular value and NCA-CNN

Automatic modulation recognition is a rapidly evolving area of signal analysis. In recent years, interest from the academic and military research institutes has focused around the research and development of modulation recognition algorithms. Any communication intelligence (COMINT) system comprises three main blocks: receiver front-end, modulation recogniser and output stage. Considerable work has been done in the area of receiver front-ends. The work at the output stage is concerned with information extraction, recording and exploitation and begins with signal demodulation, that requires accurate knowledge about the signal modulation type. There are, however, two main reasons for knowing the current modulation type of a signal; to preserve the signal information content and to decide upon the suitable counter action, such as jamming. Automatic Modulation Recognition of Communications Signals describes in depth this modulation recognition process. Drawing on several years of research, the authors provide a critical review of automatic modulation recognition. This includes techniques for recognising digitally modulated signals. The book also gives comprehensive treatment of using artificial neural networks for recognising modulation types. Automatic Modulation Recognition of Communications Signals is the first comprehensive book on automatic modulation recognition. It is essential reading for researchers and practising engineers in the field. It is also a valuable text for an advanced course on the subject.

Modulation recognition of communication signals is a key task in the fields of spectrum management and cognitive radio. Traditional methods rely on artificial feature design and have limited generalization ability in complex channel environment. In this study, a hybrid deep learning model combining convolutional neural network and long-term and short-term memory network is constructed to automatically extract the spatial local features of signals and capture their temporal dependencies through end-to-end learning. The model uses multi-modal inputs including time-frequency map, in-phase quadrature series and manual statistical features to process different modal data through parallel branch structure and fuse multi-dimensional information to enhance feature discrimination. Experimental results show that the accuracy, recall and AUC of the proposed model are superior to those of support vector machine and ResNet18 models in the recognition tasks of analog modulation, digital modulation and spread spectrum modulation on a dataset containing 120000 signal samples, which verifies the effectiveness of the joint spatial-temporal feature modeling. This study provides a robust solution for intelligent modulation recognition in complex electromagnetic environment.

Modulation recognition of non-cooperation underwater acoustic communication signals using principal component analysis

A new method of Modulation Recognition of communication signals is proposed based on Clustering Validity Indices. These indices provide a good basis for key feature extraction. To distinguish different modulation schemes, a Fuzzy C-mean (FCM) clustering is used to get the membership matrix of different clusters. Then, a clustering validity measure is applied to extract features. To enhance clustering results at low SNR, a neural network with a conjugate gradient learning algorithm is utilized. Fletcher-Reeves learning approach enhances the recognition rate and widely improves the speed and rate of convergence. Simulation results show the validity of proposed approach compared with other approaches using only clustering or using back propagation neural networks. Misclassification rate is less for low order MQAM signals. This algorithm is applicable in high order MQAM signals. In Non-cooperative Communications, the modulated signal parameters are unknown. Some Modulation Recognition algorithms rely on estimating these parameters first, then applying recognition algorithms. Proposed algorithm doesn't need any prior information to achieve modulation recognition.

To improve the recognition rate of the end-to-end modulation recognition method based on deep learning, a modulation recognition method of communication signals based on a cascade network is proposed, which is composed of two networks: Stacked Denoising Auto Encoder (SDAE) network and DCELDNN(Dilated Convolution, ECA Mechanism, Long Short-Term Memory, Deep Neural Networks) network. SDAE network is used to denoise the data, reconstruct the input data through encoding and decoding, and extract deep information from the data. DCELDNN network is constructed based on the CLDNN (Convolutional, Long Short-Term Memory, Fully Connected Deep Neural Networks) network. In the DCELDNN network, dilated convolution is used instead of normal convolution to enlarge the receptive field and extract signal features, the Efficient Channel Attention (ECA) mechanism is introduced to enhance the expression ability of the features, the feature vector information is integrated by a Global Average Pooling (GAP) layer, and signal features are extracted by the DCELDNN network efficiently. Finally, end-to-end classification recognition of communication signals is realized. The test results on the RadioML2018.01a dataset show that the average recognition accuracy of the proposed method reaches 63.1% at SNR of -10 to 15dB, compared with CNN, LSTM, and CLDNN models, the recognition accuracy is improved by 25.8%, 12.3%, and 4.8% respectively at 10dB SNR.

In modern communication, it is often required for a non-cooperative party to identify the modulation mode when no prior knowledge is given to facilitate the subsequent demodulation and analysis. However, the traditional modulation recognition process requires cumbersome and uncertain manual signal-feature extraction, making it inapplicable to the complex communication environment. In order to overcome this limitation, this paper proposes a communication-signal modulation recognition model based on the dense connection network (DenseNet) and residual connection network (ResNet). The convolutional block attention mechanism (CBAM) is introduced into the DenseNet and ResNet structures, significantly enhancing the modulation recognition accuracy of the proposed global network model. Besides DenseNet and ResNet, the long short-time memory (LSTM) network is also adopted. The experimental results show the performance of the proposed model.

Automatic modulation recognition (AMR) stands as a crucial core technology within the realm of signal processing and perception, playing a significant part in harsh electromagnetic environments. The time–frequency image (TFI) of communication signals can manifest modulation characteristics and serve as a foundation for signal modulation recognition and classification. However, under the influence of the electromagnetic environment, communication signals are exposed to varying degrees of interference, which poses a challenge to the recognition of modulation types. Taking into account the effects of interference and channel fading, this paper introduces a communication signal modulation recognition algorithm based on deep learning (DL) and time–frequency analysis. This approach employs short-time Fourier transform (STFT) to generate time–frequency diagrams from time-domain signals. Subsequently, it binarizes the image and feeds it as input data to the neural network. Our research presents a composite deep convolutional neural network (CNN) architecture known as the composite dense-residual neural network (CDRNN). This architecture focuses on enhancing the feature extraction and identification, aiming to achieve accurate recognition of modulation types in harsh electromagnetic environments. Finally, simulation results validate that the proposed deep learning algorithm holds remarkable advantages in boosting the accuracy of modulation type recognition with better adaptability. The algorithm shows better performance even in harsh electromagnetic environments. When the signal-to-noise ratio (SNR) is 18 dB, the recognition accuracy can reach 92.1%.

Automatic modulation recognition (AMR) of communication signals is a critical and challenging task in cognitive radio systems. In this work, classifications of four digital modulation types, including BPSK, QPSK, GMSK and 2FSK, are investigated. From the received radio signal, a set of cyclic spectrum features are first calculated, and a principal component analysis (PCA) is applied to extract the most discriminant feature vector for classification. A novel max-multiple layer perceptron (MaxMLP) neural network is introduced for classification of modulation feature vectors through supervised learning. In the experiments, real radio signals with different modulation types were generated from an Agilent vector signal generator, and sampled by an Agilent digital signal analyzer. The proposed AMR method is tested at various channel SNR levels. Experimental results indicate that the performance of this method is highly competitive, and the computational cost is relatively low.

In this paper, we review a variety of deep learning algorithms and models for modulation recognition and classification of wireless communication signals. Specifically, deep learning (DL) has shown overwhelming advantages in computer vision, robotics, and voice recognition. Recently, DL has been proposed to apply to wireless communications for signal detection and classification in order to better learn the active users for electromagnetic spectrum sharing purposes. Therefore, we aim to provide a survey on the most recent techniques which use DL for recognizing and classifying a wireless signal. We focus on the most widely used DL models, emphasize the advantages and limitations, and discuss the challenges as well as future directions. In addition, we also apply a DL algorithm, convolutional neural network (CNN), to demonstrate the feasibility of using CNN to recognize and classify the over-the-air wireless signals using Mathworks DL toolbox with PlutoSDR and Universal Software Radio Peripheral (USRP), respectively.

Automatic modulation recognition is very important for communication intelligence applications such as signal interception for satellite communications, civil authority, surveillance and threat analysis. Automatic digital modulation recognition is a system that automatically classifies the modulation type of the intercepted signal. This paper presents a method for the automatic recognition of digital modulations without a priori knowledge of the signal parameters. This algorithm works at low signal to noise ratio and get nearly to 100 % of correct classification percentage at 0 dB which is effective in Electronic warfare and communication surveillance applications. This method can recognize common single-carrier modulated satellite communication signals such as M-ary phase-shift keying, M-ary quadrature amplitude modulation and M-ary amplitude phase shift keying which used in DVB-S2. This algorithm is performed by a sequential steps to blindly classify the signal using PSD of instantaneous amplitude, kurtosis and HOS (cumulants).

This paper implements a deep learning‐based modulation pattern recognition algorithm for communication signals using a convolutional neural network architecture as a modulation recognizer. In this paper, a multiple‐parallel complex convolutional neural network architecture is proposed to meet the demand of complex baseband processing of all‐digital communication signals. The architecture learns the structured features of the real and imaginary parts of the baseband signal through parallel branches and fuses them at the output according to certain rules to obtain the final output, which realizes the fitting process to the complex numerical mapping. By comparing and analyzing several commonly used time‐frequency analysis methods, a time‐frequency analysis method that can well highlight the differences between different signal modulation patterns is selected to convert the time‐frequency map into a digital image that can be processed by a deep network. In order to fully extract the spatial and temporal characteristics of the signal, the CLP algorithm of the CNN network and LSTM network in parallel is proposed. The CNN network and LSTM network are used to extract the spatial features and temporal features of the signal, respectively, and the fusion of the two features as well as the classification is performed. Finally, the optimal model and parameters are obtained through the design of the modulation recognizer based on the convolutional neural network and the performance analysis of the convolutional neural network model. The simulation experimental results show that the improved convolutional neural network can produce certain performance gains in radio signal modulation style recognition. This promotes the application of machine learning algorithms in the field of radio signal modulation pattern recognition.

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An adaptive procedure for automatic modulation recognition is described. With it the automatic modulation classification and recognition of radio communication signals with a priori unknown parameters is possible effectively. The results of modulation recognition are important in the context of radio monitoring or electronic support measurements. The special features of the procedure are the possibility to adapt it dynamically to nearly all modulation types, and the capability to recognize continuous phase modulation (CPM) signals like Gaussian minimum-shift keying (GMSK) too. A time synchronization to the symbol rate is not necessary.

With the deepening development of communication technology, the technology of automatic modulation and recognition of communication signals has been more and more widely used in military and civilian fields. This paper mainly studies the implementation of automatic modulation recognition using deep learning as a computing tool, focusing on CNN neural network and LSTM neural network, and conducting simulation experiments on public data sets. Based on the original CNN neural network, this paper introduces the structure of LSTM neural network and combines the advantages of the two types of neural networks to explore a combined neural network that is superior to the originally used CNN network. The experimental results of this thesis show that introducing the features of dynamic time series modeling of LSTM networks into deep learning networks can capture the global and local information of signals more effectively and improve the accuracy of neural networks in automatic modulation recognition.

The modulation recognition of underwater acoustic signals plays a crucial role in various fields such as ocean exploration, military communication, and underwater surveillance. However, challenges such as multipath effects, noise interference, and signal attenuation in underwater channels make modulation recognition particularly difficult. To address these issues, this paper proposes a novel Frequency Domain Recurrence Plot (FDRP) algorithm, which constructs a similarity matrix of frequency domain signals to visualize the spectral characteristics in the form of images. Furthermore, a Convolutional Block Attention Module (CBAM) is incorporated into the traditional ResNet architecture to fully leverage the feature extraction capabilities of convolutional neural networks. Experimental results from lake trials demonstrate that the features constructed based on FDRP exhibit superior recognition performance compared to other methods.