Modulation Recognition Algorithm Research Articles

Acoustic modulation signal recognition based on endpoint detection

The modulation recognition technology for acoustic signals holds significant research importance in signal demodulation and communication signal reconnaissance, serving as a crucial component and key aspect. This paper investigates the modulation recognition technology for acoustic signals (< 20 kHz) from the perspectives of signal preprocessing and feature extraction. Firstly, it selects seven modulation signals 2ASK, 4ASK, 2FSK, 4FSK, 2PSK, 4PSK, and OFDM as recognition targets and systematically compares the effectiveness of four different endpoint detection algorithms in modulation signal recognition. To further enhance the performance of the short-time energy entropy ratio algorithm, this study introduces three different noise reduction algorithms for optimization. Finally, to accurately identify and distinguish between 2 and 4FSK signals, this study optimizes the related algorithms of the cyclic spectrum by using the kurtosis coefficient value Kur of the cyclic spectrum parameter matrix when the cyclic frequency α = 0 to differentiate between these two signals. The results show that at SNR of 4 dB, the proposed modulation recognition algorithm can effectively distinguish between these two signals, achieving a recognition accuracy of over 99%.

CNN-BiLSTM-DNN-Based Modulation Recognition Algorithm at Low SNR

Radio spectrum resources are very limited and have become increasingly tight in recent years, and the exponential growth of various frequency-using devices has led to an increasingly complex and changeable electromagnetic environment. Wireless channel complexity and uncertainty have increased dramatically, and automated modulation recognition (AMR) performs poorly at low signal-to-noise ratios. It is proposed to use convolutional bidirectional long short-term memory deep neural networks (CNN-BiLSTM-DNNs) as a deep learning framework to extract features from single and mixed in-phase/orthogonal (I/Q) symbols in modulated data. The framework combines the capabilities of one- and two-dimensional convolution, a bidirectional long short-term memory network, and a deep neural network more efficiently, extracting characteristics from the perspective of time and space to enhance the accuracy of automatic modulation recognition. Modulation recognition experiments on the benchmark datasets RML2016.10b and RML2016.10a show that the average recognition accuracies of the proposed model from −20 dB to 18 dB are 64.76% and 62.73%, respectively, and the improvement ranges of modulation recognition accuracy are 0.29−5.56% and 0.32−4.23% when the signal-to-noise ratio (SNR) is −10 dB to 4 dB, respectively. The CNN-BiLSTM-DNN model outperforms classical models such as MCLDNN, MCNet, CGDNet, ResNet, and IC-AMCNet in terms of modulation type recognition accuracy.

Radar Signal Recognition Based on CNN With a Hybrid Attention Mechanism and Skip Feature Aggregation

Radar signal recognition is of great significance for threat analysis and intelligence support in radar electronic warfare. However it remains a challenging task due to (1) the variety of radar signal modulation types, (2)the increasingly saturated electromagnetic environment and the more serious influence of noise interference, and (3)the intrinsic real-time requirement of this application. In this paper, a novel automatic modulation recognition algorithm based on the joint architecture of convolution neural network (CNN) and the support vector machine (SVM) is proposed to comprehensively address these challenges. In particular, this algorithm first employs time-frequency analysis to better show the pulse representation in the time-frequency domain. Then a well-designed DCNN equipped with a hybrid attention (HA) mechanism and skip feature aggregation (SFA) is used to automatically learn the feature representations of distinctiveness from time frequency images. HA is designed to quickly capture the important information of local parts in sight and enhance the ability of the backbone network to mine high-level features. SFA is applied to selectively aggregate features among channels for self-monitoring, which improves the expression and generalization ability. Sixteen types of radar signals are used to verify the feasibility and effectiveness of this algorithm. The experimental evaluation shows that the proposed method not only achieve competitive accuracy with a better timeliness than state-of-the-art methods, but also achieves effective identification of most of the signal modulation types.

PRI modulation recognition and sequence search under small sample prerequisite

Pulse repetition interval (PRI) modulation recognition and pulse sequence search are significant for effective electronic support measures. In modern electromagnetic environments, different types of inter-pulse slide radars are highly confusing. There are few available training samples in practical situations, which leads to a low recognition accuracy and poor search effect of the pulse sequence. In this paper, an approach based on bi-directional long short-term memory (BiLSTM) networks and the temporal correlation algorithm for PRI modulation recognition and sequence search under the small sample prerequisite is proposed. The simulation results demonstrate that the proposed algorithm can recognize unilinear, bilinear, sawtooth, and sinusoidal PRI modulation types with 91.43% accuracy and complete the pulse sequence search with 30% missing pulses and 50% spurious pulses under the small sample prerequisite.

An Improved Modulation Recognition Algorithm Based on Fine-Tuning and Feature Re-Extraction

Modulation recognition is an important technology in wireless communication systems. In recent years, deep learning-based modulation recognition algorithms, which can autonomously learn deep features and achieve superior recognition performance compared with traditional algorithms, have emerged. Yet, there are still certain limitations. In this paper, aiming at addressing the issue of poor recognition performance at low signal-to-noise ratios (SNRs) and the inability of deep features to effectively distinguish among all modulation types, we propose an optimization scheme for modulation recognition based on fine-tuning and feature re-extraction. In the proposed scheme, the network is firstly trained with the signals at high SNRs; then, the trained network is fine-tuned to the untrained network at low SNRs. Finally, on the basis of the features learned by the network, deeper features with enhanced discriminability for confused modulation types are obtained using feature re-extraction. The simulation results demonstrate that the proposed optimization scheme can maximize the performance of the neural network in the recognition of signals that are easily confused and at low SNRs. Notably, the average recognition accuracy of the proposed scheme was 91.28% within an SNR range of −8 dB to 18 dB, which is an improvement of 8% to 17% in comparison with four existing schemes.

A Lightweight Automatic Modulation Recognition Algorithm Based on Deep Learning

Automatic modulation recognition (AMR) plays a critical role in modern communication systems. Owing to the recent advancements of deep learning (DL) techniques, the application of DL has been widely studied in AMR, and a large number of DL-AMR algorithms with high recognition rates have been developed. Most DL-AMR algorithm models have high recognition accuracy but have numerous parameters and are huge, complex models, which make them hard to deploy on resource-constrained platforms, such as satellite platforms. Some lightweight and low-complexity DL-AMR algorithm models also struggle to meet the accuracy requirements. Based on this, this paper proposes a lightweight and high-recognition-rate DL-AMR algorithm model called Lightweight Densely Connected Convolutional Network (DenseNet) Long Short-Term Memory network (LDLSTM). The model cascade of DenseNet and LSTM can achieve the same recognition accuracy as other advanced DL-AMR algorithms, but the parameter volume is only 1/12 that of these algorithms. Thus, it is advantageous to deploy LDLSTM in resource-constrained systems.

Research and implementation of modulation recognition based on cascaded feature fusion

AbstractAiming at the problems of weak robustness of single feature and limited recognition range in modulation recognition, this paper proposes a modulation recognition algorithm based on cascaded feature fusion and multi‐classifier combination, in which time‐frequency map and instantaneous amplitude spectral density features are extracted from low intermediate frequency (IF) signal, constellation map and high‐order cumulant features are extracted from zero IF signal, and a three‐level recognition algorithm is designed through the decision fusion of decision tree, convolutional neural network, and support vector machine. In order to verify the performance of the algorithm, a modulation recognition system is designed and built based on USRP2974. The low IF and zero IF modulation signals are received through two channels, and the RF signals are received and processed in real time with the help of the built‐in CPU of the receiver. The recognition of 13 kinds of analog modulation and digital modulation signals is realized. Under the condition of wireless reception, the recognition rate of this system is more than 93% at 5 to 10 dB.

An Open-Set Modulation Recognition Scheme With Deep Representation Learning

This letter proposes a deep representation learning based automatic modulation recognition (AMR) algorithm in the open-set recognition (OSR) regime. The challenging recognition risk of unknown modulation classes is first analyzed for most state-of-the-art approaches, and interesting insights into this problem is then provided. Based on this, an open-set AMR scheme is proposed with a combination of feature representation and classification, where a triplet loss function from metric learning is employed for the representor to form distinct clusters for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> known modulation classes. Then, the degree of membership is calculated via extreme value theory (EVT) by modeling the distance between known training data to its corresponding clustering center, followed by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> binary classifiers. Comprehensive experiments on public dataset confirm that the proposed scheme outperforms the other state-of-the-arts in terms of both balanced accuracy and openness.

Research on modulation recognition of underwater acoustic communication signal based on deep learning

Marine information technology plays a important role in the development of marine resources development, marine climate early warning, and other industries. Underwater acoustic communication technology can help us better access marine information. The performance of the underwater acoustic signal modulation recognition algorithm depends on the accuracy of feature extraction. however, due to excessive underwater noise, many traditional algorithms can not recognize features well. For this reason, this paper proposes a modulation recognition network for aquatic communication signals based on deep learning. ResNet can capture the characteristics of deep features and combines ResNet with a modulation recognition network. Finally, the experiment proves the effectiveness of this method.

Signal Modulation Recognition Algorithm Based on Improved Spatiotemporal Multi-Channel Network

Automatic modulation recognition (AMR) plays an essential role in modern communication systems. In recent years, various modulation recognition algorithms based on deep learning have been emerging, but the problem of low recognition accuracy has not been solved well. To solve this problem, based on the existing MCLDNN algorithm, in this paper, we proposed an improved spatiotemporal multi-channel network (IQ-related features Multi-channel Convolutional Bi-LSTM with Gaussian noise, IQGMCL). Firstly, dividing the input IQ signals into three channels, time sequence feature extraction is carried out for route I, route Q, and route IQ, respectively. For route IQ, convolution kernel (2,1) is first used to extract relevant features. Two layers of the small convolution kernel (1,3) are used to extract time sequence features further, and the three channels are used to extract features further. Then, a two-layer short-length memory network is used to extract features from time and space more effectively. Through comparison experiments, Bi-LSTM is introduced to replace one layer of LSTM, and a fully connected layer is removed to prevent overfitting. Finally, multiplicative Gaussian noise is introduced to naturally corrode the feature parameters, further improving the robustness and accuracy of the model. Experiments are carried out on three public datasets RML2016.10a, RML2016.10b, and RML2016.04C. The experiments show that the IQGMCL network has higher recognition accuracies on all datasets, especially on the RML2016.10a dataset. When the SNR is 4 dB, the recognition accuracy reaches 93.52%. When the SNR is greater than 0 dB, the average recognition accuracy reaches 92.3%, 1.31%, and 1.2% higher than the original MCLDNN network, respectively.

Research on Modulation Recognition Algorithm Based on Channel and Spatial Self-Attention Mechanism

Research on Modulation Recognition Algorithm Based on Channel and Spatial Self-Attention Mechanism

Modulation recognition algorithm based on mixed attention prototype network

针对极少量带标签样本条件下的通信信号调制识别难题, 提出一种基于混合注意力原型网络的调制识别算法。综合元学习和度量学习的思想, 在原型网络框架下通过特征提取模块将信号映射至一个新的特征度量空间, 并通过比较该空间内各类原型与查询信号之间的距离确定查询信号调制样式。根据通信信号IQ分量的时序特点设计了由卷积神经网络和长短时记忆网络级联的特征提取模块, 并引入卷积注意力机制提升关键特征的权重; 采用基于Episode的训练策略, 使算法可泛化到新的信号识别任务中。仿真结果表明, 所提算法在每类信号只有5个带标签样本(5-way 5-shot)时平均识别率可达85.68%。

Novel Deep-Learning Modulation Recognition Algorithm Using 2D Histograms over Wireless Communications Channels.

Modulation recognition (MR) has become an essential topic in today’s wireless communications systems. Recently, convolutional neural networks (CNNs) have been employed as a potent tool for MR because of their ability to minimize the feature’s susceptibility to its surroundings and reduce the need for human feature extraction and evaluation. In particular, these investigations rely on the unrealistic assumption that the channel coefficient is typically one. This motivates us to overcome the previous constraint by providing a novel MR suited to fading wireless channels. This paper proposes a novel MR algorithm that is capable of recognizing a broad variety of modulation types, including M-ary QAM and M-ary PSK, without enforcing any restrictions on the modulation size, M. The analysis has shown that each modulation choice has a distinct two-dimensional in-phase quadrature histogram. This property is beneficially utilized to design a convolutional neural-network-based MR algorithm. When compared to the existing techniques, Monte Carlo simulations demonstrated the success of the proposed design.

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.

D-GF-CNN Algorithm for Modulation Recognition

This paper presents a novel modulation recognition algorithm based on dilated convolutional neural network with a new defined GF regularization function, named as D-GF-CNN algorithm. Firstly, an asynchronous delay sampling (ADS) technique is introduced. Via the defined ADS, the received signal is converted into an asynchronous delay histogram (ADH). The ADH of different modulation signals has distinct characteristics, which provides great convenience for the neural network to identify the modulation mode. Then, the pixel point matrix of the ADH is convolved with the dilated convolution kernel of the convolutional neural network, and the automatic extraction of signal features is completed so that the manual feature extraction processing can be effectively avoided. Finally, a novel GF regularization function is given, which can improve the constraint ability of the loss function on the weight and effectively weaken the influence of network over-fitting on the modulation recognition accuracy. Theoretical analysis and simulation experiments show that the proposed algorithm provides several advantages, for example: (1) automatically extract features; (2) effectively prevent network over-fitting; (3) significantly improve recognition accuracy in the lower SNR scenarios.

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.

Radio signal modulation recognition algorithm based on convolutional neural network

Radio signal modulation recognition algorithm based on convolutional neural network

Evolutionary Neural Network based on Quantum Elephant Herding Algorithm for Modulation Recognition in Impulse Noise

Evolutionary Neural Network based on Quantum Elephant Herding Algorithm for Modulation Recognition in Impulse Noise

A Modulation Recognition Algorithm for UAV Emitter

A Modulation Recognition Algorithm for UAV Emitter

Estimating the Instantaneous Frequency of Linear and Nonlinear Frequency Modulated Radar Signals-A Comparative Study.

Automatic modulation recognition plays a vital role in electronic warfare. Modern electronic intelligence and electronic support measures systems are able to automatically distinguish the modulation type of an intercepted radar signal by means of real-time intra-pulse analysis. This extra information can facilitate deinterleaving process as well as be utilized in early warning systems or give better insight into the performance of hostile radars. Existing modulation recognition algorithms usually extract signal features from one of the rudimentary waveform characteristics, namely instantaneous frequency (IF). Currently, there are a small number of studies concerning IF estimation methods, specifically for radar signals, whereas estimator accuracy may adversely affect the performance of the whole classification process. In this paper, five popular methods of evaluating the IF–law of frequency modulated radar signals are compared. The considered algorithms incorporate the two most prevalent estimation techniques, i.e., phase finite differences and time-frequency representations. The novel approach based on the generalized quasi-maximum likelihood (QML) method is also proposed. The results of simulation experiments show that the proposed QML estimator is significantly more accurate than the other considered techniques. Furthermore, for the first time in the publicly available literature, multipath influence on IF estimates has been investigated.