Automatic Modulation Identification Research Articles

Automatic modulation identification for underwater acoustic signals based on the space–time neural network

Automatic modulation identification for underwater acoustic signals based on the space–time neural network

GGCNN: An Efficiency-Maximizing Gated Graph Convolutional Neural Network Architecture for Automatic Modulation Identification

Automatic modulation identification (AMI) is a technique to detect the modulation type and order of a received signal, which has the potential to enhance cognitive radio capabilities for future generations of communication devices. However, AMI classifiers traditionally have exhibited low efficiency in low signal-to-noise ratio (SNR) environments. Hence, to address this problem we present our novel Gated Graph Convolutional Neural Network (GGCNN) classifier for feature-based AMI. This architecture includes a robust feature extraction stage to extract deep correlative patterns about the received symbols. Not only does this feature extraction stage use the temporal characteristics of the received symbols, but it also takes advantage of embedded signaling features from the received signal. In the proposed classifier, the received constellations are treated as a graph, allowing it to outperform state-of-the-art classifiers due to its strong performance in graph classification. This is observed clearly in the visualization of the extracted features, even for high-order modulation schemes. In this paper, we present our systematic research conducted for maximizing the efficiency obtainable by our classifier. Extensive simulation results demonstrate a significant accuracy improvement of 18.44 percentage points, and an efficiency increase by 60.78% for our GGCNN-AMI classifier compared to state-of-the-art classifiers in low-SNR environments.

Active Learning-Aided CNN-Based Entropy-Tunable Automatic Modulation Identification for Rate-Flexible Coherent Optical System

Flexible rate and real-time link monitoring are important tasks in the development of software-defined elastic optical networks (EONs). The tunable spectral efficiency characteristic of probabilistic constellation shaping (PCS) naturally provides a possibility to dynamically regulate the rate for future optical communication systems. In this work, we firstly propose an active learning-aided entropy-tunable automatic modulation identification (AL-aided ET-AMI) scheme based on convolution neural network (CNN) model for a PCS-based coherent optical system. An AL-based neural network allows monitoring of the link rate and signal-to-noise ratio (SNR) with tuning entropy or optical power fluctuation. The proposed ALaided ET-AMI scheme is demonstrated over a 350 ∼ 550-Gbps line rate 10-km dual-polarized coherent optical communication system at entropies from 3.5 to 5.5. When the entropy tuning step is 0.1, corresponding to a rate tuning step of 5 Gbps at 50 Gbaud, the recognition accuracy can reach 98% with data aggregation (DA). When the fluctuation of SNR is 1 dB, the recognition rate can reach 87% at an entropy of 4.5 over 400 samples. The verifications show that our proposed AL-aided ET-AMI solution can monitor the rate and SNR performance of PCS-based high-speed rateflexible optical links well. The solution provides a new perspective and tool for future optical systems and network monitoring.

Blind Detection of Broadband Signal Based on Weighted Bi-Directional Feature Pyramid Network.

With the development of wireless technology, signals propagating in space are easy to mix, so blind detection of communication signals has become a very practical and challenging problem. In this paper, we propose a blind detection method for broadband signals based on a weighted bi-directional feature pyramid network (BiFPN). The method can quickly perform detection and automatic modulation identification (AMC) on time-domain aliased signals in broadband data. Firstly, the method performs a time-frequency analysis on the received signals and extracts the normalized time-frequency images and the corresponding labels by short-time Fourier transform (STFT). Secondly, we build a target detection model based on YOLOv5 for time-domain mixed signals in broadband data and learn the features of the time-frequency distribution image dataset of broadband signals, which achieves the purpose of training the model. The main improvements of the algorithm are as follows: (1) a weighted bi-directional feature pyramid network is used to achieve a simple and fast multi-scale feature fusion approach to improve the detection probability; (2) the Efficient-Intersection over Union (EIOU) loss function is introduced to achieve high accuracy signal detection in a low Signal-Noise Ratio (SNR) environment. Finally, the time-frequency images are detected by an improved deep network model to complete the blind detection of time-domain mixed signals. The simulation results show that the method can effectively detect the continuous and burst signals in the broadband communication signal data and identify their modulation types.

Dendrogram-based Artificial Neural Network modulation classification for dual-hop cooperative relaying communications

This paper contributes to the growing field of Artificial Neural Networks (ANNs) strategies of Automatic Modulation Identification (AMI) for Cognitive Radio (CR). Traditional AMI-based ANN methods suffer from many drawbacks such as overfitting due to ANN architectures with complex layers, low performance caused by falling into local minima, and the increased training time that keeps them from being used in real-time applications.We propose generated Dendrogram to decompose the AMI task into sub-components using the ANN architectures for improved classification accuracy and less training time. In addition, one of the most significant challenges in AMI is identifying the modulation types and orders in cooperative systems due to the combination of several received signals propagating through different unknown channels. We propose dual-hop based on Amplify and Forward protocol (DH-AF) relaying system to generate the dataset used to train and test the proposed model. In addition, DH-AF relaying systems provide better coverage and signal reliability for existing wireless communication systems. We consider that the source communicates with the destination over a direct link (S − D) and indirect link via an intermediate relay (S − R − D) using Distributed Space–Time Block Code (DSTBC). Then we used High Order Cumulants (HOC) and the High Order Moments (HOM) originating from the DSTBC-decoded signal as features, followed by the Dendrogram-based ANN (DANN) classifier. The simulation results confirm that the proposed method outperforms an ordinary ANN and other counterparts while taking less training time.

A Semi-Supervised Modulation Identification in MIMO Systems: A Deep Learning Strategy

Accurate modulation identification of the received signals is undoubtedly a central component in multiple-input multiple-output (MIMO) communication systems, facilitating the demodulation task. This study presents a flexible and semi-supervised deep learning-driven strategy for automatic modulation identification. To this end, the multiclass classification problem is treated as multiple binary discrimination problems to address modulation identification challenges. Here, we merge the features extraction ability of the Generative Adversarial Network (GAN) model and the semi-supervised anomaly detection scheme, the one-class Support Vector Machine (1SVM). Essentially, a single GAN-based 1SVM detector is trained using training data of each class, with the samples of that class as inlier and all other samples as anomalies (i.e., one-vs.-rest). The 1SVM is trained using the features learned by the GAN model. A dataset consisting of three digital modulations (i.e., BFSK, CPFSK, and PAM4) and three analog modulations (i.e., AM-DSB, AM-SSB, and WB-FM), widely used in wireless communications systems, is employed to demonstrate the performance of the considered deep learning-based methods. Compared to Restricted Boltzmann Machine (RBM) and Deep Belief Network (DBN)-based 1SVM, the conventional GAN, DBN, and RBM with softmax layer as discriminator layer, the proposed GAN-based 1SVM detector offers superior discrimination performance of modulation types by achieving an averaged accuracy of 0.951 and F1-Score of 0.954. Results also showed that the GAN-1SVM detector dominates the state-of-the-art modulation classification techniques.

Turbo Modulation Identification Algorithm for OFDM Software-Defined Radios

The problem of automatic modulation identification (AMI) is one of the main challenges that faces orthogonal frequency division multiplexing (OFDM) software-defined radio (SDR) systems. The previously reported studies of this aspect have limited to uncoded transmission; however, practical systems typically employ error-correcting codes. In this work, we exploit the properties of bit-interleaved coded modulation iterative decoding (BICM-ID) schemes, which are widely used in wireless communications systems, to develop a novel AMI algorithm for OFDM-SDR systems. Since the exact maximum-likelihood (ML) solution is intractable in practice, we resort to an expectation-maximization (EM) procedure as a statistical analysis tool. The proposed identification algorithm works in an iterative fashion, in which the soft information provided by the channel decoder of BICM-ID scheme is utilized as a priori information to the proposed identifier. The results indicate that the identification performance of the proposed algorithm achieves a significant improvement with iterations and outperforms that of the conventional uncoded algorithms. These favorable results are achieved with reduced processing time and acceptable sensitivity to transmission impairments, such as clock timing offset, frequency offset, Doppler shift, and phase noise.

Digital Modulation Classification Based On BAT Swarm Optimization and Random Forest

The applications of digitally modulated signals are still in progress and expansion. Automatic Modulation Identification (AMI) is important to classify the digitally modulated signals ..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 .In this work, present hybrid intelligent system for the recognition related to the digitally modulated signals where used . The proposed (AMI) had been built to classify ten most popular schemes of digitally modulated signals, namely (2ASK, , 2PSK, 4PSK, 8PSK, 8QAM,16QAM ,32QAM, 64 QAM, 128QAM, and 256QAM), with the signal to noise ratio ranging from (-2 to 13) dB. High-order cumulants (HOCs) as well as high-order moments (HOMs) were utilized. .In this thesis used , Bat Swarm Optimization (BA).The Random Forest ( RF) classifier was introduced for the first time in this work. Simulation results of the System proposed , under additive white Gaussian noise channel, show that .While algorithm ( BA Swarm Optimization ) for the modulated signals we obtained a classification accuracy of around 92% for the SNR between (-2....12 ) dB.

An M-QAM Signal Modulation Recognition Algorithm in AWGN Channel

Computing the distinct features from input data, before the classification, is a part of complexity to the methods of automatic modulation classification (AMC) which deals with modulation classification and is a pattern recognition problem. However, the algorithms that focus on multilevel quadrature amplitude modulation (M-QAM) which underneath different channel scenarios is well detailed. A search of the literature revealed that few studies were performed on the classification of high-order M-QAM modulation schemes such as 128-QAM, 256-QAM, 512-QAM, and 1024-QAM. This work focuses on the investigation of the powerful capability of the natural logarithmic properties and the possibility of extracting higher order cumulant’s (HOC) features from input data received raw. The HOC signals were extracted under the additive white Gaussian noise (AWGN) channel with four effective parameters which were defined to distinguish the types of modulation from the set: 4-QAM∼1024-QAM. This approach makes the classifier more intelligent and improves the success rate of classification. The simulation results manifest that a very good classification rate is achieved at a low SNR of 5 dB, which was performed under conditions of statistical noisy channel models. This shows the potential of the logarithmic classifier model for the application of M-QAM signal classification. furthermore, most results were promising and showed that the logarithmic classifier works well under both AWGN and different fading channels, as well as it can achieve a reliable recognition rate even at a lower signal-to-noise ratio (less than zero). It can be considered as an integrated automatic modulation classification (AMC) system in order to identify the higher order of M-QAM signals that has a unique logarithmic classifier to represent higher versatility. Hence, it has a superior performance in all previous works in automatic modulation identification systems.

Blind Digital Modulation Identification for MIMO Systems in Railway Environments With High-Speed Channels and Impulsive Noise

In order to improve on existing radio communications for railways, cognitive radio (CR) is a promising concept, allowing the combination of artificial intelligence and software-defined radios. To ensure the best environmental awareness, as required by CR receivers, automatic modulation identification is a key feature for promoting efficient and secure communications in the CR context. This paper deals with blind modulation identification in the railway transmission environment and specifically considers its two major constraints: the high-speed and the impulsive nature of the noise. To achieve this goal, we propose a feature-based process of blind identification, which is made up of three subsystems: impulsive noise mitigation (which is the main contribution of this paper), feature extraction, and classification. For the purpose of mitigating the impulsive noise, we introduce a blind filtering myriad-based approach, which, in turn, requires the estimation of the noise parameters. Simulation results prove that the developed filtering approach provides a good filtering performance, and consequently, high identification performances of the overall identification system.

Identification of superposed modulations for two‐way relaying MIMO systems with physical‐layer network coding

The automatic modulation identification of a detected signal represents an essential task for an intelligent receiver and plays an important role in demodulating the intercepted signals for several communication systems. In this study, the authors propose an efficient algorithm of superposed modulations identification dedicated for two-way relaying multiple-input multiple-output systems with physical-layer network coding (PLNC). The aim of this work is to identify a pair of sources modulations from the superposed constellation, when PLNC is applied. For this purpose, the authors use the higher order statistics-based features in conjunction with genetic algorithm and information theory as a features selection method and the random forests as a classifier. Simulations are provided to assess the accuracy of the proposed algorithm through the average probability of correct identification for different modulation scheme pairs. It is shown that the algorithm achieves high-modulation identification in acceptable signal-to-noise ratio level at different relay position.

Self Healing MIMO Transreceiver Module On-Chip

study channel estimation and error recovery schemes to support self-healing Multi Input Multi Output transmitter and receiver architecture is proposed. The paper can coexist with existing Automatic modulation Identification hardware module. This technique is used for interference identification and source identification of received signal. The Decision theoretic approach uses the likelihood function where probabilistic and hypothesis-testing arguments to formulate the recognition problem at the output of the channel and accordingly classification are performed. The merits of the proposed approach are its lesser computational complexity, ease in implementation and robustness to model mismatch.

Automatic Modulation Identification Based on the Probability Density Function of Signal Phase

Automatic modulation recognition is advantageous for wireless communication systems employing adaptive modulation, software-defined radio, and cognitive radio. In this paper, we consider a phase based maximum likelihood (ML) approach for identifying the modulation format of a linearly modulated signal. Since the optimal ML scheme is computationally intensive, we propose two approximate ML alternatives, which can offer close-to-optimal performance with reduced complexity. We then present a general performance analysis for classification of K types of modulation constellations. For K<;=5, probability of correct classification (Pcc) can be evaluated via simplified integration. In the case of K>;5, we obtain a set of upper bounds on Pcc, which provide a tradeoff between accuracy and complexity in calculating the Pcc. In addition, asymptotic behavior of phase based ML classification schemes is investigated.

Automatic Modulation Identification of MPSK Signals Using Differential Signal and its Higher Order Cumulants

Automatic modulation classification plays a significant role in intelligent communication. A new method based on feature extraction is proposed for the recognition of M-ary Phase Shift Keying (MPSK) signals. As features, fourth and eighth order cumulants of the input samples and phase differential sequences were applied. It is shown that the cumulant-based features have robust anti-noise ability. Simulation results demonstrate that the correct classification probability (Pcc) with the proposed algorithm is higher than the existing approaches at low signal-to-noise ratio (SNR).

Survey of automatic modulation classification techniques: classical approaches and new trends

The automatic recognition of the modulation format of a detected signal, the intermediate step between signal detection and demodulation, is a major task of an intelligent receiver, with various civilian and military applications. Obviously, with no knowledge of the transmitted data and many unknown parameters at the receiver, such as the signal power, carrier frequency and phase offsets, timing information and so on, blind identification of the modulation is a difficult task. This becomes even more challenging in real-world scenarios with multipath fading, frequency-selective and time-varying channels. With this in mind, the authors provide a comprehensive survey of different modulation recognition techniques in a systematic way. A unified notation is used to bring in together, under the same umbrella, the vast amount of results and classifiers, developed for different modulations. The two general classes of automatic modulation identification algorithms are discussed in detail, which rely on the likelihood function and features of the received signal, respectively. The contributions of numerous articles are summarised in compact forms. This helps the reader to see the main characteristics of each technique. However, in many cases, the results reported in the literature have been obtained under different conditions. So, we have also simulated some major techniques under the same conditions, which allows a fair comparison among different methodologies. Furthermore, new problems that have appeared as a result of emerging wireless technologies are outlined. Finally, open problems and possible directions for future research are briefly discussed.

Automatic Modulation Recognition by Support Vector Machines Using Wavelet Kernel

Automatic modulation identification plays a significant role in electronic warfare, electronic surveillance systems and electronic counter measure. The task of modulation recognition of communication signals is to determine the modulation type and signal parameters. In fact, automatic modulation identification can be range to an application of pattern recognition in communication field. The support vector machines (SVM) is a new universal learning machine which is widely used in the fields of pattern recognition, regression estimation and probability density. In this paper, a new method using wavelet kernel function was proposed, which maps the input vector xi into a high dimensional feature space F. In this feature space F, we can construct the optimal hyperplane that realizes the maximal margin in this space. That is to say, we can use SVM to classify the communication signals into two groups, namely analogue modulated signals and digitally modulated signals. In addition, computer simulation results are given at last, which show good performance of the method.