Search

Classifying modulated signals for non-cooperative underwater acoustic communication is challenging due to signal distortion caused by fading and multipath effects in the underwater acoustic channel. Our proposed method utilizes channel estimation parameters to measure and correct signal distortion, thereby enhancing the recognition performance of the received signal. Modulation classification experiments were conducted on a public dataset with various modulation schemes, as well as on the same dataset with simulated underwater acoustic channels. The results indicate that our method effectively mitigates the impact of the underwater acoustic channel on modulation signal classification, improves recognition accuracy, and is broadly applicable to a wide range of machine learning classifiers. Finally, we validated these findings using real underwater communication data. [ABSTRACT FROM AUTHOR]

Classifying modulated signals for non-cooperative underwater acoustic communication is challenging due to signal distortion caused by fading and multipath effects in the underwater acoustic channel. Our proposed method utilizes channel estimation parameters to measure and correct signal distortion, thereby enhancing the recognition performance of the received signal. Modulation classification experiments were conducted on a public dataset with various modulation schemes, as well as on the same dataset with simulated underwater acoustic channels. The results indicate that our method effectively mitigates the impact of the underwater acoustic channel on modulation signal classification, improves recognition accuracy, and is broadly applicable to a wide range of machine learning classifiers. Finally, we validated these findings using real underwater communication data.

Intelligent methods have been applied to many fields for a long time. Recently, Visible Light Communication (VLC) systems widely include learning and classification models to improve their performances. The classification of L-Pulse Position Modulation (L-PPM) formats is crucial for VLC systems since the modulation order L is very effective for providing energy efficiency and increasing the transmission capacity. In this paper, therefore, it is reported for the first time, the classification of L-PPM schemes in VLC systems by using Decision Tree, K-nearest neighbor (KNN), Support Vector Machine, and a Direct Decision-based Linear Model technique. A novel feature extraction model is derived to be able to classify the type of L-PPM modulation schemes. A comparison has been given to observe the performance of classification schemes by taking into account the level of Signal to Noise Ratio and the transmission distance between receiver and transmitter. The 2-PPM scheme has the highest success rate against other schemes in terms of performance metrics at the overall distance. Additionally, the KNN method gives the best accuracy performance at the distance of 2.32 m and more, while it has been obtained the best accuracy of 97.85% by the Decision Tree Model at the distance of 2.20 m. [ABSTRACT FROM AUTHOR]

The popular directions for automatic modulation classification algorithms investigate how to develop feature extraction methods for further signal classification. In this paper, we propose a mapping algorithm for a manually designed feature extraction method by using the reconstruction component of principal component analysis (PCA), which further extracts discrimination between signal features via a PCA reconstruction component. Two supervised neural network models are studied to achieve the limits of the learning matrix in modulation signal classification. Some experimental results show that different modulation schemes can be obviously classified using matrix mapping for feature extraction. Moreover, the modulation classification accuracy based on the mapping extraction feature, which has a lower SNR requirement for training, is slightly improved compared with some triradial deep learning methods. [ABSTRACT FROM AUTHOR]

The recognition of modulation schemes in military and civilian applications is a major task for intelligent receiving systems. Various Automatic Modulation Classification (AMC) algorithms have been developed for this purpose in the literature. However, classification with low computational complexity as well as reasonable processing time is still a challenge. In this paper, a feature-based approach along with various classifiers is employed based on statistical features as well as higher-order moments and cumulants. An over-the-air (OTA) recorded dataset consisting of four analog and ten digital modulation schemes are used for testing the proposed method at 0–20 dB SNR. The overall accuracy for quadratic Support Vector Machine (SVM) is found to be as high as 98% at 10 dB. The comparison of the results with other AMC papers published in the literature indicates that the proposed method present higher accuracy, especially for realistic channel induced OTA dataset. [ABSTRACT FROM AUTHOR]

Automatic modulation categorization (AMC) is used in many applications such as cognitive radio, adaptive communication, electronic reconnaissance, and non-cooperative communications. Predicting the modulation class of an unknown radio signal without having any prior information of the signal parameters is challenging. This paper proposes a novel multiscale deep-learning-based approach for the automatic modulation classification using radio signals. The approach considered the fixed boundary range-based Empirical wavelet transform (FBREWT) based multiscale analysis technique to decompose the radio signal into sub-band signals or modes. The sub-band signals computed from the radio signal combined with the deep convolutional neural network (CNN) are used to classify modulation types. The approach is tested using the radio signals of different signal-to-noise ratio (SNR) values and four different channel types such as additive white Gaussian noise (AWGN) combination of Rayleigh fading and AWGN channels, the combination of Rician flat fading and AWGN channels, and combination of Nakagami-m fading and AWGN channels for AMC. The results show that the proposed FBREWT based deep-learning approach achieves an overall classification accuracy of 97% for AMC using the radio signals with 10dB SNR for the AWGN channel. Moreover, the proposed approach has obtained the accuracy’s as 94.56%, 95%, and 97.33% using radio signals with 10 dB SNR values for Rayleigh fading, Rician flat fading, and Nakagami-m fading channels combined through AWGN link. The comparison of the proposed multiscale deep learning-based approach with existing methods is shown for AMC. [ABSTRACT FROM AUTHOR]

The modulation classification problem can be effectively handled by modern machine learning techniques, however, the available data for training is usually limited due to costly measurements in practice. Concerning the problem of insufficient training data, we propose a conditional variational auto-encoder (CVAE)-enhanced learning model for modulation classification. Different from the conventional data augmentation approaches where the data is independently generated, we introduce a feedback unit from the classifier to the CVAE generative network, to ensure that the generated data helps improving the classification accuracy. A two-stage generating-training algorithm is proposed. Via both simulation and practical implementation on a universal software radio peripheral (USRP) platform, it is shown that the proposed method effectively improves the classification accuracy in realistic propagation environment. [ABSTRACT FROM AUTHOR]

Deep learning (DL) is a newly addressed area of research in the field of modulation classification. In this letter, a constellation density matrix (CDM) based modulation classification algorithm is proposed to identify different orders of ASK, PSK, and QAM. CDM is formed through local density distribution of the signal’s constellation generated using LabVIEW for a wide range of SNR. Two DL models, ResNet-50 and Inception ResNet V2 are trained through color images formed by filtering the CDM. Classification accuracy achieved demonstrates better performance compared to many existing classifiers in the literature. [ABSTRACT FROM AUTHOR]

In this article, a machine learning method to classify signal with Gaussian noise based on denoising auto encoder (DAE) and convolutional neural network (CNN) is proposed. We combine denoising autoencoder's denoising ability with CNN's feature extraction capability. First, a six-layer neural network is built, including three CNN layers. Then a dataset containing noiseless signal of 11 modulation is generated. In the simulation, we apply this dataset to train neural network and achieve an accuracy of 94%, which is much higher than performance with noisy signal, meaning that noise can greatly influence the accuracy of neural network. Next, we build a denoising autoencoder and train it with signal of 5 dB signal-to-noise ratio (SNR). Compared with neural network without denoising autoencoder, adding a denoising autoencoder can achieve an accuracy of 84% at signal of 18 dB SNR, improved by 58%.

In the context of Automatic Modulation Classification (AMC), some recent works have utilized multiple features to train the neural network. With an ultimate aim to develop a systematic approach to select the most diverse and unique features, we propose and demonstrate a novel method to select the most diverse $m\atopwithdelims ()2$ features from a larger feature set. Bhattacharyya distance metric for the dissimilarity between two probability distributions is utilized to select the features with the highest distance for all modulation pairs within a test pool. The proposed approach is analyzed for three different neural networks based classifiers, amidst AWGN and frequency-selective fading channels. A substantial reduction in computational complexity is achieved with an acceptable compromise on the classification performance. [ABSTRACT FROM AUTHOR]

A novel method based on constellation structure is proposed to identify PSK and QAM modulation of different orders, in the slow and flat fading channel. The proposed method does not require training for threshold optimization and considers carrier frequency, symbol rate, and phase offset unknown. The symbol rate is estimated using the spectrum of the instantaneous phase of the complex baseband signal. Carrier frequency offset (CFO) is estimated and corrected from the downconverted signal and downsampled to the estimated symbol rate for extraction of constellation points. The phase offset is determined based on the symmetrical structure of constellation. The features extracted using k-medoids are used for classification of the final modulation scheme. Results show that the proposed algorithm outperforms some existing classifiers and offers lower computational complexity compared to algorithms based on subtractive clustering. [ABSTRACT FROM AUTHOR]

In this article, a machine learning method to classify signal with Gaussian noise based on denoising auto encoder (DAE) and convolutional neural network (CNN) is proposed. We combine denoising autoencoder's denoising ability with CNN's feature extraction capability. First, a six-layer neural network is built, including three CNN layers. Then a dataset containing noiseless signal of 11 modulation is generated. In the simulation, we apply this dataset to train neural network and achieve an accuracy of 94%, which is much higher than performance with noisy signal, meaning that noise can greatly influence the accuracy of neural network. Next, we build a denoising autoencoder and train it with signal of 5 dB signal-to-noise ratio (SNR). Compared with neural network without denoising autoencoder, adding a denoising autoencoder can achieve an accuracy of 84% at signal of 18 dB SNR, improved by 58%. [ABSTRACT FROM AUTHOR]

An innovative automatic modulation classification via graph-based constellation analysis for ${M}$ -ary QAM signals is presented. In our framework, a unified mesh model for the constellation diagrams of the ${M}$ -QAM signals within the modulation candidate set is first constructed and exploited to transform the received ${M}$ -QAM signal into graph domain. The concise graph representation of the received ${M}$ -QAM signal is established from its constellation according to the positions of the recovered symbols in the mesh model. Then, the modulation feature vector is built from the eigenvector(s) corresponding to the maximum eigenvalue of its adjacency matrix. The modulation type can be identified by measuring the angle between the feature vectors resulting from the training data and the test data. Monte Carlo simulation results and theoretical analysis demonstrate that the proposed method with lower computational complexity can provide superior performance to the existing subtractive clustering technique, and is robust to the residual phase and timing offsets. [ABSTRACT FROM AUTHOR]

A novel automatic modulation classification method based on the graph presentation of the cyclic spectrum is proposed. In our proposed scheme, the periodicity and the symmetry of the cyclic spectrum will be exploited to establish a concise feature representation of multiple graphs. The modulated signal is first transformed from the cycle-frequency domain into the graph domain. Consequently, the concise graph-presentation, namely, a set of weighted directed rings, will be formulated as the robust features of the original signal. Those features can be easily expressed by the corresponding adjacency matrices. It can be verified that the adjacency matrices are sparse and the non-zero entries therein can be registered as the efficient feature parameters. Through the Hamming distance measure to enumerate the difference between the feature parameters resulting from the training data and the test data, one can perform the modulation classification. Monte Carlo simulation results demonstrate that our proposed method can achieve much better classification accuracy than the existing technique when the cyclic spectrum is used. [ABSTRACT FROM PUBLISHER]

This letter proposes a blind modulation classification (MC) algorithm applicable to single and multiple-antenna systems operating over frequency-selective channels. We show that the correlation functions of the received signals for certain modulation formats exhibit peaks at a particular set of time lags, a result which can be exploited as a discriminating feature. We also develop a new hypothesis test in order to detect the correlation-induced peaks. The proposed algorithm is general in the sense that it accommodates any number of transmit- and receive-antennas, without prior information about channel statistics. The classification performance of the proposed algorithm is assessed through Monte Carlo simulations. [ABSTRACT FROM AUTHOR]

Abstract: A new classification algorithm of M-ary FSK signals based on the extraction of signals’ features is presented in this paper. The implementation of the methodology does not require any apriori information. By extracting some basic features of intermediate frequency signals both in time domain and frequency domain, the classification of M-ary FSK signals is achieved. Simulation result shows that the proposed algorithm can classify 2FSK, 4FSK and 8FSK signals effectively under AWGN and and still keeps a good performance even at low input SNR. [Copyright &y& Elsevier]

Abstract: This paper presents a comparative study of implementation of feature extraction and classification algorithms based on wavelet neural networks (WNN) for chaos-based digital modulation (CBDM) classification. Thirteen different feature extraction methods are generated by separately using Daubechies, Biorthogonal, Coiflets, and Symlets wavelet filters. WNN model is used, which consists of two layers: wavelet entropy and multi-layer perceptron (MLP) neural networks for expert CBDM classification. The chaos-based digital modulated signals used in this experimental study are Chaos Shift Keying (CSK), Chaotic On–Off Keying (COOK), Differential Chaos Shift Keying (DCSK), Correlation Delay Shift Keying (CDSK), Symmetric Chaos Shift Keying (SCSK) and Frequency-Modulated Differential Chaos Shift Keying (FM-DCSK). The performance of this comparison system is evaluated by using total 1806 CBDM signals for each of these feature extraction methods. Mean correct classification rate is about 98.76% for the sample CBDM signals. [ABSTRACT FROM AUTHOR]

Due to the stochastic nature of wireless channels, the received radio signal is noised during transmission causing difficulty in classifying radio modulation categories. Deep learning methods are applied to radio modulation classification for its promising performance compared with traditional approaches. However, deep learning models have relied on a large number of labeled data as training data. Collecting and labeling radio signals in various environments require a lot of manpower and material resources, which will limit the application of deep learning models in modulation recognition. To improve the efficiency of labeling radio signals, in this paper, we propose a feature extraction and mapping algorithm (FEM) to extract and map radio signals to images for further classification. Specifically, we propose an LSTM-based feature extraction network to extract radio features from radio signals, which are further mapped to different categories of images via a feature mapping network. The mapping between radio features and images is automatically learned by a generative adversarial network. Experiment results show that different modulation categories can be obviously discriminated though the mapped images. Furthermore, the modulation classification accuracy based on mapped images is slighted improved comparing with the original radio signals. [ABSTRACT FROM AUTHOR]