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Journal ArticleDOI

Specific Emitter Identification Based on Variational Mode Decomposition and Spectral Features in Single Hop and Relaying Scenarios

TL;DR: This paper develops an emitter identification based on variational mode decomposition and spectral features (VMD-SF), which outperforms the proposed VMD- $EM^{2}$ method and has lowest computational cost as compared with the aforementioned methods.
Abstract: Specific emitter identification is the process of identifying or discriminating different emitters based on the radio frequency fingerprints extracted from the received signal. Due to inherent non-linearities of the power amplifiers of emitters, these fingerprints provide distinguish features for emitter identification. In this paper, we develop an emitter identification based on variational mode decomposition and spectral features (VMD-SF). As VMD decomposes the received signal simultaneously into various temporal and spectral modes, we choose to explore different spectral features, including spectral flatness, spectral brightness, and spectral roll-off for improving the identification accuracy contrary to existing temporal features-based methods. For demonstrating the robustness of VMD in decomposing the received signal into emitter-specific modes, we also develop a VMD-entropy and moments ( $EM^{2}$ ) method based on existing temporal features extracted from the Hilbert Huang transform of the emitter-specific temporal modes. Our proposed method has three major steps: received signal decomposition using VMD, feature extraction, and emitter identification. We evaluate the performance of the proposed methods using the probability of correct classification ( $P_{cc}$ ) both in single hop and in relaying scenario by varying the number of emitters. To demonstrate the superior performance of our proposed methods, we compared our methods with the existing empirical mode decomposition-(entropy-, first-, and second-order moments) (EMD- $EM^{2}$ ) method both in terms of $P_{cc}$ and computational complexity. Results depict that the proposed VMD-SF emitter identification method outperforms the proposed VMD- $EM^{2}$ method and the existing EMD- $EM^{2}$ method both in single hop and relaying scenarios for a varying number of emitters. In addition, the proposed VMD-SF method has lowest computational cost as compared with the aforementioned methods.
Citations
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Journal ArticleDOI
TL;DR: A novel SEI algorithm using deep learning architecture that combines high information integrity with low complexity, which outperforms previous studies in the literature and has the capability of adapting to signals collected under various conditions.
Abstract: Specific emitter identification (SEI) enables the discrimination of individual radio emitters with the external features carried by the received waveforms. This identification technique has been widely adopted in military and civil applications. However, many previous methods based on hand-crafted features are subject to the present expertise. To remedy these shortcomings, this paper presents a novel SEI algorithm using deep learning architecture. First, we perform Hilbert-Huang transform on the received signal and convert the resulting Hilbert spectrum into a grayscale image. As a signal representation, the Hilbert spectrum image has high information integrity and can provide abundant information about the nonlinear and non-stationary characteristics of signals for identifying emitters. Thereafter, we construct a deep residual network for learning the visual differences reflected in the Hilbert spectrum images. By using the residual architectures, we effectively address the degradation problem, which improves efficiency and generalization. From our analysis, the proposed approach combines high information integrity with low complexity, which outperforms previous studies in the literature. The simulation results validate that the Hilbert spectrum image is a successful signal representation, and also demonstrate that the fingerprints extracted from raw images using deep learning are more effective and robust than the expert ones. Furthermore, our method has the capability of adapting to signals collected under various conditions.

79 citations


Cites background or methods from "Specific Emitter Identification Bas..."

  • ...Furthermore, methods based on Hilbert-Huang transform (HHT) have been demonstrated for successful RF fingerprinting [15]–[17]....

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  • ...Each feature in [15]–[17] only describes the characteristics of the Hilbert spectrum from a certain perspective....

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  • ...As mentioned, the algorithms proposed in [15]–[17] extract the features from the Hilbert spectrum....

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  • ...Consequently, several different spectral features, including spectral flatness, spectral brightness, and spectral roll-off, are further explored for improving identification accuracy [17]....

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Journal ArticleDOI
TL;DR: Simulation results show that the proposed multi-receiver cooperative schemes can achieve the diversity gain in the identification performance, and the receive diversity can be achieved by the proposed schemes by using multiple distorted receivers even without compensating the receiver distortion prior to the identification.
Abstract: Specific emitter identification (SEI) is a technique that identifies the unique emitter from its received signal by using the specific characteristics of an emitter In this paper, we consider an SEI problem with unknown receiver distortion Two groups of SEI schemes based on signal decomposition are proposed In the proposed schemes, the received signal is pre-processed by either of the following decomposition, ie, empirical mode decomposition (EMD), intrinsic time-scale decomposition (ITD), or variational mode decomposition (VMD) In the first group of the proposed schemes, the skewness and the kurtosis are extracted from the decomposed signal, which characterize the non-Gaussian features of the signal The support vector machine (SVM) or the back-propagation (BP) neural network is applied to fuse the features extracted from the multiple distorted receivers respectively and then determine the unknown emitter In the second group of the proposed schemes, an approach based on the long short term memory (LSTM) is proposed The LSTM model learns the deep features rather than the specific non-Gaussian features from the pre-processed signal In contrast to the first group, the features used to identify the unknown emitter are extracted directly from the pre-processed signal by the trained LSTM model Simulation results show that the proposed multi-receiver cooperative schemes can achieve the diversity gain in the identification performance Moreover, we evaluate the identification performance of the proposed schemes in various channels, including the Gaussian channel and the fading channel Compared to the existing methods based on different time-frequency representations, the proposed schemes possess the merits of high identification accuracy and low complexity The significance of this paper is that the receive diversity can be achieved by the proposed schemes by using multiple distorted receivers even without compensating the receiver distortion prior to the identification

66 citations


Cites background or methods from "Specific Emitter Identification Bas..."

  • ...Table VI lists the identification performance of the two groups of proposed schemes and the existing approaches in [23], [24] and [25] at SNR = 4, 12, 20 dB....

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  • ...In [25], the authors develop an SEI method based on the VMD and the spectral features....

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  • ...We evaluate the computational complexity of the proposed methods compared to the methods based on the time-frequency representation in [23], [24] and [25]....

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  • ...identification performance than the method in [25] with the SNR from 0 dB to 20 dB....

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  • ...signal pre-processing method has lower identification accuracy when SNR > 8 dB than the method in [25], but it outperforms the method in [25] at the low SNR....

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Journal ArticleDOI
TL;DR: Numerical results indicate that the proposed framework consistently outperforms state-of-the-art algorithms for unsupervised SEI applications, both in terms of evaluation score and classification accuracy.
Abstract: Machine learning approaches are becoming increasingly popular to improve the efficiency of specific emitter identification (SEI). However, in most non-cooperative SEI scenarios, supervised and semi-supervised learning approaches are often incompatible due to the lack of labeled datasets. To solve this challenge, an unsupervised SEI framework is proposed based on information maximized generative adversarial networks (InfoGANs) and radio frequency fingerprint embedding (RFFE). To enhance individual discriminability, a gray histogram is first constructed according to the bispectrum extracted from the received signal before being embedded into the proposed framework. In addition to the latent class input and the RFFE, the proposed InfoGAN incorporates a priori statistical characteristics of the wireless propagation channels in the form of a structured multimodal latent vector to further improve the GAN quality. The probabilistic distribution of the bispectrum is derived in closed-form and the convergence of the InfoGAN is analyzed to demonstrate the influence of the RFFE. Numerical results indicate that the proposed framework consistently outperforms state-of-the-art algorithms for unsupervised SEI applications, both in terms of evaluation score and classification accuracy.

65 citations


Cites methods from "Specific Emitter Identification Bas..."

  • ...EMD-EM2 [10], VMD-SF [44], and normalized permutation entropy (NPE) [8], as the control group to illustrate the advantages of our proposed RFFE-InfoGAN....

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  • ...We choose several representative and recently proposed SEI methods, i.e., compressed bispectrum (CBS) [42], Hilbert-Huang Transform (HHT) [43], EMD-EM2 [10], VMD-SF [44], and normalized permutation entropy (NPE) [8], as the control group to illustrate the advantages of our proposed RFFE-InfoGAN....

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  • ...Following the settings in [10] and [44], we choose the basic coefficients of the Taylor polynomials of 5 different emitters, β[1] = (1, 0....

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Journal ArticleDOI
TL;DR: A hybrid classifier that can adjust the prediction of deep learning models with the estimated CFO is designed to further increase the classification accuracy of the deep learning-based RFFI scheme for Long Range (LoRa) systems.
Abstract: Radio frequency fingerprint identification (RFFI) is an emerging device authentication technique that relies on the intrinsic hardware characteristics of wireless devices. This paper designs a deep learning-based RFFI scheme for Long Range (LoRa) systems. Firstly, the instantaneous carrier frequency offset (CFO) is found to drift, which could result in misclassification and significantly compromise the stability of the deep learning-based RFFI system. CFO compensation is demonstrated to be effective mitigation. Secondly, three signal representations for deep learning-based RFFI are investigated in time, frequency, and time-frequency domains, namely in-phase and quadrature (IQ) samples, fast Fourier transform (FFT) results and spectrograms, respectively. For these signal representations, three deep learning models are implemented, i.e., multilayer perceptron (MLP), long short-term memory (LSTM) network and convolutional neural network (CNN), in order to explore an optimal framework. Finally, a hybrid classifier that can adjust the prediction of deep learning models with the estimated CFO is designed to further increase the classification accuracy. The CFO will not change dramatically over several continuous days, hence it can be used to correct predictions when the estimated CFO is much different from the reference one. Experimental evaluation is performed in real wireless environments involving 25 LoRa devices and a Universal Software Radio Peripheral (USRP) N210 platform. The spectrogram-CNN model is found to be optimal for classifying LoRa devices which can reach an accuracy of 96.40% with the least complexity and training time.

60 citations

Journal ArticleDOI
TL;DR: A convolutional neural network-based RFFI protocol is proposed that can classify 50 and 200 devices with uniformly and randomly distributed IQ imbalances and PA nonlinearities with high accuracy and has some tolerance on different receiver imbalance during training and classification.
Abstract: Device authentication is essential for securing Internet of things. Radio frequency fingerprint identification (RFFI) is an emerging technique that exploits intrinsic and unique hardware impairments as the device identifier. The existing RFFI literature focuses on experimental exploration but comprehensive modelling is missing. This paper systematically models impairments of transmitter and receiver in narrowband systems and carries out extensive experiments and simulations to evaluate their effects on RFFI. The modelled impairments include oscillator imperfections, imbalance of inphase (I) and quadrature (Q) branches of mixers and power amplifier (PA) nonlinearity. We then propose a convolutional neural network-based RFFI protocol. We carry out experimental measurements over three months and demonstrate that oscillator imperfections are not suitable for RFFI due to their unpredictable time variation caused by temperature change. Our simulation results show that our protocol can classify 50 and 200 devices with uniformly and randomly distributed IQ imbalances and PA nonlinearities with high accuracy, namely 99% and 89%, respectively. We also show that the RFFI has some tolerance on different receiver imbalances during training and classification. Specifically, the accuracy is shown to degrade less than 20% when the residual receiver’s gain and phase imbalances are small. Based on the experimental and simulation results, we made recommendations for designing a robust RFFI protocol, namely compensate carrier frequency offset and calibrate IQ imbalances of receivers.

55 citations

References
More filters
Journal ArticleDOI
TL;DR: This work proposes an entirely non-recursive variational mode decomposition model, where the modes are extracted concurrently and is a generalization of the classic Wiener filter into multiple, adaptive bands.
Abstract: During the late 1990s, Huang introduced the algorithm called Empirical Mode Decomposition, which is widely used today to recursively decompose a signal into different modes of unknown but separate spectral bands. EMD is known for limitations like sensitivity to noise and sampling. These limitations could only partially be addressed by more mathematical attempts to this decomposition problem, like synchrosqueezing, empirical wavelets or recursive variational decomposition. Here, we propose an entirely non-recursive variational mode decomposition model, where the modes are extracted concurrently. The model looks for an ensemble of modes and their respective center frequencies, such that the modes collectively reproduce the input signal, while each being smooth after demodulation into baseband. In Fourier domain, this corresponds to a narrow-band prior. We show important relations to Wiener filter denoising. Indeed, the proposed method is a generalization of the classic Wiener filter into multiple, adaptive bands. Our model provides a solution to the decomposition problem that is theoretically well founded and still easy to understand. The variational model is efficiently optimized using an alternating direction method of multipliers approach. Preliminary results show attractive performance with respect to existing mode decomposition models. In particular, our proposed model is much more robust to sampling and noise. Finally, we show promising practical decomposition results on a series of artificial and real data.

4,111 citations


"Specific Emitter Identification Bas..." refers background or methods in this paper

  • ...Recently, variational mode decomposition (VMD) was proposed for simultaneous decomposition of all the modes non-recursively both in temporal and spectral domain [24]....

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  • ...VMD is a recent decomposition technique, which decomposes a multicomponent signal in several band-limited modes non-recursively [24]....

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  • ...The sequence of steps involved in VMD can be summarized as [24]....

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  • ...where, θ(t) is a non-decreasing phase function, E(t) is the envelope and ω(t) = θ ′(t) is the instantaneous frequency [24]....

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  • ...However, the major drawback of using EMD is that it suffers from mode mixing problem [24]....

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Journal ArticleDOI
TL;DR: The automatic classification of audio signals into an hierarchy of musical genres is explored and three feature sets for representing timbral texture, rhythmic content and pitch content are proposed.
Abstract: Musical genres are categorical labels created by humans to characterize pieces of music. A musical genre is characterized by the common characteristics shared by its members. These characteristics typically are related to the instrumentation, rhythmic structure, and harmonic content of the music. Genre hierarchies are commonly used to structure the large collections of music available on the Web. Currently musical genre annotation is performed manually. Automatic musical genre classification can assist or replace the human user in this process and would be a valuable addition to music information retrieval systems. In addition, automatic musical genre classification provides a framework for developing and evaluating features for any type of content-based analysis of musical signals. In this paper, the automatic classification of audio signals into an hierarchy of musical genres is explored. More specifically, three feature sets for representing timbral texture, rhythmic content and pitch content are proposed. The performance and relative importance of the proposed features is investigated by training statistical pattern recognition classifiers using real-world audio collections. Both whole file and real-time frame-based classification schemes are described. Using the proposed feature sets, classification of 61% for ten musical genres is achieved. This result is comparable to results reported for human musical genre classification.

2,668 citations


"Specific Emitter Identification Bas..." refers background in this paper

  • ...c) Spectral roll-off: Spectral roll-off can be defined as the frequency corresponding to the bin L below which certain percentage (usually 85% or 95%) of the total spectral energy is concentrated [30]....

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  • ...It is given by the ratio of geometric mean to the arithmetic mean of the spectrum [30]....

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  • ...sum of all the magnitudes in the spectrum [30]....

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Journal ArticleDOI
TL;DR: The authors provide a comprehensive survey of different modulation recognition techniques in a systematic way, and simulated some major techniques under the same conditions, which allows a fair comparison among different methodologies.
Abstract: 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.

1,140 citations


Additional excerpts

  • ...RF fingerprints and do not use any modulation recognition techniques to classify the modulation scheme as in [31]–[39]....

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Journal ArticleDOI
01 Mar 2010
TL;DR: This paper surveys existing literature about the application of genetic programming to classification, to show the different ways in which this evolutionary algorithm can help in the construction of accurate and reliable classifiers.
Abstract: Classification is one of the most researched questions in machine learning and data mining. A wide range of real problems have been stated as classification problems, for example credit scoring, bankruptcy prediction, medical diagnosis, pattern recognition, text categorization, software quality assessment, and many more. The use of evolutionary algorithms for training classifiers has been studied in the past few decades. Genetic programming (GP) is a flexible and powerful evolutionary technique with some features that can be very valuable and suitable for the evolution of classifiers. This paper surveys existing literature about the application of genetic programming to classification, to show the different ways in which this evolutionary algorithm can help in the construction of accurate and reliable classifiers.

506 citations


"Specific Emitter Identification Bas..." refers background in this paper

  • ...algorithm which analyzes the k number of nearest reference signals in the feature space and assigns the class to a testing signal [25], [26]....

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Journal ArticleDOI
TL;DR: Findings show that the HLRT suffers from very high complexity, whereas the QHLRT provides a reasonable solution, and an upper bound on the performance of QHL RT-based algorithms, which employ unbiased and normally distributed non-data aided estimates of the unknown parameters, is proposed.
Abstract: In this paper, likelihood-based algorithms are explored for linear digital modulation classification. Hybrid likelihood ratio test (HLRT)- and quasi HLRT (QHLRT)- based algorithms are examined, with signal amplitude, phase, and noise power as unknown parameters. The algorithm complexity is first investigated, and findings show that the HLRT suffers from very high complexity, whereas the QHLRT provides a reasonable solution. An upper bound on the performance of QHLRT-based algorithms, which employ unbiased and normally distributed non-data aided estimates of the unknown parameters, is proposed. This is referred to as the QHLRT-Upper Bound (QHLRT-UB). Classification of binary phase shift keying (BPSK) and quadrature phase shift keying (QPSK) signals is presented as a case study. The Cramer-Rao Lower Bounds (CRBs) of non-data aided joint estimates of signal amplitude and phase, and noise power are derived for BPSK and QPSK signals, and further employed to obtain the QHLRT-UB. An upper bound on classification performance of any likelihood-based algorithms is also introduced. Method-of-moments (MoM) estimates of the unknown parameters are investigated and used to develop the QHLRT-based algorithm. Classification performance of this algorithm is compared with the upper bounds, as well as with the quasi Log-Likelihood Ratio (qLLR) and fourth-order cumulant based algorithms.

351 citations