Automatic Modulation Classification

Zhu, Zhechen; Nandi, Asoke K.

doi:10.1002/9781118906507

Cited by 97 publications

(54 citation statements)

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“…In this study, in order to come up with optimal techniques for the solution of the considered problem, a statistical characterization of the received signal is utilized, and hence, related literature focusing on likelihood-based techniques are summarized in the following. In the context of likelihood-based modulation classification techniques, three prominent approaches are adopted based on how the unknown channel parameters and the transmitted symbol sequence are handled in the statistical model: the average likelihood ratio test (ALRT) [1], [2], [5], [8], [10], [15], [18], the generalized likelihood ratio test (GLRT) [1], [2], [5], [13], [17], [20] and the hybrid likelihood ratio test (HLRT) [1], [2], [5], [9], [10], [13], [14], [18], [21]. In the case of ALRT, all unknowns are treated as random variables with known prior distributions so that the likelihood expressions can be marginalized over the space of channel parameters and modulation symbols.…”

Section: A Motivation and Related Workmentioning

confidence: 99%

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Optimal Joint Modulation Classification and Symbol Decoding

Kazikli

Dulek

Gezici

2019

IEEE Trans. Wireless Commun.

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In this paper, modulation classification and symbol decoding problems are jointly considered and optimal strategies are proposed under various settings. In the considered framework, there exist a number of candidate modulation formats and the aim is to decode a sequence of received signals with an unknown modulation scheme. To that aim, two different formulations are proposed. In the first formulation, the prior probabilities of the modulation schemes are assumed to be known and a formulation is proposed under the Bayesian framework. This formulation takes a constrained approach in which the objective function is related to symbol decoding performance whereas the constraint is related to modulation classification performance. The second formulation, on the other hand, addresses the case in which the prior probabilities of the modulation schemes are unknown, and provides a method under the minimax framework. In this case, a constrained approach is employed as well; however, the introduced performance metrics differ from those in the first formulation due to the absence of the prior probabilities of the modulation schemes. Finally, the performance of the proposed methods is illustrated through simulations. It is demonstrated that the proposed techniques improve the introduced symbol detection performance metrics via relaxing the constraint(s) on the modulation classification performance compared with the conventional techniques in a variety of system configurations.

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Section: A Motivation and Related Workmentioning

confidence: 99%

“…, M ( ) } means that the transmitted symbol s k is decoded as μ ( ) m . Whenever a decision is declared in 2 Although an additive noise channel is assumed in this paper, the proposed framework generalizes in a straightforward manner to other memoryless channel models thanks to the general form of the statistical characterization…”

Section: A System Modelmentioning

confidence: 99%

Optimal Joint Modulation Classification and Symbol Decoding

Kazikli

Dulek

Gezici

2019

IEEE Trans. Wireless Commun.

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“…P cc is utilized to evaluate the performance of all the classifications throughout the paper. Because digital modulation has better immunity to interference [17] , it is mostly discussed in the literature regarding MC. Here, we assume that there is a single carrier-transmitted signal, whose possible modulation types include the BPSK, quadrature phase-shift keying (QPSK), 8PSK, 16 quadrature amplitude modulation (QAM), and the 64QAM.…”

Section: System Assumptionsmentioning

confidence: 99%

Modulation classification for cognitive radios using stacked denoising autoencoders

Zhu

Fujii

2016

Satell Commun Network

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Summary This paper proposes a modulation classification method based on stacked denoising autoencoders (SDAE). This method can extract the modulation features automatically and classify the input signals based on the extracted features. The scenarios of rapid classification and high‐accuracy classification are considered. In a rapid classification scenario, the classification speed has priority over the classification accuracy. Therefore, a long‐symbol sequence is not attainable for this scenario. Moreover, expert features are not necessary for this scenario, simplifying the modulation classification procedure and rendering rapid classification more achievable. In addition, in a high‐accuracy classification scenario, higher cumulants are used as the expert features owing to their advantage over the other features at noise resistance. We use complex symbols rather than pulse shaped complex signals as the network inputs, simplifying the network topology and reducing the calculation overhead. The results of the average classification accuracy, the individual classification accuracy, the execution time and the influence of the signal sampling synchronization are presented, demonstrating significant performance advantages over the other methods. Copyright © 2016 John Wiley & Sons, Ltd.

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“…The use of cyclostationay features for signal classification has been reported by Reference [9] in detail. Machine Learning classifiers such as decision tree (DT) [10], Support Vector Machine (SVM) [11], and k-nearest neighbor (KNN) [12] have been used widely as shallow classifiers to classify wireless signals based-on the aforementioned feature set. However, these conventional FB approaches mainly rely on expert knowledge which may perform well on the proposed solution but suffers from a lack of generality and time-consumption with high computational complexity.…”

Section: Introductionmentioning

confidence: 99%

Automatic Wireless Signal Classification: A Neural-Induced Support Vector Machine-Based Approach

et al. 2019

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Automatic Classification of Wireless Signals (ACWS), which is an intermediate step between signal detection and demodulation, is investigated in this paper. ACWS plays a crucial role in several military and non-military applications, by identifying interference sources and adversary attacks, to achieve efficient radio spectrum management. The performance of traditional feature-based (FB) classification approaches is limited due to their specific input feature set, which in turn results in poor generalization under unknown conditions. Therefore, in this paper, a novel feature-based classifier Neural-Induced Support Vector Machine (NSVM) is proposed, in which the features are learned automatically from raw input signals using Convolutional Neural Networks (CNN). The output of NSVM is given by a Gaussian Support Vector Machine (SVM), which takes the features learned by CNN as its input. The proposed scheme NSVM is trained as a single architecture, and in this way, it learns to minimize a margin-based loss instead of cross-entropy loss. The proposed scheme NSVM outperforms the traditional softmax-based CNN modulation classifier by managing faster convergence of accuracy and loss curves during training. Furthermore, the robustness of the NSVM classifier is verified by extensive simulation experiments under the presence of several non-ideal real-world channel impairments over a range of signal-to-noise ratio (SNR) values. The performance of NSVM is remarkable in classifying wireless signals, such as at low signal-to-noise ratio (SNR), the overall averaged classification accuracy is > 97% at SNR = −2 dB and at higher SNR it achieves overall classification accuracy at > 99%, when SNR = 10 dB. In addition to that, the analytical comparison with other studies shows the performance of NSVM is superior over a range of settings.

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Automatic Modulation Classification

Cited by 97 publications

References 0 publications

Optimal Joint Modulation Classification and Symbol Decoding

Optimal Joint Modulation Classification and Symbol Decoding

Modulation classification for cognitive radios using stacked denoising autoencoders

Automatic Wireless Signal Classification: A Neural-Induced Support Vector Machine-Based Approach

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