Digital modulation recognition based on instantaneous information

Hu, Youqiang; Liu, Juan; Tan, Xiaoheng

doi:10.1016/s1005-8885(09)60471-4

Cited by 16 publications

(12 citation statements)

References 8 publications

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“…Basing on the definition above, we can get a 12-dimensional original vector: how to decide if the signal is non-weak one by means of threshold comparison, as described in [5,6]. Moreover, the feature parameters have high enough signal/noise ratio, so that it needn't complex de-noising, such as linear filtering and wavelet filtering described as reference [6].…”

Section: A the Original Feature Vectormentioning

confidence: 99%

“…Moreover, the feature parameters have high enough signal/noise ratio, so that it needn't complex de-noising, such as linear filtering and wavelet filtering described as reference [6].…”

Section: A the Original Feature Vectormentioning

confidence: 99%

“…Feature parameters extraction is to extract intrinsic attributes for various modulation types, which is a projection process from observation space to feature space. It can be realized from time domain [4][5][6], frequency domain [7], time-frequency domain [8], correlation domain [9], high order cumulant domain [10][11], high order statistics domain [3], fractal domain [12] or constellation [13] , as well as their combination domains [14]. Classification means making decision on the modulation type based on extracted features, which can also be regarded as projection from feature space to decision space.…”

Section: Introductionmentioning

confidence: 99%

“…If we can transform the original feature vectors into an orthogonal space, then we can remarkably reduce the dimension of feature vectors to get better classification effect, where dimension should be as low as possible for maximizing the difference between different pattern types [4][5][6][7][8][9][10][11][12][13]. In the other hand, if we can get feature vectors in multiple domains which are independent from each other, composing feature vectors of higher dimension will benefit to classification effect or be able to classify more pattern types [14].…”

Section: Introductionmentioning

confidence: 99%

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A modulation recognizer with ITD-based features

Tian

et al. 2011

2011 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC)

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This paper proposes a modulation recognizer based on the feature vectors obtained by Intrinsic Time-scale Decomposition(ITD) algorithm and Support Vector Machine(SVM). ITD is employed to extract time-frequency information of communication signals and the obtained feature vectors are transformed into lower-dimensional subspace according to Fisher analysis theory. Multiclass SVM is employed to modulation classification, including 7 types of digital modulation such as 2ASK, 4ASK, 2PSK, 4PSK, 16QAM, 2FSK and 4FSK. The ITD-based features can be directly obtained by means of waveform analysis, which not only has very low computational complexity, but also have good discriminability because of dimension reduce based on Fisher analysis. Simulation results show that the modulation recognizer can provide very high recognition accuracy with very low processing complexity.

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Section: A the Original Feature Vectormentioning

confidence: 99%

“…Moreover, the feature parameters have high enough signal/noise ratio, so that it needn't complex de-noising, such as linear filtering and wavelet filtering described as reference [6].…”

Section: A the Original Feature Vectormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A modulation recognizer with ITD-based features

Tian

et al. 2011

2011 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC)

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“…During the past few years, there have been many research papers on wireless signal modulation recognition [3][4][5][6][7], but few published papers can be found for the optical signal. Eric et al [8] have demonstrated the usage of physical layer characteristics coupled with a digital coherent detection technique for the received optical signal modulation format recognition.…”

Section: Introductionmentioning

confidence: 99%

Using Multidimensional ADTPE and SVM for Optical Modulation Real-Time Recognition

Wei

Huang

et al. 2016

Entropy

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Based on the feature extraction of multidimensional asynchronous delay-tap plot entropy (ADTPE) and multiclass classification of support vector machine (SVM), we propose a method for recognition of multiple optical modulation formats and various data rates. We firstly present the algorithm of multidimensional ADTPE, which is extracted from asynchronous delay sampling pairs of modulated optical signal. Then, a multiclass SVM is utilized for fast and accurate classification of several widely-used optical modulation formats. In addition, a simple real-time recognition scheme is designed to reduce the computation time. Compared to the existing method based on asynchronous delay-tap plot (ADTP), the theoretical analysis and simulation results show that our recognition method can effectively enhance the tolerance of transmission impairments, obtaining relatively high accuracy. Finally, it is further demonstrated that the proposed method can be integrated in an optical transport network (OTN) with flexible expansion. Through simply adding the corresponding sub-SVM module in the digital signal processer (DSP), arbitrary new modulation formats can be recognized with high recognition accuracy in a short response time.

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An Automatic Digital Modulation Classifier Using Higher-Order Statistics for Software-Defined Radios

Marriwala

Ghunsar

2021

Advances in Intelligent Systems and Computing

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Digital modulation recognition based on instantaneous information

Cited by 16 publications

References 8 publications

A modulation recognizer with ITD-based features

A modulation recognizer with ITD-based features

Using Multidimensional ADTPE and SVM for Optical Modulation Real-Time Recognition

An Automatic Digital Modulation Classifier Using Higher-Order Statistics for Software-Defined Radios

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