Principal component analysis of cyclic spectrum features in automatic modulation recognition

He, Fangming; Yin, Yafeng; Zhou, Lei; Xu, Xingzhong; Man, Hong

doi:10.1109/milcom.2010.5680239

Cited by 15 publications

(12 citation statements)

References 11 publications

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“…A cyclostationary signal is a kind of signal whose mathematical expectation and auto-correlation function change periodically [11]. It is different from the periodic signal and stationary signal; these two kinds of signals are only a special case of it.…”

Section: Symbol Rate Estimation Based On Cyclic Spectrummentioning

confidence: 99%

Symbol Rate Estimation based on Wavelet Transform and Cyclic Spectrum

Li¹,

Hao²,

Zhang³

2019

Int J Performability Eng

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Aiming at the digital modulation signals such as MASK, MPSK, and MQAM in a Gaussian white noise environment, this paper theoretically analyzes the reason and rule of the appearance of the spectrum of wavelet transform and the symbol rate spectrum line of cyclic spectrum. The simulation results show that the method based on wavelet transform is better than that based on cyclic spectrum, and the SNR threshold of the first method is reduced by 8dB compared with the latter.

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Section: Symbol Rate Estimation Based On Cyclic Spectrummentioning

confidence: 99%

Symbol Rate Estimation based on Wavelet Transform and Cyclic Spectrum

Li¹,

Hao²,

Zhang³

2019

Int J Performability Eng

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“…The PCA can be considered as a projection method, that projects the observations from the N-dimensional space of N variables to a K-dimensional space (K <<N) [15], such that maximum information is retained (the information here is measured through the total variance of the point cloud) on the first dimensions. The purpose of the PCA is condensing the original data into new group, with the aim of removing the correlation between the objects, and ordering them in terms of the variance percentage contributed by each component.…”

Section: Principal Component Analysismentioning

confidence: 99%

“…The most efficient algorithm to optimize the global error ( ) is the gradient descent [15], the aim is to start from a random point, then move to the direction of the steepest descent by applying a certain number of iterations. The algorithm converges to a local minimum.…”

Section: Multi-layer Perceptronmentioning

confidence: 99%

Signal detection and automatic modulation classification based spectrum sensing using PCA-ANN with real word signals

Elrharras¹,

Saadane²,

Wahbi³

et al. 2014

ams

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Cognitive radio has been proposed as an optimal solution to increase spectrum efficiency, by exploiting unused portions of radio spectrum. The first and the most important function in cognitive radio equipment, is spectrum sensing, wherein a cognitive user must sense his environment to detect a free band, then use it temporarily without causing any interference to the primary user (PU). It has been shown that the energy detection is the most chosen method, to detect the spectrum holes in the case where there is no a priori information about the PU. In this paper, we are describing tow stages architecture: The first stage is used to detect the presence of the PU with an improved energy detection technique, and the second is utilized to classify the modulation type, by combining principal component analysis to extract signal features and neural network to make the decision. Performance evaluation of our proposed architecture has been done by real word signals. 7960 A. Elrharras et al.

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“…In [8], the wavelet transform for feature extraction on QAM, PSK and FSK signal samples is used, while authors in [5] use features obtained from wavelet domain to perform AMC with the use of an ANN. There are also works proposing different AMC techniques based on higher order statistics and cyclostationary features from the modulated signals [4], Principal Component Analysis (PCA) [7], and ANN with Fuzzy Logic [17]. In fact, some of those pre-processing tasks demand a high computational cost, which limits its application, or even make it not practical for real-time systems with current off-the-shelf technology.…”

Section: Automatic Modulation Classification (Amc)mentioning

confidence: 99%

“…AMC techniques currently reported on literature [2]- [5], [7], [8], [10], [16], [17] employ a pre-processing module in order to extract signal features usable for classification, which may, depending on the applied mechanism, make assumptions about the received signal which may not hold (e.g. AWGN being the unique source of noise), or even can demand a high computational cost to be implemented.…”

Section: Introductionmentioning

confidence: 99%

Novel Automatic Modulation Classification using Correntropy Coefficient

Fontes¹,

Cavalcante²,

Silveira³

2012

Anais De XXX Simpósio Brasileiro De Telecomunicações

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This paper deals with automatic modulation classification (AMC) of communication signals. A new method for the automatic classification using a similarity measure derived from Information Theoretic Learning (ITL), called correntropy coefficient, is proposed. Unlike many of the conventional methods, the proposed method does not require any signal pre-processing. Further, the proposed AMC technique uses a simple scheme of evaluating the correntropy coefficients, calculated over templates containing the common features of digitally modulated signals, in the classification task. The performance of the classifier is presented in the form of classification hit-rates under AWGN noisy conditions, with Signal-to-Noise Ratios (SNRs) at the range of −5 dB to 15 dB. Simulation results with binary modulations show classification hit-rates of 83% for −5 dB and 99% for 0 dB.

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Principal component analysis of cyclic spectrum features in automatic modulation recognition

Cited by 15 publications

References 11 publications

Symbol Rate Estimation based on Wavelet Transform and Cyclic Spectrum

Symbol Rate Estimation based on Wavelet Transform and Cyclic Spectrum

Signal detection and automatic modulation classification based spectrum sensing using PCA-ANN with real word signals

Novel Automatic Modulation Classification using Correntropy Coefficient

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