Classification of digitally modulated signals in presence of non-Gaussian HF noise

Hazza, Alharbi; Shoaib, Mohammed; Alshebeili, Saleh; Fahd, Alturki

doi:10.1109/iswcs.2010.5624339

Cited by 8 publications

(8 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…1 that, by choosing the appropriate thresholds, the statistical mean value of the HS matrix can be used for identifying the modulation levels of M-ary QAM signals in the presence of the AWGN. Table I, [13]. The noise is modeled as AWGN with 0 mean.…”

Section: B Hht Based Identification Algorithmmentioning

confidence: 99%

Modulation identification of digital M-ary QAM signals by Hilbert-Huang Transform

Tanc

Akan

2013

2013 IEEE 20th International Conference on Electronics, Circuits, and Systems (ICECS)

View full text Add to dashboard Cite

The problem of identifying the modulation types of the signals at the receiver is an intermediate step between signal detection and demodulation. In this paper, Hilbert-Huang Transform is proposed for identifying the modulation level of M-ary Quadrature Amplitude Modulation (QAM) signals in the presence of the Additive White Gaussian Noise. HilbertHuang Transform decomposes the non-stationary signal into sum of oscillatory signals with different frequency and obtains the instantaneous features of the signals, like time-frequency and time-amplitude information. The statistical properties of instantaneous data sets are used to determine the modulation levels of M-ary QAM signals. The most important feature of the proposed method is that, the algorithm does not utilize any a priori knowledge about the signal. Computer simulations demonstrate the accuracy of the proposed algorithm.

show abstract

Section: B Hht Based Identification Algorithmmentioning

confidence: 99%

Modulation identification of digital M-ary QAM signals by Hilbert-Huang Transform

Tanc

Akan

2013

2013 IEEE 20th International Conference on Electronics, Circuits, and Systems (ICECS)

View full text Add to dashboard Cite

show abstract

“…Spectral peaks of the prediction coefficients are an efficient statistical feature for FSK modulation classification [21,8].…”

Section: A Number Of Peaks In Frequency Response Of Prediction Coeffmentioning

confidence: 99%

“…All literatures focus on modulated signals corrupted with A WGN, which is not the case in the HF band as the noise probability density function could follow Gaussian or Bi kappa distribution depending on the day time [5,6]. In addition, none of the literatures considered the set of modulations addressed in this work except [21], which has small average of correct classifications at low SNR. In this paper, we propose a novel AMC algorithm that uses robust features against the noise power and the change in noise model.…”

Section: Introductionmentioning

confidence: 99%

“…Second, we use these features with Support Vector Machine (SVM) for classification of PSK and QAM signals. Second, we consider features that are related to spectral peaks associated with the frequency response of estimated Auto Regressive (AR) coefficients [8,21]. We add smoothing filter before computing the spectral peaks so as to use it for the discrimination between PSKlQAM and FSK modulations.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A novel approach for automatic classification of digitally modulated signals in HF communications

Hazza

Shoaib

Alshebeili

et al. 2010

The 10th IEEE International Symposium on Signal Processing and Information Technology

Self Cite

View full text Add to dashboard Cite

Abstrac t-In this paper, we propose a novel algorithm for automatic modulation classification of single carrier digital modulations widely used in High Frequency (HF) band that serves both military and civilian applications. Specifically, the proposed algorithm addresses the classification of 2FSK, 4FSK, 8FSK, 2PSK, 4PSK, 8PSK, 16QAM. 32QAM. 64QAM, and OQPSK using two features: Dissimilarity in constellation diagram for classification of PSKlQAM signals and spectral peaks for classification of FSK signals with the use of both Decision Tree (D1) and Support Vector Machine (SVM) classifiers. The robustness of proposed algorithm is tested against noise power and changes in noise model. Additive White Gaussian Noise (A WGN) and Bi-kappa noise models are considered in this work, as both exist in HF band

show abstract

“…In [5], it is found that the traditional LB algorithms do not give optimum results in time correlated additive noise. It is found from literature that most of the classification algorithms are developed by extracting modulation dependent features from the received signal under non-Gaussian environment [2], [10]- [13]. If these feature are not robust or properly designed, performance of classification algorithms degrade.…”

Section: Introductionmentioning

confidence: 99%

Sparse decomposition framework for maximum likelihood classification under alpha-stable noise

Mohanty

Satija

Ramkumar

2015

2015 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT)

View full text Add to dashboard Cite

Recently, automatic modulation classification has gained a lot of attention in the area of cognitive radio (CR), signal detection, electronic warfare and surveillance etc. Most of the existing modulation classification algorithms are developed based on the assumption that the received signal to be identified is corrupted by only additive white Gaussian noise. The performances of these conventional algorithms degrade significantly by addition of impulse noise. In this paper, we propose a robust algorithm using sparse signal decomposition which comprises of an overcomplete dictionary for detection and classification of modulated signals. In this work, an overcomplete dictionary is constructed using the identity basis, cosine and sine elementary waveforms to capture morphological components of the impulse noise and deterministic modulated signals effectively. The proposed method of modulation classification consists of the three major steps: sparse signal decomposition (SSD) on hybrid dictionaries, modulated signal extraction, and maximum likelihood (ML) based classification. The testing and validation of both direct ML and SSD-based ML classification methods are carried out under different Gaussian and impulse noise conditions for modulation classification. Our proposed method achieves a classification accuracy of 85% at 5 dB SNR and outperforms the conventional classification methods.

show abstract

Classification of digitally modulated signals in presence of non-Gaussian HF noise

Cited by 8 publications

References 19 publications

Modulation identification of digital M-ary QAM signals by Hilbert-Huang Transform

Modulation identification of digital M-ary QAM signals by Hilbert-Huang Transform

A novel approach for automatic classification of digitally modulated signals in HF communications

Sparse decomposition framework for maximum likelihood classification under alpha-stable noise

Contact Info

Product

Resources

About