Classification of MFSK signals over time-varying flat correlated fading channels under class-A impulsive noise environment

El-Mahdy, Ahmed

doi:10.1049/ip-com:20040913

Cited by 11 publications

(8 citation statements)

References 16 publications

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“…The complexity of the proposed algorithm is approximately O(K 3 ) per iteration, and is dominated by the matrix inversion in (12). The complexity of generating a random variable with a given posterior pdf is relatively small (when compared with matrix inversion, for example).…”

Section: E Summary and A Note On Complexitymentioning

confidence: 99%

A Blind Pre-Processor for Modulation Classification Applications in Frequency-Selective Non-Gaussian Channels

Amuru

Silva

2014

IEEE Trans. Commun.

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This paper presents a new preprocessing stage that allows for the reliable classification of digital amplitude-phase modulated signals in a practical scenario where: 1) the classifier has no knowledge of the timing (symbol transition epochs) of the received signal; 2) the noise added in the channel is non-Gaussian; and 3) the fading experienced by the signal is frequency selective. The proposed preprocessor, which is based on the Gibbs sampling algorithm, is used to acquire timing information and to estimate the channel state and noise distribution parameters blindly, i.e., without knowledge of the received symbol sequence and the modulation scheme used. With the obtained estimates, in a second processing stage, the signal is then classified by using an appropriate (likelihood-or feature-based) classification algorithm. To quantify the performance of the proposed preprocessor, the probability of correct classification obtained by using the preprocessor with different classification algorithms is presented. It is shown that, by using the proposed preprocessor, modulation classification algorithms can perform well compared with clairvoyant classifiers assumed to be symbol synchronous with the received signal and to have perfect knowledge of the channel state and noise distribution.

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Section: E Summary and A Note On Complexitymentioning

confidence: 99%

A Blind Pre-Processor for Modulation Classification Applications in Frequency-Selective Non-Gaussian Channels

Amuru

Silva

2014

IEEE Trans. Commun.

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“…For example, noises from various natural and man-made sources exhibit sharp spike and hence, impulsive in characteristic [4]- [7]. This non-Gaussian noise is one of the prime source of error in digital transmission system [8], [9]. Hence, there is a need of more realistic approach to implement noise model that comprises additive mixture of Gaussian noise and non-Gaussian impulsive noise.…”

Section: Introductionmentioning

confidence: 97%

“…If these feature are not robust or properly designed, performance of classification algorithms degrade. Few literature have been addressed the modulation classification in presence of non-Gaussian noise using LB classifiers [2], [5], [8]. The classifier implements the whitening filter to minimize the complexity of ML in presence of impulse noise, where the unknown parameter and whitening filter coefficients are estimated [5].…”

Section: Introductionmentioning

confidence: 99%

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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)

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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.

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“…It is shown that the ML classifier is capable of classifying any finite set of distinctive constellations with zero error rates when the number of available data symbols goes to infinity. In [5], a classifier for MFSK signals contaminated with class-A impulsive noise and transmitted over time varying flat correlated fading channel is developed. In [6], the author introduced a modulation classifier based on the statistical moments of the intercepted signal phase to estimate the number of levels, M, in MPSK signals.…”

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confidence: 99%

Automatic Classification of MPSK Signals Using Statistical Moments

Helaly

2006

The International Conference on Electrical Engineering

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In this paper, an automatic classification algorithm for MPSK signals is proposed. In this algorithm, it is assumed that there is prior information about the received signal to be an MPSK type. The concept of the proposed classification algorithm is based on evaluating the statistical moments of the instantaneous phase of the received signal and using it as a key feature to classify the MPSK signals. The proposed algorithm comprises three main steps: 1) estimation of the instantaneous phase of the received signal, 2) computation of statistical moments of the estimated phase, and 3) decision about the number of phase states of the intercepted signal. The performance of the proposed algorithm is measured in terms of the success rate of classification using computer simulations. It is found that all digital modulation types of interest have been correctly classified with a success rate > 91 % at signal-to-noise ratio, SNR, of 10 dB.

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Classification of MFSK signals over time-varying flat correlated fading channels under class-A impulsive noise environment

Cited by 11 publications

References 16 publications

A Blind Pre-Processor for Modulation Classification Applications in Frequency-Selective Non-Gaussian Channels

A Blind Pre-Processor for Modulation Classification Applications in Frequency-Selective Non-Gaussian Channels

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

Automatic Classification of MPSK Signals Using Statistical Moments

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