Signal classification using statistical moments

Soliman, S.S.; Hsue, S.-Z.

doi:10.1109/26.141456

Cited by 239 publications

(95 citation statements)

References 7 publications

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“…the slope of the ASRR is also constant. This demonstrates that the algorithm [12] is less sensitive to noise variance compared to the algorithm in [2]. Under low SNR (from 0 to 3 dB), the statistical moments algorithm has a better performance compared to the algorithm in [2].…”

Section: Simulation Resultsmentioning

confidence: 87%

“…Under low SNR (from 0 to 3 dB), the statistical moments algorithm has a better performance compared to the algorithm in [2]. However, the algorithm in [2] outperform the algorithm in [12] after the cross-over points. As the noise variance is involved in the cost function of our proposed model fitting algorithm, it provides a robustness of noise sensitivity.…”

Section: Simulation Resultsmentioning

confidence: 96%

“…Other recognition algorithms based on higher order statistical moments in [12] are also being compared. In this simulation, we would compare the performance of ASRR Figure 7 shows the average successful recognition rate of different algorithms.…”

Section: Simulation Resultsmentioning

confidence: 99%

See 2 more Smart Citations

Clustering based distribution fitting algorithm for Automatic Modulation Recognition

Woo

Kok

2007

2007 IEEE Symposium on Computers and Communications

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Automatic Modulation Recognition (AMR) is an expert in modulation type identification. Many existing algorithms attempt to recognize the modulation candidates using phase and magnitude feature extraction. Performance is a major drawback of thisfeature extraction under noisy environment. In this paper, we proposed a new algorithm using a modified Chi-squared test on clustered received signals as components to its performance function. Simulations show that even under low SNR environment, our proposed algorithm achieved higher recognition rate than other existing algorithms.

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Section: Simulation Resultsmentioning

confidence: 87%

Section: Simulation Resultsmentioning

confidence: 96%

See 1 more Smart Citation

Clustering based distribution fitting algorithm for Automatic Modulation Recognition

Woo

Kok

2007

2007 IEEE Symposium on Computers and Communications

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“…These works assumed ideal carrier synchronization. In [4], a method using moments of the received signal phase was examined. This method classifies the modulation order for MPSK under a known SNR condition.…”

Section: Introductionmentioning

confidence: 99%

PSK and QAM Classiﬁcation by Likelihood under Unknown SNR Condition

Nishijima

Oka

Ata

2016

Journal of Signal Processing

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Modulation classification is a key technology in current wireless communication systems. The technology is used to find the modulation employed or estimate channel state information (CSI). In this paper, a modulation classification method is proposed for phase shift keying (PSK) and quadrature amplitude modulation (QAM) under an unknown signal-to-noise ratio (SNR) condition. The method works without carrier synchronization and is based on likelihood estimation using the probability density function (pdf) of the received signal envelope. The superior classification error rate of the proposed method is demonstrated by computer simulations.

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“…Observe that this involves an extra variable z as compared to the case of binary images treated in [ 11. We define spatial power moments (PM) ( p , q, s) of (integer) order p , q , s of positive integer random variables T , 8 and z as:…”

Section: Spatial Power Momentsmentioning

confidence: 99%

Digital modulation classification using power moment matrices

Hero

Hadinejad-Mahram²

Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181

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With the rising number of modulation types used in multi-user and multi-service digital communication systems, the need to find efficient methods for their discrimination in the presence of noise has become increasingly important. Here, we present a new approach based on a recently developed pattern recognition method previously applied to word spotting problems in binary images [ 11. In this approach, a large number of spatial moments are arranged in a symmetric positive definite matrix for which eigendecomposition and noise subspace processing methods can be applied. The resultant denoised moment matrix has entries which are used in place of the raw moments for improved pattern classification. In this paper, we generalize the moment matrix technique to grey scale images and apply the technique to discrimination between M-ary PSK and QAM constellations in signal space. Invariance to unknown phase angle and signal amplitude is achieved by representing the in-phase and quadrature components of the signal in the complex plane, and computing joint moments of normalized magnitude and phase components.

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Signal classification using statistical moments

Cited by 239 publications

References 7 publications

Clustering based distribution fitting algorithm for Automatic Modulation Recognition

Clustering based distribution fitting algorithm for Automatic Modulation Recognition

PSK and QAM Classiﬁcation by Likelihood under Unknown SNR Condition

Digital modulation classification using power moment matrices

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