Blind parametric identification of non-Gaussian FIR systems using higher order cumulants

Safi, Saïd; Zeroual, Abdelouhab

doi:10.1080/00207720412331297929

Cited by 19 publications

(27 citation statements)

References 24 publications

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“…A detailed presentation of the theory of cumulants estimation can be found in [9], [4]. As cumulants are expressed in terms of moments, the estimates of cumulants are obtained as follows: …”

Section: Cumulants Estimationmentioning

confidence: 99%

“…In the literature there is many important algorithms based on higher order cumulant [1][2][3][4]. In this work, structure of the model is generally used single variable, discrete time, invariant in time.…”

Section: Algorithm Based On Cumulantsmentioning

confidence: 99%

“…The first objective is to make a comparative study of these algorithms, and then we will study our proposed algorithm , this last is a combination of two other existing algorithms [2] and [3], in order to have a better channel estimation in present of white Gaussian noise.…”

Section: Introductionmentioning

confidence: 99%

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Blind Identification of Transmission Channel with the method of Higher-OrderCummulants

Badi

Boutalline

Safi

2013

IJCT

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The modern telecommunication systems require very high transmission rates, in this context, the problem of channels identification is a challenge major. The use of blind techniques is a great interest to have the best compromise between a suitable bit rate and quality of the information retrieved.In this paper, we are interested to learn the algorithms for blind channel identification. We propose a hybrid method that performs a trade-off between two existing methods in order to improve the channel estimation.

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Section: Cumulants Estimationmentioning

confidence: 99%

Section: Algorithm Based On Cumulantsmentioning

confidence: 99%

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Blind Identification of Transmission Channel with the method of Higher-OrderCummulants

Badi

Boutalline

Safi

2013

IJCT

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“…In the literature we have important results, established that blind identification of finite impulse response (FIR) single-input single-output (SISO) communication channels is possible only from the output second order statistics of the observed sequences [1]. But these approaches are sufficient only to identify Gaussian processes with minimal phase [2][3][4]. Moreover, the system to be identified has no minimum phase, excited by non Gaussian distribution input, and is contaminated by a Gaussian noise [2,3] where the autocorrelation function (second order statistics) does not allow identifying the system correctly [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

“…But these approaches are sufficient only to identify Gaussian processes with minimal phase [2][3][4]. Moreover, the system to be identified has no minimum phase, excited by non Gaussian distribution input, and is contaminated by a Gaussian noise [2,3] where the autocorrelation function (second order statistics) does not allow identifying the system correctly [2][3][4]. To overcome these problems, another approach was proposed by several authors [5-9, 11, 12, 13].This approach allow the resolution of the insoluble problems using the second order statistics.…”

Section: Introductionmentioning

confidence: 99%

Broadband Radio Access Network Channel Identification and Downlink MC-CDMA Equalization

Zidane¹,

Safi²,

Sabri³

et al. 2014

IJEIC

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In this paper we propose a new algorithm based on third order cumulants, for MultiCarrier Code Division Multiple Access (MC-CDMA) system equalization. In order to test its efficiency, we have compared with the (CUM-AZ) algorithm proposed in the literature, for that we considered five practical frequency-selective fading channels, called Broadband Radio Access Network (BRAN A, BRAN B, BRAN C, BRAN D, and BRAN E), normalized for (MC-CDMA) system, excited by non-Gaussian sequences. In the part of (MC-CDMA), we use the zero forcing (ZF) and the minimum mean square error (MMSE) equalizers techniques after the channel identification to correct the channel's distortion. The simulation results, in noisy environment and for different signal to noise ratio (SNR), are presented to illustrate the performance of the proposed algorithm.

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Identification of quadratic systems using higher order cumulants and neural networks: Application to model the delay of video-packets transmission

Antari

Chabaa

Iqdour

et al. 2011

Applied Soft Computing

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Blind parametric identification of non-Gaussian FIR systems using higher order cumulants

Cited by 19 publications

References 24 publications

Blind Identification of Transmission Channel with the method of Higher-OrderCummulants

Blind Identification of Transmission Channel with the method of Higher-OrderCummulants

Broadband Radio Access Network Channel Identification and Downlink MC-CDMA Equalization

Identification of quadratic systems using higher order cumulants and neural networks: Application to model the delay of video-packets transmission

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