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

Badi, Imad; Boutalline, Mohammed; Safi, Saïd

doi:10.24297/ijct.v10i2.6998

Cited by 3 publications

(4 citation statements)

References 3 publications

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“…Single-user detectors consider only the active user signal; other users are treated as jammers. Single-user detectors typically use a linear equalization structure [3,25], consisting of an equalizer into an outlet. Using the above matrix notation, it is possible to express G, a diagonal matrix composed of coefficients equalization G:…”

Section: Figure 1 Principle Of Single-user Detectionmentioning

confidence: 99%

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Supervised identification and equalization of transmission channel using reproducing kernel Hilbert space

Badi

Badi²,

Khamjane³

et al. 2023

reks

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The subject matter of the article is to identify and equalize the parameters of telecommunication channels. The goal is to develop a new mathematical approach based on positive definite kernels on a Hilbert space. The tasks to be solved are: (a) to formulate a mathematical procedure based on a kernel; a kernel is a function that maps pairs of data points to a scalar value, and positive definite kernels are widely used in machine learning and signal processing applications; (b) to identify the channel parameters using the proposed method; and (c) to apply the Zero Forcing and MMSE equalizer to measure the performance of the proposed system. This article introduces a new method to address the problem of supervised identification of transmission channel parameters based on the positive definite kernel on Hilbert space, which implements Gaussian kernels. The input sequence, used as an input for a system or process, is assumed to be independent, have a zero mean, a non-Gaussian distribution, and be identically distributed. These assumptions are made to simplify the analysis and modeling. The proposed method for estimating the parameters of the channel impulse response yields promising results, indicating that the estimated parameters are close to the measured parameters of the model for various channels. The convergence of the estimated parameters toward the measured parameters of the model is particularly noticeable for BRAN A (indoor) and BRAN E (outdoor) channels. The method has been tested with different channel models, and the results remain consistent. Overall, the proposed method appears to be a reliable and effective approach for estimating channel impulse response parameters. The accuracy of the estimated parameters is particularly noteworthy considering the challenges inherent in modeling wireless channels, which can be influenced by various factors such as obstacles and interference. These findings have important implications for the design and optimization of wireless communication systems. Accurate estimates of channel impulse response parameters are essential for predicting and mitigating the effects of channel distortion and interference, and the proposed method represents a promising tool for achieving this goal. Further research and testing are needed to validate and refine the method and to explore its potential applications in different settings and scenarios. We evaluated the performance of the system using the estimated parameters obtained from the proposed method. Two equalizers, MMSE and ZF, were used, and the results show that MMSE outperforms ZF. Both equalizers produced highly satisfactory outcomes.

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Section: Figure 1 Principle Of Single-user Detectionmentioning

confidence: 99%

“…The identification of channel parameters has become a major topic addressed mainly by researchers in the signal-processing field [3]. In this work, we will apply a new method based on a positive-definite kernels on Hilbert space using a Gaussian kernel to identify the channel parameters [4].…”

Section: Introductionmentioning

confidence: 99%

Supervised identification and equalization of transmission channel using reproducing kernel Hilbert space

Badi

Badi²,

Khamjane³

et al. 2023

reks

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“…Moreover, their use in the case of identification of the noisy channel by a Gaussian white noise is very frequent. Our IJEECS ISSN: 2502-4752 411 study will focus on the blind identification of linear non-Gaussian process adjusted average (MA) [2,3,4,6,7]. To achieve this goal,we will present a typology of order cumulants, algorithm using three and four cumulants to improve the performance of standard algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, in digital communication, such phenomena altering the amplitude and the phase can be modeled by a multi-path transmission channel infected with a white noise. Such a model uses digital Finite Impulse Response filter (FIR) infected by a Gaussian white noise [2,3,4,5,6,7].Identification methods allow us to determine the channel impulse response of FIR. Tacking into account on related literature, one can identify more methods and identifications that can be classified into three categories according to resolution methods [8] [9].…”

mentioning

confidence: 99%

Using A Fuzzy Number Error Correction Approach to Improve Algorithms in Blind Identification

Atify¹,

Daoui²,

Boumezzough³

2016

IJEECS

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As part of a detailed study on blind identification of Gaussian channels, the main purpose was to propose an algorithm based on cumulants and fuzzy number approach involved throughout the whole process of identification. Our objective was to compare the new design of the algorithm to the old one using the higher order cumulants, namely Alg1, Algat and the Giannakis algorithm. We were able to demonstrate that the proposed method -fuzzy number error correction- increases the performance of the algorithm by calculating the ratio of squared errors of ALGaT and AlgatF. The method can be applied to any algorithm for more improvement and effinciency.

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A robust approach for Proakis and BRAN channel identification and equalization of MC-CDMA systems

Boutalline

Antari

Gouskir

et al. 2019

2019 5th International Conference on Optimization and Applications (ICOA)

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

Cited by 3 publications

References 3 publications

Supervised identification and equalization of transmission channel using reproducing kernel Hilbert space

Supervised identification and equalization of transmission channel using reproducing kernel Hilbert space

Using A Fuzzy Number Error Correction Approach to Improve Algorithms in Blind Identification

A robust approach for Proakis and BRAN channel identification and equalization of MC-CDMA systems

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