Measured and estimated data of non-linear BRAN channels using HOS in 4G wireless communications

Zidane, Mohammed; Safi, Saïd; Sabri, Mohamed

doi:10.1016/j.dib.2018.02.005

Cited by 4 publications

(2 citation statements)

References 3 publications

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“…The estimated parameters shown in Figures 12,13,15,16,18, and 19 evidently show that both KLMS and KRLS achieved higher prediction accuracy than the binary-measurement-based algorithms and cumulant-based algorithms for all values of SNR. Meanwhile, the performance of KRLS was also marginally better than that of KLMS, although it had a greater computational complexity.…”

Section: Magnitude and Phase Estimationmentioning

confidence: 91%

“…The synchronization required between the transmitter and the receiver is also a problem in the communication process. To handle this phase estimation problem, some authors have employed higher-order cumulants (HOCs) in order to investigate the reliability of these techniques if the channel is influenced by a colored noise [2,[9][10][11][12][13][14][15][16]. HOCs are a significant topic with multiple applications in the field of systems theory, such as in digital signal recognition [17], automatic modulation classification of real signals [18], and space-time block code identification [19].…”

Section: Introductionmentioning

confidence: 99%

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Channel Identification Based on Cumulants, Binary Measurements, and Kernels

Oualla

Fateh

Darif

et al. 2021

Systems

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In this paper, we discuss the problem of channel identification by using eight algorithms. The first three algorithms are based on higher-order cumulants, the next three algorithms are based on binary output measurement, and the last two algorithms are based on reproducing kernels. The principal objective of this paper is to study the performance of the presented algorithms in different situations, such as with different sizes of the data input or different signal-to-noise ratios. The presented algorithms are applied to the estimation of the channel parameters of the broadband radio access network (BRAN). The simulation results confirm that the presented algorithms are able to estimate the channel parameters with different accuracies, and each algorithm has its advantages and disadvantages for a given situation, such as for a given SNR and data input. Finally, this study provides an idea of which algorithms can be selected in a given situation. The study presented in this paper demonstrates that the cumulant-based algorithms are more adequate if the data inputs are not available (blind identification), but the kernel- and binary-measurement-based methods are more adequate if the noise is not important (SNR≥16 dB).

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Section: Magnitude and Phase Estimationmentioning

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Channel Identification Based on Cumulants, Binary Measurements, and Kernels

Oualla

Fateh

Darif

et al. 2021

Systems

Self Cite

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Performance Evaluation of MC-CDMA Systems with Single User Detection Technique using Kernel and Linear Adaptive Method

Fateh¹,

Darif²,

Safi³

2021

JTIT

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Among all the techniques combining multi-carrier modulation and spread spectrum, the multi-carrier code division multiple access (MC-CDMA) system is by far the most widely studied. In this paper, we present the performance of the MC-CDMA system associated with key single-user detection techniques. We are interested in problems related to identification and equalization of mobile radio channels, using the kernel method in Hilbert space with a reproducing kernel, and a linear adaptive algorithm, for MC-CDMA systems. In this context, we tested the efficiency of these algorithms, considering practical frequency selective fading channels, called broadband radio access network (BRAN), standardized for MC-CDMA systems. As far as the equalization problem encountered after channel identification is concerned, we use the orthogonality restoration combination (ORC) and the minimum mean square error (MMSE) equalizer techniques to correct the distortion of the channel. Simulation results demonstrate that the kernel algorithm is efficient for practical channels.

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A Novel Kernel Algorithm for Finite Impulse Response Channel Identiﬁcation

Fateh

Darif

Boumezzough

et al. 2023

JTIT

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Over the last few years, kernel adaptive ﬁlters have gained in importance as the kernel trick started to be used in classic linear adaptive ﬁlters in order to address various regression and time-series prediction issues in nonlinear environments.In this paper, we study a recursive method for identifying ﬁnite impulse response (FIR) nonlinear systems based on binary-value observation systems. We also apply the kernel trick to the recursive projection (RP) algorithm, yielding a novel recursive algorithm based on a positive deﬁnite kernel. For purposes, our approach is compared with the recursive projection (RP) algorithm in the process of identifying the parameters of two channels, with the ﬁrst of them being a frequency-selective fading channel, called a broadband radio access network (BRAN B) channel, and the other being a a theoretical frequency-selective channel, known as the Macchi channel. Monte Carlo simulation results are presented to show the performance of the proposed algorithm.

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Measured and estimated data of non-linear BRAN channels using HOS in 4G wireless communications

Cited by 4 publications

References 3 publications

Channel Identification Based on Cumulants, Binary Measurements, and Kernels

Channel Identification Based on Cumulants, Binary Measurements, and Kernels

Performance Evaluation of MC-CDMA Systems with Single User Detection Technique using Kernel and Linear Adaptive Method

A Novel Kernel Algorithm for Finite Impulse Response Channel Identiﬁcation

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