A Maximum Entropy inspired model for the convolutional noise PDF

Freiman, Adiel; Pinchas, Monika

doi:10.1016/j.dsp.2014.12.011

Cited by 8 publications

(23 citation statements)

References 22 publications

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“…For Fig. 2, we have chosen the step-size parameters for the MMA and WNEW algorithm approximately ten times smaller than the values given in (25) and (29) respectively. According to Fig.…”

Section: Simulationmentioning

confidence: 99%

“…Let us consider for a moment the digital communication case. During transmission, a source signal undergoes a convolutive distortion between its symbols and the channel impulse response [29]. This distortion is referred to as intersymbol interference (ISI) [30], [29].…”

Section: Introductionmentioning

confidence: 99%

“…During transmission, a source signal undergoes a convolutive distortion between its symbols and the channel impulse response [29]. This distortion is referred to as intersymbol interference (ISI) [30], [29]. Thus, a blind adaptive filter is used to remove the convolutive effect of the system to produce the source signal [30].…”

Section: Introductionmentioning

confidence: 99%

“…This process is called blind deconvolution. Since the updated coefficients used in the blind adaptive filter are not the ideal values, a noise named as "convolutional noise" is observed at the output of the deconvolution process in addition to the source signal [29]. In the literature, the convolutional noise is often modeled as a zero-mean-white-noise for a zero-mean input sequence [20], [31]- [37].…”

Section: Introductionmentioning

confidence: 99%

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Convolutional Noise PDF at the Convergence State of a Blind Adaptive Equalizer

Pinchas

2018

MATEC Web Conf.

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In the literature, the convolutional noise obtained at the output of a blind adaptive equalizer, is often modeled as a Gaussian process during the latter stages of the deconvolution process where the process is close to optimality. However, up to now, no strong mathematical basis was given supporting this phenomenon. Furthermore, no closed-form or closed-form approximated expression is given that shows what are the constraints on the system's parameters (equalizer's tap-length, input signal statistics, channel power, chosen equalization method and step-size parameter) for which the assumption of a Gaussian model for the convolutional noise holds. In this paper, we consider the two independent quadrature carrier input case and type of blind adaptive equalizers where the error that is fed into the adaptive mechanism which updates the equalizer's taps can be expressed as a polynomial function of the equalized output up to order three. We show based on strong mathematical basis that the convolutional noise pdf at the latter stages of the deconvolution process where the process is close to optimality, is approximately Gaussian if complying on some constraints depending on the step-size parameter, input constellation statistics, channel power, chosen equalization method and equalizer's tap-length. Simulation results confirm our findings.

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“…For Fig. 2, we have chosen the step-size parameters for the MMA and WNEW algorithm approximately ten times smaller than the values given in (25) and (29) respectively. According to Fig.…”

Section: Simulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Convolutional Noise PDF at the Convergence State of a Blind Adaptive Equalizer

Pinchas

2018

MATEC Web Conf.

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“…In this paper, the blind adaptive deconvolution problem is addressed, which arises in many applications, such as seismology, underwater acoustic, image restoration and digital communication [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 ]. In digital communication applications, the problem is often called blind adaptive equalization.…”

Section: Introductionmentioning

confidence: 99%

A New Efficient Expression for the Conditional Expectation of the Blind Adaptive Deconvolution Problem Valid for the Entire Range ofSignal-to-Noise Ratio

Pinchas

2019

Entropy

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In the literature, we can find several blind adaptive deconvolution algorithms based on closed-form approximated expressions for the conditional expectation (the expectation of the source input given the equalized or deconvolutional output), involving the maximum entropy density approximation technique. The main drawback of these algorithms is the heavy computational burden involved in calculating the expression for the conditional expectation. In addition, none of these techniques are applicable for signal-to-noise ratios lower than 7 dB. In this paper, I propose a new closed-form approximated expression for the conditional expectation based on a previously obtained expression where the equalized output probability density function is calculated via the approximated input probability density function which itself is approximated with the maximum entropy density approximation technique. This newly proposed expression has a reduced computational burden compared with the previously obtained expressions for the conditional expectation based on the maximum entropy approximation technique. The simulation results indicate that the newly proposed algorithm with the newly proposed Lagrange multipliers is suitable for signal-to-noise ratio values down to 0 dB and has an improved equalization performance from the residual inter-symbol-interference point of view compared to the previously obtained algorithms based on the conditional expectation obtained via the maximum entropy technique.

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Joint estimation of time difference of arrival and frequency difference of arrival for cyclostationary signals under impulsive noise

Liu

Qiu

2015

Digital Signal Processing

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A Maximum Entropy inspired model for the convolutional noise PDF

Cited by 8 publications

References 22 publications

Convolutional Noise PDF at the Convergence State of a Blind Adaptive Equalizer

Convolutional Noise PDF at the Convergence State of a Blind Adaptive Equalizer

A New Efficient Expression for the Conditional Expectation of the Blind Adaptive Deconvolution Problem Valid for the Entire Range ofSignal-to-Noise Ratio

Joint estimation of time difference of arrival and frequency difference of arrival for cyclostationary signals under impulsive noise

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