A closed approximated formed expression for the achievable residual intersymbol interference obtained by blind equalizers

Pinchas, Monika

doi:10.1016/j.sigpro.2009.12.014

Cited by 21 publications

(59 citation statements)

References 19 publications

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“…Thus, this might be the reason why having a larger convergence speed for higher numbers of coefficients in the equalizer. It should be pointed out, that this was also observed in [15] where higher numbers of coefficients in the equalizer have lead to a longer convergence speed. Obviously, choosing a higher step-size parameter may increase the convergence speed but on the same time it increases also the residual ISI which might not meet any more the system's requirements.…”

Section:  mentioning

confidence: 60%

Two Blind Adaptive Equalizers Connected in Series for Equalization Performance Improvement

Pinchas¹

2013

JSIP

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A variable step-size parameter is usually used to accelerate the convergence speed of a blind adaptive equalizer with N 1 + N 2 − 1 coefficients where N 1 and N 2 are odd values. In this paper we show that improved equalization performance is achieved when using two blind adaptive equalizers connected in series where the first and second blind adaptive equalizer have N 1 and N 2 coefficients respectively compared with the case where a single blind adaptive equalizer is applied with N 1 + N 2 − 1 coefficients. It should be pointed out that the same algorithm (cost function) is used for updating the filter taps for the different equalizers and that a fixed step-size parameter is used. Simulation results show that for the low signal to noise ratio (SNR) environment and for the case where the convergence speed is slow due to the channel characteristics, the new method has a faster convergence speed with a factor of approximately two while leaving the system with approximately the same or lower residual intersymbol interference (ISI).

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Section:  mentioning

confidence: 60%

Two Blind Adaptive Equalizers Connected in Series for Equalization Performance Improvement

Pinchas¹

2013

JSIP

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“…Since blind equalizers do not require any known training sequence for the startup period, they are also useful for point-to-multipoint network applications, such as the fiber to the curb (FTTC) systems [14]. Generally, blind methods are classified according to the location of their nonlinearity in the receiver [15]. We may classify blind equalization methods [15] as follows: 1) Polyspectral algorithms; 2) Bussgang-type algorithms; 3) Probabilistic algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…Generally, blind methods are classified according to the location of their nonlinearity in the receiver [15]. We may classify blind equalization methods [15] as follows: 1) Polyspectral algorithms; 2) Bussgang-type algorithms; 3) Probabilistic algorithms. In the first type, the nonlinearity is located at the output of the channel, right before the equalizer's filter.…”

Section: Introductionmentioning

confidence: 99%

“…In the third type, the nonlinearity is combined with the data detection process. Algorithms with the third type can extract considerable information from relatively little data [15], but this is often accomplished at a huge computational cost.…”

Section: Introductionmentioning

confidence: 99%

“…According to [15], the equalization performance depends on the nature of the equalizer (on the memoryless nonlinearity as already was mentioned before), on the channel characteristics, on the added noise, on the step-size parameter used in the adaptation process which will be explained later in this paper, on the equalizer's tap length and on the input signal statistics. Fast convergence speed and reaching a residual ISI where the eye diagram is considered to be open are the main requirements from a blind equalizer.…”

Section: Introductionmentioning

confidence: 99%

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An Approximated Expression for the Residual ISI Obtained by Blind Adaptive Equalizer and Biased Input Signals

Panizel¹,

Pinchas²

2014

JSIP

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Recently, two expressions (for the noiseless and noisy case) were proposed for the residual intersymbol interference (ISI) obtained by blind adaptive equalizers, where the error of the equalized output signal may be expressed as a polynomial function of order 3. However, those expressions are not applicable for biased input signals. In this paper, a closed-form approximated expression is proposed for the residual ISI applicable for the noisy and biased input case. This new proposed expression is valid for blind adaptive equalizers, where the error of the equalized output signal may be expressed as a polynomial function of order 3. The new proposed expression depends on the equalizer's tap length, input signal statistics, channel power, SNR, step-size parameter and on the input signal's bias. Simulation results indicate a high correlation between the simulated results and those obtained from our new proposed expression.

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A novel expression for the achievable MSE performance obtained by blind adaptive equalizers

Pinchas

2011

SIViP

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A closed approximated formed expression for the achievable residual intersymbol interference obtained by blind equalizers

Cited by 21 publications

References 19 publications

Two Blind Adaptive Equalizers Connected in Series for Equalization Performance Improvement

Two Blind Adaptive Equalizers Connected in Series for Equalization Performance Improvement

An Approximated Expression for the Residual ISI Obtained by Blind Adaptive Equalizer and Biased Input Signals

A novel expression for the achievable MSE performance obtained by blind adaptive equalizers

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