scholarly journals Residual ISI Obtained by Blind Adaptive Equalizers and Fractional Noise

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Monika Pinchas
Keyword(s):  
Gaussian Process ◽  
Closed Form ◽  
Hurst Exponent ◽  
Signal To Noise Ratio ◽  
Size Parameter ◽  
Step Size ◽  
Input Noise ◽  
Fractional Noise ◽  
Blind Adaptive ◽  
Adaptive Equalizers

Recently, a closed-form approximated expression was derived by the same author for the achievable residual intersymbol interference (ISI) case that depends on the step-size parameter, equalizer’s tap length, input signal statistics, signal-to-noise ratio (SNR), and channel power. But this expression was obtained by assuming that the input noise is a white Gaussian process where the Hurst exponent (H) is equal to 0.5. In this paper, we derive a closed-form approximated expression (or an upper limit) for the residual ISI obtained by blind adaptive equalizers valid for fractional Gaussian noise (fGn) input where the Hurst exponent is in the region of0.5≤H<1. Up to now, the statistical behaviour of the residual ISI was not investigated. Furthermore, the convolutional noise for the latter stages of the deconvolutional process was assumed to be a white Gaussian process (H=0.5). In this paper, we show that the Hurst exponent of the residual ISI is close to one, almost independent of the SNR or equalizer’s tap length but depends on the step-size parameter. In addition, the convolutional noise obtained in the steady state is a noise process having a Hurst exponent depending on the step-size parameter.

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

2018 ◽  
Vol 210 ◽  
pp. 05003
Author(s):  
Monika Pinchas
Keyword(s):  
Closed Form ◽  
Gaussian Model ◽  
Size Parameter ◽  
Step Size ◽  
Adaptive Equalizer ◽  
Mathematical Basis ◽  
Channel Power ◽  
Blind Adaptive ◽  
Adaptive Equalizers ◽  
Equalization Method

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.

scholarly journals Symbol Error Rate for Nonblind Adaptive Equalizers Applicable for the SIMO and FGn Case

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Monika Pinchas
Keyword(s):  
Closed Form ◽  
Error Rate ◽  
Hurst Exponent ◽  
Symbol Error Rate ◽  
Closed Form Expression ◽  
Form Expression ◽  
Single Input Single Output ◽  
Single Output ◽  
Adaptive Equalizers ◽  
Single Input

A nonzero residual intersymbol interference (ISI) causes the symbol error rate (SER) to increase where the achievable SER may not answer any more on the system’s requirements. Recently, a closed-form approximated expression was derived by the same author for the residual ISI obtained by nonblind adaptive equalizers for the single-input single-output (SISO) case. Up to now, there does not exist a closed-form expression for the residual ISI obtained by nonblind adaptive equalizers for the single-input multiple-output (SIMO) case. Furthermore, there does not exist a closed-form expression for the SER valid for the SISO or SIMO case that takes into account the residual ISI obtained by nonblind adaptive equalizers and is valid for fractional Gaussian noise (fGn) input where the Hurst exponent is in the region of0.5≤H<1. In this paper, we derive a closed-form approximated expression for the residual ISI obtained by nonblind adaptive equalizers for the SIMO case (where SISO is a special case of SIMO), valid for fGn input where the Hurst exponent is in the region of0.5≤H<1. Based on this new expression for the residual ISI, a closed-form approximated expression is obtained for the SER valid for the SIMO and fGn case.

scholarly journals A Novel Dual Mode Decision Directed Multimodulus Algorithm (DM-DD-MMA) for Blind Adaptive Equalization

2021 ◽  
Author(s):  
Sagi Tadmor ◽  
Sapir Carmi ◽  
Monika Pinchas
Keyword(s):  
Power Input ◽  
Adaptive Equalization ◽  
Size Parameter ◽  
Constant Modulus Algorithm ◽  
Dual Mode ◽  
Step Size ◽  
Adaptive Equalizer ◽  
Blind Adaptive ◽  
Simulation Results ◽  
Blind Adaptive Equalization

In this paper, we propose for the 16 quadrature amplitude modulation (QAM) input case, a dual-mode (DM), decision directed (DD) multimodulus algorithm (MMA) algorithm for blind adaptive equalization which we name as DM-DD-MMA. In this new proposed algorithm, the MMA method is switched to the DD algorithm, based on a previously obtained expression for the step-size parameter valid at the convergence state of the blind adaptive equalizer, that depends on the channel power, input signal statistics and on the properties of the chosen algorithm. Simulation results show that improved equalization performance is obtained for the 16 QAM input case compared with the DM-CMA (where CMA is the constant modulus algorithm), DM-MCMA (where MCMA is the modified CMA) and MCMA-MDDMA (where MDDMA is the modified decision directed modulus algorithm).

scholarly journals The Tap-Length Associated with the Blind Adaptive Equalization/Deconvolution Problem

2020 ◽  
Vol 3 (1) ◽  
pp. 2
Author(s):  
Monika Pinchas
Keyword(s):  
Input Sequence ◽  
Point Of View ◽  
Adaptive Equalization ◽  
Size Parameter ◽  
Step Size ◽  
System Parameters ◽  
Large Step ◽  
Blind Adaptive ◽  
Deconvolution Problem ◽  
Blind Adaptive Equalization

The step-size parameter and the equalizer’s tap length are the system parameters in the blind adaptive equalization design. Choosing a large step-size parameter causes the equalizer to converge faster compared with applying a smaller value for the step size parameter. However, a higher step-size parameter leaves the system with a higher residual inter-symbol-interference (ISI) than does a lower step-size parameter. The equalizer’s tap length should be set large enough to compensate for the channel distortions. However, since the channel parameters are unknown, the required equalizer’s tap length is also unknown. The system parameters are usually designed via simulation trials, in such a way that the equalizer’s performance from the residual ISI point of view reaches a system desired residual ISI level. Recently, a closed-form approximated expression was derived for the residual ISI as a function of the system parameters, input sequence statistics and channel power. This expression was obtained under the assumption having a value for the equalizer’s tap length that is sufficient to compensate for the channel distortions. Based on this approximated expression, the outcome from the step-size parameter multiplied by the equalizer’s tap length can be derived when the residual ISI is given. By choosing a step-size parameter, we automatically have also the value for the equalizer’s tap length which might now not be large enough to compensate for the channel distortions and thus leaving the system with a higher residual ISI than the required one. In this work, we derive an expression that sets a condition on the equalizer’s tap length based on the input sequence statistics, on the chosen equalizer’s characteristics and required residual ISI. In addition, highlights are supplied on how to set the equalizer’s tap length for different channel cases based on this new derived expression. The findings are accompanied by simulation results.

A new adaptive variable step size natural gradient BSS algorithm

2021 ◽  
pp. 1-12
Author(s):  
Junqing Ji ◽  
Xiaojia Kong ◽  
Yajing Zhang ◽  
Tongle Xu ◽  
Jing Zhang
Keyword(s):  
Signal To Noise Ratio ◽  
Wavelet Coefficient ◽  
Gradient Algorithm ◽  
Variable Step Size ◽  
Natural Gradient ◽  
Threshold Method ◽  
Step Size ◽  
Morphological Component Analysis ◽  
Variable Step ◽  
Separation Degree

The traditional blind source separation (BSS) algorithm is mainly used to deal with signal separation under the noiseless model, but it does not apply to data with the low signal to noise ratio (SNR). To solve the problem, an adaptive variable step size natural gradient BSS algorithm based on an improved wavelet threshold is proposed in this paper. Firstly, an improved wavelet threshold method is used to reduce the noise of the signal. Secondly, the wavelet coefficient layer with obvious periodicity is denoised using a morphological component analysis (MCA) algorithm, and the processed wavelet coefficients are recombined to obtain the ideal model. Thirdly, the recombined signal is pre-whitened, and a new separation matrix update formula of natural gradient algorithm is constructed by defining a new separation degree estimation function. Finally, the adaptive variable step size natural gradient blind source algorithm is used to separate the noise reduction signal. The results show that the algorithm can not only adaptively adjust the step size according to different signals, but also improve the convergence speed, stability and separation accuracy.

Stochastic resonance in an array of integrate-and-fire neurons with threshold

2018 ◽  
Vol 32 (16) ◽  
pp. 1850169 ◽  
Author(s):  
Bingchang Zhou ◽  
Qianqian Qi
Keyword(s):  
Stochastic Resonance ◽  
Additive Noise ◽  
Signal To Noise Ratio ◽  
Array Size ◽  
Periodic Signal ◽  
Signal Parameter ◽  
Input Noise ◽  
Integrate And Fire ◽  
Noisy Input ◽  
Better Than

We investigate the phenomenon of stochastic resonance (SR) in parallel integrate-and-fire neuronal arrays with threshold driven by additive noise or signal-dependent noise (SDN) and a noisy input signal. SR occurs in this system. Whether the system is subject to the additive noise or SDN, the input noise [Formula: see text] weakens the performance of SR but the array size N and signal parameter [Formula: see text] promote the performance of SR. Signal parameter [Formula: see text] promotes the performance of SR for the additive noise, but the peak values of the output signal-to-noise ratio [Formula: see text] first decrease, then increase as [Formula: see text] increases for the SDN. Moreover, when [Formula: see text] tends to infinity, for the SDN, the curve of [Formula: see text] first increases and then decreases, however, for the additive noise, the curve of [Formula: see text] increases to reach a plain. By comparing system performance with the additive noise to one with SDN, we also find that the information transmission of a periodic signal with SDN is significantly better than one with the additive noise in limited array size N.

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