Square contour algorithm for blind equalization of QAM signals

Thaiupathump, Trasapong; He, Liang; Kassam, S.A.

doi:10.1016/j.sigpro.2006.02.035

Cited by 39 publications

(25 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Next, this new expression for the MSE is minimized in Subsection 3.3 with respect to λ 2 and λ 4 , and newly derived expressions for both λ 2 and λ 4 are obtained depending only on the source moments. Please note that in [52], only λ k (k = 0, 2, 4) were considered in (10). In addition, no closed-form expression was needed for λ 0 in [52], as is the case also in this paper due to the fact that λ 0 is reduced in (8) when using (10) with (11) for approximating the input and output pdfs.…”

Section: The New Lagrange Multipliersmentioning

confidence: 99%

New Lagrange Multipliers for the Blind Adaptive Deconvolution Problem Applicable for the Noisy Case

Pinchas

2016

Entropy

View full text Add to dashboard Cite

Abstract:Recently, a new blind adaptive deconvolution algorithm was proposed based on a new closed-form approximated expression for the conditional expectation (the expectation of the source input given the equalized or deconvolutional output) where the output and input probability density function (pdf) of the deconvolutional process were approximated with the maximum entropy density approximation technique. The Lagrange multipliers for the output pdf were set to those used for the input pdf. Although this new blind adaptive deconvolution method has been shown to have improved equalization performance compared to the maximum entropy blind adaptive deconvolution algorithm recently proposed by the same author, it is not applicable for the very noisy case. In this paper, we derive new Lagrange multipliers for the output and input pdfs, where the Lagrange multipliers related to the output pdf are a function of the channel noise power. Simulation results indicate that the newly obtained blind adaptive deconvolution algorithm using these new Lagrange multipliers is robust to signal-to-noise ratios (SNR), unlike the previously proposed method, and is applicable for the whole range of SNR down to 7 dB. In addition, we also obtain new closed-form approximated expressions for the conditional expectation and mean square error (MSE).

show abstract

Section: The New Lagrange Multipliersmentioning

confidence: 99%

New Lagrange Multipliers for the Blind Adaptive Deconvolution Problem Applicable for the Noisy Case

Pinchas

2016

Entropy

View full text Add to dashboard Cite

show abstract

“…To increase the convergence speed and other performance indices (e.g., ISI suppression, constellation eye opening, etc.) of SCA, Thaiupathump et al [13] have recently presented, a new scheme named as modified square contour algorithm (MSCA) which is explained in the next section. …”

Section: Equalizer Model and Square Contour Algorithm (Sca)mentioning

confidence: 99%

“…The proper choice of β is therefore necessary to diminish the effects of CME term in the initial phase of convergence. For p = 2, the value of β can be chosen to satisfy the following equation [10,13,16]:…”

Section: Modified Square Contour Algorithm (Msca)mentioning

confidence: 99%

“…The expressions for steady-state MSE have been obtained for SCA and MSCA in [13], which are given below:…”

Section: Steady-state Error Analysis For the Improved Square Contour mentioning

confidence: 99%

“…The proposed scheme outperforms the existing SCA in convergence speed and residual ISI suppression. Recently, a new scheme named as modified square contour algorithm (MSCA) has been presented in [13] by adding a constellationmatched error (CME) term to the SCA cost function which speeds up convergence rate and gives lower residual error when compared with the existing SCA.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

New blind equalization techniques based on improved square contour algorithm

Sheikh

Fan

2008

Digital Signal Processing

View full text Add to dashboard Cite

Soft Decision-Directed Square Contour Algorithm for Blind Equalization

Liu

Advances in Neural Networks – ISNN 2007

View full text Add to dashboard Cite

The recently introduced square contour algorithm (SCA) combines the benefits of the generalized Sato algorithm (GSA) and the constant modulus algorithm (CMA). It implicitly updates phase and is less likely to converge to incorrect solutions. But the SCA has relatively large residual error after the algorithm reaches its steady state for high-order constellations. A new blind equalization algorithm is proposed based on concurrent square contour algorithm (SCA) and soft decision-directed (SDD) adaptation. Like the SCA, the proposed concurrent SCA and SDD algorithm includes phase recovery and offers good convergence characteristics. Simulation results demonstrate that the proposed SCA+SDD algorithm offers practical alternatives to blind equalization of high-order QAM channels and provides significant equalization improvement over the CMA, GSA and SCA.

show abstract

Square contour algorithm for blind equalization of QAM signals

Cited by 39 publications

References 15 publications

New Lagrange Multipliers for the Blind Adaptive Deconvolution Problem Applicable for the Noisy Case

New Lagrange Multipliers for the Blind Adaptive Deconvolution Problem Applicable for the Noisy Case

New blind equalization techniques based on improved square contour algorithm

Soft Decision-Directed Square Contour Algorithm for Blind Equalization

Contact Info

Product

Resources

About