A Hybrid Adaptive Blind Equalization Algorithm for QAM Signals in Wireless Communications

He, Liang; Amin, Moeness G.; Reed, C.; Malkemes, R.C.

doi:10.1109/tsp.2004.828913

Cited by 71 publications

(57 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…where 'E' is the expectation operator Each equalization filter coefficient is updated according to the following rule [15]:…”

Section: System Modelmentioning

confidence: 99%

“…The first mode uses CMA for initialization while equalization is continued using a cost function that includes partial or full knowledge of signal constellations. A modified CMA (MCMA) is lately introduced in [15]. Its cost function is a hybrid of two separate well defined error terms.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper two other CME terms are introduced in the hybrid cost function instead of the one in [15]. The first function is used for the first time namely the complement of the sum of sinc squared functions centered at the message points.…”

Section: Introductionmentioning

confidence: 99%

“…Simulation results are obtained by getting the MSE and compared with the one obtained by applying the MCMA [15].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Two Proposed Blind Equalizers Using Different Constellation Matched Error Functions for QAM Signals

Mourad

2006

Wireless Pers Commun

View full text Add to dashboard Cite

Although Constant Modulus Algorithm (CMA) is effective to equalize non-minimum phase channels blindly, it suffers from residual intersymbol interference (ISI) and large Mean Square Error (MSE) when applied to higher order constellations (QAM). Methods based on cost function matched to the signal constellation namely alphabet matched algorithm (AMA) were previously reported and proves its superiority on CMA concerning the MSE. Thus dual mode algorithms between CMA and AMA were introduced. A hybrid technique combining CMA and AMA using a cosine square function as a constellation matched error (CME) was lately reported. In this paper two different CME functions are introduced. The MSE of the proposed algorithms are calculated using Matlab simulation under multipath slow fading channels for different signal to noise ratios (SNR) and different levels of QAM constellations. A comparison is established among them. Depicted results show the effectiveness of the two proposed CME functions.

show abstract

“…where 'E' is the expectation operator Each equalization filter coefficient is updated according to the following rule [15]:…”

Section: System Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Simulation results are obtained by getting the MSE and compared with the one obtained by applying the MCMA [15].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Two Proposed Blind Equalizers Using Different Constellation Matched Error Functions for QAM Signals

Mourad

2006

Wireless Pers Commun

View full text Add to dashboard Cite

show abstract

“…Various extensions have been suggested to improve equalizer performance. The modified CMA (MCMA) [4] adds a constellation-matched error (CME) function to the CMA cost function. This letter analyses similar modifications of the GSA and MMA.…”

Section: Introductionmentioning

confidence: 99%

Convergence analysis of blind equalization algorithms using constellation-matching

Kassam

2008

IEEE Trans. Commun.

View full text Add to dashboard Cite

Two modified blind equalization algorithms are analyzed for performance. These algorithms add a constellation-matched error term to the cost functions of the generalized Sato and multimodulus algorithms. The dynamic convergence behavior and steady-state performance of these algorithms, and of a related version of the constant modulus algorithm, are characterized. The analysis establishes the improved performance of the proposed algorithms. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Pennsylvania's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.This journal article is available at ScholarlyCommons: http://repository.upenn.edu/ese_papers/492 Index Terms-Adaptive equalizer, blind equalization algorithms, convergence analysis.

show abstract

Min‐norm based alphabet‐matching algorithm for adaptive blind equalisation of high‐order QAM signals

Labed¹,

Chonavel

Bey

et al. 2012

Trans. Emerging Tel. Tech.

View full text Add to dashboard Cite

An adaptive blind equalisation algorithm for high‐order quadrature amplitude modulations is proposed. It is derived by combining the multi‐modulus criterion and a new alphabet‐matching penalty term. This penalty is based on the minimum ℓ1‐norm of the deviations of the equaliser output from the alphabet elements. Numerical experiments show that the proposed criterion provides higher performance in terms of residual error and convergence speed than the conventional multi‐modulus algorithm and its penalised versions. Copyright © 2012 John Wiley & Sons, Ltd.

show abstract

A Hybrid Adaptive Blind Equalization Algorithm for QAM Signals in Wireless Communications

Cited by 71 publications

References 28 publications

Two Proposed Blind Equalizers Using Different Constellation Matched Error Functions for QAM Signals

Two Proposed Blind Equalizers Using Different Constellation Matched Error Functions for QAM Signals

Convergence analysis of blind equalization algorithms using constellation-matching

Min‐norm based alphabet‐matching algorithm for adaptive blind equalisation of high‐order QAM signals

Contact Info

Product

Resources

About