A blind signal separation method for multiuser communications

Castedo, Luis; Escudero, Carlos J.; Dapena, Adriana

doi:10.1109/78.575706

Cited by 79 publications

(37 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The covariance of this error is given by (31) The MSE of the th user is then given by (10). Similarly, using (3), we can express the error of the MMSE detector as (32) The covariance of this error is (33) and the corresponding MSE of the th user is then given by (11 Because is strictly lower triangular, the traces of and are both zero in (40).…”

Section: Discussionmentioning

confidence: 99%

Blind multiuser detection using linear prediction

Causey

Barry²

1998

IEEE J. Select. Areas Commun.

View full text Add to dashboard Cite

Abstract-We propose a blind multiuser detection technique for array processing and code division multiple access (CDMA) systems that does not require knowledge of the array geometry or transmitter signature sequences. The technique has two key elements: an adaptive algorithm for separating the signal subspace from the noise subspace and an adaptive whitener based on linear prediction. The proposed algorithm offers low complexity, fast convergence, compatibility with shaped signal constellations, near-Wiener steady-state performance, and optimal near-far resistance.

show abstract

Section: Discussionmentioning

confidence: 99%

Blind multiuser detection using linear prediction

Causey

Barry²

1998

IEEE J. Select. Areas Commun.

View full text Add to dashboard Cite

show abstract

“…The basic objective of the BSS is to recover a set of source signals from a set of observations that are mixtures of the sources with no, or very limited In this paper, we will complete the results of [6] by providing a constraint on the mixing parameter which then guarantees the global convergence of the CC-CMA. Different from the methods mentioned above, we address the problem by investigating the solutions of the semi-algebraic sets.…”

Section: Introductionmentioning

confidence: 99%

Using Computer Algebra to Certify the Global Convergence of a Numerical Optimization Process

Lazard

Rouillier

et al. 2007

Math.comput.sci.

View full text Add to dashboard Cite

The basic objective of blind signal separation is to recover a set of source signals from a set of observations that are mixtures of the sources with no, or very limited knowledge about the mixture structure and source signals. To extract the original sources, many algorithms have been proposed; among them, the cross-correlation and constant modulus algorithm (CC-CMA) appears to be the algorithm of choice due to its computational simplicity.An important issue in CC-CMA algorithm is the global convergence analysis, because the cost function is not quadratic nor convex and contains undesirable stationary points. If these undesirable points are local minimums, the convergence of the algorithm may not be guaranteed and the CC-CMA would fail to separate source signals.The main result of this paper is to complete the classification of these stationary points and to prove that they are not local minimums unless if the mixing parameter is equal to 1. This is obtained by using the theory of discriminant varieties to determine the stationnary points as a function of the parameter and then to show that the Hessian matrix of the cost function is not positive semidefinite at these stationnay points, unless if the mixing parameter is 1.

show abstract

“…More recently, some constrained approaches relying on the CM ability to capture one source from convolutive mixtures (to be recalled in Sections IV and V) have been proposed independently in [22] and [24] (see [4] for the instantaneous mixtures case). They use decorrelation constraints in order to tune several filters to simultaneously capture different sources.…”

Section: ) Second-order Statistics Methodsmentioning

confidence: 99%

“…1 Sampling at an higher rate can be considered to add temporal diversity to the spatial diversity of factor L. From the finite time-span of , it is understood that is finite, and it is called the degree of . Then, the transfer function is a matrix of polynomials, and (2) can have the following compact notation: (4) where stands for the transfer function applied to the -dimensional signal of interest . The -dimensional noise contribution is defined from .…”

Section: B Data Modelmentioning

confidence: 99%

A globally convergent approach for blind MIMO adaptive deconvolution

Touzni

Fijalkow

Larimore³

et al. 2001

IEEE Trans. Signal Process.

View full text Add to dashboard Cite

Abstract-We discuss the blind deconvolution of multiple input/multiple output (MIMO) linear convolutional mixtures and propose a set of hierarchical criteria motivated by the maximum entropy principle. The proposed criteria are based on the constant-modulus (CM) criterion in order to guarantee that all minima achieve perfectly restoration of different sources. The approach is moreover robust to errors in channel order estimation. Practical implementation is addressed by a stochastic adaptive algorithm with a low computational cost. Complete convergence proofs, based on the characterization of all extrema, are provided. The efficiency of the proposed method is illustrated by numerical simulations.Index Terms-Blind adaptive source separation, constant modulus criterion, multiple input/multiple output convolutional systems.

show abstract

A blind signal separation method for multiuser communications

Cited by 79 publications

References 11 publications

Blind multiuser detection using linear prediction

Blind multiuser detection using linear prediction

Using Computer Algebra to Certify the Global Convergence of a Numerical Optimization Process

A globally convergent approach for blind MIMO adaptive deconvolution

Contact Info

Product

Resources

About