A closed-form solution to blind equalization

Jelonnek, B.; Kammeyer, Karl‐Dirk

doi:10.1016/0165-1684(94)90026-4

Cited by 44 publications

(41 citation statements)

References 6 publications

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“…Therefore, by the similar way to as in [2], the maximization of | | Castella et al [5] have shown that from (9), a BD can be iteratively achieved by using x i (t) = ) ( t i y w (i = 1,n) as reference signals (see …”

Section: T T F Z H Z S T a Z S T mentioning

confidence: 99%

A modified eigenvector method for blind deconvolution of MIMO systems using the matrix pseudo-inversion lemma

Kawamoto

Kono

Inouye

et al. 2010

Proceedings of 2010 IEEE International Symposium on Circuits and Systems

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Recently we have developed an eigenvector method (EVM) which can achieve the blind deconvolution (BD) for MIMO systems. One of attractive features of the proposed algorithm is that the BD can be achieved by calculating the eigenvectors of a matrix relevant to it. However, the performance accuracy of the EVM depends highly on computational results of the eigenvectors. In this paper, by modifying the EVM, we propose an algorithm which can achieve the BD without calculating the eigenvectors. Then the pseudo-inverse which is needed to carry out the BD is calculated by our proposed matrix pseudo-inversion lemma. Moreover, using a combination of the conventional EVM and the modified EVM, we will show its performances comparing with each EVM. Simulation results will be presented for showing the effectiveness of the proposed methods.

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Section: T T F Z H Z S T a Z S T mentioning

confidence: 99%

A modified eigenvector method for blind deconvolution of MIMO systems using the matrix pseudo-inversion lemma

Kawamoto

Kono

Inouye

et al. 2010

Proceedings of 2010 IEEE International Symposium on Circuits and Systems

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“…Moreover, Jelonnek et al have shown in [5] that the eigenvector corresponding to the maximum magnitude eigenvalue ofR †B becomes the solution of the blind equalization problem, which is referred to as an eigenvector algorithm (EVA). It has been also shown in [9] that the BD for MIMO-IIR systems can be achieved…”

Section: Evas With Reference Signalsmentioning

confidence: 99%

“…Researches applying this idea to solving blind signal processing (BSP) problems, such as the BD, the BE, the BSS, and so on, have been made by Jelonnek et al (e.g., [5]), Adib et al (e.g., [1]), Rhioui et al [14], and Castella, et al [2]. Jelonnek et al have shown in the single-input case that by the Lagrangian method, the maximization of a contrast function leads to a closed-form solution expressed as a generalized eigenvector problem, which is referred to as an eigenvector algorithm (EVA).…”

Section: Introductionmentioning

confidence: 99%

“…To solve this problem, we use eigenvector algorithms (EVAs) [5,6,13]. The first proposal of the EVA was done by Jelonnek et al [5]. They have proposed the EVA for solving blind equalization (BE) problems of single-input single-output (SISO) systems or single-input multiple-output (SIMO) systems.…”

Section: Introductionmentioning

confidence: 99%

“…To solve this problem, we use eigenvector algorithms (EVAs) [5,6,13]. The first proposal of the EVA was done by Jelonnek et al [5].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Blind Deconvolution of MIMO-IIR Systems: A Two-Stage EVA

Kawamoto

Inouye

Kohno

Neural Information Processing

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Abstract. This paper deals with a blind deconvolution (DB) problem for multiple-input multiple-output infinite impulse response (MIMO-IIR) systems. To solve this problem, we propose an eigenvector algorithm (EVA). In the proposed EVA, two kinds of EVAs are merged so as to give a good performance: One is an EVA and the other is a Robust EVA (REVA) which works with as little sensitive to Gaussian noise as possible. Owing to this combination, two drawbacks of the conventional EVAs can be overcome. Simulation results show the validity of the proposed EVA.

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Optimal delay estimation and performance evaluation in blind equalization

Prandini

Campi

Leonardi

1997

Int. J. Adapt. Control Signal Process.

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SUMMARYThis paper deals with the problem of recovering the input signal applied to a linear time-invariant system from the measurements of its output and the a priori knowledge of the input statistics (blind equalization). Under the assumption of an i.i.d. non-Gaussian input sequence a new iterative procedure based on phase-sensitive high-order cumulants for adjusting the coefficients of a transversal equalizer is introduced. The main feature of the proposed technique is the automatic selection of the equalization delay so as to improve the equalization performance. A method for the a posteriori evaluation of the obtained accuracy in PAM systems is also introduced. It consists of the computation of an upper bound on the probability of error depending on certain moments of the equalizer output and the statistics of the channel input and therefore can be used in a blind equalization context. Based on the result of such a computation, it can be decided whether it is necessary to consider a longer equalization filter in the iterative procedure.

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A closed-form solution to blind equalization

Cited by 44 publications

References 6 publications

A modified eigenvector method for blind deconvolution of MIMO systems using the matrix pseudo-inversion lemma

A modified eigenvector method for blind deconvolution of MIMO systems using the matrix pseudo-inversion lemma

Blind Deconvolution of MIMO-IIR Systems: A Two-Stage EVA

Optimal delay estimation and performance evaluation in blind equalization

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