Improved methods for the blind system identification using higher order statistics

Jelonnek, B.; Kammeyer, Karl‐Dirk

doi:10.1109/78.175739

Cited by 14 publications

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“…Diese Optimallösung wird durch die bekannte Normalgleichung (13) beschrieben, wobei der Vektor r ia die Kreuzkorrelation zwischen gesendeten Daten d(k) und dem Empfangssignal v(k) beschreibt, r dv -.=E{vd(k-k 0 )}. Diese Optimallösung wird durch die bekannte Normalgleichung (13) beschrieben, wobei der Vektor r ia die Kreuzkorrelation zwischen gesendeten Daten d(k) und dem Empfangssignal v(k) beschreibt, r dv -.=E{vd(k-k 0 )}.…”

Section: Eigenvektorlösung (Evi)unclassified

Geschlossene Lösungen zur blinden Entzerrung und Systemidentifikation

Kammeyer¹

1995

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Übersicht: Diese Arbeit behandelt die Probleme der blinden Entzerrung und Systemidentifikation, d.h. adaptive Verfahren, bei denen keine Referenzsignale benötigt werden. Im Mittelpunkt steht die Formulierung einer geschlossenen Eigenvektorlösung für die blinde Entzerrung. Hiervon ist unmittelbar ein modifiziertes Eigenwertproblem abzuleiten, das die geschlossene Lösung der blinden Systemidentifikation beinhaltet. Auf der Basis dieser Beziehungen lassen sich iterative Algorithmen entwickeln, die gegenüber allen bisher bekannten Verfahren deutliche Vorteile bezüglich der Konvergenzgeschwindigkeit, der Robustheit bei Systemgrad-Überschätzung und der Auswirkungen additiver gaußscher Störungen aufweisen.Abstract: In this paper the problems of blind equalization and system identification are discussed, i.e. adaptive algorithms which do not use any training sequence. The main result is a closed-form eigenvector solution to blind equalization. This relationship leads directly to a closed-form solution to blind system identification which is an eigenvector problem, too. On the basis of these results iterative algorithms can be developed which show very fast convergence speeds compared with alternative methods known so far and, furthermore, are very robust with respect to a system order overfit and the influence of additive gaussian noise.Für die Dokumentation: Statistik höherer Ordnung / Kumulanten / blinde Entzerrung / blinde Systemidentifikation ulant estimatio with .

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Section: Eigenvektorlösung (Evi)unclassified

Geschlossene Lösungen zur blinden Entzerrung und Systemidentifikation

Kammeyer¹

1995

Frequenz

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“…{ym (n)} in (4.4). The two-sided z-transform of the second order cross-correlationsequence of{{Yi (n)}{y /n))}, 15, i 5, M, 15, j 5, M can be expresses as, L Rij(Z-I) = L>ij(l)Z-1 I;-LAnd {rij (I)} is the second order cross-correlation sequence given by,Yij (I) =E~[ (k)y; (k + I)}= 0";~:>i (k)h; (k + I) + O"\~o(ij) 1= 0, ± 1, ± 2,.•• ± L (4.6)Under assumptions of input signal noted in section 4.2, (4.5) can be expressed using the channel transfer function as[68],Rij (z-I) = 0";Hi (z-I )H;(1/z*) + O";"O(ij) L L = 0";IT(I-bi(/)z-I)IT(1-1/b;(/)z) + O";"O(ij)…”

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Zero-based blind channel estimation and equalization

Ye¹

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Due to multipath propagation characteristics of frequency-selective channels, inter-symbol interference (ISI) is a major obstacle in high speed data transfer. At high speeds of data transfer, blind estimation is also necessary to fully utilize the available bandwidth. Therefore, blind channel estimation and subsequent equalization are necessary at the receiver to combat lSI. In this thesis, zero based blind channel estimation and equalization algorithms are proposed. For point to point communication systems, a single input single output (SISO) finite impulse response (FIR) channel model is used to describe the equivalent baseband system. Channel coefficient estimation and channel zero estimation for a minimum phase (MP) FIR channel is initially presented. It is shown that parameter estimation based on channel zeros is a natural and efficient way to estimate the channel coefficients. Furthermore, the estimation performance of the proposed algorithms through this approach can reach the bounds of blind channel parameter estimation. Based on the development of MP channel parameter estimation, blind channel parameter estimation algorithms for non-minimum phase (NMP) channels are also ATTENTION: The Singapore Copyright Act applies to the use of this document. Nanyang Technological University Library investigated. Initial channel zeros, obtained by second order statistics based methods, can be correctly located using higher order information of the received signal. The proposed algorithms use advantages of both second order statistics and higher order statistics. For multiple-receiver systems, a single input single output (SIMO) channel model is exploited. The proposed SISO NMP channel parameter estimation methods are extended to the parameter estimation of SIMO channel. Here all zeros of all sub-channels are determined by using the cross-relationship between individual sub-channels. It is well known that an equalizer at the output can be used to combat lSI and recover the unknown transmitted signal in high fidelity. Based on the estimated channel zeros, novel equalizer designs for SISO and SIMO channels are presented in the later part of the thesis. These equalizers belong to the categories of zero forcing (ZF) equalizer, minimum mean square error (MMSE) equalizer, and decision feedback equalizer (DFE). Traditionally, all equalizers are realized in FIR form as this is a guaranteed stable architecture. An efficient, stable infinite-impulse response (IIR) form for hardware implementation is also discussed in the thesis for reducing the length of equalizers. It is shown that the use of IIR architecture saves hardware register usage by a significant amount. An example of equalizer application in underwater acoustic (UWA) communication channel is provided which is designed by considering the characteristics ofthe UWA channel. For block transmission systems, a bi-directional zero based decision feedback equalizer (BizDFE) is also proposed.

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