Application of Least Squares Lattice Algorithms to Adaptive Equalization

Satorius, E.; Pack, Jed D.

doi:10.1109/tcom.1981.1094968

Cited by 122 publications

(17 citation statements)

References 9 publications

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“…The basic idea in decision-feedback equalization is that once an information symbol has been detected, the ISI that it causes on future symbols may be estimated and subtracted out prior to symbol detection. The DFE may be realized either in the direct form or as a lattice [223], [471], [504], [505]. The direct-form structure of the DFE is illustrated in Fig.…”

Section: B Equalization Methodsmentioning

confidence: 99%

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Fading channels: information-theoretic and communications aspects

Biglieri

Proakis

Shamai³

1998

IEEE Trans. Inform. Theory

1,672

1,342

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Abstract-In this paper we review the most peculiar and interesting information-theoretic and communications features of fading channels. We first describe the statistical models of fading channels which are frequently used in the analysis and design of communication systems. Next, we focus on the information theory of fading channels, by emphasizing capacity as the most important performance measure. Both single-user and multiuser transmission are examined. Further, we describe how the structure of fading channels impacts code design, and finally overview equalization of fading multipath channels.

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Section: B Equalization Methodsmentioning

confidence: 99%

“…Fig. 12 illustrates a comparison of the convergence rate of the RLS and the LMS algorithms for an equalizer of length the and a channel with a small amount of ISI [223], [505]. We note that the difference in convergence rate is very significant.…”

Section: Adaptive Algorithms and Lattice Equalizersmentioning

confidence: 99%

Fading channels: information-theoretic and communications aspects

Biglieri

Proakis

Shamai³

1998

IEEE Trans. Inform. Theory

1,672

1,342

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“…Using equation 13 according to [7] respectively [9] the new estimation of coefficients g(i) can be calculated bŷ…”

Section: Lattice Equalisermentioning

confidence: 99%

“…In [9] and others the adaptation speed of the Lattice equaliser was presented. It was shown that it adapts much faster than the LMS but worse compared to the RLS which needs much higher computational effort.…”

Section: Lattice Equalisermentioning

confidence: 99%

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MUI-suppression with lattice structures for UTRA FDD downlink

Knoche

Rinas

Kammeyer

et al. 2003

2003 IEEE 58th Vehicular Technology Conference. VTC 2003-Fall (IEEE Cat. No.03CH37484)

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Abstract-Wideband CDMA systems with orthogonal spreading codes suffer severely due to the loss of orthogonality by multipath propagation. This yields Multiple User Interference (MUI) which gravely reduces the performance of classical systems with Rake-receivers.In our approach we attempt to restore orthogonality again by using a Lattice equaliser. These kind of equalisers are composed of two parts. The prewhitening part and the adaptive equaliser part. The Lattice structure itself has the advantage that its backward prediction errors are orthogonal to each other. Therefore the Lattice structure itself is working as a prewhitening filter. The second part is the adaptive algorithm for the equaliser coefficients. We take the stochastic gradient algorithm for this task and use the pilot channel in UMTS as a reference to calculate the equaliser coefficients. In order to show the feasibility of this approach, some simulation results for channels with low Doppler frequencies are presented and compared with the a system using the classical Rake-receiver and a system with another prefilter approach, the LMS-like Griffith equaliser [1].

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Adaptation of Butler-Cantoni method for fast start-up equalization

Shimamura

Suzuki

1999

Electron. Comm. Jpn. Pt. III

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Fast start‐up in adaptive equalization is indispensable in communication systems that require on‐line performance. In this article, a new type of adaptive linear FIR equalizer composed of two FIR filters and called the adaptive Butler–Cantoni (ABC) equalizer is proposed, and speed‐up of convergence in the training mode is attempted. The ABC equalizer consists of a channel estimator and an equalization filter. The channel estimator updates coefficients by the LMS algorithm. At the same time, the variance of the additive noise is computed. Based on these results, the coefficients of the equalization filter are successively derived by the Levinson–Trench algorithm, and the estimated value of the transmitted signal is computed. The computational complexity of the ABC equalizer is proportional to the square of the tap length but is not more than that of the RLS equalizer. By computer simulation, it is demonstrated that the ABC equalizer provides better results than the LMS and RLS equalizers. © 1999 Scripta Technica, Electron Comm Jpn Pt. 3, 82(10): 9–17, 1999

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Application of Least Squares Lattice Algorithms to Adaptive Equalization

Cited by 122 publications

References 9 publications

Fading channels: information-theoretic and communications aspects

Fading channels: information-theoretic and communications aspects

MUI-suppression with lattice structures for UTRA FDD downlink

Adaptation of Butler-Cantoni method for fast start-up equalization

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