A novel self-learning adaptive recursive equalizer with unique optimum for QAM

Rocha, Carlos A. F. da; Macchi, Odile

doi:10.1109/icassp.1994.389985

Cited by 14 publications

(7 citation statements)

References 7 publications

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“…There have already been a few approaches to solve it. In [5]- [7] the equalizer is split into a cascade of several linear filters, which involves at least one recursive filter. In particular, in [6] the following cascade has been proposed-an attenuation equalizer which is a purely recursive whitening filter , a complex-valued gain control, and a phase equalizer which is an all-pass transversal/recursive filter.…”

Section: Introductionmentioning

confidence: 99%

Adaptive decision feedback equalization: can you skip the training period?

Labat¹,

Macchi

Laot³

1998

IEEE Trans. Commun.

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126

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This paper presents a novel unsupervised (blind) adaptive decision feedback equalizer (DFE). It can be thought of as the cascade of four devices, whose main components are a purely recursive filter (R) and a transversal filter (T): Its major feature is the ability to deal with severe quickly timevarying channels, unlike the conventional adaptive DFE. This result is obtained by allowing the new equalizer to modify, in a reversible way, both its structure and its adaptation according to some measure of performance such as the mean-square error (MSE). In the starting mode, R comes first and whitens its own output by means of a prediction principle, while T removes the remaining intersymbol interference (ISI) thanks to the Godard (or Shalvi-Weinstein) algorithm. In the tracking mode the equalizer becomes the classical DFE controlled by the decision-directed (DD) least-mean-square (LMS) algorithm. With the same computational complexity, the new unsupervised equalizer exhibits the same convergence speed, steady-state MSE, and bit-error rate (BER) as the trained conventional DFE, but it requires no training. It has been implemented on a digital signal processor (DSP) and tested on underwater communications signals-its performances are really convincing.

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Section: Introductionmentioning

confidence: 99%

Adaptive decision feedback equalization: can you skip the training period?

Labat¹,

Macchi

Laot³

1998

IEEE Trans. Commun.

Self Cite

126

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“…where, according to Figure 3, we have: The adaptive procedures applied for the magnitude equalization, the CAG and the phase recovery are similar to the predictive equalization techniques proposed in [3]. The associated optimization criteria are claimed to be unimodal, as shown in this reference.…”

Section: -mentioning

confidence: 96%

“…An alternative approach using linear prediction techniques and IIR structure has been proposed in [ZI and [3]. It consists in a cascade of forward and backward predictor and uses an adaptation criterion that allows an automatic switching from a first period of prediction of the input data to the period of correct retrieval of the transmitted sequence.…”

Section: -Introductionmentioning

confidence: 99%

A unimodal adaptation criterion for a non-linear IIR self-learning equalizer

Rocha

Romano²

Proceedings of GLOBECOM'96. 1996 IEEE Global Telecommunications Conference

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In this work, we propose a non-linear structure for self-learning equalization, which can be easily updated using the direct-decision error criterion. Such a structure consists in three different systems: an IIR predictor that provides the magnitude equalization, an automatic gain control and a non-linear phase equalizer. The main contribution of the paper concerns the demonstration of the unimodality of the adaptation criterion. An original theoretical analysis is carried out for the non-linear phase equalizer. Finally an example with a severe radio-communication channel is presented. I -IntroductionSelf-learning or blind equalization consists in retrieving the input data of an unknown channel when we have only some statistical information about these data.The typical real-time constraints of the c

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“…Due to this non linearity, the vector q k -1 will be equal to t k -1 , and any error accumulation is stopped. A soft transition [3] can be made in the feedback loop of the all pass filter as shown in figure (5), in which the mixing parameter X is gradually increased from zero to unity, as the iterations progress. The X is again set to zero and increased to one whenever the block is initialised with a new set of data.…”

Section: Error Accumulation Analysismentioning

confidence: 99%

A novel blind equalization structure for deep null communication channels

Lambotharan

Chambers

Proceedings of 1997 IEEE International Symposium on Circuits and Systems. Circuits and Systems in the Information Age ISCAS '97

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A novel self-learning adaptive recursive equalizer with unique optimum for QAM

Cited by 14 publications

References 7 publications

Adaptive decision feedback equalization: can you skip the training period?

Adaptive decision feedback equalization: can you skip the training period?

A unimodal adaptation criterion for a non-linear IIR self-learning equalizer

A novel blind equalization structure for deep null communication channels

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