26 research outputs found

    On linear H∞ equalization of communication channels

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    As an alternative to existing techniques and algorithms, we investigate the merit of the H∞ approach to the linear equalization of communication channels. We first give the formulation of all causal H∞ equalizers using the results of and then look at the finite delay case. We compare the risk-sensitive H∞ equalizer with the MMSE equalizer with respect to both the average and the worst-case BER performances and illustrate the improvement due to the use of the H∞ equalizer

    MIMO decision feedback equalization from an H∞ perspective

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    We approach the multiple input multiple output (MIMO) decision feedback equalization (DFE) problem in digital communications from an H∞ estimation point of view. Using the standard (and simplifying) assumption that all previous decisions are correct, we obtain an explicit parameterization of all H∞ optimal DFEs. In particular, we show that, under the above assumption, minimum mean square error (MMSE) DFEs are H∞ optimal. The H∞ approach also suggests a method for dealing with errors in previous decisions

    MIMO linear equalization with an H∞ criterion

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    In this paper, we study the problem of linearly equalizing the multiple-input multiple-output (MIMO) communications channels from an H∞ point of view. H∞ estimation theory has been recently introduced as a method for designing filters that have acceptable performance in the face of model uncertainty and lack of statistical information on the exogenous signals. In this paper, we obtain a closed-form solution to the square MIMO linear H∞ equalization problem and parameterize all possible H∞-optimal equalizers. In particular, we show that, for minimum phase channels, a scaled version of the zero-forcing equalizer is H∞-optimal. The results also indicate an interesting dichotomy between minimum phase and nonminimum phase channels: for minimum phase channels the best causal equalizer performs as well as the best noncausal equalizer, whereas for nonminimum phase channels, causal equalizers cannot reduce the estimation error bounds from their a priori values. Our analysis also suggests certain remedies in the nonminimum phase case, namely, allowing for finite delay, oversampling, or using multiple sensors. For example, we show that H∞ equalization of nonminimum phase channels requires a time delay of at least l units, where l is the number of nonminimum phase zeros of the channel

    Blind deconvolution techniques and applications

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    Least Squares Order-Recursive Lattice Smoothers

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    Conventional Least Squares Order-Recursive Lattice (LSORL) Filters Use Present and Past Data Values to Estimate the Present Value of a Signal. This Paper Introduces LSORL Smoothers Which Use Past, Present and Future Data for that Purpose. Except for an overall Delay Needed for Physical Realization, LSORL Smoothers Can Substantially Outperform LSORL Filters While Retaining All the Advantages of an Order-Recursive Structure. © 1995 IEE

    Mixed Norm Equalization with Applications in Television Multipath Cancellation

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    Electrical Engineerin

    Channel Equalization using GA Family

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    High speed data transmissions over communication channels distort the trans- mitted signals in both amplitude and phase due to presence of Inter Symbol Inter- ference (ISI). Other impairments like thermal noise, impulse noise and cross talk also cause further distortions to the received symbols. Adaptive equalization of the digital channels at the receiver removes/reduces the e®ects of such ISIs and attempts to recover the transmitted symbols. Basically an equalizer is an inverse ¯lter which is placed at the front end of the receiver. Its transfer function is inverse to the transfer function of the associated channel. The Least-Mean-Square (LMS), Recursive-Least-Square (RLS) and Multilayer perceptron (MLP) based adaptive equalizers aim to minimize the ISI present in the digital communication channel. These are gradient based learning algorithms and therefore there is possibility that during training of the equalizers, its weights do not reach to their optimum values due to ..

    Blind channel identification/equalization with applications in wireless communications

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    Ph.DDOCTOR OF PHILOSOPH
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