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—We study the turbo equalization approach to coded data transmission over channels with intersymbol interference. In the original system invented by Douillard et al., the data are protected by a convolutional code and the receiver consists of two trellis-based detectors, one for the channel (the equalizer) and one for the code (the decoder). It has been(More)
—A number of important advances have been made in the area of joint equalization and decoding of data transmitted over intersymbol interference (ISI) channels. Turbo equalization is an iterative approach to this problem, in which a maximum a posteriori probability (MAP) equalizer and a MAP decoder exchange soft information in the form of prior probabilities(More)
A common problem that arises in adaptive ltering, autoregressive modeling, or linear prediction is the selection of an appropriate order for the underlying linear parametric model. We address this problem, but instead of xing a speciic model order we develop a sequential algorithm whose sequentially accumulated mean squared prediction error for any bounded(More)
—The bidirectional arbitrated decision-feedback equalizer (BAD), which has bit-error rate performance between a decision-feedback equalizer (DFE) and maximum a posteriori (MAP) detection, is presented. The computational complexity of the BAD algorithm is linear in the channel length, which is the same as that of the DFE, and significantly lower than the(More)
—In this paper, we consider soft decision directed channel estimation for turbo equalization. To take advantage of soft information provided by the decoder, a minimum mean square error linear channel estimator is derived under an uncor-related channel tap model, and a soft input recursive least squares algorithm is also developed by modifying the cost(More)
—Turbo codes and the iterative algorithm for decoding them sparked a new era in the theory and practice of error control codes. Turbo equalization followed as a natural extension to this development, as an iterative technique for detection and decoding of data that has been both protected with forward error correction and transmitted over a channel with(More)
—We consider the problem of sequential linear prediction of real-valued sequences under the square-error loss function. For this problem, a prediction algorithm has been demonstrated [1]–[3] whose accumulated squared prediction error, for every bounded sequence, is asymptotically as small as the best fixed linear predictor for that sequence, taken from the(More)
—In this paper, we consider a low-complexity detection technique referred to as a reduced dimension maximum-likelihood search (RD-MLS). RD-MLS is based on a partitioned search which approximates the maximum-likelihood (ML) estimate of symbols by searching a partitioned symbol vector space rather than that spanned by the whole symbol vector. The inevitable(More)
A nonlinear generalization of the family of autoregressive signal models is introduced. This generalization can be viewed as an autoregressive model with state-varying parameters. For such signals, minimum mean-square error prediction can be reformulated as an interpolation problem. A novel interpretation of the signal a s a codebook for its own prediction(More)
In this paper, we explore the use of multi-stage adaptation algorithms for a variety of adaptive filtering applications where the structure of the underlying process to be estimated is unknown. These algorithms are " multi-stage " in that they comprise multiple adaptive filtering algorithms that operate in parallel on the observation sequence, and(More)