Non-linear channel equalization using adaptive MPNN
Applied Soft Computing
A reduced complexity estimation algorithm for ultrasound images de-blurring
Computer Methods and Programs in Biomedicine
Information Sciences: an International Journal
Application of ARMA models to automatic channel equalization
Information Sciences: an International Journal
Complex-valued support vector classifiers
Digital Signal Processing
A data-dependent equalizer for an optical storage channel
Digital Signal Processing
Real-world acoustic event detection
Pattern Recognition Letters
Formal performance analysis for faulty MIMO hardware
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
| Hi-index | 754.84 |
A maximum-likelihood sequence estimator for a digital pulse-amplitude-modulated sequence in the presence of finite intersymbol interference and white Gaussian noise is developed, The structure comprises a sampled linear filter, called a whitened matched filter, and a recursive nonlinear processor, called the Viterbi algorithm. The outputs of the whitened matched filter, sampled once for each input symbol, are shown to form a set of sufficient statistics for estimation of the input sequence, a fact that makes obvious some earlier results on optimum linear processors. The Viterbi algorithm is easier to implement than earlier optimum nonlinear processors and its performance can be straightforwardly and accurately estimated. It is shown that performance (by whatever criterion) is effectively as good as could be attained by any receiver structure and in many cases is as good as if intersymbol interference were absent. Finally, a simplified but effectively optimum algorithm suitable for the most popular partial-response schemes is described.