Journal ArticleDOI
Adaptive channel memory truncation for maximum likelihood sequence estimation
D. D. Falconer,F. R. Magee +1 more
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TLDR
This work proposes a linear prefilter to force the overall impulse response of the channel/prefilter combination to approximate a desired truncated impulse response (DIR) of acceptably short duration and shows that the minimum mean-square error can be expressed as the minimum eigenvalue of a certain channel-dependent matrix, and that the corresponding eigenvector represents the optimum DIR.Abstract:Â
Maximum likelihood data sequence estimation, implemented by a dynamic programming algorithm known as the Viterbi algorithm (VA), is of considerable interest for data transmission in the presence of severe intersymbol interference and additive Gaussian noise. Unfortunately, the required number of receiver operations per data symbol is an exponential function of the duration of the channel impulse response, resulting in unacceptably large receiver complexity for high-speed PAM data transmission on many channels. We propose a linear prefilter to force the overall impulse response of the channel/prefilter combination to approximate a desired truncated impulse response (DIR) of acceptably short duration. Given the duration of the DIR, the prefilter parameters and the DIR itself can be optimized adaptively to minimize the mean-square error between the output of the prefilter and the desired prefilter output, while constraining the energy in the DIR to be fixed. In this work we show that the minimum mean-square error can be expressed as the minimum eigenvalue of a certain channel-dependent matrix, and that the corresponding eigenvector represents the optimum DIR. An adaptive algorithm is developed and successfully tested. The simulations also show that the prefiltering scheme, used together with the VA for two different channel models, compares favorably in performance with another recently proposed prefiltering scheme. Limiting results for the case where the prefilter is considered to be of infinite length are obtained; it is shown that the optimum DIR of length two must be one of two possible impulse responses related to the duobinary impulse response. Finally we obtain limiting results for the case where the transmitting filter is optimized.read more
Citations
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References
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Journal ArticleDOI
Error bounds for convolutional codes and an asymptotically optimum decoding algorithm
TL;DR: The upper bound is obtained for a specific probabilistic nonsequential decoding algorithm which is shown to be asymptotically optimum for rates above R_{0} and whose performance bears certain similarities to that of sequential decoding algorithms.
Journal ArticleDOI
Maximum-likelihood sequence estimation of digital sequences in the presence of intersymbol interference
TL;DR: In this paper, 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, which comprises a sampled linear filter, called a whitened matched filter, and a recursive nonlinear processor, called the Viterbi algorithm.
Journal ArticleDOI
Optimum mean-square decision feedback equalization
TL;DR: It is shown that, in all cases of practical interest, signaling faster than the Nyquist rate, while keeping fixed the information rate, increases the mean-square error.
Journal ArticleDOI
Generalization of a Techinque for Binary Data Communication
TL;DR: A technique for binary data transmission is described, in which each binary symbol is chosen to be a prescribed superposition of n impulses of form (sin 2\piFt)/2\ piFt , spaced at intervals 1/2F.
Journal ArticleDOI
Adaptive maximum-likelihood sequence estimation for digital signaling in the presence of intersymbol interference (Corresp.)
F. Magee,J. Proakis +1 more
TL;DR: An adaptive maximum-likelihood sequence estimator for a digital pulse-amplitude-modulated sequence in the presence of finite-duration unknown slowly time-varying intersymbol interference and additive white Gaussian noise is developed.