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dc.contributor.advisor Johnson, Don H.
dc.creatorKelly, Owen Ernest
dc.date.accessioned 2009-06-04T08:41:54Z
dc.date.available 2009-06-04T08:41:54Z
dc.date.issued 1997
dc.identifier.urihttps://hdl.handle.net/1911/19173
dc.description.abstract Codelength based inference is used to decode binary symbols distorted by an inter-symbol interference (ISI) channel with additive noise. The transmitted signals are antipodal waveforms constructed from $\pm$1 valued signature sequences. The receiver adapts to the unknown channel by training on a known preamble sequence. The training sequence is quantized to a small alphabet and the resulting discrete valued sequence is used to train a context-tree model of the combined data-source and ISI channel. In additive white Gaussian noise, the context tree equalizer has a probability of error slightly larger than an adaptive decision feedback equalizer (DFE). The difference corresponds to approximately 1 dB loss of signal-to-noise ratio. However, a context tree equalizer performs equally well in the presence of heavy-tailed noise for which the adaptive DFE essentially fails. An empirical classification result of Gutman is extended to Markov processes of alphabet size two and unknown Markov order.
dc.format.extent 66 p.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.subjectStatistics
Electronics
Electrical engineering
dc.title Intersymbol interference equalization by universal likelihood
dc.type.genre Thesis
dc.type.material Text
thesis.degree.department Statistics
thesis.degree.discipline Engineering
thesis.degree.grantor Rice University
thesis.degree.level Doctoral
thesis.degree.name Doctor of Philosophy
dc.identifier.citation Kelly, Owen Ernest. "Intersymbol interference equalization by universal likelihood." (1997) Diss., Rice University. https://hdl.handle.net/1911/19173.


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