Doctoral Dissertations

Abstract

"An adaptive receiver is designed for transmissions through a time-varying multipath channel which may include both specular and diffuse components. The design is based on the theory of unsupervised learning machines and the receiver is a recursive structure which does not grow in complexity with each new observation, but is Bayes' optimal at each instant of time. The multipath medium is modelled as an aggregate of L conditionally independent transmission paths, each consisting of random and/or fixed reflections, and is identified in terms of three components: (1) indirect diffuse scatter, (2) indirect specular reflection, and (3) direct transmission. The channel parameters are time-varying and either independent from one signaling interval to the next or at most M-th order Markov dependent. A review of machines that learn without a teacher is presented and the learning receiver for three-component multipath is designed and modelled on the digital computer. A Monte Carlo simulation is used to estimate the performance when the channel is either Rician or nonfading. This performance, in terms of probability of error, is shown to be consistent with the existing coherent receivers and improves on their performance when the correlation between observations is increased"--Abstract, page ii.

Advisor(s)

Ziemer, Rodger E.

Committee Member(s)

Haddock, Glen
Cunningham, David R.
Bain, Lee J., 1939-
Tranter, William H.
Pazdera, John S., 1941-1974

Department(s)

Electrical and Computer Engineering

Degree Name

Ph. D. in Electrical Engineering

Publisher

University of Missouri--Rolla

Publication Date

1972

Pagination

vi, 75 pages

Note about bibliography

Includes bibliographical references (pages 62-63)

Rights

© 1972 Richard Paul Brueggemann, All rights reserved.

Document Type

Dissertation - Open Access

File Type

text

Language

English

Library of Congress Subject Headings

Radio -- Transmitter-receivers -- Design
Radio -- Transmitter-receivers -- Mathematical models
Machine learning

Thesis Number

T 2770

Print OCLC #

6034310

Electronic OCLC #

893625144

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