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On the performance of subspace SIMO blind channel identification methods

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TitleInfo
Title
On the performance of subspace SIMO blind channel identification methods
Name (type = personal)
NamePart (type = family)
Bonna
NamePart (type = given)
Kareem Y.
NamePart (type = date)
1986-
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Kareem Y. Bonna
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author
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Spasojevic
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Predrag
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Predrag Spasojevic
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Advisory Committee
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chair
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Gajic
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Zoran
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Zoran Gajic
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Advisory Committee
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internal member
Name (type = personal)
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Yates
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Roy
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Roy Yates
Affiliation
Advisory Committee
Role
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internal member
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Rutgers University
Role
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degree grantor
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School of Graduate Studies
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school
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Text
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theses
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DateCreated (qualifier = exact)
2017
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2017-10
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2017
Place
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xx
Language
LanguageTerm (authority = ISO639-2b); (type = code)
eng
Abstract (type = abstract)
Channel Identification is an important part of wireless communication systems. Radio-Frequency (RF) signals are subject to reflection, refraction, and diffraction, attenuation, and other effects, that result in a distorted signal at a receiver, particularly over what are known as frequency-selective channels. Traditionally, such distortion is estimated using a ``training sequence" which is a known reference signal used to estimate, and then correct for, the distortion. However, use of training sequences is not always possible, for example in military applications where the source signal is not known, or in broadcast environments where there is a high cost of transmitting a signal. One potential solution is to estimate the channel blindly, that is, without knowledge of the transmitted signal. Blind Channel Identification (BCI) and Equalization has been a extensive topic of research since at least 1975. One strategy in Blind Channel Identification is to use the structure of the received signals in a Single Input Multiple Output (SIMO) system to estimate the channel. Research has occurred on a number of methods that exploit this in the past several decades. The subspace methods form the channel estimate in terms of a one-dimensional subspace constructed using the estimated second-order statistics of the received signals. Additionally, the use of sparsity in signal estimation has been a topic of interest as well, and has recently been used in certain cases to improve the robustness of the subspace methods in a number of works. In this thesis, the Cross-Relations and Noise-Subspace methods, both of which are SIMO BCI methods, as well as their sparse variant, are examined for a deterministic channel. The expected Normalized Projection Misalignment (NPM) is analytically approximated for all considered methods. In addition, it is compared to simulation results for a random source signal and several measured RF channels from earlier literature. Finally, the sensitivity of the sparse variant of the subspace methods as a function of the regularization parameter is studied using simulation for a set of measured RF channels from earlier literature.
Subject (authority = RUETD)
Topic
Electrical and Computer Engineering
Subject (authority = ETD-LCSH)
Topic
Radio frequency
RelatedItem (type = host)
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Title
Rutgers University Electronic Theses and Dissertations
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ETD
Identifier
ETD_8486
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electronic resource
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application/pdf
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text/xml
Extent
1 online resource (ix, 47 p. : ill.)
Note (type = degree)
M.S.
Note (type = bibliography)
Includes bibliographical references
Note (type = statement of responsibility)
by Kareem Y. Bonna
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TitleInfo
Title
School of Graduate Studies Electronic Theses and Dissertations
Identifier (type = local)
rucore10001600001
Location
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NjNbRU
Identifier (type = doi)
doi:10.7282/T3WW7MT0
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The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
Bonna
GivenName
Kareem
MiddleName
Y.
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2017-10-02 13:20:14
AssociatedEntity
Name
Kareem Bonna
Role
Copyright holder
Affiliation
Rutgers University. School of Graduate Studies
AssociatedObject
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Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
Copyright
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Copyright protected
Availability
Status
Open
Reason
Permission or license
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2017-10-02T12:09:30
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