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Maximally sparse convex estimation and equalization

Lourakis Georgios

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Extent78 pagesen
TitleMaximally sparse convex estimation and equalizationen
CreatorLourakis Georgiosen
CreatorΛουρακης Γεωργιοςel
Contributor [Thesis Supervisor]Liavas Athanasiosen
Contributor [Thesis Supervisor]Λιαβας Αθανασιοςel
Contributor [Committee Member]Digalakis Vasilisen
Contributor [Committee Member]Διγαλακης Βασιληςel
Contributor [Committee Member]Bletsas Aggelosen
Contributor [Committee Member]Μπλετσας Αγγελοςel
PublisherΠολυτεχνείο Κρήτηςel
PublisherTechnical University of Creteen
Academic UnitTechnical University of Crete::School of Electronic and Computer Engineeringen
Academic UnitΠολυτεχνείο Κρήτης::Σχολή Ηλεκτρονικών Μηχανικών και Μηχανικών Υπολογιστώνel
Content SummarySparse multipath channels are wireless links commonly found in communication systems such as High Frequency radio channels, horizontal and vertical underwater acoustic channels and terrestrial broadcasting channels for High Definition Television. Their impulse responses are characterized by a few significant terms that are widely separated in time. With high speed transmission, the length of a sampled sparse channel can reach hundreds of symbol interval. Thus, the amount of Intersymbol Interference (ISI) at the receiver is very high. Consequently, the presence of an ISI mitigating structure at the receiver, such as the Decision Feedback Equalizer (DFE) is essential. Due to the sparse impulse responses of these channels, traditional estimation techniques such as Least Squares (LS) result in over-parameterization and thus poor performance of the estimator. Also, classical equalizers become too complex for tackling these channels. The problem of estimating and equalizing sparse multipath channels is considered in this thesis. We formulate the sparse channel estimation and the computation of the sparse DFE filters as sparse approximation problems. A usual approach in sparse approximation problems is regularization with an l_1 norm penalty term and usage of convex optimization techniques in order to acquire a solution. Other sparsity promoting penalty functions are available, but the l_1 norm has the advantage to be a convex function, making the l_1 norm regularized approximation problem a convex one. When a problem is formulated as a convex optimization problem, it can be solved by very fast, efficient and reliable algorithms. In order to achieve sparser solutions and still gain from the benefits of the convex optimization theory, the Maximally Sparse Convex (MSC) algorithm utilizes a non-convex regularization term, that promotes sparsity more strongly than the l_1 norm, but chosen such that the total cost function remains convex.en
Type of ItemΔιπλωματική Εργασίαel
Type of ItemDiploma Worken
Date of Item2014-10-06-
Date of Publication2014-
SubjectConvex optimizationen
SubjectCommunication systems, Wirelessen
SubjectWireless data communication systemsen
SubjectWireless information networksen
SubjectWireless telecommunication systemsen
Subjectwireless communication systemsen
Subjectcommunication systems wirelessen
Subjectwireless data communication systemsen
Subjectwireless information networksen
Subjectwireless telecommunication systemsen
Bibliographic CitationGeorgios Lourakis, "Maximally sparse convex estimation and equalization", Diploma Work, School of Electronic and Computer Engineering, Technical University of Crete, Chania, Greece, 2014en
Bibliographic CitationΓεώργιος Λουράκης, "Maximally sparse convex estimation and equalization", Διπλωματική Εργασία, Σχολή Ηλεκτρονικών Μηχανικών και Μηχανικών Υπολογιστών, Πολυτεχνείο Κρήτης, Χανιά, Ελλάς, 2014el

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