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Algorithms for blind equalization ba...

Wu, Zhengwei.

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Algorithms for blind equalization based on relative gradient and toeplitz constraints.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Algorithms for blind equalization based on relative gradient and toeplitz constraints./
作者:	Wu, Zhengwei.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2016,
面頁冊數:	244 p.
附註:	Source: Dissertation Abstracts International, Volume: 78-04(E), Section: B.
Contained By:	Dissertation Abstracts International78-04B(E).
標題:	Electrical engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10190226
ISBN:	9781369339697

Algorithms for blind equalization based on relative gradient and toeplitz constraints.
Wu, Zhengwei.

Algorithms for blind equalization based on relative gradient and toeplitz constraints. - Ann Arbor : ProQuest Dissertations & Theses, 2016 - 244 p.

Source: Dissertation Abstracts International, Volume: 78-04(E), Section: B.

Thesis (Ph.D.)--University of Pennsylvania, 2016.

This item is not available from ProQuest Dissertations & Theses.

Blind Equalization (BE) refers to the problem of recovering the source symbol sequence from a signal received through a channel in the presence of additive noise and channel distortion, when the channel response is unknown and a training sequence is not accessible. To achieve BE, statistical or constellation properties of the source symbols are exploited. In BE algorithms, two main concerns are convergence speed and computational complexity.

ISBN: 9781369339697Subjects--Topical Terms:

649834
Electrical engineering.

Algorithms for blind equalization based on relative gradient and toeplitz constraints.
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245 1 0 $a Algorithms for blind equalization based on relative gradient and toeplitz constraints.
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300 $a 244 p.
500 $a Source: Dissertation Abstracts International, Volume: 78-04(E), Section: B.
500 $a Adviser: Saleem A. Kassam.
502 $a Thesis (Ph.D.)--University of Pennsylvania, 2016.
506 $a This item is not available from ProQuest Dissertations & Theses.
520 $a Blind Equalization (BE) refers to the problem of recovering the source symbol sequence from a signal received through a channel in the presence of additive noise and channel distortion, when the channel response is unknown and a training sequence is not accessible. To achieve BE, statistical or constellation properties of the source symbols are exploited. In BE algorithms, two main concerns are convergence speed and computational complexity.
520 $a In this dissertation, we explore the application of relative gradient for equalizer adaptation with a structure constraint on the equalizer matrix, for fast convergence without excessive computational complexity. We model blind equalization with symbol-rate sampling as a blind source separation (BSS) problem and study two single-carrier transmission schemes, specifically block transmission with guard intervals and continuous transmission. Under either scheme, blind equalization can be achieved using independent component analysis (ICA) algorithms with a Toeplitz or circulant constraint on the structure of the separating matrix. We also develop relative gradient versions of the widely used Bussgang-type algorithms. Processing the equalizer outputs in sliding blocks, we are able to use the relative gradient for adaptation of the Toeplitz constrained equalizer matrix. The use of relative gradient makes the Bussgang condition appear explicitly in the matrix adaptation and speeds up convergence.
520 $a For the ICA-based and Bussgang-type algorithms with relative gradient and matrix structure constraints, we simplify the matrix adaptations to obtain equivalent equalizer vector adaptations for reduced computational cost. Efficient implementations with fast Fourier transform, and approximation schemes for the cross-correlation terms used in the adaptation, are shown to further reduce computational cost.
520 $a We also consider the use of a relative gradient algorithm for channel shortening in orthogonal frequency division multiplexing (OFDM) systems. The redundancy of the cyclic prefix symbols is used to shorten a channel with a long impulse response. We show interesting preliminary results for a shortening algorithm based on relative gradient.
590 $a School code: 0175.
650 4 $a Electrical engineering. $3 649834
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710 2 $a University of Pennsylvania. $b Electrical and Systems Engineering. $3 3169654
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793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10190226