Data-aided and blind stochastic gradient algorithms for widely linear MMSE MAI suppression for DS-CDMA

TitleData-aided and blind stochastic gradient algorithms for widely linear MMSE MAI suppression for DS-CDMA
Publication TypeJournal Article
Year of Publication2004
AuthorsSchober, R., W. H. Gerstacker, and L. Lampe
JournalSignal Processing, IEEE Transactions on
Volume52
Pagination746 - 756
Date Publishedmar.
ISSN1053-587X
Keywordsadaptive filters, adaptive signal processing, blind stochastic gradient algorithm, code division multiple access, computational complexity, convergence speed, data-aided algorithm, direct sequence code division multiple access, DS-CDMA, gradient methods, interference suppression, least mean square algorithm, least mean squares methods, linear adaptive algorithm, linear filter, minimum mean square error, minimum output energy algorithm, multiple access interference, signal-to-interference-plus-noise ratio, spread spectrum communication, steady-state SINR, stochastic processes, widely linear MMSE MAI suppression, widely linear processing
Abstract

In this paper, three novel stochastic gradient algorithms for adjustment of the widely linear (WL) minimum mean-squared error (MMSE) filter for multiple access interference (MAI) suppression for direct-sequence code-division multiple access (DS-CDMA) are introduced and analyzed. In particular, we derive a data-aided WL least-mean-square (LMS) algorithm, a blind WL minimum-output-energy (MOE) algorithm, and a WL blind LMS (BLMS) algorithm. We give analytical expressions for the steady-state signal-to-interference-plus-noise ratios (SINRs) of the proposed WL algorithms, and we also investigate their speed of convergence. Wherever possible, comparisons with the corresponding linear adaptive algorithms are made. Both analytical considerations and simulations show, in good agreement, the superiority of the novel WL adaptive algorithms. Nevertheless, all proposed WL algorithms require a slightly lower computational complexity than their linear counterparts.

URLhttp://dx.doi.org/10.1109/TSP.2003.822359
DOI10.1109/TSP.2003.822359

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