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

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 de...

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Vydané v:	IEEE transactions on signal processing Ročník 52; číslo 3; s. 746 - 756
Hlavní autori:	Schober, R., Gerstacker, W.H., Lampe, L.H.-J.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York, NY IEEE 01.03.2004 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Adaptive algorithm Algorithm design and analysis Applied sciences Detection, estimation, filtering, equalization, prediction Direct-sequence code-division multiple access Exact sciences and technology Information, signal and communications theory Least squares approximation Multiaccess communication Multiple access interference Nonlinear filters Signal analysis Signal and communications theory Signal, noise Steady-state Stochastic processes Telecommunications and information theory Performance evaluation Adaptive algorithm Adaptive algorithms Stochastic method multiple-access interference suppression Mean square error Interference suppression DS-CDMA Simulation Code division multiple access widely linear processing Linear filtering Gradient method Direct sequence Least mean squares methods
ISSN:	1053-587X, 1941-0476
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Popis
Shrnutí:	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.
Bibliografia:	ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 content type line 23
ISSN:	1053-587X 1941-0476
DOI:	10.1109/TSP.2003.822359