Constrained Adaptive Linear Multiuser Detection Schemes

Authors:
George V. Moustakides
Affiliations:
Institut National de Recherche en Informatique et en Automatique (INRIA), Rennes 35042, France&semi/ Department of Computer Engineering and Informatics, University of Patras, Greece
Venue:
Journal of VLSI Signal Processing Systems
Year:
2002

Citing 9
Cited 2

Adaptive algorithms and stochastic approximations

Adaptive algorithms and stochastic approximations
Adaptive signal processing algorithms: stability and performance

Adaptive signal processing algorithms: stability and performance
Multiuser Detection

Multiuser Detection
A constrained optimization approach to multiuser detection

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Locally optimum adaptive signal processing algorithms

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Probability of error in MMSE multiuser detection

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Blind multiuser detection: a subspace approach

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On the relative error probabilities of linear multiuser detectors

IEEE Transactions on Information Theory
Blind adaptive multiuser detection

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Steady-state analysis of constrained normalized adaptive filters for MAI reduction by energy conservation arguments

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An adaptive projected subgradient approach to learning in diffusion networks

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Abstract

By using a fair comparison method we show that contrary to the general belief the conventional LMS, when in training mode, does not necessarily outperform the popular blind LMS (BLMS). With the help of a constrained MMSE criterion we identify the correct trained version which is guaranteed to have uniformly superior performance over BLMS since it maximizes the SIR over an algorithmic class containing BLMS. Because the proposed optimum trained version requires knowledge of the amplitude of the user of interest we also present simple and efficient techniques that estimate the amplitude in question. The resulting algorithm in both modes, training and decision directed, is significantly superior to BLMS.