Simultaneous principal-component extraction with application to adaptive blind multiuser detection

Authors:
Deniz Erdogmus;Yadunandana N. Rao;Kenneth E. Hild, II;Jose C. Principe
Affiliations:
Computational NeuroEngineering Laboratory, Electrical and Computer Engineering Department, University of Florida, Gainesville, FL;Computational NeuroEngineering Laboratory, Electrical and Computer Engineering Department, University of Florida, Gainesville, FL;Computational NeuroEngineering Laboratory, Electrical and Computer Engineering Department, University of Florida, Gainesville, FL;Computational NeuroEngineering Laboratory, Electrical and Computer Engineering Department, University of Florida, Gainesville, FL
Venue:
EURASIP Journal on Applied Signal Processing
Year:
2002

Citing 7
Cited 2

Original Contribution: Least mean square error reconstruction principle for self-organizing neural-nets

Neural Networks
Multiuser Detection

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Inverse filtering criteria for CDMA systems

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Blind multiuser detection: a subspace approach

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Group-blind multiuser detection for uplink CDMA

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Artificial neural networks for feature extraction and multivariate data projection

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Perturbation-Based Eigenvector Updates for On-Line Principal Components Analysis and Canonical Correlation Analysis

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Recursive principal components analysis using eigenvector matrix perturbation

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Abstract

SIPEX-G is a fast-converging, robust, gradient-based PCA algorithm that has been recently proposed by the authors. Its superior performance in synthetic and real data compared with its benchmark counterparts makes it a viable alternative in applications where subspace methods are employed. Blind multiuser detection is one such area, where subspace methods, recently developed by researchers, have proven effective. In this paper, the SIPEX-G algorithm is presented in detail, convergence proofs are derived, and the performance is demonstrated in standard subspace problems. These subspace problems include direction of arrival estimation for incoming signals impinging on a linear array of sensors, nonstationary random process subspace tracking, and adaptive blind multiuser detection.