NDLI: A general covariance-based optimization framework using orthogonal projections

Content Provider	IEEE Xplore Digital Library
Author	Hunger, R. Schmidt, D.A. Joham, M. Utschick, W.
Copyright Year	2008
Description	Author affiliation: Associate Inst. for Signal Process., Tech. Univ. Munchen, Munich (Hunger, R.; Schmidt, D.A.; Joham, M.; Utschick, W.)
Abstract	We present a general framework for the minimization of a function which is parametrized by a set of covariance matrices over a constraint set. Since all covariance matrices have to obey the property of being positive semidefinite, this characteristic has to be reflected in the constraint set. In addition, the sum of all traces of the covariance matrices shall be upper bounded. Using a preconditioned gradient descent algorithm, we derive an orthogonal projection onto this constraint set in an easy to follow monolithic way such that it directly results from the definition of the projection. Interestingly, this projection allows for a descriptive water-spilling interpretation in the style of the well-known water-filling algorithm. Two possible applications are investigated: the sum mean-square-error minimization and the weighted sum-rate maximization for the MIMO broadcast channel. Simulations finally reveal the excellent performance of the proposed framework.
Starting Page	76
Ending Page	80
File Size	1830993
Page Count	5
File Format	PDF
ISBN	9781424420452
DOI	10.1109/SPAWC.2008.4641573
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-07-06
Publisher Place	Brazil
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Covariance matrix Optimization MIMO Minimization Signal processing algorithms Downlink Cost function
Content Type	Text
Resource Type	Article

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