Asymptotic analysis of MIMO wireless systems with spatial correlation at the receiver

A framework to analyze inference performance in densely connected single-layer feed-forward networks is developed for situations where a given data set is composed of correlated patterns. The framework is based on the assumption that the left and right singular value bases of the given pattern matrix are generated independently and uniformly from Haar measures. This assumption makes it possible to characterize the objective system by a single function of two variables which is determined by the eigenvalue spectrum of the cross-correlation matrix of the pattern matrix. Links to existing methods for analysis of perceptron learning and Gaussian linear vector channels and an application to a simple but nontrivial problem are also shown.

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Statistical mechanical analysis of the linear vector channel in digital communication

Takeda

Hatabu

Kabashima

2007

J. Phys. A: Math. Theor.

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A statistical mechanical framework to analyze linear vector channel models in digital wireless communication is proposed for a large system. The framework is a generalization of that proposed for code-division multiple-access systems in Europhys. Lett., 76 (2006) 1193 and enables the analysis of the system in which the elements of the channel transfer matrix are statistically correlated with each other. The significance of the proposed scheme is demonstrated by assessing the performance of an existing model of multi-input multi-output communication systems.

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Asymptotic analysis of MIMO wireless systems with spatial correlation at the receiver

Cited by 21 publications

References 38 publications

Generic Multiuser Detection and Statistical Physics

Generic Multiuser Detection and Statistical Physics

Inference from correlated patterns: a unified theory for perceptron learning and linear vector channels

Statistical mechanical analysis of the linear vector channel in digital communication

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