Optimization of MIMO Systems Capacity Using Large Random Matrix Methods

This paper provides a comprehensive introduction of large random matrix methods for input covariance matrix optimization of mutual information of MIMO systems. It is first recalled informally how large system approximations of mutual information can be derived. Then, the optimization of the approxim...

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Main Authors: Philippe Loubaton, Florian Dupuy
Format: Article
Language:English
Published: MDPI AG 2012-11-01
Series:Entropy
Subjects:
Online Access:http://www.mdpi.com/1099-4300/14/11/2122
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spelling doaj-0070990c173f403a9bebca912b3086112020-11-24T22:39:37ZengMDPI AGEntropy1099-43002012-11-0114112122214210.3390/e14112122Optimization of MIMO Systems Capacity Using Large Random Matrix MethodsPhilippe LoubatonFlorian DupuyThis paper provides a comprehensive introduction of large random matrix methods for input covariance matrix optimization of mutual information of MIMO systems. It is first recalled informally how large system approximations of mutual information can be derived. Then, the optimization of the approximations is discussed, and important methodological points that are not necessarily covered by the existing literature are addressed, including the strict concavity of the approximation, the structure of the argument of its maximum, the accuracy of the large system approach with regard to the number of antennas, or the justification of iterative water-filling optimization algorithms. While the existing papers have developed methods adapted to a specific model, this contribution tries to provide a unified view of the large system approximation approach.http://www.mdpi.com/1099-4300/14/11/2122large random matricesMIMO systemsaverage mutual informationoptimization of the input covariance matrixiterative water-filling
collection DOAJ
language English
format Article
sources DOAJ
author Philippe Loubaton
Florian Dupuy
spellingShingle Philippe Loubaton
Florian Dupuy
Optimization of MIMO Systems Capacity Using Large Random Matrix Methods
Entropy
large random matrices
MIMO systems
average mutual information
optimization of the input covariance matrix
iterative water-filling
author_facet Philippe Loubaton
Florian Dupuy
author_sort Philippe Loubaton
title Optimization of MIMO Systems Capacity Using Large Random Matrix Methods
title_short Optimization of MIMO Systems Capacity Using Large Random Matrix Methods
title_full Optimization of MIMO Systems Capacity Using Large Random Matrix Methods
title_fullStr Optimization of MIMO Systems Capacity Using Large Random Matrix Methods
title_full_unstemmed Optimization of MIMO Systems Capacity Using Large Random Matrix Methods
title_sort optimization of mimo systems capacity using large random matrix methods
publisher MDPI AG
series Entropy
issn 1099-4300
publishDate 2012-11-01
description This paper provides a comprehensive introduction of large random matrix methods for input covariance matrix optimization of mutual information of MIMO systems. It is first recalled informally how large system approximations of mutual information can be derived. Then, the optimization of the approximations is discussed, and important methodological points that are not necessarily covered by the existing literature are addressed, including the strict concavity of the approximation, the structure of the argument of its maximum, the accuracy of the large system approach with regard to the number of antennas, or the justification of iterative water-filling optimization algorithms. While the existing papers have developed methods adapted to a specific model, this contribution tries to provide a unified view of the large system approximation approach.
topic large random matrices
MIMO systems
average mutual information
optimization of the input covariance matrix
iterative water-filling
url http://www.mdpi.com/1099-4300/14/11/2122
work_keys_str_mv AT philippeloubaton optimizationofmimosystemscapacityusinglargerandommatrixmethods
AT floriandupuy optimizationofmimosystemscapacityusinglargerandommatrixmethods
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