Optimization of MIMO Systems Capacity Using Large Random Matrix MethodsReport as inadecuate




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1

Thales Communications & Security, 4 av. des Louvresses, 92622 Gennevilliers, France

2

Université Paris-Est-Marne la Vallée, LIGM, UMR CNRS 8049, 5 Bd. Descartes, Champs-Marne, 77454 Marne la Vallée Cedex 2, France





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Abstract 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. View Full-Text

Keywords: large random matrices; MIMO systems; average mutual information; optimization of the input covariance matrix; iterative water-filling large random matrices; MIMO systems; average mutual information; optimization of the input covariance matrix; iterative water-filling





Author: Florian Dupuy 1,* and Philippe Loubaton 2,*

Source: http://mdpi.com/



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