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Optimization of MIMO Systems Capacity Using Large Random Matrix Methods
Thales Communications & Security, 4 av. des Louvresses, 92622 Gennevilliers, France
Université Paris-Est/Marne la Vallée, LIGM, UMR CNRS 8049, 5 Bd. Descartes, Champs/Marne, 77454 Marne la Vallée Cedex 2, France
* Authors to whom correspondence should be addressed.
Received: 12 September 2012; in revised form: 19 October 2012 / Accepted: 24 October 2012 / Published: 1 November 2012
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.
Keywords: large random matrices; MIMO systems; average mutual information; optimization of the input covariance matrix; iterative water-filling
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MDPI and ACS Style
Dupuy, F.; Loubaton, P. Optimization of MIMO Systems Capacity Using Large Random Matrix Methods. Entropy 2012, 14, 2122-2142.
Dupuy F, Loubaton P. Optimization of MIMO Systems Capacity Using Large Random Matrix Methods. Entropy. 2012; 14(11):2122-2142.
Dupuy, Florian; Loubaton, Philippe. 2012. "Optimization of MIMO Systems Capacity Using Large Random Matrix Methods." Entropy 14, no. 11: 2122-2142.