Optimization of MIMO Systems Capacity Using Large Random Matrix Methods

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 approximations is discussed, and important methodological points that are not necessarily covered by the ...

Capacity Approximation for Uncorrelated Mimo Channels Using Random Matrix Methods

In this paper, we present random matrix methods for determining the capacity of uncorrelated MIMO channels having partial channel state information, i.e., statistical knowledge of the channel parameters. We show that it is possible to approximate the MIMO channel capacities by Taylor series expansions in order to apply large-scale analysis using random matrix methods. For an adequate approximat...

A General Framework for Covariance Matrix Optimization in MIMO Systems

For multi-input multi-output (MIMO) systems, many transceiver design problems involve the optimization of the covariance matrices of the transmitted signals. Karush-Kuhn-Tucker (KKT) conditions based derivations are the most popular method, and many derivations and results have been reported for different scenarios of MIMO systems. We propose a unified framework in formulating the KKT condition...

Capacity Limits of MIMO Systems

Capacity Analysis of MIMO Systems

Digital communication using multiple-input multiple-output (MIMO) wireless links has recently emerged as one of the most significant technical breakthroughs in modern communications. This thesis presents an overview of some important theoretical concepts of MIMO systems. After describing the basic ideas of MIMO transmissions in the introduction, we mainly focused on information theoretical conc...