ABSTRACT: We investigate the linear precoding policy that maximizes the mutual information for general multiple-input multiple-output (MIMO) Gaussian channels with arbitrary input distributions, by capitalizing on the relationship between mutual information and minimum mean-square error. The optimal linear precoder can be computed by means of a fixed-point equation as a function of the channel and the input constellation. We show that diagonalizing the channel matrix does not maximize the information transmission rate for nonGaussian inputs. A non-diagonal precoding matrix in general increases the information transmission rate, even for parallel non-interacting channels. We also determine the optimal power allocation policy for MIMO channels. In this context, we put forth a novel interpretation of the optimal power-allocation procedure that generalizes the mercury/waterfilling algorithm, previously proposed for parallel non-interfering channels. In this generalization the mercury level accounts for the suboptimal (nonGaussian) input distribution and the interferences between inputs. Finally, we also investigate the use of correlated input distributions, which further increase the transmission rate for low and medium-snr ranges.
Optimal Linear Precoding for Multiple-Input Multiple-Output Gaussian Channels with Arbitrary Inputs
Weekly Seminar
25 May 2009Speaker: Fernando Pérez-Cruz, Ph.D.
Place: CTTC Auditorium/ 10:00h
Dr. Pérez, Princeton and Carlos III Univ., will study the linear precoder that maximizes the mutual information by capitalizing on the relationship between mutual information and minimum mean-square error recently unveiled by Guo, Shamai, and Verdú.
SPEAKER: Fernando Pérez-Cruz (IEEE Senior Member) was born in Sevilla, Spain, in 1973. He received a PhD. in Telecommunication Engineering in 2000 from the Technical University of Madrid and an MSc/BSc in Telecommunication Engineering from the University of Sevilla in 1996. He is an Associate Professor with the Department of Signal Theory and Communication at University Carlos III in Madrid. He is currently on sabbatical at Princeton University under an outgoing Marie Curie Fellowship. He has held positions and visited Gatsby Unit (London), Max Planck Institute for Biological Cybernetics (Tuebingen), BioWulf Technologies (New York) and the Technical University of Madrid and Alcala University (Madrid). His current research interest lies in machine learning algorithmic and theoretical developments and its application to signal processing and information theory. He has authored over 60 contributions to international journals and conferences. For more information visit http://www.princeton.edu/~fp.
