TY - JOUR
T1 - MIMO gaussian channels with arbitrary inputs
T2 - Optimal precoding and power allocation
AU - Perez-Cruz, Fernando
AU - Rodrigues, Miguel R.D.
AU - Verdú, Sergio
N1 - Funding Information:
Manuscript received October 03, 2008; revised August 14, 2009. Current version published March 10, 2010. This work was supported in part by the National Science Foundation (NSF) under Grants NCR-0074277 and CCR-0312879 and collaborative participation in the Communications and Networks Consortium sponsored by the U.S. Army Research Laboratory under the Collaborative Technology Alliance Program, Cooperative Agreement DAAD19-01-2-0011. This work was also supported in part by the Spanish Government (Ministerio de Educación y Ciencia TEC2006-13514-C02-01/TCM, TEC2009-14504-C02-01, Consolider-Ingenio 2010 CSD2008-00010). The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation thereon. The work of F. Pérez-Cruz was supported by Marie Curie Fellowship 040883-AI-COM. The work of M. R. D. Rodrigues was supported by Fundação para a Ciência e a Tecnologia through the research project PTDC/EEA-TEL/100854/2008, by Instituto de Telecomunicações through the research project IT/LA/430/2008, by Fundação Luso-Americana through the research project FLAD/2-05/08, and by Fundação Calouste Gulbenkian. The material in this paper was presented in part at the IEEE International Conference on Communications, Beijing, China, May 2008 and at the 45th Annual Allerton Conference on Communication, Computing and Control, Monticello, IL, September 2007.
PY - 2010/3
Y1 - 2010/3
N2 - In this paper, we investigate the linear precoding and power allocation policies that maximize the mutual information for general multiple- inputmultiple-output (MIMO) Gaussian channels with arbitrary input distributions, by capitalizing on the relationship between mutual information and minimum mean-square error (MMSE). The optimal linear precoder satisfies a fixed-point equation as a function of the channel and the input constellation. For non-Gaussian inputs, a nondiagonal precoding matrix in general increases the information transmission rate, even for parallel noninteracting channels. Whenever precoding is precluded, the optimal power allocation policy also satisfies a fixed-point equation; we put forth a generalization of the mercury/waterfilling algorithm, previously proposed for parallel noninterfering channels, in which the mercury level accounts not only for the non-Gaussian input distributions, but also for the interference among inputs.
AB - In this paper, we investigate the linear precoding and power allocation policies that maximize the mutual information for general multiple- inputmultiple-output (MIMO) Gaussian channels with arbitrary input distributions, by capitalizing on the relationship between mutual information and minimum mean-square error (MMSE). The optimal linear precoder satisfies a fixed-point equation as a function of the channel and the input constellation. For non-Gaussian inputs, a nondiagonal precoding matrix in general increases the information transmission rate, even for parallel noninteracting channels. Whenever precoding is precluded, the optimal power allocation policy also satisfies a fixed-point equation; we put forth a generalization of the mercury/waterfilling algorithm, previously proposed for parallel noninterfering channels, in which the mercury level accounts not only for the non-Gaussian input distributions, but also for the interference among inputs.
KW - Gaussian noise channels
KW - Minimum mean-square error (MMSE)
KW - Multiple-inputmultiple-output (MIMO) systems
KW - Mutual information
KW - Optimum power allocation
KW - Precoding
KW - Waterfilling
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U2 - 10.1109/TIT.2009.2039045
DO - 10.1109/TIT.2009.2039045
M3 - Article
AN - SCOPUS:77949529945
SN - 0018-9448
VL - 56
SP - 1070
EP - 1084
JO - IEEE Transactions on Information Theory
JF - IEEE Transactions on Information Theory
IS - 3
M1 - 5429131
ER -