Linear Precoding for Finite-Alphabet Inputs Over MIMO Fading Channels With Statistical CSI

This paper investigates the linear precoder design that maximizes the average mutual information of multiple-input multiple-output fading channels with statistical channel state information known at the transmitter. It formulates the design from the standpoint of finite-alphabet inputs, which leads...

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Bibliographic Details
Published in:IEEE transactions on signal processing Vol. 60; no. 6; pp. 3134 - 3148
Main Authors: Zeng, Weiliang, Xiao, Chengshan, Wang, Mingxi, Lu, Jianhua
Format: Journal Article
Language:English
Published: New York, NY IEEE 01.06.2012
Institute of Electrical and Electronics Engineers
Subjects:
Applied sciences
Coding, codes
Detection, estimation, filtering, equalization, prediction
Exact sciences and technology
Finite-alphabet inputs
Information, signal and communications theory
MIMO
multiple-input multiple-output
Mutual information
Optimization
precoder design
Resource management
Signal and communications theory
Signal processing algorithms
Signal, noise
statistical channel state information
Telecommunications and information theory
Transmitters
Vectors
Alphabet
Fading channels
Performance evaluation
MIMO system
Lower bound
Parameter estimation
Coding circuit
Spatial correlation
Transmitter
multiple-input multiple-output
Finite-alphabet inputs
precoder design
Algorithm
Computational complexity
Optimization
Upper bound
Coding
Channel estimation
Signal processing
statistical channel state information
Numerical simulation
Pretreatment
Reduced order model
Mutual information
ISSN:1053-587X, 1941-0476
Online Access:Get full text
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Summary:This paper investigates the linear precoder design that maximizes the average mutual information of multiple-input multiple-output fading channels with statistical channel state information known at the transmitter. It formulates the design from the standpoint of finite-alphabet inputs, which leads to a problem that is very important in practice but extremely difficult in theory: First, the average mutual information lacks closed-form expression and involves prohibitive computational burden. Second, the optimization over the precoder is nonconcave and thus easily gets stuck in local maxima. To address these issues, this study first derives lower and upper bounds for the average mutual information, in which the computational complexity is reduced by several orders of magnitude compared to calculating the average mutual information directly. It proves that maximizing the bounds is asymptotically optimal and shows that, with a constant shift, the lower bound actually offers a very accurate approximation to the average mutual information for various fading channels. This paper further proposes utilizing the lower bound as a low-complexity and accurate alternative for developing a two-step algorithm to find a near global optimal precoder. Numerical examples demonstrate the convergence and efficacy of the proposed algorithm. Compared to its conventional counterparts, the proposed linear precoding method provides significant performance gain over existing precoding algorithms. The gain becomes more substantial when the spatial correlation of MIMO channels increases.
ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2012.2188717