Robust Superimposed Training Designs for MIMO Relaying Systems Under General Power Constraints

In this paper, we investigate the superimposed training matrix designs for the channel estimation of amplify-and-forward (AF) multiple-input multiple-output (MIMO) relaying systems under general power constraints. Furthermore, the imperfect channel and colored noise statistical information models wi...

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Bibliographic Details
Published in:IEEE access Vol. 7; pp. 80404 - 80420
Main Authors: Rong, Beini, Zhang, Zhongshan, Zhao, Xin, Yu, Xiaoyun
Format: Journal Article
Language:English
Published: Piscataway IEEE 2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
AF MIMO relaying systems
Algorithms
Channel estimation
Codes
Colored noise
Complexity
Error analysis
general power constraints
Iterative methods
LMMSE channel estimation
Mathematical models
Matrices (mathematics)
Maximization
MIMO (control systems)
MIMO communication
Optimization
Receivers
Relaying
Relays
robust training designs
Robustness (mathematics)
Semidefinite programming
Training
unitarily-invariant uncertainty sets
Upper bounds
ISSN:2169-3536, 2169-3536
Online Access:Get full text
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Summary:In this paper, we investigate the superimposed training matrix designs for the channel estimation of amplify-and-forward (AF) multiple-input multiple-output (MIMO) relaying systems under general power constraints. Furthermore, the imperfect channel and colored noise statistical information models with the corresponding nominal terms being Kronecker structure and the corresponding statistical errors belonging to the unitarily-invariant uncertainty sets are adopted. Based on the above analysis, the linear minimum mean-squared-error (LMMSE)-based robust training optimization problem is formulated, which is generally nonconvex and intractable. In order to effectively address this problem, we propose an iterative semidefinite programming (SDP) algorithm and two low-complexity upper bound optimization schemes. Particularly, for the proposed two upper bound optimization schemes, the diagonal structured optimal solutions of the relaxed robust training problems can be verified. Besides, the low-complexity iterative bisection search (IBS) can also be applied to derive the diagonal training matrix. Furthermore, we extend our work into the robust mutual information maximization of the AF MIMO relaying channel and demonstrate that all proposed robust training designs are still applicable. Finally, the numerical simulations illustrate the excellent performance of the proposed robust training designs in terms of the channel estimation MSE minimization and mutual information maximization.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2922970