Statistically robust precoder design over correlated Rician MIMO channels

被引:1
|
作者
Luo, Zhen [1 ,2 ]
Zhang, Lin [1 ,2 ]
Leung, Shu-hung [1 ,2 ]
Yu, Xiang-bin [3 ]
机构
[1] City Univ Hong Kong, Dept Elect Engn, Hong Kong, Hong Kong, Peoples R China
[2] City Univ Hong Kong, State Key Lab Millimeter Waves, Hong Kong, Hong Kong, Peoples R China
[3] Nanjing Univ Aeronaut & Astronaut, Nanjing 210016, Jiangsu, Peoples R China
来源
SIGNAL PROCESSING | 2014年 / 102卷
关键词
Precoder; MIMO; Space-time block code; Statistical CSI; TIME BLOCK-CODES; POWER ALLOCATION; ESTIMATION ERROR; FADING CHANNELS; SYSTEMS; INFORMATION; ALGORITHMS; FEEDBACK; DELAY;
D O I
10.1016/j.sigpro.2014.03.025
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
In this paper, a statistically robust precoding scheme for multiple-input multiple-output systems with orthogonal space-time block code over double correlated Rician fading channels of Kronecker correlation structure is presented. We consider only statistical channel state information (CSI) known at the transmitter for the sake of saving feedback overhead. We adopt eigenrepresentation to express the precoder design in terms of unitary matrix and eigenvalues to minimize pairwise error probability (PEP) subject to an average power constraint for the purpose of reducing computation and fast convergence. A closed-form formulation of the PEP bound in terms of eigenvalue decomposition is derived. By introducing new variables for expressing the power constraint, an optimal power allocation algorithm for given unitary matrix and an optimal unitary matrix update procedure are developed. Based on asymptotic analysis, a near-optimal unitary matrix is derived. With this unitary matrix, the precoder design can be reduced to power allocation that can provide near optimal performance. Using the unitary matrix as initialization, the proposed algorithm can reach the optimal solution in a few iterations. Numerical results show that the proposed optimal scheme provides performance better than existing methods and has low computational complexity and fast convergence in comparison with the fixed-point method. (C) 2014 Elsevier B.V. All rights reserved.
引用
收藏
页码:177 / 187
页数:11
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