Adaptive Widely Linear Reduced-Rank Beamforming Based on Joint Iterative Optimization

被引：23
|
作者：
Song, Nuan [1 ]
Alokozai, Waheed Ullah [1 ]
de Lamare, Rodrigo C. [2 ,3 ]
Haardt, Martin [1 ]
机构：
[1] Ilmenau Univ Technol, Commun Res Lab, D-98684 Ilmenau, Germany
[2] CETUC PUC Rio, Rio De Janeiro, Brazil
[3] Univ York, Commun Res Grp, Dept Elect, York YO10 5DD, N Yorkshire, England
来源：
IEEE SIGNAL PROCESSING LETTERS | 2014年 / 21卷 / 03期
关键词：
Adaptive beamforming; linear constrained minimum variance; non-circular data; recursive least squares algorithms; reduced-rank methods; widely linear processing; LEAST-SQUARES ALGORITHM; MULTIUSER RECEIVERS;
D O I：
10.1109/LSP.2013.2295943
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：
We propose a reduced-rank beamformer based on the rank-Joint Iterative Optimization (JIO) of the modified Widely Linear Constrained Minimum Variance (WLCMV) problem for non-circular signals. The novel WLCMV-JIO scheme takes advantage of both the Widely Linear (WL) processing and the reduced-rank concept, outperforming its linear counterpart as well as the full-rank WL beamformer. We develop an augmented recursive least squares algorithm and present an improved structured version with a much more efficient implementation. It is shown that the improved adaptive scheme achieves the best convergence performance among all the considered methods with a low computational complexity.
引用
收藏
页码：265 / 269
页数：5
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