Optimal step size of the adaptive multichannel LMS algorithm for blind SIMO identification

被引：27
作者：
Huang, YT
Benesty, J
Chen, JD
机构：
[1] Bell Labs, Lucent Technol, Murray Hill, NJ 07974 USA
[2] Univ Quebec, INRS EMT, Montreal, PQ H5A 1K6, Canada
来源：
IEEE SIGNAL PROCESSING LETTERS | 2005年 / 12卷 / 03期
关键词：
blind channel identification (BCI); least mean square (LMS); multichannel signal processing; SIMO systems; variable step-size adaptive algorithm;
D O I：
10.1109/LSP.2004.842286
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：
Adaptive algorithms for blindly identifying single-input multiple-output (SIMO) systems are appealing because of their computational efficiency and capability of continuously tracking a time-varying system. Adaptive multichannel least-mean-square (MCLMS) algorithms (with and without the unit-norm constraint) are analyzed, and the optimal step size is derived. A simple yet effective variable step-size MCLMS algorithm is proposed, and its performance is evaluated with simulations.
引用
收藏
页码：173 / 176
页数：4
相关论文
共 5 条
[1]  
AVENDANO C, 1999, P IEEE INT C AC SPEE, V4, P1797
[2]   Convergence of stochastic-approximation-based algorithms for blind channel identification [J].
Chen, HF ;
Cao, XR ;
Zhu, J .
IEEE TRANSACTIONS ON INFORMATION THEORY, 2002, 48 (05) :1214-1225
[3]   Adaptive multi-channel least mean square and Newton algorithms for blind channel identification [J].
Huang, YA ;
Benesty, J .
SIGNAL PROCESSING, 2002, 82 (08) :1127-1138
[4]   On the evaluation of estimated impulse responses [J].
Morgan, DR ;
Benesty, J ;
Sondhi, MM .
IEEE SIGNAL PROCESSING LETTERS, 1998, 5 (07) :174-176
[5]   A least-squares approach to blind channel identification [J].
Xu, GH ;
Liu, H ;
Tong, L ;
Kailath, T .
IEEE TRANSACTIONS ON SIGNAL PROCESSING, 1995, 43 (12) :2982-2993
1