摘要
针对SIMO信道的经典盲估计方法 ,如子空间法 (SS)等 ,都是基于接收端样本自相关阵的特征值分解(EVD)或奇异值分解 (SVD)来实现信道估计的 ,而基于QR分解的SIMO信道盲辨识方法是最近提出的一种性能优良的新算法 .本文将该算法推广为MIMO信道盲辨识算法 ,并且证明了在一定的假设下 ,即使各路源信号为空间相关且其统计特性未知时 ,该算法仍然保持有效 .实验结果表明这种MIMO辨识算法具有收敛速度快、计算量小、无须对噪声做额外的处理、对噪声不敏感等优点 .我们还将这种算法与经典的MIMO辨识算法进行了性能比较 .
Traditional blind identification methods for SIMO channels, such as subspace method (SS), etc., are based on the eigen value decomposition (EVD) or singular value decomposition (SVD) of the received signal's correlation matrix, which may need more data to make the estimation of correlation matrix be accurate. Recently a QR factorization based blind identification algorithm for SIMO channels with good performance and without calculating the correlation matrix is proposed. We extend the QR factorization based algorithm for the blind identification of MIMO channels, and we then proved the extended algorithm to be valid for a more generalized case, that is, the source signals are spatially related. Simulations demonstrate that the QR-based identification algorithms for MIMO channels preserves advantages of fast convergence, low computational costs, and robustness to noise. We also compare the performance of the QR-based method with that of traditional MIMO blind identification algorithms.
出处
《电子学报》
EI
CAS
CSCD
北大核心
2004年第10期1589-1593,共5页
Acta Electronica Sinica
基金
国家自然科学基金 (No 60 2 72 0 4 6)
江苏省自然科学基金 (No BK2 0 0 2 0 51 )
教育部博士点基金 (No 2 0 0 2 0 2 860 1 4 )
国家 863计划重大项目(No 2 0 0 2AA1 2 30 31 )
关键词
QR分解
MIMO系统
盲辨识
盲均衡
空间有色信号
Algorithms
Computer simulation
Identification (control systems)
Mathematical models
Matrix algebra
