子空间约束的最小二乘常模算法  

A Subspace Constrained LSCM Algorithm

作　　者：赵丽娟[1] 酆广增[1] 

机构地区：[1]南京邮电大学通信与信息工程学院,江苏南京210003

出　　处：《信号处理》2009年第3期389-393,共5页Journal of Signal Processing

基　　金：国家自然科学基金资助项目--快速常模算法及其在MIMO信道盲估计与均衡中的应用研究(60472104);江苏省高校自然科学研究计划项目(04KJB510094);江苏省高校研究生创新计划(xm04-32);江苏省高校研究生创新计划(CX07B_106z)。

摘　　要：常模算法(CMA)是一种性能优良的盲多用户检测算法。最小二乘常模算法(LSCMA)因其全局收敛性和稳定性而备受关注,但是在信噪比低时性能不理想。本文将最小二乘常模算法,紧缩近似投影子空间(PASTd)算法和奇异值分解(SVD)相结合,提出了一种子空间约束的最小二乘常模算法,称为SUB_LSCMA,其复杂度比已有的基于直接对接收信号自相关矩阵做特征值分解(ED)的LSCMA_SUB复杂度低。仿真结果表明这种算法的收敛速度、跟踪性能和误码性能和LSC- MA_SUB基本相同。The constant modulus algorithm（CMA） is a blind algorithm. It can be applied in multiuser detection. The least squares constant modulus algorithm（LSCMA） is concerned because of its globally convergence and stability, but its performance is not superior when the SNR is low. In this paper we combine LSCMA ,Projection Approximate Subspace Tracking with deflation（PASTd） algorithm and Singular Value Decomposition （SVD）, and propose a subspace constrained LSCM algorithm（SUB_LSCMA）. Moreover, the computational complexity of the proposed SUB_LSCMA is lower than that of LSCMA SUB which is based on the eigenvalue decomposition （ED）of the received signal autocorrelation matrix. Simulation results show that the proposed SUB_LSCMA is similar to the LSCMA_SUB on the convergence rate, the tracking ability and the BER performance.

关 键 词：子空间 LSCMA LSCMA—SUB SUB_LSCMA PASTd SVD 

分 类 号：TN929.5[电子电信—通信与信息系统]