基于子空间的次分量分析恒模盲多用户检测算法  

Blind Multiuser Detection Based on Subspace Minor Component Analysis Constant Modulus Algorithm

作　　者：刘顺兰[1] 张鹏[1] 

机构地区：[1]杭州电子科技大学通信工程学院,杭州310018

出　　处：《科技通报》2011年第2期190-194,共5页Bulletin of Science and Technology

摘　　要：为了有效的抑制多址干扰,本文提出了一种基于子空间的次分量分析恒模盲多用户检测算法。该方法是将子空间方法与次分量分析恒模算法相结合,有效的消除来自噪声子空间分量的影响。仿真结果表明,在相同的多址干扰情况下,本文建议方法的输出信干噪比比次分量分析恒模算法提高了11dB,比线性约束最小二乘恒模算法提高了17 dB;在不同的多址干扰情况下,当误码率为10-3时,本文建议的方法比次分量分析恒模算法约有1 dB的信噪比增益,比线性约束最小二乘恒模算法的信噪比增益更多。因此本文建议的方法在输出信干比和误码率等性能上都有显著的提高,且有更强的抗多址干扰能力,检测性能好。To suppress multi-access interference（MAI）, a muhiuser detection approach based on subspace minor component analysis constant modulus algorithm（SUB-MCACMA） was proposed in this paper. The proposed algorithm combined subspace-based methods with the basic MCACMA, can reduce the impact from noise subspace component effectively. Simulation results showed that, in the same MAI, the SINR of proposed algorithm is 11 dB better than the MCACMA and 17 dB better than the LCLSCMA （linearly constrained least squares constant modulus blind muhiuser detection algorithm）. While the MAI was different, when the bit error rate（BER） is 10^-3, the proposed algorithm is 1 dB lower than the MCACMA in SNR, and it＇s much more lower than the LCLSCMA. Hence, the proposed sub-MCACMA algorithm was more robust to MAI, and can imps＇ore the SINR and bit error rate etc dramatically, the detection performance was good.

关 键 词：码分多址 多用户检测 恒模算法 次分量恒模算法 子空间方法 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]