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Adaptive Blind Multiuser Detection Under Impulsive Noise Using Principal Components

机译：基于主成分的脉冲噪声下的自适应盲多用户检测

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摘要

In this paper we consider blind signal detection for an asynchronous code division multiple access (CDMA) system with Principal component analysis (PCA) in impulsive noise. The blind multiuser detector requires no training sequences compared with the conventional multiuser detection receiver. The proposed PCA blind multiuser detector is robust when compared with knowledge based signature waveforms and the tim ing of the user of interest. PCA is a statistical method for reducing the dimension of data set, spectral decomposition of the covariance matrix of the dataset i.e first and second order statistics are estimated. Principal component analysis makes no assumption on the independence of the data vectors PCA searches for linear combinations with the largest variances and when several linear combinations are needed, it considers variances in decreasing order of importance. PCA improves SNR of signals used for differential side channel analysis. In different to other approaches, the linear minimum mean-square-error (MMSE) detector is obtained blindly; the detector does not use any training sequence like in subspace methods to detect multi user receiver. The algorithm need not estimate the subspace rank in order to reduce the computational complexity. Simulation results show that the new algorithm offers substantial performance gains over the traditional subspace methods
机译：在本文中，我们考虑在脉冲噪声中使用主成分分析（PCA）的异步码分多址（CDMA）系统的盲信号检测。与常规的多用户检测接收器相比，盲多用户检测器不需要训练序列。与基于知识的签名波形和感兴趣的用户的时间进行比较时，所提出的PCA盲多用户检测器是鲁棒的。 PCA是一种用于减少数据集维的统计方法，对数据集的协方差矩阵进行频谱分解，即估算一阶和二阶统计量。主成分分析不假设数据向量的独立性PCA搜索具有最大方差的线性组合，并且当需要几个线性组合时，它会按重要性递减的顺序考虑方差。 PCA改善了用于差分侧信道分析的信号的SNR。与其他方法不同，线性最小均方误差（MMSE）检测器是盲目获得的;检测器不像子空间方法那样使用任何训练序列来检测多用户接收器。该算法不需要估计子空间等级以降低计算复杂度。仿真结果表明，与传统子空间方法相比，新算法具有明显的性能提升。

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