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New Blind Multiuser Detection in DS-CDMA Based on Extension of Efficient Fast Independent Component Analysis (EF-ICA)

机译:基于高效快速独立分量分析(EF-ICA)扩展的DS-CDMA中的新盲多用户检测

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This Paper develops a novel blind Detection algorithm based on Extension of Efficient FAST Independent Component Analysis (EF-ICA). classifying the received data to many parts in order to use a fit non-quadratic function to separate a non-Gaussian source. Blind detection is to estimate multiple symbol sequences associated with all users in the downlink of the DS-CDMA communication system using only the received wireless data and without any knowledge of the user spreading codes. Bit error rate (BER) simulation evidence of the improved performance of this novel algorithm are shown for different number of users, signal to noise ratio (SNR) and different number of symbols per user. The performance is compared with several Blind Detectors based on Efficient FastICA and Fast ICA algorithms. The results show that the proposed algorithm performs well and outperforms the other Detectors in estimating the symbol signals from the mixed CDMA received signals.
机译:本文开发了一种基于高效快速独立分量分析(EF-ICA)扩展的新型盲检测算法。 将接收的数据分类为许多部分,以便使用拟合非二次函数来分离非高斯源。 盲检测是估计与DS-CDMA通信系统的下行链路中的所有用户相关联的多个符号序列,仅使用所接收的无线数据,并且没有用户扩展码的任何知识。 钻头错误率(BER)模拟本新颖算法性能的仿真证据显示为不同数量的用户,信号到噪声比(SNR)和每个用户的不同数量的符号。 基于高效的Fastica和FAST ICA算法的几个盲检扰进行了比较了性能。 结果表明,该算法在估计来自混合CDMA接收信号的符号信号时,所提出的算法良好并且优于其他探测器。

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