【摘要】 针对独立分量分析算法计算复杂度较高,不利于其在多用户检测中应用,为了消除这个缺陷,在进行ICA运算前,先对接收数据进行预处理。在预处理中分别采用特征值分解、奇异值分解、三角分解和正交分解等四种方法,研究了其在减少ICA后续运算复杂度的同时对多用户检测性能的影响,实验结果表明:在基于ICA多用户检测算法中采用特征值分解预处理能取得最优的误码性能。更多还原

【Abstract】 Independent component analysis(ICA) algorithm is of high computation complexity and not applicable to multi-user detection.In order to eliminate this deficiency,the received data is preprocessed first before carrying out ICA algorithm.Four methods,including eigenvalue decomposition(Eig),singular value decompose(SVD),triangle decomposition(LU) and orthogonal decomposition(QR),are adopted in the preprocessing,and the effects of these four methods on multi-user detection performance are analyzed while the computation complexity of ICA algorithm is reduced.The experimental results show that the eigenvalue decomposition(Eig) method based on ICA-based multi-user detection could achieve optimal bit-error performance.更多还原