Independent component analysis(ICA) is a new method in statistical signal processing and neural networks.As a result of its outstanding performance in blind identification and feature extraction or representation,ICA has been used wildly in various fields,for instance,communication systems,speech processing,image enhancement,and biomedical signal processing.Because the linear ICA model has the same form as the CDMA signal model,it can be used in multi-user detection.Utilizing ICA,the desired user information sequence can be separated from the mixture signals polluted by MAI.The main research works of this paper are presented as follows:1.The Cramer-Rao lower bound of ICA is established.Cramer-Rao lower bound is the variance lower bound of unbiased parameters estimation in estimation theory and the common measurement of evaluation performance of parameters estimation.A detail derivation of the Fisher information matrix for demixing matrix w is provided. Gain matrix G is defined,which can give the accuracy measurement of the estimation of the original signals.Cramer-Rao lower bound for gain matrix is deduced in the end and can give a general performance criterion for independent component analysis.2.Algorithm for generating generalized Gaussian distribution random variable and adaptive FastlCA algorithm are proposed.In order to simply create random variables of generalized Gaussian distribution with any shape parameter and any variance,an algorithm is proposed by combining transformations of random variable method and abandon-selection sampling method.The algorithm is derived by proper transformations based on gamma distribution random variables.The algorithm is computationally simple.Random variables of generalized Gaussian distribution with any shape parameter and any variance can be generated easily by adjusting the numerical values of these parameters.Different from traditional FastICA algorithms where the activation function is pre-designed by prior knowledge and fixed for all sources even in different types,an adaptive FastlCA algorithm is presented based on the generalized Gaussian model where the iterations for the paracmeters of the generalized Gaussian and the demixing vector are combined.The stability analysis is also given as well as computer simulation demonstrations.3.Nonparametric generalized Gaussian kernel ICA algorithm is designed.When parametric ICA algorithms can perform suboptimally or even fail to separate the source signals,nonparametric generalized gaussian kernel ICA algorithm can be used,which is based on generalized Gaussian kernel function,and is truly blind to the source signals.Nonparametric density estimation is directly evaluated from the observed signal samples.An important problem,of choose nonlinear functions as the probability density function estimation of the sources is solved in ICA.The algorithm changes window width adaptively in terms of higher statistics so that it is able to separate a wide range of source signals.4.A FastICA blind multi-user detection algorithm based on Neg-entropy is implemented.Function with power 4 is adopted as non-quadratic function,therefore, neg-entropy based non-Gaussianity measurement can be transformed into kurtosis form,which can decrease the computational complexity.Meanwhile,by exploiting the independence of the source signals of different users and utilizing spreading codes of target user as training sequence and initialization of unmixing matrix,excellent symbol estimation performances are obtained through stochastic gradient method while the codes of the interfering users in downlink are unknown.Analysis for computational complexity of our algorithm shows that computational complexity increases with length of receiving data and number of users.In this work,the ICA blind detection method is compared with traditional matched filter and well-known linear MMSE multi-user detector.Numerical simulations indicate that ICA based detection performance is comparable to MMSE detection when MAI is lower in synchronous CDMA channels.With the increase of MAI,the superior performance of ICA has significant improvement over exact-MMSE.5.A novel ICA blind detection algorithm based on nonparametric likelihood ratio criterionr is improved.Classical ICA algorithms rely on simple assumptions on the source statistics.Therefore,such algorithms can not perform optimally or even fail to produce the desired source separation when the assumed statistical model is inaccurate. This method is completely blind to the sources and has ability to separate the mixed sources simultaneously.The signal of desired user is detected by using kemel function to estimate probability density function and combining gradient descent method and likelihood ratio criterion.Finally,comparing this algorithm with others,namely FastlCA detection,MMSE,RAKE,the results show that this algorithm provides MAI interference suppression between FastICA detection and MMSE.