Adaptive Minimum Mean-Square Estimator Multiuse Detection Based On Fast-ICA Algorithm

Code Division Multiple Access is self-interfered system, where multi-access interference (MAI) and Near-far effect are existing as the main factors restricting CDMA system capacity. These years, be considered as one of the key techniques for the 3rd Generation (3G) mobile communication system, Multiple User Detection (MUD) for CDMA system made its obvious progress. MUD reduces the multi-access interference and solves the Near-far problem and improves system capacity. The blind adaptation schemes do not require training sequences and the interference user's information. It only needs the prior knowledge of the signature waveform and the timing of the desired user. The blind adaptation schemes are the promising one with acceptable implementation complexity, while simultaneously eliminating the need of a plenty of priori system information and can be implemented as a blind MUD.This paper has summarized the basis concept and theory on the independent component analysis (ICA) and the multi-user detection. Delay estimation of Channel is introduced, and the algorithms which had used are improved. We build the delay estimation based on Negentropy ICA. At last, the matrix DS-CDMA (Direct Sequence-CDMA) signal model and the noisy linear Independent Component Analysis (ICA) model are analyzed in this paper. Comparing these models shows that they have same form. The adaptive Minimum Mean-Square Estimator (MMSE) multiuse detection based on ICA is proposed. It lets the output of adaptive MMSE multiuse detection to initialize the ICA iterations, not only the known spread information of interesting user is used to overcome the uncertainness of ICA, but also the character of statistical independence is used. In this thesis, all algorithms are done with numerical simulations and performance analysis. The simulation results show that it advances the performance of adaptive MMSE multiuse detection when the signal-noise-ratio is high.