Reaserch On Blind Multiuser Detection Algorithm In Asynchronous DS-CDMA

DS-CDMA is an important part of the CDMA standards, and it has been widely used. In certain circumstances, asynchronous DS-CDMA system is more suitable because of its random accessing and convenient networking. Although DS-CDMA system has many advantages, it is inevitable there is a serious problem--MAI. Fortunately, multiuser detection technology can effectively reduce or even completely eliminate the MAI. In this thesis, based on the project “Study of aircraft CDMA communication system”,mainly focuses on the multiuser detection techniques without power control in asynchronous DS-CDMA. It includes the following aspects:On the basis of extensive literature, The writer mainly focuses on the Blind Multiuser Detection Algorithm theory based on subspace decomposition, and the following work is completed:(1)Based on asynchronous DS-CDMA signal model, the writer studies the conventional multiuser detection algorithms, including comparative analysis. Limitations and improved methods of these algorithms are put forward.(2)Proposed a new blind multiuser detection algorithm based on space decomposition to against the problems of typical subspace tracking algorithms, such as converging slowly or eigenvectors are not strictly orthogonal. The algorithm project the receiving signal to signal space and constructs a linear MMSE detection weight vector without subspace matrix of eigenvalues by solved inverse of autocorrelation matrix. In addition, the algorithm tracks subspace by the modified algorithm. The simulation results show that the new algorithm converges fast and better performance in BER.(3)To resist the near-far resistance, combine the algorithm proposed above and the interference-subspace theory. Thus, an blind adaptive multiuser detection algorithm based on interference-subspace. The algorithm uses interference subspace eigenvector and eigenvector diagonal matrix to construct weight vector of MMSE multiuser detection, and applies modified FAPI to estimate the interference subspace eigenvector. and overcome the problem of general interference-subspace tracking algorithms bringing approximation error accumulation. The MATLAB simulation results show that the algorithm proposed resists the near-far resistance in the range of 20 dB and performs well.