Research On Blind Multiuser Detection Algorithms In α Stable Distribution Noise

Code Division Multiple Access (CDMA) is one of the key techniques of the third and fourth generation mobile communication system (4G). However, multiple access interference (MAI) will become serious with the increase of the number of users or enhancement of signal power, which seriously degrades the performance of system and limits the capacity of the system.Multi-User Detection (MUD) technology can suppress the interference between different users to improve the performance of the receiver; it can also significantly increases the capacity of the system. Blind Multi-User Detection (BMUD) technology has become a hot topic of current research as it does not need the prior knowledge, such as signature sequences of all users and timing information. The conventional signal processing methods, which are based on second order statistics and Gaussian noise, is relatively easy to analyze. However, many channel noises do not obey Gaussian distribution in practical applications, especially communication systems. Instead, a lot of impulse noise exists in communication systems, which cannot be analyzed according to the models of Gaussian distribution,a-stable distribution is suitable for modeling spikes and impulsiveness noise. In this paper, blind multi-user detection algorithm in a-stable distribution noise were studied. The main works and conclusions are listed as follows:(1) The modeling and simulation are performed according to the characteristics of a-stable distribution random variable.The performance of traditional blind multiuser detection algorithms both in Gaussian noise and impulse noise are studied and compared. Two basic robust methods in adaptive signal processing and anti-alpha stable distribution impulse noise signal processing were analyzed, including M-estimation and fractional lower order statistics. Inspired by M-estimation method, we modified the cost function of the constant modulus BMUD algorithms, and then created two new robust algorithm namely M-estimator-based constant modulus algorithm (M-CMA) and M-estimator-based fractional lower-order constant modulus algorithm (M-FLOS-CMA). The computer simulation results show that the anti-pulse noise immunity of the improved algorithms is greatly enhanced.(2) The BMUD algorithms based on subspace method are very sensitive to impulsive noise and the performance can be degraded sharply. Robust subspace estimation methods, such as:the scatter matrix instead of the covariance matrix estimating and fractional lower order subspace method, can improve the robustness of blind multi-user detector based on subspace. In this paper, a new robust subspace BMUD, which combines fractional lower statistics and M estimator, is proposed. Simulation results show that the proposed algorithm has stronger offers improved robustness against impulsive noise than conventional subspace algorithms. The tracking performance of traditional PAST and OPAST algorithms are seriously degraded by impulse noise. This paper modified the OPAST algorithm by using M-estimator to achieve good performance in impulsive noise environment.(3) In order to improve the robustness of blind multiuser detection algorithm based on Kalman filtering under a-stable noise, a robust Kalman filtering scheme is formulated by the introduction of M-estimation. Finally, a new blind adaptive multiuser detection scheme based on a hybrid of robust Kalman filter and robust subspace estimation is proposed. The proposed detectors are much more robust against the a-stable noise as demonstrated by computer simulations.