Multi-user Detection Based on Gaussian Sum Particle Filter in Impulsive Noise

Li Zhihui, xian Jin-Long


In order to improve the performance of multi-user detector, this paper analyses a new algorithm, Gaussian sum particle filter (GSPF). This algorithm approximates the filtering and predictive distributions by weighted Gaussian mixtures and is basically banks of Gaussian particle filters (GPF). Then, GSPF is used in dynamic state space (DSS) models with non-Gaussian noise. The non-Gaussian noise is approximated by Laplace noise and Alpha noise. As a result, GSPF can effectively reduce the bit error rate of the system. The simulation results show that the GSPF has the versatility and super performance in MUD. It proves that the improved algorithm has important value for the research of MUD system.




Particle filter; Gaussian sum particle filter; Multi-user detection; Bayesian estimation

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