The EM iterative algorithm is commonly used in recent years for missing data, which has the character of easy and popular applicability. But the EM algorithm has a fatal weakness that the convergence speed is slowly; Acceleration of the EM algorithm using the Aitken method is proposed in order to solve this problem.In Multi-user Detection, via this accelerated algorithm, we get a good performance which trends to ML performance, and compared its speed of convergence with the EM algorithm that Aitken-acceleration algorithm has faster convergence than the standard EM algorithm, and we also illustrate the performance of simulation.

The EM iterative algorithm is a very general and popular algorithm that commonly used for missing data to find maximum likelihood in recent years, which has the character of stability,flexibility and simlicity.However, the EM algorithm has a great weakness that the convergence speed is slowly; Acceleration of the EM algorithm using the Vector-ε method is proposed in order to solve this problem in this paper.In Multi-user Detection, via this accelerated algorithm, we get a good performance which trends to ML performance and improving the computational efficiency, and compared its speed of convergence with the EM algorithm that Vector-ε acceleration algorithm has faster convergence than the standard EM algorithm.

To solve the problem of particle degeneracy and sample impoverishment in conventional particle filter, we propose the weight approaching particle filter(WAPF) to increase the particle diversity before resampling step for adaptive multiuser detection (MUD) in synchronous code division multiple access (CDMA) system.. In the resampling step, particles are classified into two groups according to their particle-weights, and then the particles with the smaller weights are replaced by the mean of the two group particles, so that the particles can approach from the low likelihood region to the high likelihood region. Similar to the carrier wavemethod, the chaotic perturbation resampling method adopts the chaotic variable with the property of global ergodicity to ameliorate the diversity of samples and reduce the computation load. Simulation results demonstrate the feasibility of the improved particle filter.

In this paper, we proposed a regularized particle filter (RPF) algorithm for multi-user detection (MUD) in synchronous code division multiple access (CDMA) system. It is especially suitable for non-linear non-Gaussian system. In the standard particle filter algorithm. If particles weights have vast difference, we needed to re-sampling, but it replicates particles with larger weights and discards particles with small weights, so that it will lose the diversity of particles. Regularized particle filter algorithm can effectively overcome this shortcoming.