The Research Of Blind Multiuser Detection Algorithm Based On Particle Swarm Optimization

CDMA system is the main access ways of the 3rd generation mobile communication system. However, multiple access interference and near-far effect which CDMA possesses restrict system capability, with the increase of the users or the enhancement of the signal power, MAI is becoming the main interference in the system. Consequently MAI has been becoming the fatal bottleneck of the further development of CDMA system. Multiuser detection is a key technique erasing MAI and NFE due to think fully about user structure making MAI and interference information. Among them, blind multiuser detection without requiring training sequences and information of interference users has attracted considerable attention and significant researches.The particle swarm optimization (PSO) algorithm was a parallel optimization. Since the PSO is proposed, more and more scholars in the field of evolutionary computation have paid attention to it, because it has profound intelligence background and is easy to implement.1. The principle of MUD is introduced in this paper, and the characteristics, performance and drawbacks of the existed algorithms is discussed. In detail summarized and discussed in the blind multiuser examination three criteria, discusses the basic principle of MOE blind multiuser detector with emphasis, Has laid the foundation for the behind discussion. The future development of MUD is discussed at the end.2. The paper introduces intelligent bionic algorithm, and it throughly presents developing state, basic theory and steps of particle swarm optimization, and its application in multi-user detection. Based on particle swarm optimization and minimum output energy rule, proposes multi-user detection algorithm based on basic particle swarm optimization, and simulation results show that the new algorithm improves greatly in its convergence speed, reduces BER, and improves SIR.3. According to the weekness along with particle swarm optimization such as it can't find gloable optimum, the paper proposes two kinds of improved multi-user detection based on particle swarm optimization, the two new algorithms own merits better gloable optimum property, higher convergence speed, and simpler in parameter adjusting. And simulation results show that the new algorithm improves greatly in its convergence speed, reduces BER, and improves SIR.