Blind Multi-user Detection Algorithm Based On Neural Network

In the third generation(3G) mobile communication Code Division Multiple Access(CDMA) system,the main interference are multiple access interference (MAI),multi-path fading,"near-far"effect(NFE),the noise in the system and some narrow-band interference.Multiuser detection is a key technique in CDMA communications system.Because CDMA system is a kind of system whose performance is limited by the interference.The capability and performance of the system will be improved with the elimination of the interference.The multiuser detection can jointly detect the signals transmitted by each user,this method decrease the"near-far"effect,cancel the MAI and increase the system capacity.However, the realization is high complexity, it is generally used in the base station, the detection for mobile stations generally uses blind multiuser detection into. With the rapid development of neural networks, pepole also strengthenes research of its applications in communications area. In recent years, blind multiuser detection based neural networks combined with blind multiuser detection of small amount of known information and neural networks of rapidly computing speed, the advantages of parallel processing ability, become a research hotspot.In this paper, the two algorithms of blind multiuser detection in infinite multi-path fading channel are studied and analyzed. The first is the BP-MMSE algorithm which combined the BP neural network and the MMSE criterion. The second is the combination of the improved genetic algorithm and BP neural networks and applies blind multiuser detection. Simulation results show that the first algorithm has better convergence properties relative to the blind multiuser detection algorithm of simple MMSE criteria, but its NFE capacity need to be further improved. The second combine genetic algorithms globally searching optimization and traditional BP neural network model locally searching optimization, each other, not only can reduce the search space of genetic algorithms to improve search efficiency, but also easily converge to the optimal Solutions to make the algorithm is practical. And simulation results show that: Based on genetic algorithm, BP neural network blind multiuser detection algorithm whose convergence compared to the first convergence is better has lower bit error rate. To some extent, it is able to effectively suppress the multiple access interference and ability of resisting near-far is more stong.