Multi-User Detector Based On Swarm Intelligence Optimization Algorithm

In CDMA communication system, different users are distinguished by different spread-spectrum codes. It is very difficult to keep completely orthogonal between spread-spectrum codes, which lead to interference between different users in the same channel. We call it Multiple Access Interference (MAI). Along with the increase of the number of users and the signal power, MAI may become one of the main factors which affect the quality of the CDMA communication system and restrict the capacity of the system seriously. The Multi-User Detection (MUD) is one of the effective methods to resolve the Multiple Access Interference. The traditional matching filter looks the signals of other users as disturbance. The Multi-User Detection utilizes the information of all users to extract the object user from other users, which can decrease MAI and has better Near-Far Resistance. So the resource of frequency spectrum is fully utilized, and the capacity of the system is enlarged.In this paper, clone selection algorithm (CS) is combined with traditional dispersed particle swarm optimization (DPSO) and two improved artificial fish school algorithm (AAFSA_FP and AAFSA_SP). An artificial operator is imported into them. Then, a new advanced self-adaptation clone selection particle swarm optimization (ACSPSO) and two kinds of advanced adaptation clone selection artificial fish school algorithm (IAFSA_FP and IAFSA_SP) are proposed and used for Multi-User Detection. Computer simulation results have shown that these new detector are better than DPSO, CS and AFSA in BER and convergence speed.