Study On Multiuser Detection Techniques In Wireless Communication

The capacity and performance of a code division multiple access (CDMA) system is essentially dominated by multiple access interference (MAI). Therefore, the suppression and cancellation of MAI is a crucial issue in CDMA system design. The well cancellation of MAI that degrades the capacity and performance could depend on multiuser receiver, i.e. multiuser detection (MUD).This paper deals with the linear MUD. Several improved algorithms are proposed to cope with the problems in Kalman filtering based MUD. As for subspace based MUD, two improved algorithms are shown from the point of view of subspace tracking to solve the problems of slow tracking and unstable performance in traditional methods. The main research works and results are listed as follows.1. U-D factor algorithm could get over the instability of numerical calculation. By this characteristic, a high performance blind adaptive MUD algorithm is proposed by introducing U-D factor Kalman filter to estimate the optimal decision vector of CDMA multiple-users receiver. This algorithm can converge rapidly, track in high accuracy and has satisfied numeric stability. Simulation results show that this algorithm can eliminate MAI efficiently and is robust for numerical calculation.2. Strong tracking filter (STF) has extensive applications in multiple target tracking and radar signal procession. A high reliability blind adaptive MUD is proposed by introducing STF to estimate the optimal decision vector of CDMA multiuser receiver. This algorithm can converge rapidly, track in high accuracy and has satisfied Bit Error Rate (BER). Simulation results show that STFMUD can eliminate paroxysmal MAI efficiently and has excellent capability of anti-multiple access interference and high reliability.3. The performance of Kalman filtering based MUD depraves sharply with model errors and a robust algorithm is needed in this situation. A fictitious noise compensation approach available is used for reference in this paper, and from which a robust Kalman filtering based MUD is proposed. Simulation results show that this algorithm can eliminate MAI efficiently and is a robust algorithm.