A Blind Adaptive SOR/JGS Iterative Kalman MUD Algorithm for Multiple Access Communication System

Based on the fast stable convergence characteristics of successive over relaxation (SOR) iterative and Jacobi GaussSeidel (JGS) iterative, a blind adaptive SOR/JGS iterative Kalman multi-user detection (MUD) algorithm (SJK) is proposed for multiple access communication system as direct sequence spread spectrum code division multiple access (DSCDMA) system with multi-path fading channel. The proposed combination of blind adaptive Kalman filtering theory, SOR and JGS iterative method can adaptively control the selection of relaxation parameters and damping parameter, and then effectively deal the problem as time-varying noise statistics estimation. Compared with traditional standard Kalman filter (SKF), fading Kalman filter (FKF) and robust adaptive Kalman filter (RAKF) algorithm, the proposed algorithm can effectively estimate unknown noise statistics characteristics on-line while conducting state filtering, totally track the time-varying channel, minimize the detection error diffusion, and thus effectively reduce multiple access interference (MAI). Simulation results show that the SJK algorithm is of better detection accuracy, convergence ability, dynamic tracking capability, and lower bit error rate (BER) performance.  Index Terms Gauss-Seidel iterative, multiple access interference, Kalman.