A Blind Adaptive Kalman-PIC MUD Algorithm for the Multiple Access Communication Network

In the parallel signal processing, the multi-user detection precision of parallel interference cancellation (PIC) detector is always easily affected by the decision error diffusion. Based on the fast convergence and low complexity of blind adaptive Kalman algorithm, a new blind adaptive Kalman-PIC (KPIC) multi-user detection (MUD) algorithm is proposed for the direct sequence spread spectrum code division multiple access (DS-CDMA) system network with strong multiple access interference (MAI). Compared with traditional standard Kalman filter and PIC algorithm, the proposed combined program can totally track the timevarying channel, effectively estimate unknown noise statistics characteristics on-line while conducting state filtering, possibly minimize the detection error diffusion in the interference cancellation processing of single PIC algorithm, thus effectively suppress MAI. Simulation results show that the KPIC algorithm is of better convergence, dynamic tracking ability and precision.