Blind adaptive multiuser detection based on Kalman filtering

Although several Kalman filtering algorithms have been presented for adaptive multiuser detection, none is “blind” due to requiring training data sequences and/or more knowledge than the spreading waveform and delay of the desired user. This paper proposes a novel blind adaptive multiuser detector based on Kalman filtering and compares it with previously published LMS and RLS algorithms for blind adaptive multiuser detection. It is shown that the steady-state excess output energy of the Kalman filtering algorithm is identically zero for stationary environment. Simulation results show the effectiveness of the new Kalman filtering algorithm.