Blind Multiuser Detection Using Adaptive Filter

A new multi-user detection scheme based on signal subspace estimation is proposed under fading channels in this paper. Different from several Kalman filtering algorithms presented for adaptive multi-user detection, which adapts training data sequences and needs more knowledge about the spreading waveform and delay of the desired user, this paper proposes a blind adaptive multi-user detector based on sub-space RLS filtering. It is shown that the detector can be expressed as an anchored signal in the signal subspace and the coefficients can be estimated by the RLS filter using only the signature waveform and the timing of the desired user. For enhancing the robustness of RLS, a new cost function is defined in the algorithm, which can be used to suppress the effect of impulse noise on the filter weights. Simulation shows that enhanced RLS is less sensitive to consecutive impulse noise and has better convergence ability than conventional LMS algorithms.