Blind multi-user detection based on inerference subspace

Abstract

A new blind adaptive MMSE multi-user detection(MUD) based on subspace tracking is presented. The new detector doesn't employ interference eigenvalue estimation but the interference subspace estimation, and it avoids performance deterioration induced by eigenvalue estimation error. The proposed MUD exploits the normalized orthogonal Oja (NOOja) subspace tracking algorithm for subspace estimation, since it guarantees the orthogonality of the weight matrix spanned by the interference subspace in every iteration, which must be meet in the new detector. The numerical simulation results the proposed MMSE detector has faster convergence rate, better output SIR and BER and lower the computational complexity.