Semi-blind Multiuser Detection Based on Improved Pastd Subspace Tracking for Mc-cdma Uplink

In this paper, an adaptive semi-blind multiuser detection (MUD) algorithm based on improved projecting approximation subspace tracking with deflation (PASTd) subspace tracking is proposed for multicarrier code division multiple access (MC-CDMA) uplink where the base station receiver has the knowledge of the spreading sequences of all the users within the cell, but not that of the users from other cells. It is known that the PASTd algorithm has the drawback of slow convergence rate. Based on this, we develop an improved PASTd algorithm and apply it to the adaptive linear hybrid semi-blind multiuser detection. The improved PASTd algorithm guarantees the orthonormality between the estimated eigenvectors such that a fast convergence rate can be achieved. Simulation results show the proposed semi-blind MUD has a fast convergence rate and provides the similar output signal-to-interference-plus-noise-ratio (SINR) and bit error rate (BER) as the Singular Value Decomposition (SVD) semi-blind MUD.