Improved independent component analysis for blind multiuser detector

In this paper, we propose an improved independent component analysis(ICA) based CDMA receiver for multiple access communication channels. ICA is a statistical method for transforming an observed multi-dimensional random vector into components that are statistically as independent from each other as possible. We apply Noisy-ICA as a post processor attached to a subspace based CDMA receiver in the presence of Gaussian noise. The proposed algorithm reduces the bias caused by the channel noise in ordinary ICA algorithms and further decreases the noise by dimension reduction. Then we propose an improved version of the FastICA algorithm which is asymptotically efficient, its accuracy given by the residual error variance attains the Cramér-Rao lower bound. The error is thus as small as possible. Simulation results demonstrate that the proposed method offers high performance.