Blind adaptive detection for CDMA systems based on regularized independent component analysis

We present a new approach to blind adaptive detection for CDMA systems based on regularized independent component analysis (ICA). Classical ICA algorithms are effective in separating linearly weighted signal mixtures consisting of subGaussian and superGaussian signals. However, they do not incorporate any information of the weighting matrix, in this case, the user´s signature sequence into the formulation. This results in underutilization of the information available. To address this difficulty, we propose a new ICA algorithm that combines a contrast function and a regularization functional to integrate the information of the user´s signature. A blind adaptive detector based on stochastic gradient optimization of the new cost function is derived. Simulation results show that the new technique provides good interference suppression, fast convergence and low BER performance when compared with other blind detectors