ICA-based semi-blind spatial equalization for MIMO systems

Blind equalization is one challenging problem for multiple-input multiple-output systems, to which independent component analysis (ICA) is applicable. However direct application of ICA could yield low convergence speed and poor performance. In this paper we propose two semi-blind ICA-based algorithms, which incorporate information both from the training and unknown sequences. During each iterative/adaptive step, the training information is utilized to supervise the unconstrained blind ICA-based measure. Simulation results show that the two proposed semi-blind approaches can outperform both the training-based and conventional ICA method. Furthermore we report a special case of MIMO systems which does not require the algorithm of source separation, whose proof is also provided.