Independent component analysis (ICA) for blind equalization of frequency selective channels

In this paper we address the problem of blind source separation (BSS) in frequency selective multiple-input multiple-output (MIMO) channels, when the only available prior knowledge about the transmitted signals is their mutual statistical independence. The novelty of the paper is two-fold. Firstly, we analytically show that when orthogonal frequency division multiplexing (OFDM) is employed, the original BSS problem is transformed into a set of standard ICA problems with complex mixing matrices. Each ICA problem is associated with one of the orthogonal subcarriers. Secondly, we show that the statistical correlation between the different frequency bins (at each orthogonal subcarrier) can be exploited to avoid the frequency-bin dependent permutation and scaling problems, which are intrinsic to the ICA solution. Our approach is also tested on a realistic channel model.