Blind Equalization Based on Complexity Measures: Is It Feasible?

This work investigates an alternative approach to the problem of blind equalization. The approach is based on complexity measures and is inspired by preceding successful application of the same framework to the problem of blind source separation. We draw the relationship between algorithmic com- plexity, a measure for randomness within the area of algorithmic information theory, and recurrence quantification analysis, a tool for recurrent structure analysis of dynamical data. The evaluation of the hypotheses is carried out in the context of chaotic signals. The results show that such approach is effective under some circumstances (minimum-phase or stable and invertible channel).