On super-exponential algorithm, constant modulus algorithm and inverse filter criteria for blind equalization

The super-exponential algorithm (SEA), constant modulus algorithm (CMA) and inverse filter criteria (IFC) using higher-order statistics have been widely used for blind equalization. Chi et al., (2000) have reported that SEA and IFC are equivalent under certain conditions. We further prove that SEA, IFC and CMA are equivalent under certain conditions, and their convergence speed and computational load can be significantly improved as the given data are preprocessed by the well-known lattice linear prediction error (LPE) filter for both off-line processing and adaptive processing. Some simulation results are presented to support the analytic results and the proposed off-line and adaptive implementations.