A globally convergent adaptive blind equalizer based on second and fourth order statistics

The author proposes a new approach to recursive estimation of the unknown channel parameters and to adaptive blind deconvolution for data communication systems. The problem is addressed in a blind setting, i.e., only the channel output is observed, not the input to it. The proposed recursive parameter estimator is globally convergent, i.e., the parameter estimator converges to the true model regardless of its initialization. Therefore, the blind equalizer designed based upon the estimated channel parameters is also globally convergent. The proposed parameter estimator is based upon a two model decomposition approach: a SEMP (spectrally equivalent minimum phase) system in cascade with an allpass system. The author exploits the fourth-order cumulant statistics, in addition to the usual second-order statistics, of the data. A computer simulation example is also presented using a 4-PAM (pulse amplitude modulation) signal