Second-order cyclic statistics based blind channel identification and equalization

Blind channel identification and equalization based on second-order statistics by subspace fitting and linear prediction have received a lot of attention lately. On the other hand, the use of cyclic statistics in fractionally sampled channels has also raised considerable interest. We propose to use these statistics in subspace fitting and linear prediction for (possibly multiuser) channel identification. The main benefit expected is to get rid of the dependence on the color of the additive noise, due to the properties of the cyclocorrelations. We also present some simulations to illustrate the effectiveness of the method.