Linear prediction and subspace fitting blind channel identification based on cyclic statistics

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 and multiple antennas) channel identification. We base our identification schemes on the cyclic statistics, using the stationary multivariate representation introduced by Gladyshev (1961) and by Miamee (1990, 1993). This leads to the use of all cyclic statistics. The methods proposed appear to have good performance.