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Blind channel identification based on cyclic statistics

Use of cyclic statistics in fractionally sampled channels in subspace fitting and linear prediction for channel identification is proposed, possibly for multiuser and multiple antennas. Identification schemes are based on cyclic statistics using the stationary multivariate representation, leading to the use of all cyclic statistics. Compared with classical approaches, the methods proposed have an equivalent performance for subspace fitting, and an enhanced performance for linear prediction.