Linear prediction error method for blind identification of periodically time-varying channels

Blind channel estimation for single-input multiple-output (SIMO) periodically time-varying channels is considered using only the second-order statistics of the data. The time-varying channel is assumed to be described by a complex exponential basis expansion model (CE-BEM). The linear prediction error method for blind identification of time-invariant channels is extended to time-varying channels represented by a CE-BEM. Sufficient conditions for identifiability are investigated. The cyclostationary nature of the received signal is exploited to consistently estimate the time-varying correlation function of the data from a single observation record. The proposed method requires the knowledge of the active basis functions but not the channel length (an upper bound suffices). Several existing methods require precise knowledge of the channel length. Equalization of the time-varying channel, given the estimated channel, is investigated. Computer simulation examples are presented to illustrate the approach and to compare it with two existing approaches.