On channel estimation using superimposed training and first-order statistics

Channel estimation for single-input multiple-output (SIMO), possibly time-varying, channels is considered using only the first-order statistics of the data. The time-varying channel is assumed to be described by a complex exponential basis expansion model (CE-BEM). A periodic (non-random) training sequence is arithmetically added (superimposed) at a low power to the information sequence at the transmitter before modulation and transmission. Recently superimposed training has been used for time-invariant channel estimation assuming no mean-value uncertainty at the receiver. We propose a different method that explicitly exploits the underlying cyclostationary nature of the periodic training sequences. It is applicable to both time-invariant and time-varying systems. Unlike existing approaches we allow mean-value uncertainty at the receiver. Illustrative computer simulation examples are presented.