Channel Estimation Using Superimposed Training and First-Order Statistics

摘要

Channel estimation for single-input multiple-output (SIMO) time- invariant channels is considered using only the first-order statistics of the data, A periodic (nonrandom) training sequence is added (superimposed) at a low power to the information sequence at the transmitter before modulation and transmission, Recently superimposed training has been used for channel estimation assuming no mean-value uncertainty at the receiver and using periodically inserted pilot symbols, We propose a different method that allows more general training sequences and explicitly exploits the underlying cyclostationary nature of the periodic training sequences, We also allow mean- value uncertainty at the receiver, Illustrative computer simulation examples are presented.