Abstract-Channel estimation for single-input multiple-output (SIMO) time-invariant channels is considered using only the firstorder 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.
Abstract-Channel estimation for single-input multiple-output (SIMO) time-invariant channels is considered using only the firstorder 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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.