Iterative Joint Channel Estimation and Data Detection Using Superimposed Training: Algorithms and Performance Analysis

Channel estimation for single-input multiple-output time-invariant channels is considered using superimposed training. A periodic (nonrandom) training sequence is arithmetically added (superimposed) at low power to the information sequence at the transmitter before modulation and transmission. We extend a recently proposed first-order statistics-based channel estimation approach (IEEE Commun. Lett., vol. 7, p. 413, 2003) to iterative joint channel estimation and data detection using a conditional maximum likelihood approach where the information sequence is exploited to enhance performance instead of being viewed as interference. An approximate performance analysis of the iterative channel estimation method is also presented under certain simplifying assumptions. Illustrative computer simulation examples are presented.