On channel estimation using superimposed training and first-order statistics |
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Authors: | Tugnait JK Weilin Luo |
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Affiliation: | Dept. of Electr. & Comput. Eng., Auburn Univ., AL, USA; |
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Abstract: | 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. |
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