On Channel Estimation Using Superimposed Training and First-Order Statistics
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,
Document Details
- Document Type
- Technical Report
- Publication Date
- Oct 06, 2003
- Accession Number
- ADA422839
Entities
People
- Jitendra K. Tugnait
- Weilin Luo
Organizations
- Auburn University