Channel estimation for single-input multipleoutput (SIMO), possibly time-varying, channels is considered using only the first-order statistics of the data. The time-varying channel is assumed to be described by a complex exponential basis expansion model (CEBEM). A periodic (non-random) training sequence is arithmetically added (superimposed) at a low power to the information sequence at the transmitter before modulation and transmission. Recently superimposed training has been used for time-invariant channel estimation assuming no mean-value uncertainty at the receiver. We propose a different method that explicitly exploits the underlying cyclostationary nature of the periodic training sequences. It is applicable to both time-invariant and time-varying systems. Unlike existing approaches we allow mean-value uncertainty at the receiver. Illustrative computer simulation examples are presented.
In REF , a channel estimation methodology based on first order statistics is proposed.
On channel estimation using superimposed training and first-order statistics
2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03).
2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03).
Computer Science