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Doubly-Selective Channel Estimation Using Data-Dependent Superimposed Training and Exponential Basis Models

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2 Author(s)
Jitendra K. Tugnait ; Auburn Univ., Auburn ; Shuangchi He

Channel estimation for single-user frequency- selective time-varying channels is considered using superimposed training. The time-varying channel is assumed to be well- approximated by a complex exponential basis expansion model (CE-BEM). A periodic (non-random) training sequence is arithmetically added (superimposed) at low power to the information sequence at the transmitter before modulation and transmission. In existing first-order statistics-based channel estimators, the information sequence acts as interference resulting in a poor signal-to-noise ratio (SNR). In this paper a data-dependent superimposed training sequence is used to cancel out the effects of the unknown information sequence at the receiver on channel estimation. A performance analysis is presented. We also consider the issue of superimposed training power allocation. Several illustrative computer simulation examples are presented.

Published in:

IEEE Transactions on Wireless Communications  (Volume:6 ,  Issue: 11 )