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Doubly-selective channel estimation using data-dependent superimposed training and exponential basis models
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DOI：10.1109/TWC.2007.060246.png)
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Abstract
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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.
Keywords:
channel estimation
doubly-selective channels
ISI channels
superimposed training
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