Doubly-Selective Channel Estimation Using Data-Dependent Superimposed Training and Exponential Bases Models
Abstract: 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 a 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. We extend recent time-invariant channel results to CE-BEM-based doubly-selective channels using a block transmission approach. A performance analysis is presented. An illustrative computer simulation example is also presented.
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