Self-interference suppression in doubly-selective channel estimation using superimposed training

Channel estimation for frequency-selective time-varying channels is considered using superimposed training. We employ a discrete prolate spheroidal basis expansion model (DPS-BEM) to describe the time-varying channel. 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 either totally or partially cancel out the effects of the unknown information sequence at the receiver on channel estimation. In total cancellation, at certain frequencies, the information-bearing components are nulled. To compensate for this information loss, we propose a partially-data-dependent (PDD) superimposed training scheme where a trade-off is made between interference cancellation and frequency integrity. An iterative method is also used to enhance channel estimation and data detection and illustrated via a simulation example.