Estimation of doubly-selective channels in block transmissions using data-dependent superimposed training

We propose to estimate time-varying frequency-selective channels using data-dependent superimposed training (DDST) and a basis expansion model (BEM). The proposed method is an extension of the DDST-based method recently proposed for time-invariant channels. The superimposed training consists of the sum of a known sequence and a data-dependent sequence, which is unknown to the receiver. The data-dependent sequence cancels the effects of the unknown data on channel estimation. Simulation results show that the proposed method compares favorably with time-division multiplexing training.