Semi-blind time-varying channel estimation using superimposed training

Channel estimation for single-input multiple-output (SIMO) time-varying channels is considered using superimposed training. The time-varying channel is assumed to be described 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. A two-step approach is adopted where, in the first step, we estimate the channel using only the first-order statistics of the data. Using the channel estimate from the first step, a Viterbi detector is used to estimate the information sequence. In the second step, a deterministic maximum likelihood (DML) approach is used to estimate the SIMO channel iteratively and the information sequences sequentially. An illustrative computer simulation example is presented where a frequency-selective channel is randomly generated with different Doppler spreads via Jakes´ model.