Semi-blind channel estimation and detection using superimposed training

Channel estimation for single-input multiple-output (SIMO) time-invariant or slowly time-varying channels is considered using superimposed training. 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. Two versions of a two-step approach are adopted where in the first step, following [11], we estimate the channel using only the first-order statistics of the data. Using the estimated channel from the first step, a linear MMSE equalizer and hard decisions, or a Viterbi detector, are used to estimate the information sequence. In the second step a deterministic maximum likelihood (DML) approach or an approximation to it, is used to iteratively estimate the SIMO channel and the information sequences sequentially. Illustrative computer simulation examples are presented where we compare the proposed approaches to the conventional (time-multiplexed) training based approach.