Doubly-selective MIMO channel estimation using superimposed training

Channel estimation for multiple-input multiple-output (MIMO) frequency- and time-selective channels is considered using superimposed training. The time-varying channel is assumed to be described by a complex exponential basis expansion model (CB-BEM). A user-specific periodic (non-random) training sequence is arithmetically added (superimposed) at a low power to each user´s 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 estimated channel 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 iteratively estimate the MIMO channel and the information sequences sequentially. An illustrative computer simulation example is presented.