On superimposed training for MIMO channel estimation and symbol detection

Channel estimation for multiple-input multipleoutput (MIMO) time-invariant channels using superimposed training is considered. A user-specific periodic (nonrandom) training sequence is arithmetically added (superimposed) at a low power to each user's information sequence at the transmitter before modulation and transmission. Two versions of a two-step approach are 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 linear minimum mean-square error (MMSE) equalizer and hard decisions, or a Viterbi detector, are used to estimate the information sequence. In the second step of the two-step approach a deterministic maximum-likelihood (DML) approach based on a Viterbi detector or a linear MMSE equalizer-based approach is used to iteratively estimate the MIMO channel and the information sequences sequentially. We also present a performance analysis of the first-order statistics-based approach to obtain a closed-form expression for the channel estimation variance. We then ad ress the issue of superimposed training power allocation for complex Gaussian random (Rayleigh) channels for MIMO systems arising from spatial multiplexing of a single-user signal. Illustrative simulation examples are provided.