Optimal training sequences for low complexity ML multi-channel estimation in multi-user MIMO OFDM-based communications

The goal of this paper is to propose new binary training sequences that allow for optimal joint estimation of multiple channels by relying explicitly on the cyclo-stationary structure of the signals in the OFDM-based communication systems. The new estimator is perfectly suited for multiple-user MIMO types of communication systems where multiple channels have to he estimated simultaneously at each receive antenna. It is shown that the resulting estimator is not only optimal in the sense that it minimizes the channel estimation error variance, but also requires a very low complexity computational effort. Considering two different types of systems, it is shown that the proposed estimator allows for significant SNR gain in comparison to existing methods.