Superimposed Training Designs for Spatially Correlated MIMO-OFDM Systems

Optimal training design and channel estimation for spatially correlated multiple-input multiple-output systems with orthogonal frequency-division multiplexing (MIMO-OFDM) is still an open research topic of great interest. Only one asymptotic design for a special case of channel correlations was proposed in the literature. To fill this gap, this paper applies tractable semi-definite programming (SDP) to obtain the optimal superimposed training signals for the general case of channel correlations. To improve computational efficiency, an approximate design in closed-form is also proposed. This approximate design is formed by minimizing an upper bound of the channel estimation mean-square error. Since the superimposed training approach is taken, the derivation of an optimal non-redundancy precoder for data detection enhancement is also given. Analytical and simulation results demonstrate the excellent performance of the proposed designs and their superior performance compared to the previously proposed design.