Design of superimposed training sequence for spatially correlated multiple-input-multiple-output channels under interference-limited environments

In this study, the design of a superimposed training (ST) for an interference-limited spatially correlated multiple-input–multiple-output (MIMO) system is addressed and a closed-form solution for designing the training signal is proposed in a sub-optimal way. Earlier papers have considered noise limited MIMO systems. The authors also propose random/orthogonal variable spread factor (OVSF) codes as a choice for the ST signal. The mean-squared error of the channel estimate and symbol error rate performances are obtained through simulation. Considering the power allocation issue between the data and training symbols, a sub-optimal average training power that maximises the lower bound on the effective signal-to-interference ratio is also proposed. Simulation results show that the bit error rate performance of the ST is indistinguishable from the OVSF/random ST sequence.