Performance of Superimposed Training-Based Channel Estimation in MIMO-CDMA Systems

In this paper, we investigate the performance of superimposed training-based channel estimation techniques considering an asynchronous code-division multiple-access (CDMA) uplink transmission over frequency-selective fading channels. In that, we analyze the performance of a channel estimation and data detection scheme based on superimposed training for space-time spreading (STS) systems. Our results show that the scheme enhances the performance of the space-time system by eliminating the interference effect from both the channel and data estimates using two decorrelators; channel and data decorrelators. Compared with other conventional estimation techniques, the underlying system is more robust to channel estimation errors. Furthermore, both simulations and analytical results indicate that full system diversity is achieved.