Channel estimation for MIMO systems using data-dependent superimposed training

Multiple antenna systems have been shown to increase system capacity and provide resiliency to random fading. However, these attractive features require accurate channel estimation. This can be achieved by time-division multiplexing (TDM) an adequately large training sequence. An alternative to TDM training is the superimposed training (ST) scheme which trades-off power for bandwidth (or rate). However, the performance of STbased channel estimation is affected by the unknown embedded data which acts like input noise. Here, we propose a data-dependent superimposed training (DDST) technique where the training sequence consists of a known sequence and a data-dependent sequence, which is unknown to the receiver. The data-dependent sequence cancels the effects of the unknown data on channel estimation. We consider both spatial multiplexing and space-time coded systems. For the latter, we focus on the Alamouti scheme. The proposed method is compared to the TDM and ST schemes in terms of the mean square error of the channel estimates, and the bit error rate. The proposed method is shown to offer good trade-offs between bandwidth and performance.