Abstract
In a recent study, the authors evaluated the performance of channel estimation using Superimposed Training (ST) in a Single-Input Single-Output (SISO) communication system. It was shown that optimal performance could be obtained if the training sequence is balanced and has a specific correlation property. One drawback of the ST method is that the data sequence interferes with the channel estimation process and degrade its performance. In this paper, we generalize our approach to Data Dependent Superimposed Training (DDST) where a data-dependent sequence is also added to the data sequence, thus eliminating the effects of the latter sequence on channel estimation.
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