Partial Data-Dependent Superimposed Training Based Iterative Channel Estimation for OFDM Systems over Doubly Selective Channels

In this paper, partial data-dependent superimposed training based channel estimation for OFDM systems over doubly selective channels (DSCs) is addressed. Due to the presence of unknown data as interference, we first derive a minimum mean square error (MMSE) channel estimator by treating the effect of unknown data as noise. To further improve the performance, a novel iterative algorithm which jointly estimates channel and suppresses interference from data is proposed via variational inference approach. Simulation results show that the proposed algorithm converges after a few iterations. Furthermore, after convergence, the performance of the proposed channel estimator is very close to that with full training at high SNRs.