Superimposed Training Based Channel Estimation for OFDM Modulated AF Relay Networks

In this paper, we consider channel estimation for amplify-and-forward (AF) relay network with orthogonal frequency division multiplexing (OFDM) modulation. We propose a superimposed training strategy at relay that allows the destination node to obtain the separate channel information of the source-->relay link and the relay-->destination link. The proposed training strategy only requires two transmission phases and is thus compatible with the two-phase data transmission scheme, i.e., the training can be embedded into data transmission. Since the optimal minimum mean square error (MMSE) estimator and the maximum a posteriori (MAP) estimator cannot be expressed in close-forms, we propose to obtain the initial channel estimates from the low complexity suboptimal linear estimators, e.g., linear minimum mean-square error (LMMSE) or least square (LS), and then resort to iterative approaches to improve the estimation accuracy.