Semi-blind channel estimation for OFDM-based amplify-and-forward two-way relay networks

We consider the problem of channel estimation for OFDM-based amplify-and-forward (AF) two-way relay networks (TWRNs). Unlike previous works which were based on a fully pilot-based approach, we propose a semi-blind approach that exploits both the transmitted pilots as well as the received data samples to provide an enhanced estimation performance. Superimposed training is adopted at the relay to assist in the estimation of the individual channels. We base our semi-blind estimator on the maximum-likelihood (ML) criterion and employ an iterative low-complexity Quasi-Newton method to obtain the ML semi-blind channel estimates. As a performance benchmark we derive the semi-blind Cramer-Rao bound (CRB). Using simulation studies, we show that the proposed approach provides a substantial improvement in estimation accuracy over the conventional pilot-based approach.