Sum-rate maximization of two-way amplify-and-forward relay networks with imperfect channel state information

Considering a two-way amplify-and-forward (AF) relay network and aiming to simultaneously maximize the two users' mutual information lower bounds in the presence of channel estimation errors, we study the Pareto-front of users' mutual information lower bounds. Based on the Pareto-front we investigate the optimal power allocation among the two users and the relay, as well as the optimal power allotment between training and data symbols that maximize the average sum-rate lower bound, and explore the variations of these optimal power allocation as the relay position changes. We also show that the mean square error (MSE) of channel estimation is minimized when training vectors transmitted by the two users are orthogonal.