On Channel Estimation for Amplify-and-Forward Two-Way Relay Networks

In this paper, we study the channel estimation problem for the two-way wireless relay network (TWRN) where two terminals exchange their information through a relay node in a bi-directional manner. We derive the maximum likelihood (ML) channel estimator as well as a new estimator called the linear maximum signal-to-noise ratio (LMSNR) estimator. It is shown that our proposed methods give superior performance compared to the common channel estimators like the least-square (LS) and the linear minimum-mean-squared-error (LMMSE) in the TWRN scenario. The provided study is based on any given training sequence, while the optimal training sequence design will be presented in a separate work due to the lack of the space. Simulations are conducted to corroborate the proposed studies.