Superimposed training based doubly selective channel estimation for OFDM modulated amplify-and-forward relay networks

This paper is concerned with the problem of superimposed training based channel estimation for orthogonal frequency division multiplexing (OFDM) modulated amplify-and-forward (AF) relay networks in doubly selective environment. A `subblockwise' linear assumption based channel model is proposed to represent the mobile-to-mobile doubly selective channels. We then propose novel strategy that allows the destination node to separately obtain the channel information of the source to relay link and the relay to destination link, from which the optimal ST signals are derived by minimizing the channel mean-square-error. Extensive numerical results are provided to corroborate the proposed studies.