Superimposed training for channel estimation of OFDM modulated amplify-and-forward relay networks

This paper is concerned with the problem of channel estimation for amplify-and-forward （AF） cooperative relay networks with orthogonal frequency division multiplexing （OFDM） modulation. The algorithm is based on both the least square （LS） and minimum mean square error （MMSE） technique with a superimposed training strategy. Specifically, both the source and relay superimpose their own training signal onto data stream prior to transmission so as to estimate the separate channel state information of the source to relay link and the relay to destination link. We also present the performance analysis and derive the approximated closed-form expressions for the MSE of separate channel estimation of source to relay link and the relay to destination link, respectively, from which we compute the optimal training signal as well as the relay power-amplification factor. To further improve the performance of channel estimation, we adopt a weighted average process to enhance the estimation performance over multiple OFDM blocks, from which we compute the optimal tracking factor. Simulation results are provided to corroborate the proposed scheme.