Research on Channel Estimation Technology Based on Superimposed Training in Diamond Relay Networks

In order to obtain the channel state information (CSI) of cascaded and single-hop links in amplify-and-forward (AF) diamond relay networks efficiently, this paper proposes a channel estimation scheme based on superimposed training to eliminate multi-access interference and mutual interference between trainings. Then the optimal multi-training sequence design is performed. The new scheme superimposes relay training on the source training sequence. Through joint optimization design of relay identification symbols and relay training groups, a generation algorithm of orthogonal spreading sequence group based on frequency domain cyclic shift is designed. In order to eliminate the serious interference caused by non-Gaussian compound noise on single-hop channel estimation, a relay noise cancellation algorithm is proposed. Diversity combining can be implemented at destination through the copies of information obtained from the two relay links. This can improve symbol detection performance effectively. The simulation experiment compares the same type of channel estimation scheme, and the analysis verifies the effectiveness of this scheme.