Individual channel estimation for amplify‐and‐forward relay networks using in‐band superimposed training
Gaoqi Dou, Xianwen He, Ran Deng, Jun Gao, Qingbo Wang · IET Communications · 2018
In amplify‐and‐forward relay networks, accurate channel estimation of individual links is essential for coherent reception and optimal design. To acquire the channel state information of individual links, a novel in‐band superimposed training is proposed, where relay superimposes its own training sequence directly on top of the information‐bearing received data. As a result, the training sequences from the source and relay nodes are independent of each other, thus making it more flexible and robust in relay‐training design. However, the proposed training scheme induces extra data interference that degrades the estimation performance. The authors first adopted the detected symbols to mitigate the data‐induced interference during channel estimation. However, the estimation performance is degraded by the symbol detection error and relay propagated noise. To resolve this problem, the interference nulling at the relay is proposed, where some special frequency components of the received data are nulled prior to training superimposition. As a result, both the data‐induced interference and relay propagated noise are totally removed during channel estimation. Simulation results are provided to assess the performance of the proposed schemes.