In this paper, graph attention network (GAT) is firstly utilized for the channel estimation. In accordance with the 6G expectations, we consider a high-altitude platform station (HAPS) mounted reconfigurable intelligent surface-assisted two-way communications and obtain a low overhead and a high normalized mean square error performance. The performance of the proposed method is investigated on the two-way backhauling link over the RIS-integrated HAPS. The simulation results denote that the GAT estimator overperforms the least square in full-duplex channel estimation. Contrary to the previously introduced methods, GAT at one of the nodes can separately estimate the cascaded channel coefficients. Thus, there is no need to use time-division duplex mode during pilot signaling in full-duplex communication. Moreover, it is shown that the GAT estimator is robust to hardware imperfections and changes in small-scale fading characteristics even if the training data do not include all these variations.
@article{arxiv.2010.12004,
title = {Channel Estimation for Full-Duplex RIS-assisted HAPS Backhauling with Graph Attention Networks},
author = {Kürşat Tekbıyık and Güneş Karabulut Kurt and Chongwen Huang and Ali Rıza Ekti and Halim Yanikomeroglu},
journal= {arXiv preprint arXiv:2010.12004},
year = {2021}
}
Comments
This paper has been accepted for the presentation in IEEE ICC'2021