TY - GEN
T1 - Channel Estimation for Full-Duplex RIS-assisted HAPS Backhauling with Graph Attention Networks
AU - Tekbiyik, Kursat
AU - Kurt, Gunes Karabulut
AU - Huang, Chongwen
AU - Ekti, Ali Riza
AU - Yanikomeroglu, Halim
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/6
Y1 - 2021/6
N2 - 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.
AB - 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.
KW - Reconfigurable intelligent surfaces
KW - channel estimation
KW - graph attention networks
KW - high-altitude platform station systems
UR - http://www.scopus.com/inward/record.url?scp=85108565892&partnerID=8YFLogxK
U2 - 10.1109/ICC42927.2021.9500697
DO - 10.1109/ICC42927.2021.9500697
M3 - Conference contribution
AN - SCOPUS:85108565892
T3 - IEEE International Conference on Communications
BT - ICC 2021 - IEEE International Conference on Communications, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE International Conference on Communications, ICC 2021
Y2 - 14 June 2021 through 23 June 2021
ER -