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.
翻译:在本文中,图形关注网络(GAT)首先用于频道估算。 根据 6G 的预期, 我们考虑高高度平台站(HAPS) 安装了可重新配置智能地表辅助双向通信, 并获得了低管理费和高正态平均正方差性能。 所建议方法的性能在RIS- 集成 HAPS 的双向背航链路上进行了调查。 模拟结果显示, GAT 估计器在全复式频道估测中超过最低平方值。 与先前采用的方法相反, 一个节点的GAT 可以分别估算串联通道系数。 因此, 在全复式通信的试点信号中, 不需要使用时间分解模式。 此外, 显示, GAT 估计器对于硬件的缺陷和小规模淡化特性的改变是强大的, 即使培训数据不包括所有这些变异。