Direct-to-satellite (DtS) communication has gained importance recently to support globally connected Internet of things (IoT) networks. However, relatively long distances of densely deployed satellite networks around the Earth cause a high path loss. In addition, since high complexity operations such as beamforming, tracking and equalization have to be performed in IoT devices partially, both the hardware complexity and the need for high-capacity batteries of IoT devices increase. The reconfigurable intelligent surfaces (RISs) have the potential to increase the energy-efficiency and to perform complex signal processing over the transmission environment instead of IoT devices. But, RISs need the information of the cascaded channel in order to change the phase of the incident signal. This study proposes graph attention networks (GATs) for the challenging channel estimation problem and examines the performance of DtS IoT networks for different RIS configurations under GAT channel estimation.

翻译：最近,直接对卫星(DtS)的通信在支持全球连通的物联网网络方面变得日益重要,但是,地球周围部署的卫星网络距离相对较长,造成高路径损失;此外,由于在IoT装置中必须部分地进行诸如波束成形、跟踪和均匀等高度复杂的操作,因此,IoT装置的硬件复杂性和高容量电池需求都有所增加,可重新配置的智能表面有可能提高能源效率,在传输环境中而不是在IoT装置上进行复杂的信号处理。但是,RIS需要级联频道的信息,以改变事件信号的阶段。本研究报告建议为具有挑战性的频道估计问题建立图形关注网络,并审查GAT频道估计下不同RIS配置的DtS IoT网络的性能。