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. It is shown that the proposed GAT both demonstrates a higher performance with increased robustness under changing conditions and has lower computational complexity compared to conventional deep learning methods. Moreover, bit error rate performance is investigated for RIS designs with discrete and non-uniform phase shifts under channel estimation based on the proposed method. One of the findings in this study is that the channel models of the operating environment and the performance of the channel estimation method must be considered during RIS design to exploit performance improvement as far as possible.
翻译:直接对卫星(DtS)的通信最近越来越重要,以支持全球连通的物联网网络;然而,地球周围密集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高复杂操作,例如波束成形、跟踪和衡平性等,硬件复杂性和对高容量的IoT装置电池的需求都有所增加;可重新配置的智能表面(RIS)有可能提高能源效率,在传输环境而不是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;此外,这项研究提议对具有挑战性的频道估算问题进行图形关注网络(GATs),并审查DtS IoT网络在GAT装置高容量电池方面的性能。