Dataset for Channel Estimation in RIS-assisted Satellite IoT Communications

The proposed GAT-based channel estimation method examines the performance of the DtS IoT networks for different RIS configurations to solve the challenging channel estimation problem. 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. 

所提出的基于图注意力网络（Graph Attention Network，GAT）的信道估计方法，针对不同可重构智能表面（Reconfigurable Intelligent Surface，RIS）配置下的DtS物联网（Internet of Things，IoT）网络性能展开研究，旨在解决具有挑战性的信道估计问题。结果表明，与传统深度学习方法相比，该GAT不仅在变化条件下表现出更高的性能与更强的鲁棒性，还具有更低的计算复杂度。