Channel Modeling and Estimation for Reconfigurable-Intelligent-Surface-Based 6G SAGIN IoT
Xi Meng, Nan Zhang, Mengnan Jian, Michel Kadoch, Dacheng Yang
Abstract
The design of the Internet of Things (IoT) system over space–air–ground-integrated network (SAGIN) is still in its infancy. It is critical to get 6G technology involved, in order to address the current problems in the combined SAGIN and IoT ecosystem. This work utilizes a reconfigurable intelligent surface (RIS) to enhance the existing SAGIN infrastructure. We highlight channel modeling and estimate as a major component of RIS beam control and present a sparse Bayesian super-resolution estimation approach to achieve a good balance of accuracy and computing complexity. Our numerical results justify that, with our proposed models and algorithms, RIS will become a cost-effective and spectral-efficient solution for SAGIN-based 6G networks.