LAUSR

Low-Earth orbit (LEO) satellites are recognized as one of the most promising infrastructures for realizing global Internet of Things (IoT) services. With the explosive growth of user terminals (UTs) and data traffic, the integration of massive multiple-input multiple-output (mMIMO) techniques and LEO satellite communication systems has been regarded as a novel idea to enhance system capacity and realize global seamless high-speed interconnection. However, obtaining effective downlink channel state information (CSI) and establishing a simple and efficient hybrid beamforming mechanism are challenging tasks due to the limitations of objective factors, such as high dynamic, long delay, and low payload in LEO satellite scenarios. It is embodied in three aspects: 1) the untenable channel reciprocity in time division duplex (TDD) systems; 2) the training feedback costs and feedback delay in frequency division duplex (FDD) systems; and 3) the complex nonconvex optimization process faced by hybrid beamforming design. Driven by the performance advantages of the deep learning (DL) technology to deal with various problems in the field of physical layer communications, this article proposes to use a deep neural network (DNN) to solve the above challenges, and constructs SatCP and SatHB schemes for realizing downlink CSI acquirement and hybrid beamforming design, respectively. By deeply mining the potential correlation of the uplink-downlink channels between LEO satellites and UTs and exploring the mapping relationship between CSI and beamformers, the SatCP can assist LEO satellites to directly predict the future downlink CSI based on the observed uplink CSI with no need for downlink channel estimation, while the SatHB can easily generate the corresponding beamformers based on the downlink CSI predicted by the SatCP without requiring complex optimization. Numerical results demonstrate that the proposed SatCP and SatHB can play an effective auxiliary role in LEO satellite mMIMO communication systems.