  Low earth orbit (LEO) satellite internet of things (IoT) is a promising way
achieving global Internet of Everything, and thus has been widely recognized as
an important component of sixth-generation (6G) wireless networks. Yet, due to
high-speed movement of the LEO satellite, it is challenging to acquire timely
channel state information (CSI) and design effective multibeam precoding for
various IoT applications. To this end, this paper provides a deep learning
(DL)-based joint channel prediction and multibeam precoding scheme under
adverse environments, e.g., high Doppler shift, long propagation delay, and low
satellite payload. {Specifically, this paper first designs a DL-based channel
prediction scheme by using convolutional neural networks (CNN) and long short
term memory (LSTM), which predicts the CSI of current time slot according to
that of previous time slots. With the predicted CSI, this paper designs a
DL-based robust multibeam precoding scheme by using a channel augmentation
method based on variational auto-encoder (VAE).} Finally, extensive simulation
results confirm the effectiveness and robustness of the proposed scheme in LEO
satellite IoT.
