| dc.description.abstract | In the current 5G network, to obtain higher throughput, a high-band spectrum is often used for 5G communication. Since the signal in high-band has a higher declining rate than the signal in the low-band, 5G encounter low coverage problem. Users in areas such as mountains, oceans, and deserts where base stations cannot be built are not able to enjoy the convenience brought by 5G. In the specification of beyond 5G, to cope with the problem of low coverage, combining low orbit satellites (LEO) with the terrestrial network to achieve global coverage network. However, LEOs have high mobility and ability to long-range transmission characteristic, which makes the Doppler effect and path loss severely aggravated, which easily makes the communication channel to be in an unstable state, therefore, users cannot detect the accurate signal. In 5G, to solve the problem of the above mentioned, several Adaptive Transmission Technique are proposed. Adaptive transmission techniques usually operate on the transmitter side, and the adjustment of which is tuning by the Channel State Information (CSI), which is obtained through channel estimation and feedbacked from the receiver side. However, in the 5G satellite network, the obtained CSI might be already outdated before its actual usage due to the long-range satellite link. Therefore, the CSI prediction algorithm has gained more research interests. Most of the prediction methods need to collect a great number of environmental parameters to complete the predict process. In a time-variant satellite channel, the obtained estimates expire quickly with the change of propagation environments, which will cause high computational cost. In this paper, we propose a lightweight CSI prediction method for LEO communication. We use a small amount of historical CSI to train the Recurrent Neural Network (RNN) and predict the future CSI to reduce the use of traditional channel estimation. | en_US |