  5G networks provide more bandwidth and more complex control to enhance user's
experiences, while also requiring a more accurate estimation of the
communication channels compared with previous mobile networks. In this paper,
we propose a channel quality indicator (CQI) prediction method in a deep
learning approach in that a Long Short-Term Memory (LSTM) algorithm. An online
training module is introduced for the downlink scheduling in the 5G New Radio
(NR) system, to reduce the negative impact of outdated CQI for communication
degradation, especially in high-speed mobility scenarios. First, we analyze the
impact of outdated CQI in the downlink scheduling of the 5G NR system. Then, we
design a data generation and online training module to evaluate our prediction
method in ns-3. The simulation results show that the proposed LSTM method
outperforms the Feedforward Neural Networks (FNN) method on improving the
system performance of the downlink transmission. Our study may provide insights
into designing new deep learning algorithms to enhance the network performance
of the 5G NR system.
