  While the 5G New Radio (NR) network promises a huge uplift of the uplink
throughput, the improvement can only be seen when the User Equipment (UE) is
connected to the high-frequency millimeter wave (mmWave) band. With the rise of
uplink-intensive smartphone applications such as the real-time transmission of
UHD 4K/8K videos, and Virtual Reality (VR)/Augmented Reality (AR) contents,
uplink throughput prediction plays a huge role in maximizing the users' quality
of experience (QoE). In this paper, we propose using a ConvLSTM-based neural
network to predict the future uplink throughput based on past uplink throughput
and RF parameters. The network is trained using the data from real-world drive
tests on commercial 5G SA networks while riding commuter trains, which
accounted for various frequency bands, handover, and blind spots. To make sure
our model can be practically implemented, we then limited our model to only use
the information available via Android API, then evaluate our model using the
data from both commuter trains and other methods of transportation. The results
show that our model reaches an average prediction accuracy of 98.9\% with an
average RMSE of 1.80 Mbps across all unseen evaluation scenarios.
