Time series forecasting has gained significant traction in LTE networks as a way to enable dynamic resource allocation, upgrade planning, and anomaly detection. This work investigates short-term key performance indicator (KPI) forecasting for rural fixed wireless LTE networks. We show that rural fixed wireless LTE KPIs have shorter temporal dependencies compared to urban mobile networks. Second, we identify that the inclusion of environmental exogenous features yields minimal accuracy improvements. Finally, we find that sequence-to-sequence-based (Seq2Seq) models outperform simpler recurrent neural network (RNN) models, such as long short-term memory (LSTM) and gated recurrent unit (GRU), and random forest (RF).
This work was supported by a MITACS Accelerate grant and Xplore, Inc.
https://www.ece.unb.ca/petersen/pubs/CoPe23/