Dynamic Traffic Prediction Model Retraining for Autonomous Network Operation

In general, the availability of an accurate machine learning (ML) model plays a particularly important role in the
development of new networking solutions and is one of the main drivers for the development of 5G and beyond
networking. Although an option is to update the model once inaccurate data is detected, such approach requires
high computational effort, specially once the data history is large. In this paper, we propose an approach that
combines a traffic prediction model based on Long Short-Term Memory (LSTM) with an analysis module for
dynamic connection capacity allocation. Once the model is generated, re-training can be triggered after
inaccuracies are detected by the analysis module. Illustrative numerical results show the benefits from the
proposed decision-based re-training approach to reduce the number of re-training rounds while maintaining
model accuracy.