Published May 3, 2023 | Version 1
Conference paper Open

Dynamic Traffic Prediction Model Retraining for Autonomous Network Operation

  • 1. Centre Tecnològic de Telecomunicacions de Catalunya
  • 2. Universitat Politecnica de Catalunya
  • 3. Université Côte d'Azur


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.


[ICTON] Dynamic Traffic Prediction Model Retraining for Autonomous Network Operation.pdf

Additional details


DESIRE6G – Deep Programmability and Secure Distributed Intelligence for Real-Time End-to-End 6G Networks 101096466
European Commission