Usage of Network Simulators in Machine-Learning-Assisted 5G/6G Networks
This addresses trustworthiness and reliability concerns for network operators in ML-assisted communications, though it is incremental as it builds on existing simulator use.
The paper tackles the challenge of applying machine learning to 5G/6G networks by proposing an architectural integration of network simulators for training, testing, and validating ML models before deployment, demonstrated through a proof-of-concept Wi-Fi testbed.
Without any doubt, Machine Learning (ML) will be an important driver of future communications due to its foreseen performance when applied to complex problems. However, the application of ML to networking systems raises concerns among network operators and other stakeholders, especially regarding trustworthiness and reliability. In this paper, we devise the role of network simulators for bridging the gap between ML and communications systems. In particular, we present an architectural integration of simulators in ML-aware networks for training, testing, and validating ML models before being applied to the operative network. Moreover, we provide insights on the main challenges resulting from this integration, and then give hints discussing how they can be overcome. Finally, we illustrate the integration of network simulators into ML-assisted communications through a proof-of-concept testbed implementation of a residential Wi-Fi network.