Dataset Quality Assessment in Autonomous Networks with Permutation Testing

The development of autonomous or self-driving networks is one of the main challenges faced by the telecommunication industry. Future networks are expected to realise a number of tasks, including network optimization and failure recovery, with minimal human supervision. In this context, the network community (manufacturers, operators, researchers, etc.) is looking at Machine Learning (ML) methods with high expectations. However, ML models can only be as good as the data they are trained on, which means that autonomous networks also require a sound autonomous procedure to assess, and if possible improve, data quality. Although the application of ML techniques in communication networks is ample in the literature, analyzing the quality of the network data seems an ignored problem. This paper presents work in progress on the application of permutation testing to assess the quality of network datasets. We illustrate our approach on a number of simple synthetic datasets with pre-established quality and then we demonstrate its application in a publicly available network dataset.