Trust Me: Explainable ML in Self-Organized Network Management

Abstract

Machine Learning (ML) powered Self-Organizing Network (SON) functions are an integral part of the 5G(+) network management to automatically learn and optimize the network performance. Third Generation Partnership Project (3GPP) Release 17 confirmed it by providing a foundation for studying ML-based solutions to tackle network management problems. However, despite their ability to provide high-quality solutions, most ML algorithms lack interpretability, leading to a lack of trust from Mobile Network Operators (MNOs) and delaying the fast integration of ML solutions into operational networks. To address this issue, Explainable Machine Learning (xML) techniques can be used to make complex ML models more interpretable, manageable, and trustworthy. In this work, we apply xML methods to explain an implicit coordination scenario between two conflicting SON functions (SFs): Coverage and Capacity optimization (CCO) and Inter Cell Interference Coordination (ICIC). We show how xML methods can be used to see the coordination problem from an ML model point of view, get meaningful insights, and confirm that the ML model captured all the relevant relationships correctly. This in turn helps to build trust in the ML model which allows it to be used for automatic network management.