A Technical Framework For Formally Verifying AI Models Supporting 6G Network Functions

In this paper we propose a technical framework for applying formally verifiable AI (FVAI) methods to certify AI models that support 6G network functions. The certifications state that the input/output behavior of an AI model conforms to a specification or identifies potential issues with the model. This paper contributes to the broad spectrum of methods necessary to ensure that deep learning models, known to have instability issues, are sufficiently trustworthy for supporting functions in critical infrastructure like 6G networks. The proposed framework gives a unified way of representing network functions supported by AI models, certifications, construction of those certifications from model behaviour, and incorporating technical tools for bounding AI model input/output behaviour. We also illustrate the use of this framework in verifying AI models in two 6G use cases on optimisations in virtual network functions and radio access network resources. Furthermore, we discuss how this framework can be extended in the future.