Contradiction Management in Intent-driven Cognitive Autonomous RAN

Intent Based Networks (IBN) is a prominent feature in the design of the AI-enabled B5G networks. Intents are primarily used to transform the intention of a human operator into network configuration, operation, and maintenance strategies. Although IBN provides future network automation technologies, it also raises the risk of contradiction(s) in an intent which arises during the runtime and cannot be predicted or resolved beforehand. In this paper we propose a new design which helps to detect and remove contradiction(s) in the optimal way during the runtime and evaluate it. We evaluate our proposed solution in a simulation environment and also provide a brief overview of standardization impact of our work to show that it conforms with the worldwide mobile network standardization efforts.