Cognitive Autonomous Networks require the network to be able to derive and execute intelligent decisions, and thereby elevating the human operator’s role to a higher level of abstraction where the operator can only specify the desired outcomes from the network. These abstract inputs, called intents, must be supported by corresponding intent-driven capabilities in the network or its management functions. Although, Intent-Driven Management (IDM) has been published in multiple works, there is still no globally agreed end-to-end view of such IDM systems, let alone a globally agreed definition of intents. This paper provides a comprehensive discussion on the core aspects of IDM systems and combines them into an end-to-end system view with the related example solutions. Contrasting against a short review of related scientific and standards literature, the paper introduces a flexible, generic definition of intents and an End-to-End IDM System Architecture as well as the related modeling of intents to support their standardization. The paper also introduces implementation examples fitting the architecture and discusses advanced IDM features that need to be provided, including the ability to detect and resolve conflicts among intents.

Intent Based Networks (IBNs) are mainly used to transform a user’s intent into network configuration, operation, and maintenance strategies. IBN is a prominent feature for designing the AI-enabled next generation networks. In this paper we propose intent-driven orchestration of cognitive autonomous RAN for managing network control parameters. Our proposed design enables the Controller to detect and remove contradiction, which may arise from a single intent, in the runtime. We also provide a brief overview of existing standards on this aspect and standardization impact of our research to show that it conforms with the worldwide mobile network management standardization efforts.