Environment Modeling and Abstraction of Network States for Cognitive Functions

Cognitive Autonomous Networks (CANs) promise to overcome the shortcomings of current Self-Organizing Network (SON) implementations, i.e., the limited flexibility and adaptability to changing environments, by applying cognition. In CAN, intelligent network automation functions, herein called Cognitive Functions (CFs), apply machine learning techniques to learn context-specific behavioral policies with which to automate network operations. For proper operation, the CAN system needs to learn the environment in which the functions are operating and to abstract the environment and performance observations into states to which the CFs must respond. This paper proposes a design and implementation of an Environmental-state Modeling and Abstraction (EMA) engine that could be tasked to learn the required abstract states in a consistent way across multiple CFs.