Modeling and Abstraction of Network and Environment States Using Deep Learning

CANs promise to apply cognition to overcome shortcomings of self-organizing networks, such as limited flexibility and adaptability to changing environments. in CAN, machine-learning-based network automation functions, called CFs, learn context-specific policies for automating network operations. For this, CFs need a common abstract description of the network states to which they respond. This article presents a design and implementation of an EMA engine that could be tasked with learning the required abstract states in a consistent way across multiple CFs.