Objective: The goal of KICK is to significantly simplify and improve the operation of future 5G campus networks by using AI methods. The focus here is on Industry 4.0 environments with their high reliability and latency requirements. Specifically, an AI framework for the tactical and operational management and control of such communication networks is being developed and investigated, taking into account the limited communication and computing resources.
Approaches: On the one hand, the research work includes the definition of requirements for AI algorithms and the identification of suitable data and data formats. On the other hand, questions concerning the training, adaptation, compression, exchange and interaction of AI algorithms are addressed. In order to meet the high requirements of industrial applications, hybrid, i.e. data- and model-based approaches are pursued and data from production is linked with data from communication networks. Transfer Learning based on AI network state detection is used to derive adequate behaviour also on the basis of similar situations or similar logical network instances. This enables robust, continuous configuration, optimization and error handling, thus fully exploiting the potential of campus networks and networked Industry 4.0 systems.
Applications: The project is based on experimental work with real communication and production data from a real factory environment. The automation advantages achieved are also demonstrated in an exemplary manner in such an environment.