Toward Control and Coordination in Cognitive Autonomous Networks

The incorporation of Artificial Intelligence (AI) and Machine Learning (ML) in mobile networks is expected to raise the degree of automation by proposing Cognitive Autonomous Networks (CAN). In CAN, learning based functions, called Cognitive Functions (CFs), adjust network control parameters to optimize specific Key Performance Indicators (KPIs). The CFs share the same resources, and this very often introduces an overlap among their target control parameter adjustment, i.e., at one point of time, multiple CFs may want to change the same control parameter albeit by different amounts depending on their respective levels of interest in that parameter. Correspondingly, a Controller is required in CAN to coordinate the sharing of the parameter among the independent CFs to meet their varying extents of interests. Although a Nash Social Welfare Function (NSWF) based Controller was introduced at first, to overcome the problems of this Controller a second Controller was introduced based on Eisenberg-Gale Solution (EGS). To use an EGS based Controller, impact of each network control parameter on each CF, called Config-Weight (CW), needs to be calculated. In this paper we propose a Shapley value based method for CW calculation, prove the optimality of the method mathematically and by simulation, provide a comparison between the Controllers in a simulation environment that resembles 5G network and find that up to 9.18% improvement can be obtained using the EGS based Controller.