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SON Function Coordination in Campus Networks Using Machine Learning

With the advent of 5G, network lifecycle operations such as service initial deployment, configuration changes, upgrades, optimization, and self-healing to name a few, should be fully automated processes to reduce capital expenditure (CAPEX) and operational expenditure (OPEX), and also to allow new players such as industry owners, to come into the scene as nontraditional network operators. To this end, self-organized networks functions (SF) have been proposed as a first attempt to provide self-adaptation capabilities to mobile networks on different fronts and to reduce the error-prone human intervention. Nevertheless, deploying multiple optimization functions in a network brings demanding challenges in terms of conflicting objectives in coordination. Automatically coordinating all those functions is paramount for industry owners in campus networks (CN) since they often do not have a deep expertise to carry out network optimization in an agile manner. Typically, each SF aim at individual goals modifying coupled network parameters, generally in dissonant directions with respect to other SF, jeopardizing the global stability of the system. This work presents an explicit formulation of the joint optimization problem when load balancing optimization (LBO) and coverage and capacity optimization (CCO) are instantiated in a CN.