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Mobility load balancing using machine learning with case study in live network

In this paper, we propose a new tool that enhance user throughput by better exploiting current network resources and optimizing mobility management using machine learning algorithm. The dramatically increase in data traffic and the demand for high data rates services in UMTS (Universal Mobile Telecommunications Service) become a challenge that faces Mobile Operators due to the shortage of spectrum resources. Hence, to fulfill users' requirements with the current network resources, by the aid of data analytics technique like regression, a tool is being designed to comprehensively analyze the current cell Key Performance Indicators (KPIs) that impact the average user throughput, afterwards it advises with the optimum resources utilization to achieve the required user throughput. Subsequently, it optimizes the mobility parameters to re-distribute and balance traffic among cells, which accordingly increases the overall network capacity, resources utilization and user throughput without any extra expenditures.