LAUSR

Self-Organizing Networks (SON) which automate the mobile networks in a cost-efficient way can provide extensive benefits for mobile network operators that are facing several challenges, such as providing maximum coverage and balanced/efficient usage of spectrum and energy. However, a major challenge in future wireless cellular systems is the design of self-organizing architecture that can enable re-configurable, scalable, flexible, low-cost, and efficient solutions for supporting increasingly diverse applications, products, and services of user and network requirements. In this paper, we discuss the benefits and opportunities of mobility-aware clustering capability on different conventional SON functions and advanced Long-Term Evolution features. We further propose a novel cell clustering methodology, which utilizes handover attempt data and aims to collect the cells that have higher mobility activity with each other in a single cluster. One of the main benefits of the clustering mechanism is to reduce the complexity of network optimization processes via checking smaller number of cells instead of evaluating entire network, as their number is gradually increasing. We consider real-world data of evolved Node-Bs (eNodeBs) in one of the major cities while evaluating our novel clustering technique that considers the mobility activities and location information of eNodeBs. In our evaluations, ${k}$ -means clustering is used as a benchmark. The clustering technique provides the detection of the cells whose serving regions are overlapped. Our results indicate that up to %6 increment is achieved in number of the ratio of intra-cluster handover attempts to total handover attempts. Additionally, the results further show a reduction in the standard deviation of the number of cells within a single cluster with a ratio of %8, which yields more uniform distribution of the cells across different clusters.