Design of handover self-optimization using big data analytics

As the cloud computing platform and the cluster file system become more mature so that more applications are developed on the distributed parallel processing, they could be usefully applied to the analytics of the cellular network service quality. Thus, in this paper, handover optimization is selected as the use of big data analysis techniques to improve the service quality of the cellular network. Handover is one of the most critical issues for the quality of cellular network services, while configuration of neighbor cell list of each cell is one very important job. A traditional way is that network operators manually configure the neighbor cell list for each cell and cannot rapidly respond to changes of the network to provide handover optimization. In this paper, a method for handover self-optimization is introduced, including initialization of self-configuration of neighbor cell list and self-optimization to further refine the neighbor cell list based on performance measurements, in order to improve the success rate of handover, thus improving the service quality of the cellular network. Because of needs of a large scale measurement data analyzing, a big data platform is built. All the self-optimization algorithms are deployed on Hadoop computing platform to enhance efficiency.