Big Data Enabled Mobility Robustness optimization for Commercial LTE Networks

Abstract

Mobility Robustness optimization (MRO) is widely considered as an important self-organizing network (SON) usecase for tackling mobility management problems in LTE/LTEAdvanced networks. In this paper, we propose a data-driven centralized SON based MRO approach that relies on data from network configuration and performance management data sources to improve mobility performance in a fully automated manner. In particular, early and late handover statistics are used by the algorithm to make decisions regarding modification of mobility parameters. Based on performance management data from a live network, it is first observed that intra-frequency handovers provide the majority of handover problems, and that problems are predominantly cell-pair specific, not cellspecific. To increase mobility robustness, the cell individual offset configuration parameter is adjusted accordingly. The algorithm is deployed in a cluster of cells in a commercial LTE network. Results show that the algorithm is able to reduce radio link failure rates by up to 40 percent within two weeks, which underscores the potential of the proposed approach for commercial LTE networks.