AQ-learning approach for mobility robustness optimization in Lte-Son

Self-organizing networks (SON) for cellular systems is emerging as an important technology to reduce the cost of network deployment and maintenances. Mobility robustness optimization (MRO) is one of the main use cases of SON and has been intensively studied in 3GPP working groups. This paper proposes a dynamic self-optimization algorithm for handover (HO) parameters using the Q-Learning method. The proposed algorithm is mobility robustness, which means that the HO performance is robust against the change in UE mobility. In order to realize the mobility robustness, the proposed algorithm adaptively adjusts the HO parameters through Q-learning. This paper examines the performance of the proposed algorithm through the computer simulations and confirms the mobility robustness. The simulation results show that the success rate of Handover (HO) is improved and user experience is enhanced by the Q-MRO algorithm.