Minimizing Handover Performance Degradation Due to LTE Self Organized Mobility Load Balancing

Self-Organization (SO) has been proposed to reduce capital and operational expenses as well as to improve cellular network performance. Mobility Load Balancing (MLB) and Mobility Robustness Optimization (MRO) are two of the major proposed SO use cases. Typically, MRO sets the cell´s Handover (HO) Hysteresis and Time to Trigger, to select the optimum point at which a HO is initiated. Conversely, MLB can be achieved by advancing HOs from overloaded to less loaded cells, commonly by adjusting the Cell Individual Offset (CIO). However, MLB affects HO metrics specifically because advancing HOs inadvertently increases Radio Link Failures (RLF) arising from overly early HOs and/or the number of HOs and Ping-Pong HOs. In this work we present a Q-Learning algorithm that learns the best MLB action to take in different load states so as to achieve a desired load transfer, but with the least effect on HO performance. The learning agent minimizes the negative HO effects by applying a penalty to MLB actions that cause high RLFs, thereby reducing the effects on HO metrics by up to 30%.