Double Threshold Design for Mobility Load Balancing in Self-Optimizing Networks

Mobility load balancing (MLB) is an important use case of Self-Optimizing Networks (SONs). To combat the commonly encountered issues in conventional MLBs, such as the blind offloading without equilibrium and optimality guarantees, we propose an Enhanced MLB (ELB) scheme to conquer these problems with the double threshold design including the common trigger threshold and the fairness-aware ending one. First, we introduce the rationale behind our idea with simple analysis, then the newly presented the fairness-aware ending threshold is given by modeling the fairness metric during the optimal target cell selection process. Based on these analysis, we propose the ELB scheme with a double-threshold design. Simulation results show that the presented ELB scheme can well improve the system performance and the user experience quality.