Context-Aware Self-Optimization: Evolution Based on the Use Case of Load Balancing in Small-Cell Networks

Abstract

Self-organizing network (SON) mechanisms in cellular systems automate network management procedures to improve performance while reducing operational expenditure (OPEX) and capital expenditure (CAPEX). Within the area of SON research and development, self-optimization is an especially hot research topic, and key use cases have been defined by Third-Generation Partnership Project (3GPP) standards. This article proposes to apply the context-awareness concept to self-optimizing small-cell networks. Context information provides valuable additional data to enhance self-optimization mechanisms. In this context, a framework for context-aware (CA) self-optimization in smallcell environments is described, and a particular self-optimization use case, load balancing (LB), is used to evaluate the benefits of the proposed approach. The results show that the proposed CA-LB algorithms increase the users? satisfaction, while the optimization time is significantly reduced.