Bi-SON: Big-Data Self Organizing Network for Energy Efficient Ultra-Dense Small Cells

Abstract

In this paper, we present a big-data self organizing network (Bi-SON) framework aiming to optimize energy efficiency of ultra-dense small cells. Although small cell can enhance the capacity of cellular mobile networks, ultra-dense small cells suffer from severe interference and poor energy efficiency. The self organizing network (SON) can automatically manage and optimize the system performance. However, current SON-enable mechanisms mostly focus on indoor femtocells. Our proposed Bi-SON suggests a data flow framework from data collection, analysis and optimization to reconfiguration. We adopt the statistics analysis approach to determine the optimal system parameters to improve the energy efficiency of a huge number of outdoor small cells. The Bi-SON mechanism periodically collects the management data of small cells, e.g. transmission power, reference signal receiving power and the number of users per cell. We find that simple sorting and filtering data analysis from huge number of small cells can already effectively find the almost optimal solution. Our simulation results show that Bi-SON can improve throughput and energy efficiency by 50% and 135% respectively, compared to the scheme without energy saving approach.