Self-Organizing Ultra-Dense Small Cells in Dynamic Environments: A Data-Driven Approach

This paper presents a data-driven biadaptive self-organizing network (Bi-SON) for ultra-dense small cells (UDSC), which can improve energy efficiency and reduce interference in dynamic environments, taking account of cell switching <sc>on</sc>/<sc>off</sc>, transmission power adjustment, and traffic loads simultaneously. In the first adaptation of Bi-SON, a joint traffic load and interference aware cell ranking mechanism first determines the necessary active small cells based on traffic loads, and then ranks all the active small cells based on their carried traffic load and resulting interference. Top ranked cells will transmit at the maximum power. The last ranked <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula> cells will adjust the transmission power for interference reduction in the second adaptation function of Bi-SON, while maintaining the required quality of service. According to a polynomial regression learning approach, the total system throughput of UDSC is characterized as a function of <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula>. Compared to the baseline case when all the cells transmit with the maximum power, our proposed Bi-SON framework can improve the throughput and energy efficiency of UDSC by 73% and 169%, respectively. However, the pure switching <sc>on/off</sc> approach can only improve the throughput and the energy efficiency of UDSC by 52% and 115%, respectively. As demonstrated, even with a simple power adaptation algorithm, a learning-based Bi-SON framework can improve the performance of UDSC by taking advantage of the pervasive availability of voluminous data.