Load analysis and sleep mode optimization for energy-efficient 5G small cell networks

Abstract

Dense deployment of small cells is seen as one of the major approaches for addressing the traffic demands in next-generation wireless networks. However, dense deployment of large number of small cells necessitates effective techniques for placing under-loaded small cells into sleep mode, so as to save energy. Such techniques should be low complexity and should not also compromise quality of service of users such as short access delay, while they can also result in significant energy savings for delay-tolerant network traffic. In this study, we introduce energy efficient, low-complexity techniques for load-based sleep mode optimization in densely deployed 5G small cell networks. We define a new analytic model to characterize the distribution of the traffic load of a small cell using a Gamma distribution, find its distribution parameters, and verify the validity of the model using computer simulations. We also compare the throughput of various sleep mode techniques as a function of different delay tolerance levels, where our simulation results show that the proposed technique achieves the highest throughput.