Load-awareness energy saving strategy via success probability constraint for heterogeneous small cell networks

In this paper, we investigate the energy consumption issue which stems from the enormous number of running small cells deploying in the heterogeneous networks. We first propose two power consumption models so as to characterize the active state and the idle state of small cells respectively. Then two sleep modes for small cells tier, random sleep mode and load-awareness dynamic sleep mode, are proposed. The random sleep is designed based on a binomial distribution of the small cell operation probability. Through the analysis on activeness of small cell base stations (SBSs), we define the operation probability for the small cell applying the proposed dynamic sleep mode is associated to its traffic load level. The closed-form expressions of success probability, which is used to decide whether an active user can connect to a small cell successfully, are derived for the proposed two sleep modes. Energy consumption minimizations are presented for each of the proposed sleep modes with condition on success probability constraint. Simulation results prove the effectiveness of the proposed two sleep modes. Different energy saving gains can be achieved via using of the concrete sleep mode. The superior of the dynamic sleep mode by comparing the random sleep is also verified in terms of energy consumption, success probability and power efficiency.