Research On Base Station Sleeping Strategy In Heterogeneous Cellular Networks

With the rapid increase in the number of mobile users and the maturity of various smart devices,the capacity of mobile communication systems faces enormous challenges.Heterogeneous cellular networks came into being and it can effectively respond to appeals.However,with the continuous expansion of the scale of heterogeneous networks,the energy consumption in the network is becoming increasingly serious.It is particularly important to reduce energy consumption and improve energy efficiency.As an effective energy-saving technology,the base station sleeping technology has attracted the attention of many scholars.At present,research on the base station sleeping technology in heterogeneous cellular networks still has problems in considering the dynamic changes of traffic load and the impact of the association problem between users and base stations on the sleeping strategy.In response to these problems,this thesis proposes two different base station sleeping strategies in a Macro-Femto heterogeneous cellular network,which are as follows:Firstly,from the viewpoint of energy saving,the semi-Markov decision process is used to model the dynamic switching process of Femto Base Stations(FBSs)in the heterogeneous networks.The number of users served by the base station and the mode of FBSs are used as system states,and how to control the status of FBSs are used as actions.The system energy saving is regarded as an immediate reward function,and it is converted into a long-term revenue model by derivation,which realizes the control of the system energy consumption.At the same time,the UA-Basic algorithm has been formulated to effectively handle the impact of the association between users and base stations on the sleeping strategy during each round of decision-making.Then,a value iterative algorithm is used to solve the optimal sleeping strategy of FBSs.The simulation results show that this strategy achieves a good dynamic switching of FBSs and effectively reduces the system energy consumption.Second,from the perspective of energy efficiency,the three-exponential smoothing method is used to track the data in the FBSs' historical periods,predict the number of users in the next cycle.And the predicted result will compare with the threshold to allow some FBSs to enter sleep mode to reduce system energy consumption.Then,a UA-QoS algorithm is developed to realize the association between the users and the base stations based on optimizing the quality of service of users,and also to solve the influence of the association problem on the sleeping decision.The simulation results show that the strategy can reduce the energy consumption of the system,and ensure the quality of service of users,then improve energy efficiency of the system.