Sleep Control Strategy To Optimize The Energy Efficiency Of The Base Station In Dense Network

Key technology research in the next generation of mobile communication network,intensive networking is one of the effective means to increase network capacity.The intensive deployment of the base station can cause huge power demand and further increase the pressure on the operator.Therefore,the energy consumption research of communication network is a key research point of next generation mobile communication network.In the actual scene,the service requirements of the base station are random at the time,and the randomness of the user distribution is made,which makes it possible for the base station to sleep in certain periods.At present,we study the base station energy saving while also concerned about the system's other performance.This thesis puts forward the energy saving strategy of micro base station dormancy in the energy consumption of dense system,while limiting the average latency of the service.In a small time scale,aiming at the different characteristics of macro base stations and micro base stations,this thesis presents a base station collaboration energy efficiency optimization strategy based on service classification.First,when the micro base station is dormant,the macro base station cooperates in order to reduce the waiting time of the user service.Second,in the dense scene,the number of micro base stations is more,macro base station can give each micro base station collaborative processing resources is limited.While the micro base station access to the user less,we can access the user at a higher rate of service.According to the characteristics of the macro base station and the micro base station,the service is distinguished according to the type capacity,and the large capacity type business is handed over to the micro base station as much as possible.The small capacity type service is handed over to the macro base station to improve the energy efficiency of the system.Finally,the base station collaboration and business process are modeled as Markov models to analyze their energy efficiency.The simulation results show that the classification of the business,fitting the macro base station and micro base station their own characteristics to deal with,can effectively improve the system's energy efficiency.In a large time scale,a micro-cell service is changing over time.Using the currently set control parameters and can not adapt to business changes.For this problem,this thesis presents an energy efficiency optimization strategy based on wavelet neural network prediction service.First,a wavelet neural network is used to predict the next time traffic of the micro base station.Second,a multi-sleep mode and base station cooperative energy efficiency optimization strategy is proposed,the sleep time length parameter is used to control the micro base station sleep.The practical Markov model models the service process and finally obtains the optimal base station sleep control parameters under the traffic volume.Finally,the corresponding base station sleep control parameters are set according to the prediction of traffic volume at the next time according to the wavelet neural network.Simulation shows that at large scale time,after the business forecast,the base station can more accurately combine the traffic to perform the sleep strategy,effectively reducing the base station's energy consumption and business average delay.