Graph Theory Based Energy Consumption Management Algorithm And Application Of LTE Base Station

Recently,there has been a trend for explosion in mobile data traffic driven by smart phones and devices,which provide ubiquitous Internet access and diverse multimedia applications with high availability and convenience.In addition,this trend also offers ever-increasing energy consumption(EC)and greenhouse gas emission to the mobile communication industry.However,base station(BS)is the principal entity of ICT power consumption(PC)and occupation will step up with the development of Mobile Communications Industry.In conclusion,power reservation(PR)and optimization in the wireless cellular networks is bound to become a problem that should not be ignored in the progress of the future green wireless network analysis and development.To ensure quality of service(QoS)of BS in the peak time,there is potential energy waste in the design of wireless access internet.Therefore,turning off some underutilized BSs during off-peak period and performing effective compensation without delay are the most efficient way to save energy.However,the problem that long convergence time and poor convergence precision in the largescale network needs to be solved.At the same time,large-scale energy conservation yet remains to be well investigated at macro level.In addition,without well investigated mechanism of traffic prediction model,switching operation of BSs can be continual,thus influencing QoS of network.For LTE scenarios,we propose a BS topology-aware based energy saving(ES)model,whose core is cell adjacency graph(CAG)with vertexes and links representing eNodeBs(eNBs)and their neighboring relationship.In addition,we introduce new metrics,predicted energy efficiency(PEE)and quality of compensation(QoC),as the weights of nodes and links respectively.Consequently,the model transforms the ES problem into average weights maximization in CAG.In view of the model presented,centralized and hybrid algorithms are put forward to solve the problem.To promote the efficiency of time series analysis and prediction,we design S-ARIMA model as a tool for predicting traffic.Compared with classic distributed algorithm,simulation results claim that our hybrid approach achieves the maximization of ES with guaranteed QoC while our centralized approach maximize the PEE.In addition,we design a general system-level platform to verify our proposed energy consumption management algorithm.The workbench is able to achieve energy consumption management algorithm at different time granularity.With displaying the situation of power reservation,user SINR and base station CDF of dormant BSs,we prove that our energy-saving algorithm can be carried out effectively in real-world project.Moreover,there is no "ping pong" effect in that the workbench depicts the dormant base stations in detail during different time interval.