Power Consumption Modeling Of Base Stations Based On Dynamicy Factors

With the development of wireless communication network,So the mobile communication base station power consumption is also increasing.However,it wastes much energy consumption in base station.In order to create green communication environment,it is more and more important to save the energy in base station.Due to the large number of mobile communication base station,it is difficult to build the energy consumption monitoring station.The energy consumption of base stations is affected by traffic,temperature and other multiple dynamic factors.In order to save the energy in base station effectively,we should establish a base station energy consumption monitoring system.Based on the actual data,we can establish the energy consumption model.Currently there is little research in this area.In addition,the current energy-saving mechanisms are all about to close the base stations.But this way will affect the communication quality of the network.So it has a very important significance to find an energy-saving mechanism which do not affect the communication quality.Based on the above analysis,we propose a method to establish the energy consumption model for mobile communication base stations based on the measurement data.Firstly,we propose a system for collecting energy consumption data based on communication base stations and the network architecture.Then,based on the collected data,a mathematical model is built combined with the traffic,indoor and outdoor temperature and other parameters.We analyze the quantify value of various parameters in energy consumption model and determine the energy consumption proportion in each section.Finally we verify that the mathematical model has a high accuracy.Based on the base station energy consumption model,we propose an energy-saving mechanism driven by forecasting traffic,indoor and outdoor temperature.According to its characteristics of the dynamic factors,we estimation the prediction model using the ARIMA model and sine fuzzy.And we propose an energy-saving method to verify the effectiveness of energy-saving method.The results prove that this method can save the energy up to 30%without affect network performance.In summary,this paper presents a comprehensive energy model based on the current network data which can tell us the composition and factors in the current base station.Based on this model,a forecast-driven energy-saving mechanism is proposed.This energy-saving mechanism can save the energy consumption of a single base station more than 20 percent without affecting the network.The research has strong economic significance and green values.