CN115002889A - 5G base station power consumption curve fitting method based on user behaviors - Google Patents
5G base station power consumption curve fitting method based on user behaviors Download PDFInfo
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- CN115002889A CN115002889A CN202210001203.7A CN202210001203A CN115002889A CN 115002889 A CN115002889 A CN 115002889A CN 202210001203 A CN202210001203 A CN 202210001203A CN 115002889 A CN115002889 A CN 115002889A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
- H04W52/04—TPC
- H04W52/18—TPC being performed according to specific parameters
- H04W52/22—TPC being performed according to specific parameters taking into account previous information or commands
- H04W52/228—TPC being performed according to specific parameters taking into account previous information or commands using past power values or information
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
- H04W52/04—TPC
- H04W52/06—TPC algorithms
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
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Abstract
The invention relates to a 5G base station power consumption curve fitting method based on user behaviors. The functional relation between the power consumption of the base station and the power failure duration after the power failure is researched, reflects the power consumption condition of the base station after the power failure, and can provide a new method for calculating the capacity of the standby battery of the 5G base station. And based on the user behavior after power failure, establishing a response model of the user transfer rate to the power failure duration by adopting a sigmoid function. And (3) fitting a curve by adopting a mathematical method to obtain parameters required by the response model and a relation curve between the number of the network users connected with the base station after power failure and the power failure duration. The power consumption of the base station and the number of the connected internet users have a function mapping relation, the power consumption of the base station can be obtained according to the number of the internet users after power failure, and a power consumption curve of the base station after power failure is obtained. And calculating the standby battery capacity of the base station according to the power consumption curve of the base station and the standby time length requirement.
Description
Technical Field
The invention belongs to the research field of 5G base station power consumption change trend after power failure, and obtains a 5G base station power consumption curve after power failure by combining the internet access behavior of users after power failure and researching the relation between the number of internet access users connected with a base station and the power failure duration.
Background
With the comprehensive opening of the digital economy era, a new generation of information communication technology represented by 5G becomes an important engine for assisting the high-quality development of the economy society. Particularly, in recent years, the society is accelerated to digitalization, networking and intellectualization, and the Chinese medicinal sheep plays a powerful role in promoting the digitalized transformation of various industries as 5G of a new capital construction 'leading sheep'. However, the development of the 5G technology also faces many challenges, and the first time is the problem of energy consumption. The energy consumption, power density and number of communication devices have increased substantially from 4G networks to 5G networks. Compared with a 4G base station, the power consumption of the 5G base station is about 3-4 times of that of the 4G base station. Meanwhile, the high-frequency signal attenuation adopted by the 5G technology is serious, the number of 5G base stations required for covering the same range is multiplied by 4G, the number of the 5G base stations reaches 600 ten thousand at present, and the number of the 5G base stations can reach 3000 ten thousand in the future. The base station has large power consumption and large quantity, and causes huge power supply pressure to a power grid, thereby influencing the power supply reliability of the base station.
In the future, the 5G technology is closely related to the fields of industrial application, unmanned driving, medicine and the like, and the requirement on the power supply reliability of the base station is higher. In order to ensure the reliability of the power supply of the base station, a spare battery needs to be allocated to the base station, and the selection of the appropriate capacity of the spare battery becomes a difficult problem. Studying the power consumption profile of a base station as a function of the length of the power outage helps to solve this problem. The change of the power consumption of the base station has a function mapping relation with the number of the base station internet users, and a relation curve of the number of the internet users based on user behaviors and the power failure duration is researched, so that the power consumption curve of the base station after power failure can be obtained. Based on the curve and the standby time, the total discharge capacity of the standby storage battery can be calculated, and a new method is provided for selecting proper standby battery capacity, reducing the investment and operation cost of the standby battery and prolonging the service life of the battery.
Disclosure of Invention
The invention provides a user internet access demand response model based on user behaviors for researching a power consumption curve of a base station after power failure. When power is off, the internet access behavior of the user changes along with the extension of the power off time, a certain rule is met, and a relation curve between the number of the internet access users connected with the base station and the power off time can be obtained according to the historical data of the 5G base station and by combining a mathematical fitting method.
At present, wifi's prevalence is higher, and most users both used the flow and also connected wifi, and during the power failure, unable normal work and study can arouse user's the desire to surf the internet, arouse user's online action, because can't connect wifi, but have the online demand, the user will switch to the flow and surf the internet. When the traffic is used for surfing the internet, the user is connected with the nearby 5G base station, and the power consumption of the base station and the number of connected internet users have a function mapping relation. The invention starts from the change of user behavior caused by power failure and researches the power consumption change of the base station, and the specific research process is as follows:
step 1, establishing a response model of the user transfer rate to the power failure duration by adopting a sigmoid function based on user behaviors.
And 2, referring to historical data statistics of the 5G base station, fitting a curve by adopting a mathematical method to obtain parameters required by a response function, and obtaining a relation curve between the number of the network users connected with the base station and the power failure time length.
And 3, obtaining a power consumption change curve of the base station after power failure by using a function mapping relation of the power consumption of the base station and the number of connected network users and combining the relation of the number of the network users connected with the base station and the power failure time length.
Drawings
The drawings used in the embodiments will be briefly described below.
FIG. 1 is a flow chart of a 5G base station power consumption curve fitting method based on user behaviors
FIG. 2Sigmoid curve
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention relates to a 5G base station power consumption curve fitting method based on user behaviors, the flow of which is shown in figure 1, and the specific implementation steps are as follows:
step 1, based on the behavior of the user after power failure, the length of the power failure time has the smallest perceived difference to the user response, namely a dead zone threshold value, and in the threshold value range, the power failure basically does not affect the user response, and the interval is called as a dead zone. When the power failure time is greater than the dead zone threshold, the user response generates obvious change, the variation of the user response is in direct proportion to the variation of the power failure time, and the interval is called as a normal response area; when the power failure duration is long to a certain degree, so that the user response degree reaches an upper limit, the power failure duration is a saturation region threshold, and an interval larger than the saturation region threshold is called a saturation region. And establishing a functional relation between the user transfer rate alpha and the power failure duration by using a sigmoid function, and acquiring a user demand response model. The user transfer rate is the number of users transferred to the base station after power failure divided by the number of users at the time of power failure. The sigmoid function curve is shown in fig. 2.
α max The maximum user transfer rate is u, the inclination is u, the power failure duration is t, a is a dead zone threshold value, b is a saturation zone threshold value, and d is the average value of a and b. The sigmoid function is a good threshold function, is continuously derivable and has no inflection point, the value of u determines the inclination degree of the graph, and the smaller u is, the larger the inclination is.
L k ＝L k0 (1+ alpha) (formula 2)
L k For the number of network users connected to the base station after power failure, L k0 Is the number of network users, L ', connected with the base station before power failure' kmax The upper limit for connecting the base station with the internet user.
And 2, fitting curves by adopting mathematical fitting methods such as a method for approximating discrete data by an analytical expression and a least square method and the like to obtain parameters required by the model according to the user behavior-based demand response model obtained in the step 1 and by combining historical data of the 5G base station during power failure. Need filter historical data when carrying out curve fitting, there is certain influence in power failure time point to user's transfer rate, at 0 o' clock to 7 o 'clock, the user is in rest state, and the power failure is less to user's influence, if adopt power failure data fitting curve this moment, will make the consumption variation volume of calculation littleer, and the reserve battery capacity that calculates with this curve simultaneously also can be littleer, and reserve battery will unable support when having a power failure daytime for a long time in reserve. In order to more clearly illustrate the process of the present invention, the least square method is used to fit the curve, the least square method is to compare the estimated value with the measured value to minimize the square difference between the two values, and the parameters a, b, u are obtained by using (formula 5) as the objective function, and other mathematical methods can be used to fit the curve in the actual fitting process.
An objective function:
L k,t ，L′ k,t the estimated value and the actual measured value of the number of the internet surfing users with the power failure duration of t are obtained. Because the population distribution conditions of the areas where the base stations are located are different, the transfer rates of the users of the base stations are different when power is off, and the obtained parameters can be corrected according to historical data of different areas to obtain the user response curves suitable for the base stations.
Step 3, according to the 5G base station data, the functional relation f (L) between the power consumption of the base station and the number of the network users connected with the base station can be known k ). And combining (formula 4), obtaining a relation curve f (t) of the power consumption of the base station and the power failure time length. And (5) according to the spare time requirement of each area, combining with the f (t) curve to calculate the spare battery capacity.
The method obtains the functional relation between the power consumption of the base station and the power failure duration based on the user behavior, and uses the function for calculating the capacity of the standby battery, so that the method is more suitable for the actual situation than the traditional standby battery capacity calculating method, and the selected standby battery capacity is fully utilized on the premise of meeting the normal power supply of the base station. The above description is only for the preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention shall fall within the protection scope of the present invention.
Claims (2)
1. And establishing a response model of the user transfer rate to the power failure time length by adopting a sigmoid function, wherein the sigmoid function is only one choice, and other functions can be adopted to establish the response model of the user transfer rate to the power failure time length.
2. And (4) combining historical data statistics of the 5G base station, and fitting a relation curve between the number of the users connected to the network and the power failure duration of the base station after power failure by adopting a mathematical method. The least squares method used in the embodiments of the present invention may be used to fit the curve using other mathematical methods.
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Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101242549A (en) * | 2007-11-22 | 2008-08-13 | 中国移动通信集团山东有限公司 | Neutral network construction method for communication network alarm association |
| CN101568122A (en) * | 2008-04-25 | 2009-10-28 | 中兴通讯股份有限公司 | Method for realizing base station electricity saving in time division synchronous code division multiple access (TDS-CDMA) system |
| CN103167186A (en) * | 2011-12-12 | 2013-06-19 | 联想(北京)有限公司 | Internet access way control method and electronic terminal |
| CN104581808A (en) * | 2015-01-19 | 2015-04-29 | 广东欧珀移动通信有限公司 | Method and device for testing mobile network performance and mobile terminal |
| CN105635960A (en) * | 2014-11-19 | 2016-06-01 | 中兴通讯股份有限公司 | Terminal management method, terminal management apparatus and terminal management system |
| CN105703457A (en) * | 2016-01-20 | 2016-06-22 | 慈松 | Method and device for managing and controlling base station machine room UPS lead-acid cell |
| EP3051884A1 (en) * | 2013-10-24 | 2016-08-03 | Huawei Technologies Co., Ltd. | Uplink power control method and device |
| CN107037373A (en) * | 2017-05-03 | 2017-08-11 | 广西大学 | Battery dump energy Forecasting Methodology based on neutral net |
| CN110495221A (en) * | 2017-04-13 | 2019-11-22 | 微软技术许可有限责任公司 | Power-efficient base station |
| CN111356191A (en) * | 2020-03-04 | 2020-06-30 | 维沃移动通信有限公司 | Cell access method and electronic equipment |
| CN112672438A (en) * | 2019-10-15 | 2021-04-16 | 深圳市万普拉斯科技有限公司 | Network link control method, mobile terminal and computer storage medium |
| CN112996090A (en) * | 2021-01-21 | 2021-06-18 | 西藏先锋绿能环保科技股份有限公司 | Energy-saving management system and method |
| CN113766573A (en) * | 2021-09-18 | 2021-12-07 | 国网上海市电力公司 | Base station energy storage facility adjustable capacity evaluation method considering load balance and standby |
| CN113810954A (en) * | 2021-09-08 | 2021-12-17 | 国网宁夏电力有限公司信息通信公司 | Virtual resource dynamic expansion and contraction method based on flow prediction and deep reinforcement learning |
-
2022
- 2022-01-04 CN CN202210001203.7A patent/CN115002889A/en active Pending
Patent Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101242549A (en) * | 2007-11-22 | 2008-08-13 | 中国移动通信集团山东有限公司 | Neutral network construction method for communication network alarm association |
| CN101568122A (en) * | 2008-04-25 | 2009-10-28 | 中兴通讯股份有限公司 | Method for realizing base station electricity saving in time division synchronous code division multiple access (TDS-CDMA) system |
| CN103167186A (en) * | 2011-12-12 | 2013-06-19 | 联想(北京)有限公司 | Internet access way control method and electronic terminal |
| EP3051884A1 (en) * | 2013-10-24 | 2016-08-03 | Huawei Technologies Co., Ltd. | Uplink power control method and device |
| CN105635960A (en) * | 2014-11-19 | 2016-06-01 | 中兴通讯股份有限公司 | Terminal management method, terminal management apparatus and terminal management system |
| CN104581808A (en) * | 2015-01-19 | 2015-04-29 | 广东欧珀移动通信有限公司 | Method and device for testing mobile network performance and mobile terminal |
| CN105703457A (en) * | 2016-01-20 | 2016-06-22 | 慈松 | Method and device for managing and controlling base station machine room UPS lead-acid cell |
| CN110495221A (en) * | 2017-04-13 | 2019-11-22 | 微软技术许可有限责任公司 | Power-efficient base station |
| CN107037373A (en) * | 2017-05-03 | 2017-08-11 | 广西大学 | Battery dump energy Forecasting Methodology based on neutral net |
| CN112672438A (en) * | 2019-10-15 | 2021-04-16 | 深圳市万普拉斯科技有限公司 | Network link control method, mobile terminal and computer storage medium |
| CN111356191A (en) * | 2020-03-04 | 2020-06-30 | 维沃移动通信有限公司 | Cell access method and electronic equipment |
| CN112996090A (en) * | 2021-01-21 | 2021-06-18 | 西藏先锋绿能环保科技股份有限公司 | Energy-saving management system and method |
| CN113810954A (en) * | 2021-09-08 | 2021-12-17 | 国网宁夏电力有限公司信息通信公司 | Virtual resource dynamic expansion and contraction method based on flow prediction and deep reinforcement learning |
| CN113766573A (en) * | 2021-09-18 | 2021-12-07 | 国网上海市电力公司 | Base station energy storage facility adjustable capacity evaluation method considering load balance and standby |
Non-Patent Citations (2)
| Title |
|---|
| CHANGHAO DU , ZHONGSHAN ZHANG: ""Deep Learning Based Power Allocation for Workload Driven Full-Duplex D2D-Aided Underlaying Networks"", 《IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY》, 10 November 2020 (2020-11-10), pages 7 * |
| 祁鑫;王福忠;张丽;王瑞;王晓慧: ""基于SVD-LSTM的高校学生宿舍空调负荷预测"", 《电子科技》, 22 November 2019 (2019-11-22) * |
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