WO2013075330A1 - Procédé pour sélectionner précisément un point en phase de planification de déploiement de point d'accès sans fil wi-fi, et modèle - Google Patents

Procédé pour sélectionner précisément un point en phase de planification de déploiement de point d'accès sans fil wi-fi, et modèle Download PDF

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Publication number
WO2013075330A1
WO2013075330A1 PCT/CN2011/082943 CN2011082943W WO2013075330A1 WO 2013075330 A1 WO2013075330 A1 WO 2013075330A1 CN 2011082943 W CN2011082943 W CN 2011082943W WO 2013075330 A1 WO2013075330 A1 WO 2013075330A1
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WIPO (PCT)
Prior art keywords
data
users
candidate area
terminal
candidate
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PCT/CN2011/082943
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English (en)
Chinese (zh)
Inventor
任波
郝应涛
钱苏敏
任翠红
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华为技术有限公司
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Priority to PCT/CN2011/082943 priority Critical patent/WO2013075330A1/fr
Priority to CN2011800025361A priority patent/CN102726089A/zh
Publication of WO2013075330A1 publication Critical patent/WO2013075330A1/fr

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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/18—Network planning tools

Definitions

  • the present invention relates to the field of communications technologies, and in particular, to a precise method and model for selecting a Wi-Fi hotspot deployment planning stage.
  • WLAN Wireless Local Area Networks
  • Wi-Fi Wireless Fidelity
  • PDAs and terminals personal computers and handheld devices
  • Wi-Fi hotspot The place where Wi-Fi can access the Internet is called a Wi-Fi hotspot. Accurate deployment of Wi-Fi hotspots is a problem that must be considered in the healthy development of the macro network and the normal operation of the WLAN.
  • the prior art there are usually two ways to determine whether a Wi-Fi hotspot needs to be deployed: one is to determine whether the cell is suitable for deploying a Wi-Fi hotspot by the data traffic level of the cell in the cellular network, and the other is to be geographicalized.
  • the data traffic map is used to determine the latitude and longitude of the potential Wi-Fi hotspot location.
  • the embodiments of the present invention provide a method and a model for accurately selecting a P-segment of a Wi-Fi hotspot deployment plan to improve the accuracy of Wi-Fi hotspot deployment.
  • the technical solution adopted by the embodiment of the present invention is:
  • a Wi-Fi hotspot deployment plan P-segment precise selection method including:
  • the statistical data of each candidate area is evaluated according to the service type index, and the business evaluation score is obtained; and the statistical data of each candidate area is evaluated according to one or two types of the user behavior type indicator and the terminal type indicator, and the user is obtained.
  • a behavior evaluation score or/and a terminal penetration assessment score a summary calculation according to various evaluation scores corresponding to each candidate region, and obtaining a heat value for each candidate region;
  • a candidate area having a heat value greater than a set threshold is used as a Wi-Fi hotspot area.
  • a Wi-Fi hotspot deployment plan P-segment precise selection model including:
  • a data collection unit configured to collect statistics of each candidate area in the statistical period
  • the business indicator evaluation unit evaluates the statistical data of each candidate area according to the service type indicator, and obtains a business evaluation score
  • the other indicator evaluation unit is configured to evaluate the statistical data of each candidate region according to one or two types of the user behavior index and the terminal class index, and obtain a user behavior assessment score or/and a terminal penetration assessment score;
  • a calculation unit configured to perform a summary calculation according to the various evaluation scores of the corresponding candidate regions obtained by the service indicator evaluation unit and the other indicator evaluation unit, to obtain a heat value of each candidate region;
  • the hotspot area determining unit is configured to use the candidate area whose heat value is greater than the set threshold as the Wi-Fi hotspot area i or .
  • the precise selection method and model of the Wi-Fi hotspot deployment planning stage in the embodiment of the present invention comprehensively evaluate one or two types of service class indicators, user behavior indicators and terminal class indicators in each candidate region. Calculate the heat value of each candidate area based on the evaluation result, so as to quickly and accurately identify and determine the optimal Wi-Fi deployment location.
  • DRAWINGS In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the description of the prior art will be briefly described below. Obviously, the drawings in the following description It is merely some embodiments of the present invention, and those skilled in the art can obtain other drawings according to the drawings without any creative work.
  • FIG. 1 is a flowchart of a method for accurately selecting a segment of a Wi-Fi hotspot deployment plan P segment according to an embodiment of the present invention
  • FIG. 2 is a schematic structural diagram of a precise point selection model of a Wi-Fi hotspot deployment planning phase according to an embodiment of the present invention
  • FIG. 3 is another schematic structural diagram of a precise point selection model in a Wi-Fi hotspot deployment planning phase according to an embodiment of the present invention.
  • the Wi-Fi hotspot deployment plan of the Wi-Fi hotspot deployment plan P-segment precise selection method and model of the Wi-Fi hotspot deployment planning stage, the service type indicators for each candidate area, and the user behavior indicators or/and terminals
  • the statistical indicators of the class indicators are comprehensively evaluated by the model, and the heat value of each candidate area is calculated according to the evaluation result, thereby quickly and accurately identifying and determining the optimal Wi-Fi deployment place.
  • FIG. 1 it is a flowchart of a method for accurately selecting a Wi-Fi hotspot deployment planning stage according to an embodiment of the present invention, which includes the following steps:
  • Step 101 Collect statistics of each candidate area in the statistical period.
  • the above statistical period can be arbitrarily set, for example, three days to one week.
  • the candidate area refers to a candidate area of the existing network cellular network, and may be an area of a cell level or a grid level (for example, a grid of 25 meters x 25 meters, 50 meters x 50 meters, 100 meters x 100 meters, etc.).
  • the foregoing statistics may include any one or more of the following: data service statistics, user operation or operation and maintenance statistics, different brand terminal statistics, and of course, other data may be included as needed, for the embodiment of the present invention Not limited.
  • Step 102 The statistical data of each candidate area is evaluated according to the service type indicator, and the business evaluation score is obtained.
  • the service type indicator may include any one or more of the following: Line resource utilization, busy time data service traffic, data traffic channel bearer efficiency, data traffic channel congestion rate, data traffic channel resource occupancy rate, low value service ratio; for the grid level candidate area, the service type indicator may include Any of the above indicators can be refined to any one or more of the grid levels.
  • a threshold value needs to be set, and the value may be set by referring to the average value of the whole network, for example, it may be 1.2 times of the average value of the whole network, or may be based on the user.
  • the expectations are set to suit the needs of different scenarios.
  • the threshold value corresponding to the corresponding service type indicator in the statistical data of the candidate area may be first compared, if the threshold value is less than or equal to the corresponding threshold value, Then it is not considered, the business evaluation score is 0; if it is greater than the corresponding threshold, the evaluation score is calculated.
  • the evaluation score is the ratio of the actual statistical data to the threshold value corresponding to the corresponding business type indicator multiplied by a certain weight.
  • the weight of each indicator can be set according to actual needs. The greater the weight, the higher the importance of the indicator.
  • Step 103 Perform statistics on each candidate region according to one or two types of user behavior index and terminal class index, and obtain a user behavior assessment score or/and a terminal penetration assessment score.
  • the user behavior indicator may include any one or more of the following: proportion of high-end users, proportion of users using Wi-Fi data packages, proportion of users who frequently use Wi-Fi, use The average data traffic of users who do not support Wi-Fi terminals, the average data traffic of users who use Wi-Fi terminals, and the total PS (Packet Switch) traffic contributed by Wi-Fi-enabled terminals account for total cell traffic.
  • the terminal class indicator may include any one or more of the following: a Wi-Fi-enabled terminal ratio, a star terminal ratio, and a data card ratio.
  • a threshold value needs to be set, and the value can be set by referring to the average value of the whole network, for example, it can be 1.2 times of the average value of the whole network, or can be performed according to the expectation of the user. Set to suit different scenes.
  • the statistical data related to the user behavior in the statistical data of the candidate region may be first compared with the threshold value of the corresponding user behavior index, if less than Or equal to the corresponding user behavior class indicator corresponding to the gate The limit is not considered, the business evaluation score is 0; if it is greater than the corresponding threshold, the evaluation score is calculated.
  • the evaluation score is the ratio of the actual statistical data to the corresponding threshold value multiplied by a certain weight.
  • the weight of each indicator can be set according to actual needs. The greater the weight, the higher the importance of the indicator.
  • Step 104 Perform a summary calculation according to each type of evaluation score corresponding to each candidate area, and obtain a heat value of each candidate area.
  • the evaluation scores of the different types of index items may be weighted and averaged to obtain the heat value of the corresponding candidate region.
  • the weight of the evaluation score of each indicator item can be set according to actual needs.
  • the weights corresponding to different indicator items can be the same or different. Of course, it can be positive or negative.
  • Step 105 A candidate area whose heat value is greater than the set threshold is used as a Wi-Fi hotspot area.
  • the Wi-Fi potential hotspot distribution map may be generated according to the heat value of each candidate region, thereby more intuitively reflecting the Wi-Fi potential hotspot distribution.
  • Example 1 Candidate area for cell granularity Indicators Indicator Threshold Determination Weight and score meter Indicator description
  • the average channel bearer efficiency of the cell is different, such as the value *0.8 GSM can use the downlink throughput rate of the RLC layer, here is not Make a mandatory limit.
  • the average channel congestion rate of the cell is different, such as the value *1.2 GSM can use the TBF congestion rate, here is not mandatory limited.
  • the average channel resource occupancy rate of the cell is not defined as *1.2.
  • the WCDMA standard can use the code of the H carrier. Resource utilization, there is no mandatory limit here.
  • Low-value service refers to multi-valued *1.2 services suitable for the average WLAN network in the cell, and refers to the video and download services of the Internet.
  • the definition of high-end users compares the behavior of the cell to the average flexibility, such as the high ARPU value with the analysis value *1.2 households.
  • the average cell definition is flexible, for example, 30 values*1.2 days using Wi-Fi more than 5 times
  • the flat channel congestion rate of the grid is different, such as the average value * 1.2 GSM can use the TBF congestion rate, do not do here Mandatory.
  • Low value service refers to the average value of the flat WLAN network that is suitable for the grid. *1.2, which refers to the Internet video, download service, etc.
  • the fixed user is defined as the number of the number of behaviors.
  • the average value of the grid is analyzed by the average value of 1.2. More than a certain number of users.
  • the grid is flexible, such as high ARPU value with mean * 1.2 households.
  • the flat definition of the grid is more flexible, for example, 30 averages * Wisdom within 1.2 days -Fi users over 5 times
  • the heat value of each candidate area obtained in the above step 104 may be corrected, for example, the score integrity parameter is used as a correction.
  • Coefficient, ie heat value* score integrity parameter corrected heat value. Then, the Wi-Fi hotspot area is determined according to the heat value after the correction.
  • the above-mentioned principle of setting the score integrity parameter can be embodied by one or more aspects, such as: (1) Excluding the abnormal data hotspots caused by the extreme data service behavior of a few users, for example, if the top 5 of the community The user contributes 95% of the data traffic, and the score integrity parameter value is 0.
  • the specific principles can be reasonably adjusted according to the actual situation of the network.
  • the score integrity parameter value is 1; if none of the indicators in the class has no score, the score integrity The parameter value is 2/3; if there are no indicators in each of the two types of indicators, the score integrity parameter value is 1/3.
  • the specific principles can be reasonably adjusted according to the actual situation of the network.
  • the statistical data of each candidate area collected is based on one of a service indicator, a user behavior indicator, and a terminal penetration index.
  • the two types are comprehensively evaluated, and the heat value of each candidate area is calculated according to the evaluation result, thereby quickly and accurately identifying and determining the optimal Wi-Fi deployment location.
  • the embodiment of the present invention further provides a precise selection point model of a Wi-Fi hotspot deployment planning stage, as shown in FIG. 2, which is a structural schematic diagram of the model.
  • the model includes:
  • the data collection unit 201 is configured to collect statistical data of each candidate area in the statistical period; the service indicator evaluation unit 202 estimates the statistical data of each candidate area according to the service type indicator, and obtains a service evaluation score;
  • the other indicator evaluation unit 203 is configured to evaluate statistical data of each candidate region according to one or two types of user behavior index and terminal class index, and obtain a user behavior assessment score or/and a terminal penetration assessment score. ;
  • the calculating unit 204 is configured to perform a summary calculation on each type of evaluation scores corresponding to each candidate area obtained by the service indicator evaluation unit 202 and the other indicator evaluation unit 203, to obtain a heat value of each candidate area;
  • the hotspot area determining unit 205 is configured to use the candidate area whose heat value is greater than the set threshold as the Wi-Fi hotspot area i or .
  • the above statistical period can be arbitrarily set, for example, three days to one week.
  • the candidate area refers to a candidate area of an existing network cellular network, and may be a cell level or a grid level area.
  • the foregoing statistics may include any one or more of the following: data service statistics, user operation or operation and maintenance statistics, different brand terminal statistics, and of course, other data may be included as needed, for the embodiment of the present invention Not limited.
  • the above-mentioned various types of indicators may be determined according to factors such as the geographical location of the candidate area, the environment, and the like, and different indicator items are set for the candidate areas of different granularities.
  • factors such as the geographical location of the candidate area, the environment, and the like, and different indicator items are set for the candidate areas of different granularities.
  • Wi-Fi hotspot deployment plan P of the previous embodiment of the present invention The description in the precise selection method of the segment will not be repeated here.
  • the service metrics evaluation unit 202 may first perform the threshold value corresponding to the service-related statistical data and the corresponding service type indicator in the statistical data of the candidate area. If the value is less than or equal to the corresponding threshold, it is not considered, and the service evaluation score is 0. If it is greater than the corresponding threshold, the evaluation score is calculated.
  • the specific calculation method can be various, for example, it can be actual statistics.
  • the ratio of the threshold value to the corresponding threshold value, or the difference between the actual statistical data and the corresponding threshold value, is not limited in this embodiment of the present invention.
  • the process in which the other indicator evaluation unit 203 evaluates the statistical data of each candidate region according to one or both of the user behavior type index and the terminal type index is similar to the processing of the above-described business index evaluation unit 202, where No longer praise.
  • the calculating unit 204 is specifically configured to perform weighted averaging on each evaluation score of each candidate region to obtain a heat value of the corresponding candidate region.
  • the weight of the evaluation score of each indicator item can be set according to actual needs.
  • the weights corresponding to different indicator items may be the same or different. Of course, it may be a positive number or a negative number.
  • the statistical data of each candidate area collected is based on one of a service indicator, a user behavior indicator, and a terminal penetration index.
  • the two are comprehensively evaluated, and the heat value of each candidate area is calculated according to the evaluation result, so that the optimal Wi-Fi deployment location can be quickly and accurately identified and determined.
  • FIG. 3 it is another structural schematic diagram of a precise point selection model in the Wi-Fi hotspot deployment planning stage according to the embodiment of the present invention.
  • the model further includes: a distribution map generating unit 301, configured to generate a Wi-Fi hotspot distribution according to the heat value of each candidate region obtained by the calculating unit 204.
  • a distribution map generating unit 301 configured to generate a Wi-Fi hotspot distribution according to the heat value of each candidate region obtained by the calculating unit 204.

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  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

L'invention concerne un procédé pour sélectionner précisément un point en phase de planification de déploiement de point d'accès sans fil Wi-Fi et un modèle. Le procédé consiste à : collecter des données statistiques de chaque zone candidate dans une période de statistiques; évaluer les données statistiques de chaque zone candidate en fonction d'un indicateur de classe de service afin d'obtenir un score d'évaluation de service; évaluer les données statistiques de chaque zone candidate en fonction d'une classe de comportement d'utilisateur et/ou d'une classe de terminal afin d'obtenir un score d'évaluation de comportement d'utilisateur et/ou un score d'évaluation de taux de pénétration de terminal; effectuer un calcul de résumé en fonction du score d'évaluation de chaque classe correspondant à chaque zone candidate afin d'obtenir une valeur de chaleur de chaque zone candidate; et prendre une zone candidate dont la valeur de chaleur est supérieure à une valeur seuil à titre de zone de point d'accès sans fil Wi-Fi.
PCT/CN2011/082943 2011-11-25 2011-11-25 Procédé pour sélectionner précisément un point en phase de planification de déploiement de point d'accès sans fil wi-fi, et modèle WO2013075330A1 (fr)

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Application Number Priority Date Filing Date Title
PCT/CN2011/082943 WO2013075330A1 (fr) 2011-11-25 2011-11-25 Procédé pour sélectionner précisément un point en phase de planification de déploiement de point d'accès sans fil wi-fi, et modèle
CN2011800025361A CN102726089A (zh) 2011-11-25 2011-11-25 Wi-Fi热点部署规划阶段的精确选点方法及模型

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CN102932848B (zh) * 2012-10-11 2015-07-08 北京拓明科技有限公司 一种基于栅格辅助的网络分流方法
CN103796218B (zh) * 2012-11-01 2018-10-12 中国移动通信集团山西有限公司 无线接入点的选址方法及装置
CN102984712A (zh) * 2012-11-02 2013-03-20 王攀 一种基于移动互联网业务感知的wlan精确建站方法
CN103916866A (zh) * 2012-12-29 2014-07-09 中国移动通信集团河北有限公司 一种wlan选址的方法及装置
CN104144429B (zh) * 2013-05-10 2018-03-23 中国电信股份有限公司 Wifi热点选址决策方法和系统
CN104185308B (zh) 2013-05-23 2018-05-29 华为技术有限公司 小区的业务热点检测方法及装置
CN104754590B (zh) * 2013-12-31 2018-10-26 中国移动通信集团山东有限公司 一种评估长期演进lte网络站址的方法及装置
CN104581743B (zh) * 2015-01-04 2018-06-26 中国联合网络通信集团有限公司 一种实现wlan部署的方法及装置
CN105873079A (zh) * 2015-01-23 2016-08-17 中国移动通信集团浙江有限公司 网络规划方法及装置
CN104754591B (zh) * 2015-02-28 2018-12-28 重庆信科设计有限公司 一种应用于网络布局规划的热点用户移动行为分析方法
CN106714202B (zh) * 2015-11-16 2020-08-14 中国移动通信集团公司 一种网络容量优化方法及装置
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