WO2021008393A1 - 一种无线小区的覆盖黑洞识别方法及系统 - Google Patents

一种无线小区的覆盖黑洞识别方法及系统 Download PDF

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WO2021008393A1
WO2021008393A1 PCT/CN2020/100249 CN2020100249W WO2021008393A1 WO 2021008393 A1 WO2021008393 A1 WO 2021008393A1 CN 2020100249 W CN2020100249 W CN 2020100249W WO 2021008393 A1 WO2021008393 A1 WO 2021008393A1
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event
black hole
cell
coverage
wireless cell
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PCT/CN2020/100249
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English (en)
French (fr)
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杨伟伟
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中兴通讯股份有限公司
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/08—Testing, supervising or monitoring using real traffic
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/04—Arrangements for maintaining operational condition
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H04W4/029—Location-based management or tracking services
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management

Definitions

  • the embodiments of the present disclosure relate to, but are not limited to, the field of communication technologies, and in particular to a method and system for identifying coverage black holes of wireless cells.
  • MR Measurement Report
  • the MR method has a large amount of data and limited data collection time, resulting in poor real-time judgment results; secondly, the MR method is mainly based on network signal-related indicators for judgment, and good network signal-related indicators do not represent user perception.
  • the wireless equipment of operators generally comes from multiple vendors, and multi-vendor systems are required to monitor the entire network through MR. It is difficult to implement the entire network monitoring by requiring all vendors to report data according to the same interface standard; in addition, , When there is no signal, it is basically impossible to report MR data, which makes it impossible to judge the cell coverage.
  • the embodiments of the present disclosure provide a coverage black hole identification method and system for a wireless cell, which can detect the coverage black hole problem of the wireless cell in time and locate the coverage black hole position, thereby greatly reducing the operation and maintenance cost of the wireless network.
  • the embodiments of the present disclosure provide a coverage black hole identification method for a wireless cell, which includes: collecting control plane data and user plane data in a detection area through a probe deployed between an access network and a core network; The obtained control plane data and user plane data identify the wireless cell covering the black hole in the detection area and the location information of the covering black hole in the wireless cell.
  • an embodiment of the present disclosure provides a coverage black hole identification system for a wireless cell, which includes: a data acquisition module configured to collect control plane data in the detection area through a probe deployed between the access network and the core network And user plane data; a processing module configured to identify a wireless cell covering a black hole in the detection area and location information of the covering black hole in the wireless cell based on the collected control plane data and user plane data.
  • embodiments of the present disclosure provide a computer-readable storage medium that stores a computer program that, when executed, implements the steps of the above-mentioned coverage black hole identification method for a wireless cell.
  • a computer program that, when executed, implements the steps of the above-mentioned coverage black hole identification method for a wireless cell.
  • FIG 1 is a networking diagram of part of the process of the Long Term Evolution (LTE) Core Network (Evolved Packet Core, EPC);
  • LTE Long Term Evolution
  • EPC Evolution Core Network
  • FIG. 2 is a flowchart of a method for identifying a coverage black hole of a wireless cell provided by an embodiment of the disclosure
  • FIG. 3 is an example diagram of a coverage black hole cell identification and a coverage black hole location identification process of a wireless cell provided by an embodiment of the disclosure
  • Figure 4 is a schematic diagram of the OTT positioning principle
  • FIG. 5 is an example diagram of an OTT location recognition method based on a time window in an embodiment of the disclosure
  • FIG. 6 is an example diagram of a covered black hole position obtained by a covered black hole identification method provided by an embodiment of the present disclosure
  • FIG. 7 is another example diagram of a covered black hole position obtained by the covered black hole identification method provided by an embodiment of the present disclosure.
  • FIG. 8 is a schematic diagram of a coverage black hole identification system of a wireless cell provided by an embodiment of the disclosure.
  • the embodiments of the present disclosure provide a wireless cell coverage black hole identification method and system.
  • the wireless cell covering the black hole in the detection area and the location of the covering black hole in the wireless cell are identified Information to support the proactive and timely discovery of coverage black holes in wireless cells, thereby providing precise positioning for wireless network optimization, narrowing the test range, and greatly reducing the operation and maintenance costs of wireless networks.
  • the coverage black hole identification method and system of a wireless cell provided by the embodiments of the present disclosure can be applied to an LTE system.
  • the embodiment of the present disclosure does not limit this.
  • the embodiments of the present disclosure can also be applied to other communication systems, such as a fifth-generation mobile communication technology (5G) new air interface communication system.
  • 5G fifth-generation mobile communication technology
  • FIG. 1 is a schematic diagram of the networking of part of the LTE EPC process. It should be noted that FIG. 1 only shows a part of the networking diagram related to the embodiment of the present disclosure.
  • the LTE radio access network includes a radio base station (eNodeB), and LTE EPC includes a mobility management entity (Mobility Management Entity, referred to as MME), a serving gateway (Serving Gateway, referred to as SGW), and packet data.
  • MME mobility management entity
  • SGW serving gateway
  • PDN Gateway PDN Gateway
  • the MME is a network element responsible for processing signaling in the core network, and is a signaling entity, mainly responsible for functions such as mobility management, bearer management, user authentication and authentication, SGW and PGW selection.
  • SGW is mainly responsible for user plane processing, responsible for data packet routing and forwarding functions, and supports the switching of different access technologies of the Third Generation Partnership Project (3GPP), and acts as the anchor point of the user plane when switching occurs For each user terminal (User Equipment, UE for short) related to the Evolved Packet System (EPS), at a time point, there is an SGW to serve it.
  • 3GPP Third Generation Partnership Project
  • S10 interface is an interface between any two MMEs
  • S11 is an interface between MME and SGW
  • S5 or S8 is an interface between SGW and PGW.
  • S1-MME and S1-U are the two main interfaces of the EPC network
  • S1-MME is the interface between eNodeB and MME
  • S1-U is the interface between eNodeB and SGW.
  • the user terminal accesses the SGW from the radio base station.
  • the first probe is connected between the radio base station and the SGW to collect LTE data services S1-U interface user plane data.
  • the user terminal accesses the MME from the wireless base station.
  • the second probe is connected between the wireless base station and the MME to collect the control plane data of the S1-MME port of the LTE data service.
  • the wireless cells with coverage black holes in the entire network can be preliminarily identified, and based on the spatial clustering machine learning model, the locations of the coverage black holes of the wireless cells can be further identified.
  • the wireless network optimization department can be provided with a clear network optimization object, thereby greatly reducing the operation and maintenance cost of the wireless network.
  • FIG. 2 is a flowchart of a method for identifying a coverage black hole of a wireless cell provided by an embodiment of the disclosure. As shown in FIG. 2, the covering black hole identification method provided in this embodiment includes:
  • S202 According to the collected control plane data and user plane data, identify the wireless cell covering the black hole in the detection area and the location information of the covering black hole in the wireless cell.
  • the coverage black hole can refer to the network coverage area where the user terminal cannot normally access the current network system, and it can also be called the coverage blind spot.
  • a wireless cell with a covering black hole can be referred to as a covering black hole cell for short.
  • the detection area can be determined according to the deployment range of the probe, for example, a city, a province, etc.
  • the embodiment of the present disclosure does not limit this.
  • S202 may include: filtering out the first event redirected from the first mobile communication system to the second mobile communication system within the first time period according to the collected control plane data, where the first event The network quality of the mobile communication system is higher than the network quality of the second mobile communication system; based on the first event in the first time period, identify the wireless cell covering the black hole in the detection area; according to the collected user plane data and the first time The first event in the segment determines the location information of the coverage black hole in the wireless cell.
  • the first mobile communication system may be a fourth-generation mobile communication technology (4G) system
  • the second mobile communication system may be a third-generation mobile communication technology (3G) system or a second-generation mobile communication technology (2G) system
  • the first mobile communication system can be a fifth-generation mobile communication technology (5G) system
  • the second mobile communication system can be a 2G system, a 3G system, or a 4G system.
  • 5G fifth-generation mobile communication technology
  • the embodiment of the present disclosure does not limit this.
  • the first time period can be set according to actual needs, for example, one day or one week.
  • the embodiment of the present disclosure does not limit this.
  • identifying a wireless cell covering a black hole in the detection area may include:
  • the single redirection duration corresponding to the first event can be obtained by subtracting the time point of the first event from the time point of the second event associated with the first event.
  • the number of the first event that the user has occurred in a certain wireless cell during the first time period, or the number of occurrences of the first event and the redirection stay duration is used as a basic indicator for determining the coverage black hole cell to identify the coverage black hole cell.
  • identify the wireless cell that covers the black hole in the detection area Can include:
  • first threshold to the fourth threshold may be set according to actual requirements, which is not limited in the embodiment of the present disclosure.
  • the first time period and the second time period can be set according to actual needs, for example, the first time period can be one week, and the second time period can be one day.
  • the embodiment of the present disclosure does not limit this.
  • identify the wireless cell that covers the black hole in the detection area Can include:
  • the number of users whose number of times the first event is met in the first time period is greater than the fifth threshold and the redirection stay duration is greater than the sixth threshold, as the number of users who are not satisfied with cell coverage; According to the number of users who are not satisfied with the cell coverage and the total number of users in the first time period, the proportion of users who are not satisfied with the cell coverage in the first time period is calculated, and the proportion of users who are not satisfied with the cell coverage in the first time period is calculated.
  • a cell larger than the seventh threshold is identified as a wireless cell covering a black hole.
  • the fifth threshold to the seventh threshold may be set according to actual requirements, which is not limited in the embodiment of the present disclosure.
  • determining the location information of the coverage black hole in the wireless cell based on the collected user plane data and the first event in the first time period may include: obtaining and reporting from the collected user plane data OTT location bill; for the wireless cell identified to have coverage black holes, based on the time window, associate the first event in the wireless cell within the first time period with the bill reporting the OTT location, and determine the OTT associated with the first event Location; the OTT location associated with the first event in the wireless cell is summarized and clustered to obtain the location information of the coverage black hole in the wireless cell.
  • OTT Over The Top
  • OTT refers to various services provided to users via the Internet.
  • the services developed by Internet companies using operators' broadband networks can be called OTT applications.
  • Some OTT service providers provide users with positioning and navigation services.
  • Applications Apps, referred to as APPs
  • APPs may report location information in plain text. Based on this, the latitude and longitude information can be extracted to describe the user's movement track. Since the latitude and longitude information obtained by this positioning method comes from OTT applications, it is called OTT positioning.
  • the OTT associated with the first event is filtered from the user plane data.
  • the location can provide a data basis for identifying the location of the coverage black hole, thereby further outputting the location of the coverage black hole of the wireless cell.
  • the first event in the wireless cell in the first time period is associated with the bill for reporting the OTT location, and the first event is determined to be associated
  • the OTT location may include: for any first event in the wireless cell in the first time period, searching for the time point closest to the first event within a time window determined by using the time point of the first event as a reference point
  • the OTT location reported in the CDR is determined as the OTT location associated with the first event.
  • the time window determined by using the time point of the first event as a reference point may include: a time window obtained by setting the time length forward with the time point of the first event as the end point, or the time point of the first event The time window obtained by the first set duration forward and the second set duration backward.
  • the embodiment of the present disclosure does not limit this.
  • the OTT locations associated with the first event in the wireless cell are summarized and clustered to obtain the location information of the coverage black hole in the wireless cell, which may include: In the cell, the OTT location associated with the first event in the wireless cell is subjected to coordinate system one, and then the machine learning model based on the clustering algorithm is input to obtain the location information of the covered black hole.
  • the redirected OTT location information of all users in the cell during the first time period is summarized according to the latitude and longitude, and the spatial location correlation analysis is performed, and the adjacent location points
  • the clustering algorithm-based machine learning model is clustered into one category. After removing outliers, multiple clustered locations are obtained as multiple covering black holes in the cell, and the center coordinates of each covering black hole are given. Assist network optimization personnel in optimization and positioning.
  • the probes deployed between the access network and the core network include: the first probe deployed between the radio base station (eNodeB) and the mobility management entity (MME), the first probe deployed between the radio base station and The second probe between the service gateways (SGW); wherein the control plane data collected by the first probe includes: S1-MME port data; the user plane data collected by the second probe includes: S1-U port data.
  • eNodeB radio base station
  • MME mobility management entity
  • SGW service gateways
  • the first probe may transmit the collected control plane data to the covering black hole identification system
  • the second probe may transmit the collected user plane data to the covering black hole identification system
  • the black hole recognition system performs data processing to identify the coverage black hole cell and the location information of the coverage black hole in the detection area.
  • the coverage black hole recognition system can be deployed on a server, or can be deployed in a server cluster.
  • the embodiment of the present disclosure does not limit this.
  • FIG. 3 is an exemplary diagram of a coverage black hole cell identification and a coverage black hole location identification process of a wireless cell provided by an exemplary embodiment of the present disclosure.
  • the collected data of the first time period Q can be analyzed, and then the covering black hole cell can be identified according to the covering black hole cell determination rule.
  • the first time period Q may be seven days, and the second time period may be one day.
  • the coverage black hole cell determination rule may include: the coverage black hole cell requirement meets the coverage black hole problem on three or more days in the previous seven days, and the number of unsatisfied users on these days with problems is not less than R 4 (corresponding to The fourth threshold above); among them, the coverage hole problem in the cell on a certain day is defined as the proportion of users with unsatisfactory coverage in the cell that is greater than R 3 (corresponding to the third threshold above); users with unsatisfactory coverage in the cell on a certain day It is defined as a user whose number of occurrences of the first event in the cell that day is greater than R 1 (corresponding to the above-mentioned first threshold) and redirected stay longer than R 2 seconds (corresponding to the above-mentioned second threshold).
  • the identification process of the covered black hole cell includes:
  • the judgment condition of the first event may include: UE Context Release, and the reason is interrat-redirection.
  • the judgment condition of the second event may include: Tracking Area Update (TAU) or ATTACH event that occurs for the first time after the first event (4G system is redirected to 2G system).
  • TAU Tracking Area Update
  • ATTACH event that occurs for the first time after the first event (4G system is redirected to 2G system).
  • the first event may include: an event redirected from a 4G system to a 2G system, and an event redirected from a 4G system to a 3G system.
  • the embodiment of the present disclosure does not limit this.
  • the records associated with the first event and the second event include IMSI, cell ECI, time point time_src of the first event (4G system redirection to 2G system event), and time of the second event (2G system return to 4G system event) Click time_dst.
  • the residence time of a single redirection of the first event can be obtained by subtracting time_src from time_dst.
  • S303 Associating the results of S302, gather them according to the two dimensions of IMSI and cell ECI, and calculate the number of the first event (4G system redirection to 2G system event) of user IMSI j in a certain cell ECI i in a day and Redirection duration.
  • the number of the first event can be obtained by counting the number of records in the dialog, and the redirection duration can be obtained by subtracting time_src from time_dst and then summing.
  • the number of the first event of the user IMSI j and the redirection duration can be described by the following formula:
  • the redirection residency duration of the user IMSI j SUM(time_dst i- time_src i ).
  • the results obtained in S303 are aggregated according to the cell ECI dimension, and it can be obtained that the number of the first event (4G system redirection to 2G system event) occurring in any wireless cell in a day is greater than R 1 and the redirection stay duration
  • the number of users greater than R 2 seconds that is, the number of users who are not satisfied with the coverage defined in the black hole cell determination rule
  • the proportion of users with unsatisfactory coverage in the cell on the day is equal to the ratio of the number of users with unsatisfactory coverage in the cell currently and the total number of users in the cell that day.
  • S305 Repeat the calculation of the number of users with dissatisfied coverage and the proportion of users with dissatisfied coverage in each wireless cell of the entire network for seven days according to the steps from S301 to S304.
  • the analysis process in this example is as follows: read the control surface S1-MME port data collected by the probe on June 3, 2019 and the previous 6 days, and output June 3, 2019 according to the coverage black hole cell identification steps (S301 to S306) List of coverage black hole cells, and output the average of the percentage of unsatisfied users with coverage and the number of days with coverage black hole problems in each cell within seven days, sorted in descending order according to the average percentage of unsatisfied users with coverage, and take the top 50 Record, that is, get the list of Top50 covered black hole cells and corresponding indicators in the city, and then dispatch the order to solve it.
  • the OTT location when the user occurs when the first event occurs may be calculated based on the time window.
  • FIG. 4 is a schematic diagram of the OTT positioning principle. As shown in Figure 4, the OTT positioning principle is as follows:
  • APP application for example, APP mobile phone terminal
  • the APP application can access the map server through the Application Programming Interface (API for short).
  • API Application Programming Interface
  • the map server After receiving the encrypted positioning request, the map server sends the longitude and latitude information to the APP mobile phone in the form of a compressed packet in the downstream http 200OK response in the post mode of the http protocol after calculation; among them, the S1-U interface
  • the latitude and longitude information is decoded in the payload of the http original code stream in.
  • the APP application reports the longitude and latitude information to its own server in the form of clear text in the uplink Uniform Resource Location (URL) in the get mode of the http protocol. Server side); Among them, the latitude and longitude information can be directly extracted from the Uniform Resource Identifier (URI) field of the http type XDR file in the S1-U interface.
  • URI Uniform Resource Identifier
  • the longitude and latitude information of the APP mobile terminal can be obtained. Since the acquired longitude and latitude information comes from OTT applications, it can be called OTT positioning.
  • obtaining the user redirection position based on the time window can provide a data basis for the coverage black hole position identification , So as to achieve coverage black hole recognition.
  • FIG. 5 is an example diagram of an OTT location recognition method based on a time window in an embodiment of the disclosure. As shown in Figure 3 and Figure 5, based on the time window, the process of calculating the OTT location when the user is redirected from the 4G system to the 2G system is as follows:
  • S501 Filter out the bills that report OTT location information (for example, information such as longitude, latitude, and coordinate system) from the XDR detailed list of the first time period of the S1-U port.
  • OTT location information for example, information such as longitude, latitude, and coordinate system
  • the TN before the time point of the bill of the first event is used as the time window to find the distance within the time window.
  • the most recent bill of the OTT location is reported, and the found OTT location is determined as the OTT location of the first event.
  • Fig. 5 shows an example of OTT locations corresponding to two first events.
  • the time window of TN before the time point of the bill of the first event is used as the time window.
  • the embodiment of the present disclosure does not limit this.
  • the T N1 duration forward and the backward T N2 duration of the time point of the bill of the first event may be used as the time window.
  • the location information of the covering black hole may be obtained based on the machine learning model of the clustering algorithm.
  • the process of obtaining the location information of the covered black hole by the machine learning model based on the clustering algorithm may include:
  • S601. Determine the OTT location (e.g., including the 4G system redirection to the 2G system event) of a user in a covered black hole cell during the first time period (for example, the first 7 days) when the first event (4G system is redirected to the 2G system event) by means of time window Information such as longitude, latitude and coordinate system).
  • time window Information such as longitude, latitude and coordinate system
  • S602. Convert the latitude and longitude of the different coordinate systems in the result of S601 to the latitude and longitude information under a unified coordinate system (such as unified conversion to the GCJ-02 Mars coordinate system). After the conversion, the latitude and longitude can be gathered according to the latitude and longitude, and different latitude and longitude can be calculated The number of redirects and the number of redirected users.
  • a unified coordinate system such as unified conversion to the GCJ-02 Mars coordinate system.
  • S603. Use the latitude and longitude information obtained in S601 and S602 for a certain covered black hole cell as input data and input the machine learning model to obtain a clustering result.
  • the clustering result automatically divides different longitude and latitude coordinates into multiple groups.
  • each set of latitude and longitude coordinates corresponds to a covering black hole in Figure 6.
  • a density-based clustering algorithm is required for model training to obtain a machine learning model suitable for this example.
  • the density-based clustering algorithm can use the latitude and longitude information obtained in the manner of S601 and S602 as the input features of the clustering algorithm, training and adjusting the input parameters, and the expected clustering algorithm model can be obtained as the machine of this example. Learning model.
  • the number of redirects and the number of redirected users at different latitudes and longitudes can be calculated through S602, and the total number of redirects and the number of redirected users that occur under each coverage black hole can be calculated backward.
  • the severity level of the coverage black hole Based on this judgment, the severity level of the coverage black hole.
  • the judgment condition regarding the severity level can be set according to requirements, which is not limited in the embodiment of the present disclosure.
  • an optimization solution may be determined.
  • the operator’s network optimization staff can adjust the azimuth angle, antenna height, and increase base stations for targeted optimization.
  • the location of the coverage black hole corresponding to the cell may be as shown in FIG. 6.
  • the location of the coverage black hole corresponding to the cell may be as shown in FIG. 7.
  • probes are deployed between the radio base station and the core network element MME, and the radio base station and the core network gateway SGW, respectively, to obtain the LTE data service entire network control plane S1-MME Port data and user plane S1-U port data, and then combine the number of times the 4G system is redirected to the 2G system in the S1-MME port data and the 4G system redirection residence time to identify the coverage black hole cell; for a coverage black hole cell .
  • the clustering algorithm is used to obtain multiple coverage black hole cells.
  • the embodiments of the present disclosure can actively discover the coverage black hole problem of the entire network's wireless cells that affect the user's real Internet perception, and can output the coverage black hole location of the problem cell and expose it to the operator to provide the operator with the wireless network Optimization provides precise positioning, reduces the scope of testing, thereby reducing maintenance costs.
  • FIG. 8 is a schematic diagram of a coverage black hole identification system of a wireless cell provided by an embodiment of the disclosure.
  • the covering black hole identification system provided by the embodiment of the present disclosure includes:
  • the data acquisition module 801 is configured to collect control plane data and user plane data in the detection area through a probe deployed between the access network and the core network;
  • the processing module 802 is configured to identify the wireless cell covering the black hole in the detection area and the location information of the covering black hole in the wireless cell based on the collected control plane data and user plane data.
  • embodiments of the present disclosure also provide a computer-readable storage medium storing a computer program that, when executed, implements the steps of the above-mentioned covering black hole identification method, such as the steps shown in FIG. 2.
  • Such software may be distributed on a computer-readable medium
  • the computer-readable medium may include a computer storage medium (or non-transitory medium) and a communication medium (or transitory medium).
  • the term computer storage medium includes volatile and non-volatile memory implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data).
  • Computer storage media include but are not limited to RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disk (DVD) or other optical disk storage, magnetic cassette, tape, magnetic disk storage or other magnetic storage device, or Any other medium used to store desired information and that can be accessed by a computer.
  • communication media usually contain computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as carrier waves or other transmission mechanisms, and may include any information delivery media .
  • the black hole problem provides precise positioning for wireless network optimization, reduces the test range, and greatly reduces the operation and maintenance costs of the wireless network.

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Abstract

本公开实施例提供了一种无线小区的覆盖黑洞识别方法及系统，上述方法包括：通过部署在接入网与核心网之间的探针，采集检测区域内的控制面数据和用户面数据；根据采集到的控制面数据和用户面数据，识别检测区域内存在覆盖黑洞的无线小区以及无线小区内覆盖黑洞的位置信息。本公开实施例可以及时发现无线小区的覆盖黑洞问题，并定位覆盖黑洞位置，从而大大降低无线网络的运行维护成本。

Description

一种无线小区的覆盖黑洞识别方法及系统 技术领域
本公开实施例涉及但不限于通信技术领域，尤指一种无线小区的覆盖黑洞识别方法及系统。
随着现代通信科技的高速发展，用户对网络质量的要求越来越高，通信覆盖要求日益极端化，除了城市、室内需要覆盖，荒山野林也需要覆盖。在当前各大运营商通信系统中，无线通信网的年维护费用及人力成本非常高。
在传统方式中，通过测量报告(Measurement Report，简称为MR)方式来判断小区的覆盖问题。然而，MR方式的数据量大，且数据采集时间有限，导致判断结果的实时性不佳；其次，MR方式主要基于网络信号相关指标进行判断，而网络信号相关指标好并不能代表用户感知就好；另外，运营商的无线设备一般均来自多个厂商，通过MR方式进行全网监控需要使用多厂商系统，而要求所有厂商按照相同的接口标准上报数据进行全网监控是较难实现的；此外，在没有信号时基本无法上报MR数据，导致无法判断小区覆盖情况。
发明内容
本公开实施例提供了一种无线小区的覆盖黑洞识别方法及系统，可以及时发现无线小区的覆盖黑洞问题，并定位覆盖黑洞位置，从而大大降低无线网络的运行维护成本。
一方面，本公开实施例提供一种无线小区的覆盖黑洞识别方法，包括：通过部署在接入网与核心网之间的探针，采集检测区域内的控制面数据和用户面数据；根据采集到的控制面数据和用户面数据，识别所述检测区域内存在覆盖黑洞的无线小区以及所述无线小区内覆盖黑洞的位置信息。
另一方面，本公开实施例提供一种无线小区的覆盖黑洞识别系统，包 括：数据获取模块，设置为通过部署在接入网与核心网之间的探针，采集检测区域内的控制面数据和用户面数据；处理模块，设置为根据采集到的控制面数据和用户面数据，识别所述检测区域内存在覆盖黑洞的无线小区以及所述无线小区内覆盖黑洞的位置信息。
另一方面，本公开实施例提供一种计算机可读存储介质，存储有计算机程序，所述计算机程序被执行时实现如上所述的无线小区的覆盖黑洞识别方法的步骤。在本公开实施例中，通过分析探针采集到的控制面数据和用户面数据，可以识别检测区域内存在覆盖黑洞的无线小区，并识别出无线小区的覆盖黑洞的位置，从而可以及时发现无线小区的覆盖黑洞问题，进而给无线网络优化提供精准定位，缩小了测试范围，大大降低了无线网络的运行维护成本。
本公开实施例的其它特征和优点将在随后的说明书中阐述，并且，部分地从说明书中变得显而易见，或者通过实施本公开实施例而了解。本公开实施例的目的和其他优点可通过在说明书、权利要求书以及附图中所特别指出的结构来实现和获得。
附图说明
附图用来提供对本公开实施例技术方案的进一步理解，并且构成说明书的一部分，与本公开的实施例一起用于解释本公开的技术方案，并不构成对本公开实施例技术方案的限制。
图1为长期演进(Long Term Evolution，简称为LTE)核心网(Evolved Packet Core，简称为EPC)部分流程的组网示意图；
图2为本公开实施例提供的无线小区的覆盖黑洞识别方法的流程图；
图3为本公开实施例提供的无线小区的覆盖黑洞小区识别和覆盖黑洞位置识别过程的示例图；
图4为OTT定位原理的示意图；
图5为本公开实施例中基于时间窗的OTT位置识别方式的示例图；
图6为通过本公开实施例提供的覆盖黑洞识别方法得到的覆盖黑洞位置的一种示例图；
图7为通过本公开实施例提供的覆盖黑洞识别方法得到的覆盖黑洞位置的另一种示例图；
图8为本公开实施例提供的一种无线小区的覆盖黑洞识别系统的示意图。
具体实施方式
为使本公开实施例的目的、技术方案和优点更加清楚明白，下文中将结合附图对本公开的实施例进行详细说明。需要说明的是，在不冲突的情况下，本公开中的实施例及实施例中的特征可以相互任意组合。
在附图的流程图示出的步骤可以在诸如一组计算机可执行指令的计算机系统中执行。并且，虽然在流程图中示出了逻辑顺序，但是在某些情况下，可以以不同于此处的顺序执行所示出或描述的步骤。
本公开实施例提供一种无线小区的覆盖黑洞识别方法及系统，通过分析探针采集到的控制面数据和用户面数据，识别检测区域内存在覆盖黑洞的无线小区以及无线小区内覆盖黑洞的位置信息，从而支持主动及时发现无线小区的覆盖黑洞问题，进而给无线网络优化提供精准定位，缩小测试范围，并大大降低无线网络的运行维护成本。
本公开实施例提供的无线小区的覆盖黑洞识别方法及系统，可以应用于LTE系统中。然而，本公开实施例对此并不限定。本公开实施例还可以应用于其他通信系统中，比如，第五代移动通信技术(5G)新空口通信系统等。
图1为LTE EPC部分流程的组网示意图。需要说明的是，图1中仅绘示出了与本公开实施例相关的部分组网图。如图1所示，LTE无线接入网(RAN)包括无线基站(eNodeB)，LTE EPC包括移动管理实体(Mobility Management Entity，简称为MME)、服务网关(Serving Gateway，简称 为SGW)和分组数据网网关(PDN Gateway，简称为PGW)。
其中，MME是核心网中负责处理信令的网元，是一个信令实体，主要负责移动性管理、承载管理、用户的鉴权认证、SGW和PGW的选择等功能。SGW主要负责用户面处理，负责数据包的路由和转发等功能，支持第三代合作伙伴计划(Third Generation Partnership Project，简称为3GPP)不同接入技术的切换，发生切换时作为用户面的锚点，对每一个与演进的分组系统(Evolved Packet System，简称为EPS)相关的用户终端(User Equipment，简称为UE)，在一个时间点上，都有一个SGW为之服务。
在图1中，多个MME组成了MME池(Pool)，S10接口为任两个MME之间的接口，S11为MME和SGW之间的接口，S5或S8为SGW与PGW之间的接口。S1-MME和S1-U是EPC网络的两个主要接口，S1-MME是eNodeB和MME之间的接口，S1-U是eNodeB和SGW之间的接口。
基于图1所示的组网结构，用户终端(UE)从无线基站接入到SGW，在该流程中，第一探针(Probe)接于无线基站与SGW之间，用于采集LTE数据业务S1-U口用户面数据。用户终端从无线基站接入到MME，在该流程中，第二探针(Probe)接于无线基站与MME之间，用于采集LTE数据业务S1-MME口控制面数据。然后，通过分析探针采集到的控制面和用户面数据，即可以对全网存在覆盖黑洞的无线小区进行初步识别，并基于空间聚类的机器学习模型，进一步识别无线小区的覆盖黑洞位置。如此一来，可以给无线网络优化部门提供明确的网络优化对象，从而大大降低无线网络的运行维护成本。
图2为本公开实施例提供的无线小区的覆盖黑洞识别方法的流程图。如图2所示，本实施例提供的覆盖黑洞识别方法包括：
S201、通过部署在接入网与核心网之间的探针，采集检测区域内的控制面数据和用户面数据；
S202、根据采集到的控制面数据和用户面数据，识别检测区域内存在 覆盖黑洞的无线小区以及无线小区内覆盖黑洞的位置信息。
其中，覆盖黑洞可以指用户终端不能正常访问当前网络系统的网络覆盖区域，亦可称为覆盖盲点。在本文中，存在覆盖黑洞的无线小区可以简称为覆盖黑洞小区。
其中，检测区域可以根据探针的部署范围确定，比如，一个城市、一个省份等。然而，本公开实施例对此并不限定。
在一示例性实施方式中，S202可以包括：根据采集到的控制面数据，筛选出第一时间段内从第一移动通信系统重定向到第二移动通信系统的第一事件，其中，第一移动通信系统的网络质量高于第二移动通信系统的网络质量；基于第一时间段内的第一事件，识别检测区域内存在覆盖黑洞的无线小区；根据采集到的用户面数据以及第一时间段内的第一事件，确定无线小区内覆盖黑洞的位置信息。
其中，第一移动通信系统可以为第四代移动通信技术(4G)系统，第二移动通信系统可以为第三代移动通信技术(3G)系统或第二代移动通信技术(2G)系统；或者，第一移动通信系统可以为第五代移动通信技术(5G)系统，第二移动通信系统可以为2G系统、3G系统或4G系统。然而，本公开实施例对此并不限定。
其中，第一时间段可以根据实际需求进行设定，比如，一天或一周等。然而，本公开实施例对此并不限定。
在一示例性实施方式中，基于第一时间段内的第一事件，识别检测区域内存在覆盖黑洞的无线小区，可以包括：
从采集到的控制面数据中，筛选出第一事件关联的从第二移动通信系统返回到第一移动通信系统的第二事件；根据第一事件的时间点以及第一事件关联的第二事件的时间点，确定该第一事件对应的单次重定向驻留时长；根据第一时间段内检测区域内任一无线小区下每个用户对应的第一事件的次数和重定向驻留时长，识别检测区域内存在覆盖黑洞的无线小区；
或者，根据第一时间段内检测区域内任一无线小区下每个用户对应的 第一事件的次数，识别检测区域内存在覆盖黑洞的无线小区。
其中，第一事件对应的单次重定向驻留时长可以通过该第一事件关联的第二事件的时间点减去该第一事件的时间点得到。
在本示例性实施方式中，可以将用户在第一时间段内且在某一无线小区下发生的第一事件的次数，或者，发生第一事件的次数和重定向驻留时长(即多个第一事件的单次重定向驻留时长的累加值)作为判定覆盖黑洞小区的基本指标，来识别覆盖黑洞小区。
在一示例性实施方式中，根据第一时间段内检测区域内任一无线小区下每个用户对应的第一事件的次数和重定向驻留时长，识别检测区域内存在覆盖黑洞的无线小区，可以包括：
将第一时间段划分为至少N个第二时间段，N为大于1的整数；针对检测区域内的任一无线小区，在任一第二时间段，确定在第二时间段内满足第一事件的次数大于第一阈值且重定向驻留时长大于第二阈值的用户数，作为不满意小区覆盖的用户数；并根据不满意小区覆盖的用户数与第二时间段内的总用户数，计算得到该第二时间段内不满意小区覆盖的用户占比，将第二时间段内不满意小区覆盖的用户占比大于第三阈值的小区，记录为在第二时间段内出现覆盖黑洞问题的小区；筛选出满足以下条件的小区为存在覆盖黑洞的无线小区：在第一时间段中的至少M个第二时间段中出现覆盖黑洞问题，M为正整数，且M小于N；出现覆盖黑洞问题的小区在至少M个第二时间段内不满意小区覆盖的用户平均数大于或等于第四阈值。
需要说明的是，第一阈值至第四阈值可以根据实际需求进行设定，本公开实施例对此并不限定。第一时间段和第二时间段可以根据实际需求进行设定，比如，第一时间段可以为一周，第二时间段可以为一天。然而，本公开实施例对此并不限定。
在一示例性实施方式中，根据第一时间段内检测区域内任一无线小区下每个用户对应的第一事件的次数和重定向驻留时长，识别检测区域内存 在覆盖黑洞的无线小区，可以包括：
针对检测区域内的任一无线小区，确定在第一时间段内满足第一事件的次数大于第五阈值且重定向驻留时长大于第六阈值的用户数，作为不满意小区覆盖的用户数；并根据不满意小区覆盖的用户数与第一时间段内的总用户数，计算得到第一时间段内不满意小区覆盖的用户占比，将第一时间段内不满意小区覆盖的用户占比大于第七阈值的小区，识别为存在覆盖黑洞的无线小区。
需要说明的是，第五阈值至第七阈值可以根据实际需求进行设定，本公开实施例对此并不限定。
在一示例性实施方式中，根据采集到的用户面数据以及第一时间段内的第一事件，确定无线小区内覆盖黑洞的位置信息，可以包括：从采集到的用户面数据中，获取上报OTT位置的话单；针对识别出存在覆盖黑洞的无线小区，基于时间窗，将第一时间段内该无线小区内的第一事件与上报OTT位置的话单进行关联，确定该第一事件关联的OTT位置；对该无线小区内的第一事件关联的OTT位置进行汇总和聚类分析，得到该无线小区内的覆盖黑洞的位置信息。
其中，OTT(Over The Top)指通过互联网向用户提供的各种服务。互联网企业利用运营商宽带网络发展的业务可以称为OTT应用。某些OTT服务商对用户提供定位及导航类服务，应用程序(Application，简称为APP)存在明文上报位置信息的情况，基于此可以提取出经纬度信息，用来描绘用户移动轨迹。由于这种定位方式获取的经纬度信息来源于OTT应用，故称之为OTT定位。
相较于传统方式中在没有信号时基本无法上报MR数据，进而无法判断覆盖黑洞的位置，在本示例性实施例方式中，基于时间窗，从用户面数据中筛选出第一事件关联的OTT位置，可以提供覆盖黑洞位置识别的数据基础，从而进一步输出无线小区的覆盖黑洞的位置。
在本示例性实施方式中，针对识别出存在覆盖黑洞的无线小区，基于 时间窗，将第一时间段内无线小区内的第一事件与上报OTT位置的话单进行关联，确定第一事件关联的OTT位置，可以包括：针对第一时间段内无线小区内的任一第一事件，在以该第一事件的时间点为参考点确定的时间窗内，查找与该第一事件的时间点最近的上报OTT位置的话单，将该话单上报的OTT位置确定为该第一事件关联的OTT位置。
其中，以第一事件的时间点为参考点确定的时间窗，可以包括：以该第一事件的时间点为结束点向前设定时长得到的时间窗，或者以该第一事件的时间点向前第一设定时长且向后第二设定时长得到的时间窗。然而，本公开实施例对此并不限定。
在本示例性实施方式中，对无线小区内的第一事件关联的OTT位置进行汇总和聚类分析，得到该无线小区内的覆盖黑洞的位置信息，可以包括：针对识别出存在覆盖黑洞的无线小区，将该无线小区内的第一事件关联的OTT位置进行坐标系统一后，输入基于聚类算法的机器学习模型，得到覆盖黑洞的位置信息。
在一示例中，针对识别出的任一覆盖黑洞小区，将所有用户在该小区下第一时间段内的重定向OTT位置信息按照经纬度进行汇总，进行空间位置的关联分析，将邻近的位置点通过基于聚类算法的机器学习模型聚成一类，剔除离群位置点后，得到聚类后的多组位置点，作为该小区的多个覆盖黑洞，并给出每个覆盖黑洞的中心坐标，辅助网优人员进行优化和定位。
下面以图1所示的LTE系统环境为例，对本公开实施例进行举例说明。在本示例性实施例中，部署在接入网与核心网之间的探针包括：部署在无线基站(eNodeB)和移动管理实体(MME)之间的第一探针、部署在无线基站和服务网关(SGW)之间的第二探针；其中，第一探针采集的控制面数据包括：S1-MME口数据；第二探针采集的用户面数据包括：S1-U口数据。
在本示例性实施例中，第一探针可以将采集到的控制面数据传送至覆 盖黑洞识别系统，第二探针可以将采集到的用户面数据传送到覆盖黑洞识别系统，然后，由覆盖黑洞识别系统进行数据处理，来识别检测区域内的覆盖黑洞小区和覆盖黑洞的位置信息。其中，覆盖黑洞识别系统可以部署在一台服务器上，或者，可以部署在服务器集群中。然而，本公开实施例对此并不限定。
图3为本公开示例性实施例提供的无线小区的覆盖黑洞小区识别和覆盖黑洞位置识别过程的示例图。其中，可以对第一时间段Q的采集数据进行分析，然后，根据覆盖黑洞小区判定规则来识别出覆盖黑洞小区。
在本示例中，第一时间段Q可以为七天，第二时间段可以为一天。
在本示例中，覆盖黑洞小区判定规则可以包括：覆盖黑洞小区要求满足前七天中有三天或以上出现覆盖黑洞问题，且有问题的这些天日均覆盖不满意用户数不少于R 4(对应上述的第四阈值)个；其中，小区某一天出现覆盖空洞问题定义为小区当天出现覆盖不满意的用户占比大于R 3(对应上述的第三阈值)；小区某一天出现覆盖不满意的用户定义为当天在该小区的第一事件的发生次数大于R 1(对应上述的第一阈值)且重定向驻留时长大于R 2秒(对应上述的第二阈值)的用户。
需要说明的是，覆盖黑洞小区的判定规则可以根据实际需求进行调整。本公开实施例对此并不限定。
如图3所示，在本示例中，覆盖黑洞小区的识别过程包括：
S301、从S1-MME口一天的外部数据表示(External Data Representation，简称为XDR)详单中过滤出4G系统重定向到2G系统事件(对应上述的第一事件)的话单和2G系统返回至4G系统事件(对应上述的第二事件)的话单。
在本示例中，第一事件的判别条件可以包括：终端上下文释放(UE Context Release)，且原因为异系统重定向(interrat-redirection)。第二事件的判别条件可以包括：第一事件(4G系统重定向到2G系统)之后第一次发生的跟踪区更新(Tracking Area Update，简称为TAU)或附着 (ATTACH)事件。
需要说明的是，在其他实现方式中，第一事件可以包括：从4G系统重定向到2G系统的事件、从4G系统重定向到3G系统的事件。本公开实施例对此并不限定。
S302、通过过滤出的话单中的国际移动用户识别码(International Mobile Subscriber Identification Number，简称为IMSI)将第一事件的话单和第二事件的话单进行关联，即，将4G系统重定向到2G系统之后第一次发生的TAU或者ATTACH事件的话单与这条4G系统重定向到2G系统的话单关联成一条记录(未关联上的话单可以废弃)；其中，关联方式可以为根据第一事件话单的时间点，向后查找和这个话单具有相同IMSI和小区标识(eNodeB ID Cell ID，简称为ECI)、且在这个话单事件点之后第一次出现的事件类型为TAU或者ATTACH的话单，查找到的话单的时间点作为第二事件的时间点。第一事件和第二事件关联后的记录包括：IMSI、小区ECI、第一事件(4G系统重定向到2G系统事件)的时间点time_src、第二事件(2G系统返回至4G系统事件)的时间点time_dst。其中，第一事件的单次重定向驻留时长可以通过time_dst减去time_src得到。
S303、对S302关联后的结果，按照IMSI和小区ECI两个维度进行聚集，计算一天内某一小区ECI i下，用户IMSI j的第一事件(4G系统重定向到2G系统事件)的次数和重定向驻留时长。
其中，第一事件的次数可以通过对话单的记录数进行统计得到，重定向驻留时长可以通过time_dst减去time_src然后求和得到。其中，用户IMSI j的第一事件的次数和重定向驻留时长可以通过以下式子进行描述：
用户IMSI j的第一事件话单为XDR i(i＝1,2,..,k)；
用户IMSI j的第一事件的次数Count j＝k；
用户IMSI j的重定向驻留时长Time j＝SUM(time_dst i-time_src i)。
如此一来，可以得到一天内小区ECI i下不同用户IMSI j的4G系统重 定向到2G系统事件的次数和重定向驻留时长。
S304、对S303得到的结果，按照小区ECI维度进行聚集，可以得到任一无线小区在一天内发生的第一事件(4G系统重定向到2G系统事件)的次数大于R 1且重定向驻留时长大于R 2秒的用户数(即覆盖黑洞小区判定规则中定义的覆盖不满意用户的数量)，然后结合该小区当天总用户数(可以通过采集的S1-MME口数据另行计算得到)，即可得到该小区当天出现覆盖不满意的用户占比。其中，该小区当天出现覆盖不满意的用户占比等于该小区当前出现覆盖不满意用户的数量与该小区当天总用户数的比值。
S305、按照S301至S304的步骤重复计算七天内全网每个无线小区每天的覆盖不满意用户数和覆盖不满意用户占比。
S306、基于S305得到的结果，统计每个无线小区前七天中出现覆盖空洞问题的天数，并计算日均覆盖不满意用户数。然后，筛选出满足本示例的覆盖黑洞小区判定规则的无线小区，即为覆盖黑洞小区。
在一应用例子中，以模拟现网数据为例，识别某地市2019年6月3日的覆盖黑洞小区。本示例中的分析流程如下：读取2019年6月3日及前6天探针采集的控制面S1-MME口数据，按照覆盖黑洞小区识别步骤(S301至S306)输出2019年6月3日的覆盖黑洞小区列表，并且输出每个小区在七天内的覆盖不满意用户占比的平均值、出现覆盖黑洞问题的天数，按照覆盖不满意用户占比的平均值进行降序排列，取前50条记录，即得到该地市的Top50覆盖黑洞小区列表及对应指标，然后可以进行派单解决。
表1覆盖黑洞小区列表
小区ECI 出现覆盖黑洞问题天数 覆盖不满意用户占比(％)
208898564 6 66.9
208898565 5 64.9
215510273 7 63.3
215600899 6 61.7
208898563 5 59.6
215600898 6 56.9
215511297 7 55.6
215669250 6 52.9
215548933 5 52
203037185 7 49.6
74699584 6 49.6
208898306 5 49.6
215885826 7 49
215600901 3 48.6
74666049 4 45.5
215282435 6 43.5
202877962 7 39.9
203486723 4 39
203441158 6 38.6
208898308 5 38.6
74665281 7 37.7
215170315 7 37.4
215104517 7 37.3
208955649 7 37.3
215897089 7 36.8
185024093 7 35.6
185202795 7 35.4
203047425 5 34.9
208901892 4 34.2
202931460 5 34.1
74675779 5 34
215619075 3 33.5
209020173 3 33
215938308 7 32.6
203429377 4 32.2
91684616 7 31.8
203045890 7 31.7
215170312 7 31.5
214971139 7 31.3
203117314 7 31.2
74675776 5 31
215170310 5 30.8
91926795 6 30.2
203384833 4 29.4
215574805 7 29.1
215604747 7 28.4
91805207 6 28.4
75121235 4 28
91774217 7 27.9
209167620 7 27
基于图3所示，在本示例性实施例中，在通过S306识别出全网内的覆盖黑洞小区之后，可以基于时间窗，计算用户发生第一事件时的OTT位置。
图4为OTT定位原理的示意图。如图4所示，OTT定位原理如下：
S401、APP应用(比如，APP手机端)以http协议的post方式在上行上报含坐标系、GPS经纬度(室外，且开启GPS)、WIFI介质访问控制(Media Access Control，简称为MAC)地址、类似MR等信息的加密定位请求。APP应用可以通过应用程序编程接口(Application Programming Interface，简称为API)访问地图服务器端。
S402、地图服务器端接收到加密定位请求后，经计算，以http协议的post方式在下行的http 200OK响应中以压缩包的形式将经纬度信息发给APP手机端；其中，可从S1-U接口中的http原始码流的有效载荷(payload)中解码得到经纬度信息。
S403、APP应用(比如，APP手机端)以http协议的get方式，在上行的统一资源定位符(Uniform Resource Location，简称为URL)中以明文的形式将经纬度信息上报给自身的服务器(即APP服务器端)；其中，可从S1-U接口中的http类型XDR文件的统一资源标识符(Uniform Resource Identifier，简称为URI)字段中直接提取经纬度信息。
由此可知，通过APP手机端与地图服务器端或APP服务器端的交互过程，可以得到APP手机端所在的经纬度信息。由于获取的经纬度信息来源于OTT应用，故可以称之为OTT定位。
相较于传统方式中在没有信号时基本无法上报MR数据，也就无法判断覆盖黑洞位置，在本示例性实施例中，基于时间窗获取用户重定向位置，可以给覆盖黑洞位置识别提供数据基础，从而实现覆盖黑洞识别。
图5为本公开实施例中基于时间窗的OTT位置识别方式的示例图。结合图3和图5所示，基于时间窗，计算用户发生从4G系统重定向到2G系统时的OTT位置流程如下：
S501、从S1-U口第一时间段的XDR详单中过滤出上报了OTT位置信息(比如，经度、纬度和坐标系等信息)的话单。
S502、针对S302过滤得到的第一事件的任一话单，以该话单的时间点为时间窗T N的结束点，查找在时间窗内距离该话单的时间点最近的OTT位置信息，作为该第一事件的OTT位置。
如图5所示，在S1-MME口数据的时间序列和S-U口数据的时间序列中，以第一事件的话单的时间点向前的T N作为时间窗，查找该时间窗内距离该话单的时间点最近的上报了OTT位置的话单，则将查找到的OTT位置确定为该第一事件的OTT位置。在图5中绘示出了两个第一事件对应的OTT位置的示例。
在本示例中，由于用户发生第一事件(4G系统重定向至2G系统事件)时很有可能OTT信息已经无法上报，故以第一事件的话单的时间点向前的T N作为时间窗。然而，本公开实施例对此并不限定。在其他实现方式中，可以第一事件的话单的时间点向前的T N1时长和向后的T N2时长作为时间窗。
在本示例性实施例中，在确定第一事件关联的OTT位置之后，针对存在覆盖黑洞的小区，可以基于聚类算法的机器学习模型获得覆盖黑洞的位置信息。本示例中，基于聚类算法的机器学习模型获得覆盖黑洞的位置信息的流程可以包括：
S601、通过时间窗方式，确定某一覆盖黑洞小区在第一时间段(比如，前7天)内的用户发生第一事件(4G系统重定向到2G系统事件)时的OTT位置(比如，包括经度、纬度和坐标系等信息)。
S602、对S601的结果中不同坐标系的经纬度进行转换，转换为统一坐标系(如统一转换为GCJ-02火星坐标系)下的经纬度信息，转换之后 可以按照经纬度进行聚集，并计算不同的经纬度下的重定向次数和重定向用户数。
S603、将针对某一覆盖黑洞小区，按照S601和S602得到的经纬度信息，作为输入数据，输入机器学习模型，得到聚类结果，聚类结果将不同的经、纬度坐标自动划分为多组。在本示例中，每组经纬度坐标即对应为图6中的一个覆盖黑洞。
在本示例中，在采用S603使用机器学习模型之前，需要基于密度的聚类算法进行模型训练，以得到适用于本示例的机器学习模型。其中，可以基于密度的聚类算法，以按照S601和S602方式得到的经纬度信息作为聚类算法的输入特征，进行训练并且调整输入参数，可以得到符合预期的聚类算法模型，作为本示例的机器学习模型。
S604、在确定不同的覆盖黑洞的位置之后，可以通过S602计算得到的不同经纬度下的重定向次数和重定向用户数，反向计算每个覆盖黑洞下发生的总重定向次数和重定向用户数，基于此判断覆盖黑洞的严重级别。其中，关于严重级别的判定条件可以根据需求进行设定，本公开实施例对此并不限定。
S605、根据S604确定的覆盖黑洞的严重级别，可以确定优化方案。比如，运营商网优人员可以采用调整方位角、天线高度、增加基站等方式，进行针对性优化。
在一应用例子中，仍以模拟现网数据为例，在识别某地市2019年6月3日的覆盖黑洞小区(比如表1)后，可以按照以下分析流程继续计算每个覆盖黑洞小区的覆盖黑洞位置：读取2019年6月3日及前6天探针采集的控制面S1-MME口数据和用户面S1-U口数据，对每天的数据按照S501至S502以及S601至S602，计算每个覆盖黑洞小区的覆盖黑洞列表及每个覆盖黑洞下发生的总重定向次数和重定向用户数。
以小区ECI：215510273为例，对应的覆盖黑洞列表及指标如表2所示。
表2覆盖黑洞列表一及指标
覆盖黑洞ID 黑洞中心点坐标 重定向次数 重定向用户数
1 117.080396,36.652073 205 47
2 117.07434,36.6517 60 28
3 117.07392,36.651162 6 3
4 117.074244,36.652567 2 1
5 117.079642,36.651982 2 2
6 117.081407,36.652095 2 1
7 117.073897,36.652026 1 1
8 117.079769,36.652214 1 1
9 117.079778,36.651824 1 1
12 117.081016,36.651886 1 1
10 117.08047,36.651157 1 1
11 117.080711,36.651696 1 1
按照S603处理之后，该小区对应的覆盖黑洞位置可以如图6所示。
以小区ECI：215600899为例，对应的覆盖黑洞列表及指标如表3所示。
表3覆盖黑洞列表二及指标
黑洞ID 中心点坐标 重定向次数 重定向用户数
1 117.085764,36.684346 33 20
2 117.079622,36.683853 26 18
5 117.086977,36.684721 3 2
4 117.086066,36.684213 3 1
3 117.074922,36.684714 3 1
7 117.084846,36.684033 2 2
10 117.085439,36.684483 2 2
8 117.085421,36.683849 2 1
6 117.084473,36.684532 2 1
9 117.085429,36.684253 2 1
11 117.079192,36.683429 1 1
12 117.0792,36.683789 1 1
16 117.078272,36.683944 1 1
14 117.08015,36.683797 1 1
15 117.080464,36.684534 1 1
17 117.084106,36.684041 1 1
21 117.083793,36.684236 1 1
18 117.085518,36.684054 1 1
19 117.078854,36.684017 1 1
20 117.07881,36.684266 1 1
13 117.078907,36.683803 1 1
按照S603处理之后，该小区对应的覆盖黑洞位置可以如图7所示。
在一实际应用例子中，以本公开实施例提供的覆盖黑洞识别方法应用在济南市的某一运营商网络的使用情况为例，采用本实施例方案运行十天内共输出Top覆盖黑洞小区71个，经过派单核算和验证，其中需要新建站点的有17个，确认为故障的为45个，无法确定的为9个，即发现的可以确认需要进行优化的小区占比高达87.3％。由此可见，本实施例可以基于用户真实感知，及时发现全网小区覆盖黑洞问题，并定位出问题小区具 体的覆盖黑洞位置，从而为无线网络优化提供了明确的解决对象，大大降低了无线网络的运行维护成本。
在本示例性实施例中，在LTE组网中，通过在无线基站与核心网网元MME以及无线基站与核心网网关SGW之间分别部署探针，获取LTE数据业务全网控制面S1-MME口数据和用户面S1-U口数据，然后，结合S1-MME口数据中的4G系统重定向到2G系统的次数和4G系统重定向驻留时长来识别覆盖黑洞小区；对于某个覆盖黑洞小区，通过S1-U口的OTT数据，来获取用户发生4G系统重定向到2G系统时具体的经纬度位置，并基于一定时间段内的经纬度位置，采用聚类算法得到该覆盖黑洞小区内的多个覆盖黑洞的位置。如此一来，本公开实施例可以主动发现影响用户真实上网感知的全网无线小区的覆盖黑洞问题，并且可以将问题小区的覆盖黑洞位置进行输出，并暴露给运营商，为运营商进行无线网络优化提供精准定位，缩小测试范围，从而降低维护成本。
图8为本公开实施例提供的无线小区的覆盖黑洞识别系统的示意图。如图8所示，本公开实施例提供的覆盖黑洞识别系统包括：
数据获取模块801，设置为通过部署在接入网与核心网之间的探针，采集检测区域内的控制面数据和用户面数据；
处理模块802，设置为根据采集到的控制面数据和用户面数据，识别检测区域内存在覆盖黑洞的无线小区以及该无线小区内覆盖黑洞的位置信息。
关于本实施例提供的覆盖黑洞识别系统的相关说明可以参照上述方法实施例的描述，故于此不再赘述。
此外，本公开实施例还提供一种计算机可读存储介质，存储有计算机程序，所述计算机程序被执行时实现上述的覆盖黑洞识别方法的步骤，比如图2所示的步骤。
本领域普通技术人员可以理解，上文中所公开方法中的全部或某些步骤、系统、装置中的功能模块/单元可以被实施为软件、固件、硬件及其适 当的组合。在硬件实施方式中，在以上描述中提及的功能模块/单元之间的划分不一定对应于物理组件的划分；例如，一个物理组件可以具有多个功能，或者一个功能或步骤可以由若干物理组件合作执行。某些组件或所有组件可以被实施为由处理器，如数字信号处理器或微处理器执行的软件，或者被实施为硬件，或者被实施为集成电路，如专用集成电路。这样的软件可以分布在计算机可读介质上，计算机可读介质可以包括计算机存储介质(或非暂时性介质)和通信介质(或暂时性介质)。如本领域普通技术人员公知的，术语计算机存储介质包括在用于存储信息(诸如计算机可读指令、数据结构、程序模块或其他数据)的任何方法或技术中实施的易失性和非易失性、可移除和不可移除介质。计算机存储介质包括但不限于RAM、ROM、EEPROM、闪存或其他存储器技术、CD-ROM、数字多功能盘(DVD)或其他光盘存储、磁盒、磁带、磁盘存储或其他磁存储装置、或者可以用于存储期望的信息并且可以被计算机访问的任何其他的介质。此外，本领域普通技术人员公知的是，通信介质通常包含计算机可读指令、数据结构、程序模块或者诸如载波或其他传输机制之类的调制数据信号中的其他数据，并且可包括任何信息递送介质。
工业实用性
通过本公开实施例，通过分析探针采集到的控制面数据和用户面数据，识别检测区域内存在覆盖黑洞的无线小区以及无线小区内覆盖黑洞的位置信息，从而支持主动及时发现无线小区的覆盖黑洞问题，进而给无线网络优化提供精准定位，缩小测试范围，并大大降低无线网络的运行维护成本。

Claims (11)

  1. 一种无线小区的覆盖黑洞识别方法，包括：
    通过部署在接入网与核心网之间的探针，采集检测区域内的控制面数据和用户面数据；
    根据采集到的控制面数据和用户面数据，识别所述检测区域内存在覆盖黑洞的无线小区以及所述无线小区内覆盖黑洞的位置信息。
  2. 根据权利要求1所述的方法，其中，所述根据采集到的控制面数据和用户面数据，识别所述检测区域内存在覆盖黑洞的无线小区以及所述无线小区内覆盖黑洞的位置信息，包括：
    根据采集到的控制面数据，筛选出第一时间段内从第一移动通信系统重定向到第二移动通信系统的第一事件，其中，所述第一移动通信系统的网络质量高于所述第二移动通信系统的网络质量；
    基于所述第一时间段内的第一事件，识别所述检测区域内存在覆盖黑洞的无线小区；
    根据采集到的用户面数据以及所述第一时间段内的第一事件，确定所述无线小区内覆盖黑洞的位置信息。
  3. 根据权利要求2所述的方法，其中，所述基于所述第一时间段内的第一事件，识别所述检测区域内存在覆盖黑洞的无线小区，包括：
    从采集到的控制面数据中，筛选出所述第一事件关联的从所述第二移动通信系统返回到所述第一移动通信系统的第二事件；根据所述第一事件的时间点以及所述第一事件关联的第二事件的时间点，确定所述第一事件对应的单次重定向驻留时长；根据所述第一时间段内所述检测区域内任一无线小区下每个用户对应的所述第一事件的次数和重定向驻留时长，识别所述检测区域内存在覆盖黑洞的无线小区；
    或者，
    根据所述第一时间段内所述检测区域内任一无线小区下每个用户对应的所述第一事件的次数，识别所述检测区域内存在覆盖黑洞的无线小区。
  4. 根据权利要求3所述的方法，其中，所述根据所述第一时间段内所述检测区域内任一无线小区下每个用户对应的所述第一事件的次数和重定向驻留时长，识别所述检测区域内存在覆盖黑洞的无线小区，包括：
    将所述第一时间段划分为至少N个第二时间段，N为大于1的整数；针对所述检测区域内的任一无线小区，在任一第二时间段内，确定在所述第二时间段内满足第一事件的次数大于第一阈值且重定向驻留时长大于第二阈值的用户数，作为所述第二时间段内不满意小区覆盖的用户数；并根据所述第二时间段内不满意小区覆盖的用户数与所述第二时间段内的总用户数，计算得到所述第二时间段内不满意小区覆盖的用户占比；将所述第二时间段内不满意小区覆盖的用户占比大于第三阈值的小区，记录为在所述第二时间段内出现覆盖黑洞问题的小区；筛选出满足以下条件的小区为存在覆盖黑洞的无线小区：在所述第一时间段中的至少M个第二时间段中出现覆盖黑洞问题，M为正整数，且M小于N；所述出现覆盖黑洞问题的小区在所述至少M个第二时间段内不满意小区覆盖的用户数的平均值大于或等于第四阈值；
    或者，
    针对检测区域内的任一无线小区，确定在所述第一时间段内满足第一事件的次数大于第五阈值且重定向驻留时长大于第六阈值的用户数，作为不满意小区覆盖的用户数；并根据不满意小区覆盖的用户数与所述第一时间段内的总用户数，计算得到所述第一时间段内不满意小区覆盖的用户占比；将所述第一时间段内不满意小区覆盖的用户占比大于第七阈值的小区，识别为存在覆盖黑洞的无线小区。
  5. 根据权利要求2所述的方法，其中，所述根据采集到的用户面数据以及所述第一时间段内的第一事件，确定所述无线小区内覆盖黑洞的位置信息，包括：
    从采集到的用户面数据中，获取上报OTT位置的话单；
    针对识别出存在覆盖黑洞的无线小区，基于时间窗，将所述第一时间 段内所述无线小区内的第一事件与所述上报OTT位置的话单进行关联，确定所述第一事件关联的OTT位置；
    对所述无线小区内的第一事件关联的OTT位置进行汇总和聚类分析，得到所述无线小区内的覆盖黑洞的位置信息。
  6. 根据权利要求5所述的方法，其中，所述针对识别出存在覆盖黑洞的无线小区，基于时间窗，将所述第一时间段内所述无线小区内的第一事件与所述上报OTT位置的话单进行关联，确定所述第一事件关联的OTT位置，包括：
    针对所述第一时间段内所述无线小区内的任一第一事件，在以所述第一事件的时间点为参考点确定的时间窗内，查找与所述第一事件的时间点最近的上报OTT位置的话单，将所述话单上报的OTT位置确定为所述第一事件关联的OTT位置。
  7. 根据权利要求5所述的方法，其中，所述对所述无线小区内的第一事件关联的OTT位置进行汇总和聚类分析，得到所述无线小区内的覆盖黑洞的位置信息，包括：
    针对识别出存在覆盖黑洞的无线小区，将所述无线小区内的第一事件关联的OTT位置进行坐标系统一后，输入基于聚类算法的机器学习模型，得到所述覆盖黑洞的位置信息。
  8. 根据权利要求3所述的方法，其中，在长期演进LTE系统中，所述第一事件的判别条件包括：事件类型为终端上下文释放，且原因为异系统重定向interrat-redirection；所述第一事件关联的第二事件的判别条件包括：在所述第一事件之后第一次发生的跟踪区更新TAU或附着ATTACH事件。
  9. 根据权利要求1至8中任一项所述的方法，其中，在长期演进LTE系统中，所述部署在接入网与核心网之间的探针包括：部署在无线基站和移动管理实体MME之间的第一探针、部署在所述无线基站和服务网关SGW之间的第二探针；其中，所述第一探针采集的控制面数据包括： S1-MME口数据；所述第二探针采集的用户面数据包括：S1-U口数据。
  10. 一种无线小区的覆盖黑洞识别系统，包括：
    数据获取模块，设置为通过部署在接入网与核心网之间的探针，采集检测区域内的控制面数据和用户面数据；
    处理模块，设置为根据采集到的控制面数据和用户面数据，识别所述检测区域内存在覆盖黑洞的无线小区以及所述无线小区内覆盖黑洞的位置信息。
  11. 一种计算机可读存储介质，存储有计算机程序，所述计算机程序被执行时实现如权利要求1至9中任一项所述的覆盖黑洞识别方法的步骤。
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