WO2020113845A1 - 小区经纬度预测方法、装置、服务器、基站及存储介质 - Google Patents

小区经纬度预测方法、装置、服务器、基站及存储介质 Download PDF

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WO2020113845A1
WO2020113845A1 PCT/CN2019/078444 CN2019078444W WO2020113845A1 WO 2020113845 A1 WO2020113845 A1 WO 2020113845A1 CN 2019078444 W CN2019078444 W CN 2019078444W WO 2020113845 A1 WO2020113845 A1 WO 2020113845A1
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Prior art keywords
sample
latitude
longitude
samples
distance
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PCT/CN2019/078444
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English (en)
French (fr)
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李益刚
陈云霄
范国田
孙凯文
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中兴通讯股份有限公司
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Priority to EP19893019.0A priority Critical patent/EP3890361B1/en
Publication of WO2020113845A1 publication Critical patent/WO2020113845A1/zh

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/0009—Transmission of position information to remote stations
    • G01S5/0018—Transmission from mobile station to base station
    • G01S5/0027—Transmission from mobile station to base station of actual mobile position, i.e. position determined on mobile
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
    • G01S5/14—Determining absolute distances from a plurality of spaced points of known location
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/10—Scheduling measurement reports ; Arrangements for measurement reports
    • 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
    • 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
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
    • G01S5/0205—Details
    • G01S5/0242—Determining the position of transmitters to be subsequently used in positioning
    • 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
    • H04W64/003—Locating users or terminals or network equipment for network management purposes, e.g. mobility management locating network equipment

Definitions

  • This application relates to a technology for positioning a cell in a mobile communication network, and in particular, to a method, device, server, base station, and storage medium for predicting a cell's latitude and longitude.
  • Engineering parameters also known as base station information tables, mainly contain information such as the latitude and longitude of the base station and the cell, antenna height, azimuth, and downtilt.
  • the longitude and latitude information of the cell is usually obtained and recorded by the engineer using GPS handheld terminals on the spot when the base station is installed and opened. Because the engineers are all manual operations in the process of measurement and recording, errors are inevitable.
  • Embodiments of the present application provide a method, device, server, base station, and storage medium for cell longitude and latitude prediction.
  • a cell latitude and longitude prediction method comprising: determining the MR sample to be located according to the position information and/or time advance TA corresponding to the MR sample of the measurement report, the MR sample being the GNSS carrying the global navigation satellite system within the set period of the target cell MR samples of information; determine the target area including the target cell according to the MR sample to be located; select a to-be-determined position according to the target area, according to the location information and the location carried in the MR sample to be located The location information of the to-be-determined location and the distance represented by the corresponding TA predict the latitude and longitude of the target cell.
  • a cell latitude and longitude prediction device comprising: a sample screening module configured to determine MR samples to be located according to the location information and/or time advance TA corresponding to the measurement report MR samples, the MR samples being the target cell within the set period MR samples carrying global navigation satellite system GNSS information; a position determination module configured to determine a target area including the target cell based on the MR samples to be located; a prediction module configured to select a pending position based on the target area, Predict the latitude and longitude of the target cell according to the location information carried in the MR sample to be located and the location information of the to-be-determined location, and the distance represented by the corresponding TA.
  • a server includes a processor and a memory for storing a computer program that can run on the processor; wherein, when the processor is configured to run the computer program, the cell latitude and longitude prediction method described in the embodiment of the present application is executed .
  • a base station includes a processor and a memory for storing a computer program that can run on the processor; wherein, when the processor is configured to run the computer program, the cell longitude and latitude prediction method described in the embodiment of the present application is executed .
  • a storage medium stores executable instructions, and when the executable instructions are executed by a processor, the method for predicting cell latitude and longitude according to an embodiment of the present application is implemented.
  • the method, device, server, base station, and storage medium for cell longitude and latitude prediction provided in the above embodiments are based on the location information and/or the amount of time advancement corresponding to the MR samples carrying the GNSS information of the global navigation satellite system within the set period of the target cell,
  • the MR samples are screened to determine the MR samples to be located, and the latitude and longitude of the target cell are predicted according to the to-be-determined positions contained in the target area determined by the MR samples to be located.
  • the MR samples to be located are the MR samples collected
  • Corresponding location information and/or TA are screened and determined, so that the method for predicting the longitude and latitude of the cell has no special requirements for the station type of the cell, and it is for omnidirectional stations, directional stations, remote stations, micro stations, and room divisions.
  • the station and other station types are suitable, and the measurement accuracy is high, which improves the efficiency of cell latitude and longitude calibration in the engineering parameters, lays a good foundation for network operation and maintenance, and greatly reduces the measurement cost.
  • FIG. 1 is a schematic diagram of an application scenario of a cell longitude and latitude prediction method in an embodiment of this application;
  • FIG. 2 is a schematic flowchart of a method for predicting longitude and latitude of a cell in an embodiment of the present application
  • FIG. 3 is a schematic diagram of excluding multi-path MR samples in an embodiment of the present application.
  • FIG. 4 is a schematic diagram of removing MR samples carrying inaccurate GNSS information and showing obvious solitary point features in an embodiment of the present application;
  • FIG. 5 is a schematic diagram of removing MR samples carrying inaccurate GNSS information and showing non-obvious solitary point features in an embodiment of the present application;
  • FIG. 6 is a schematic diagram of excluding multi-path MR samples in another embodiment of the present application.
  • FIG. 7 is a schematic diagram of identifying and excluding MR samples carrying inaccurate GNSS information and showing obvious outlier characteristics in an embodiment of the present application;
  • FIG. 8 is a schematic diagram of identifying and excluding MR samples carrying inaccurate GNSS information and showing non-obvious solitary point features in an embodiment of the present application;
  • FIG. 9 is a schematic diagram of determining a target area including a target cell according to an MR sample to be located in an embodiment of this application;
  • FIG. 10 is a schematic flowchart of a method for predicting a cell's latitude and longitude in another embodiment of the present application.
  • FIG. 11 is a schematic flowchart of a method for predicting a longitude and latitude of a cell in another embodiment of the present application.
  • FIG. 12 is a schematic structural diagram of a device for predicting a longitude and latitude of a cell in an embodiment of the present application
  • FIG. 13 is a schematic structural diagram of a server in an embodiment of the present application.
  • Base station which is a form of radio station, which refers to the transmission of information between a mobile communication terminal and a mobile phone terminal in a certain radio coverage area through a mobile communication switching center Radio transceiver station.
  • NodeBID is usually used to identify different base stations under the same RNC (Radio Network Controller), and in LTE networks, EnodeBID is usually used to identify different base stations in the same local network.
  • a cell a wireless logical area that provides services to users.
  • CellID is usually used to identify a unique cell under the same base station.
  • Measurement Report is the main method for obtaining wireless information of the terminal on the network side in the mobile communication system. It mainly includes two parts: uplink measurement and downlink measurement information.
  • the downlink measurement information is measured and collected by the terminal and reported to the base station through Measurement Signal Report of the air interface, and the uplink measurement information is measured and collected by the base station.
  • MR is divided into event type and periodic type. Periodic MR is usually used to evaluate network coverage, interference, quality, performance, traffic and other indicators. Based on the evaluation results, it is used in wireless network planning and optimization.
  • Timing Advanced refers to the difference between the actual time when the mobile station signal reaches the base station and the time when the mobile station signal reaches the base station when the distance between the mobile station and the base station is 0.
  • the base station always monitors the arrival time of the mobile station signal, and sends a TA command to the mobile station according to the change of the arrival time, so that the mobile station sends uplink information in advance so that the uplink information sent by the mobile station can be correctly received by the base station.
  • 1 TA unit represents the distance between the mobile station and the base station is about 78 meters, 1 TA contains 16 TS (Time Slot, time slot), the distance of each TS is about 4.9 meters.
  • Grid It refers to the area determined according to the position data. For example, if the surface of the earth is divided into an array of cells of uniform size and closely adjacent to each other, each cell is used as a pixel or pixel by rows and columns. Define and include a code to identify the attribute type or magnitude of the pixel. Therefore, the grid structure is a data organization that represents the distribution of spatial features or phenomena in a regular array, and each data in the organization represents the non-collective attribute characteristics of features or phenomena. In the field of mobile communications, rasterizing MR and other data is a common means of data tools for wireless networks, and is usually used to geographically describe the characteristics of network coverage, quality, interference, performance, and traffic in a cell.
  • RRU radio remote unit
  • RRU is a device that converts a baseband optical signal into a radio frequency signal at the far end and amplifies it to the antenna.
  • RRU is a new type of distributed network coverage mode. It places large-capacity macrocell base stations in the available central computer room, baseband part of the centralized processing, and uses optical fibers to extend the radio frequency module in the base station to the remote radio frequency unit. Separately placed on the site determined by the network plan.
  • the inventor of the present application found in research that in order to reduce the signal loss caused by feeder transmission during actual engineering installation, the feeder length between the RRU and the antenna should be as short as possible, so the RRU is usually installed next to or near the antenna Location, so the longitude and latitude of the cell can be considered to be equivalent to the longitude and latitude of RRU and antenna.
  • the actual location of each RRU in the cell needs to be located, for a multi-RRU scenario It defaults to the need to locate each RRU.
  • the latitude and longitude of the base station and the current base station are equal or nearly equal to each other, that is, the longitude and latitude of the cell is the longitude and latitude of the base station BBU to which the current cell belongs; and when the base station is an RRU remote station, such as a centralized type In the scenario of Centralized Radio Access Network (CRAN), the latitude and longitude of the cell are different from the latitude and longitude of the baseband processing unit (BBU) of the base station.
  • CRAN Centralized Radio Access Network
  • WCDMA Wideband Code Division Multiple Access
  • FDD-LTE Frequency Division Duplexing-Long Term Evolution
  • the requirements for clock synchronization are very high. Therefore, most base stations install GPS modules on the BBU side, which are mainly used for clock synchronization of the system. Measure and obtain BBU location information.
  • the location information of the BBU cannot be equal to the location information of the RRU. Therefore, the latitude and longitude of the BBU and the latitude and longitude of the cell are not directly equivalent, and cannot be directly applied to actual network operation and maintenance. At work.
  • the application obtains the periodic measurement report (MR) data carrying Global Navigation Satellite System (GNSS) information, and determines the time advance TA according to the MR data corresponding to the actual location of the cell.
  • MR periodic measurement report
  • GNSS Global Navigation Satellite System
  • the relationship between the representative distance and the linear distance between the antenna and the terminal (UE), and the horizontal distance between the antenna and the UE must be satisfied.
  • the distance represented by the time advancement TA determined by the MR data corresponding to the actual location of the cell and the linear distance between the antenna and the terminal (UE), and the horizontal distance between the antenna and the UE must be satisfied
  • the relationship is expressed by the following formula 1:
  • Distance_TA the distance represented by the time advance.
  • the time advance in MR can be obtained and converted into the corresponding distance; the time advance can be measured by BBU, and its measurement reference point actually represents the distance from BBU to UE, including the fiber length from BBU to RRU, and the distance from RRU to antenna Feeder length and antenna to UE propagation distance.
  • Distance_Horizon horizontal distance between antenna and UE. After obtaining the latitude and longitude of the UE and the latitude and longitude of the cell in the MR, the distance formula between two points can be used.
  • Distance_AntennatoUE linear distance between the antenna and the UE. There is a height difference between the antenna height and the UE height, so the linear distance between the antenna and the UE (Distance_AntennatoUE) and the horizontal distance between the antenna and the UE (Distance_Horizon) are not completely equal, and the height difference between the antenna and the UE needs to be considered.
  • the antenna height (Antenna Height) of each cell is different. After obtaining the more accurate antenna height of each cell, use the Pythagorean theorem formula to obtain the relationship between Distance_AntennatoUE and Distance_Horizon, the formula is
  • Antenna Height Antenna height.
  • UE Height UE height. When the UE can report MR data with GNSS information, the UE height is considered to be 1.5 meters.
  • Multipath error that is, the TA measurement value of the UE at a certain point due to signal reflection, refraction and other multipath propagation MR (referred to as multipath MR) relative to the UE's direct path propagation MR at this point (referred to as direct path MR)
  • multipath MR multipath propagation MR
  • direct path MR direct path propagation MR
  • the signal propagation distance between the UE and RRU calculated by TA and the actual distance between the UE and RRU The greater the distance error.
  • multipath errors cannot be completely avoided, and the wireless signal propagation environment is too complicated to accurately predict, but some methods can be used to identify and eliminate some multipath MRs.
  • Measurement Error measurement error, that is, the error generated by the base station when measuring TA. Affected by the sensitivity of system components and software algorithms, there are some errors in the system measurement TA itself. For the specified BBU, the measurement error can be approximated to a fixed value.
  • Measurement Interval Error measurement granularity error.
  • one TA is about 78 meters and one TS is about 4.9 meters. Therefore, if the TAs of two UEs measured in the same cell are equal, the distance between the two UEs and the cell is the largest. The error is about 78 meters; if the TA of the time slot granularity of two UEs measured in the same cell is equal, the maximum error of the distance between the two UEs and the cell is about 4.9 meters. The measurement granularity error cannot be avoided.
  • the TA of the time slot granularity can be obtained when performing cell longitude and latitude prediction. The measurement granularity error of the time slot granularity is small to avoid the influence of the measurement granularity error on the prediction result.
  • Fiber Length fiber length, fiber length from BBU to RRU.
  • the fiber length difference of each RRU under the same BBU may be large and cannot be accurately obtained. But for the same cell, the fiber length is fixed.
  • Feeder Length the length of the feeder, the length of the feeder from the RRU to the antenna. Since the feeder length of each cell is different, and the feeder length of each cell is not accurately recorded when the base station is installed, the feeder length of each cell cannot be accurately obtained. But for the same cell, the feeder length is fixed.
  • Measurement Error, Fiber Length and Feeder length can be regarded as fixed values in a cell, so for a designated cell, when only the direct path MR is selected, and only if the predicted longitude and latitude of the cell are at the actual location of the cell ,
  • Each MR in the MR set of the cell should theoretically be equal and fixed according to the X result calculated in Equation 2, that is, the distance represented by the time advance TA determined by the MR data corresponding to the actual location of the cell and the antenna
  • the relationship between the linear distance to the terminal (UE) and the horizontal distance between the antenna and the UE must be satisfied can also be expressed by the following formula 2.
  • the radius of the earth is R meters
  • the circumference of the earth at any longitude is 2* ⁇ *R meters.
  • the current latitude is Latitude
  • the earth circumference at latitude is R*cos(Latitude).
  • R 6371000 meters
  • the calculation method of calculating the distance between two points Taking point 1 and point 2 as an example, according to the latitude and longitude of point 1 and point 2, the distance between point 1 and point 2 can be calculated as follows: As shown:
  • Point1 represents point 1
  • Point2 represents point 2
  • Lon1 and Lon1 represent the longitude of point 1
  • point 2
  • Lat1 and Lat2 represent the latitude of point 1
  • point 2
  • 6371000 is the approximate radius of the earth, the unit is meter.
  • FIG. 2 is a cell longitude and latitude prediction method provided by an embodiment of the present application, including the following steps:
  • Step 103 Determine the MR sample to be located according to the location information and/or the time advance TA corresponding to the MR sample of the measurement report.
  • the MR sample is the MR sample of the target cell that carries the GNSS information of the global navigation satellite system within the set period.
  • the MR data carrying GNSS information may be obtained by the terminal UE reporting GNSS information or a request that the base station issues an RRC message that requires the UE to carry GNSS information in the MR.
  • the UE reporting GNSS information described in the 3GPPT S36.331 and 36.355 protocols as an example, when the UE accesses the network, the UE-BasedNetwPerfMeasParameters-r10 structure is identified as standaloneGNSS-Location- in the UECapabilityInformation message reported by the UE r10 supported, indicating that the UE supports the reporting of GNSS location information.
  • the base station may request the UE to carry GNSS information in the MR by configuring it as: includeLocationInfo-r10true in the measConfig in the RRC Connection Reconfiguration message. After the UE reports the GNSS information in the MR, the base station can record and save each MR carrying the GNSS information through post-processing.
  • the length of the setting period can be set according to the actual situation.
  • the main purpose is to ensure that the data volume of the collected MR samples is sufficient.
  • the setting period is usually 3 days or more, and for micro-stations or room sub-stations, the setting period is usually 5 days or more to ensure sufficient sample size and improve the accuracy of calculation results.
  • the location information corresponding to the MR samples refers to the GNSS information reported by the UE.
  • the MR sample contains a base station number and a cell number.
  • the cell number in the MR may refer to each RRU number, and the predicted cell latitude and longitude correspondingly refers to the location of each RRU under the cell.
  • the time advance TA refers to the TA of the time slot granularity.
  • determining the MR sample to be located according to the location information and/or time advance TA corresponding to the MR sample mainly refers to identifying and excluding multipath MR based on the location information and/or TA corresponding to the MR sample and MRs with inaccurate latitude and longitude are identified and eliminated, and MR samples with accurate latitude and longitude and direct-path MR are obtained as MR samples to be located.
  • Recognition and rejection of multi-path MR and rejection of MR data with inaccurate latitude and longitude can include the following ways:
  • the GNSS information reported by the MR is inaccurate and is used to identify and eliminate the MR that covers the isolated point.
  • the preset first threshold distance around the MR the other MRs belonging to the same cell The method of determining whether the density is lower than the preset second threshold to determine whether the corresponding MR is the coverage solitary point MR for identification and elimination.
  • the target cell is The MR corresponding to the TA with the highest proportion of MR sample points in each grid is set to the MR with the highest confidence, and among the MRs in the same grid belonging to the target cell, the TA value closest to the one with the highest confidence is selected.
  • the MR is set to a MR with a higher confidence, and the target cell is identified and eliminated in the MR with a non-highest and a non-high confidence in each grid.
  • the multipath MR with a higher probability is eliminated. Since the larger the TA, the multipath MR with a higher probability exists, so the MR set with a relatively small TA is selected. Ensure that the number of MR samples in the MR set meets a preset third threshold, and remove the remaining MR.
  • the identification and rejection of multipath MR and the removal of MR data with inaccurate latitude and longitude can reduce the calculation error introduced by multipath MR and the introduction of inaccurate MR carrying GNSS information. Calculation error, improve calculation accuracy.
  • determining the MR sample to be located according to the position information and/or TA corresponding to the MR sample when the predicted latitude and longitude of the target cell is located at the actual location of the target cell, each of the MR samples of the target cell reports the accurate latitude and longitude and is the direct path of the MR.
  • the distance represented by TA minus the straight-line distance between the antenna and the UE in MR is the closest set of equal and fixed values.
  • Step 105 Determine a target area including the target cell according to the MR sample to be located.
  • a closed area including the actual location of the target cell and the smallest possible area can be obtained, which can be reduced The amount of calculation to predict the actual location of the target cell in the target area and improve the accuracy of the prediction result.
  • Step 107 Select a to-be-determined location according to the target area, and predict the latitude and longitude of the target cell according to the location information and the location information of the to-be-determined location carried in the MR sample to be located, and the distance represented by the corresponding TA.
  • the to-be-determined position according to the target area, and use the to-be-determined position as the assumed actual position of the target cell, using the MR data corresponding to the actual position of the cell to determine the distance represented by TA and the linear distance between the antenna and the UE, and the level between the antenna and the UE
  • the relationship between the distances will inevitably be satisfied, and the predicted latitude and longitude of the target cell are calculated using the to-be-determined location as the assumed actual location of the target cell.
  • a plurality of the to-be-determined positions can be selected according to the setting rules.
  • a to-be-determined position point can be found by traversing and iterating, and the to-be-determined position point is used as the assumed actual position of the target cell.
  • the X sets calculated according to the association relationship are closest to be equal, so that the latitude and longitude of the target cell can be predicted through the to-be-determined location point.
  • the MR samples are screened to determine the MR samples to be located according to the location information and/or timing advance of the MR samples carrying GNSS information within the set period of the target cell, and determined according to the MR samples to be located Predict the latitude and longitude of the target cell.
  • MR samples to be located are based on the location information and/or TA of the collected MR samples.
  • the method of screening and determining, so as to predict the longitude and latitude of the cell has no special requirements for the station type of the cell. It is suitable for omnidirectional stations, directional stations, remote stations, micro stations, and room substations. The measurement accuracy is high. It improves the efficiency of cell latitude and longitude calibration in engineering parameters, lays a good foundation for network operation and maintenance, and greatly reduces measurement costs.
  • step 103 before determining the MR sample to be located according to the position information and/or the time advance TA corresponding to the MR sample of the measurement report, the method further includes:
  • the collection may refer to the MR sample data corresponding to the GNSS information reported by the UE, or the MR sample data corresponding to the GNSS information reported by the UE based on the configuration request in the RRC message sent by the base station that requires the UE to carry the GNSS information in the MR.
  • determining the MR sample to be located according to the location information and/or time advance TA corresponding to the MR sample of the measurement report includes:
  • the position information and time advance TA corresponding to the MR samples of the measurement report determine that the MR samples whose positions meet the set conditions and the TAs are different form the MR set;
  • Deduplication processing is performed according to whether the TA in the MR set meets the setting requirements.
  • the position meets the setting condition means that the positions corresponding to the MR samples are the same or similar, and can be determined according to the GNSS information carried in the MR samples. Whether the TA meets the setting requirements may refer to one or more of the TAs that are relatively small. Referring to FIG. 3, under the same cell, the signal TA propagated by the direct path between the antenna and the antenna at the same position is the smallest. By selecting the MR set formed by the MR samples at the same position or close to The other MRs in the set can be deduplicated, and multipath MRs with the same or similar positions can be eliminated.
  • the deduplication processing according to whether the TA in the MR set meets the setting requirements refers to excluding the MR other than the smallest TA for the same MR set at the same position or close to each other, such as corresponding to the first position Position1
  • determining the MR sample to be located according to the location information and/or the time advance TA corresponding to the MR sample of the measurement report includes:
  • the MR samples to be identified are rejected.
  • whether the distribution density meets the setting condition may refer to that the distribution density is less than a preset distribution density threshold.
  • MR samples that actually contain inaccurate latitude and longitude information can be identified and rejected to Avoid the influence of introducing MR with inaccurate latitude and longitude on the prediction result of the actual location of the cell.
  • determining the MR sample to be located according to the location information and/or the time advance TA corresponding to the MR sample of the measurement report includes:
  • a target TA is determined according to the ratio of the number of MR samples in each grid, and a target MR sample with a confidence level that meets the requirements is determined according to the target TA. For the other than the target MR sample in the grid MR samples are rejected.
  • the target MR sample whose confidence level meets the requirements may refer to one or more MR samples with relatively high confidence in the position information in each grid.
  • determining the target TA according to the proportion of the number of MR samples in each grid, and determining the target MR sample with confidence according to the target TA according to the target TA may include: matching the corresponding MR sample in each grid The TA with the highest number ratio is regarded as the target TA, and the MR sample corresponding to the target TA is regarded as the MR with the highest confidence, and the TA that is the closest to the target TA among the MR samples in the same grid The MR sample corresponding to the neighboring TA is regarded as the MR with higher confidence.
  • determining the MR sample to be located according to the location information and/or the time advance TA corresponding to the MR sample of the measurement report includes:
  • a set number of target MR samples with a relatively small TA in the TA ranking are selected, and MR samples other than the target MR sample are excluded.
  • the set number can be adjusted according to the actual situation, and the size of the set number should ensure that the number of MR samples can meet the sampling accuracy.
  • FIG. 6 Generally, the larger the TA, the higher the probability that there will be multipath MR samples. As shown in FIG. 6, TA>4 MR samples indicated by white dots, TA is relatively small by selecting TA Set the number of target MR samples, select the MR with a smaller TA from the MR samples as the target MR sample, and make the number of target MR samples corresponding to the target cell meet the preset number threshold, and remove the remaining MR samples To remove MR samples with relatively high probability as multipath MR samples. In this way, under the condition that the number of MR samples can meet the accuracy of calculating the cell position, MR data with a small TA is preferentially selected to reduce errors caused by multipath MR.
  • determining the MR sample to be located according to the location information and/or the time advance TA corresponding to the MR sample of the measurement report includes:
  • the size of the grid can be a set size, such as 10 meters.
  • the position information corresponding to the MR samples belonging to the same grid is the same or similar. Since the signal TA propagated by the direct path between the antenna and the antenna at the same position in the same cell is the smallest, it is formed by MR samples at the same position or similar In the MR set of the MR set, that is, the MR set formed by the MRs in the same grid, the MR with the smallest TA is selected and the other MRs in the MR set are deduplicated. The multipaths with the same position or similar MR is rejected.
  • Rasterize the MR data of the target cell where the grid can be GridSize, and the size of the grid can be set according to TA, such as between 1TA and 2TA, and establish between each MR and the home grid Correspondence.
  • This correspondence can be recorded in a table, as shown in Table 1 below:
  • step 103 determining the MR sample to be located according to the location information corresponding to the MR sample of the measurement report and the time advance TA, further including:
  • the number of adjacent grids contained within the first threshold distance relative to the position of the initial grid is determined, and when it is determined that the number of adjacent grids is less than a preset value, the MR samples contained in the initial grid are eliminated.
  • the number of adjacent grids is used to determine the corresponding MR sample surrounding the initial grid. Distribution density.
  • the number of neighboring grids is less than the preset value, that is, when the distribution density of MR samples around the initial grid is small, it can be recognized that the MR points in the initial grid exhibit the characteristics of isolated points, so that the The MR samples in the initial grid are eliminated.
  • the first threshold distance can be set as Between GirdSize*2, it can be considered that there are 8 adjacent grids around the grid.
  • the first threshold distance may be GridSize*1.5, and the preset value is 4.
  • the number of Gird2's neighboring grids within the first threshold distance is less than the preset value, which shows obvious characteristics of isolated points, which can be identified as MRs carrying inaccurate latitude and longitude information and eliminated.
  • step 103 determining the MR sample to be located according to the location information corresponding to the MR sample of the measurement report and the time advance TA, further including:
  • the target TA is determined according to the TA with the highest ratio, and the MR samples other than the MR samples corresponding to the target TA in each grid are eliminated.
  • counting the proportion of the corresponding TA in the MR samples contained in each grid may refer to taking the integer corresponding to the TA corresponding to the MR samples contained in each grid, for example, by removing the fractional part of the corresponding TA, Or, the fractional part of TA is processed according to the rule of four provinces and five enters to obtain the corresponding TA integer value, and the respective proportions of the TAs after the integers are taken statistically.
  • counting the proportion of the corresponding TA in the MR samples contained in each grid may also refer to directly counting the TA without taking an integer The proportion of the corresponding TA in the MR samples contained in each grid.
  • Determining the target TA based on the TA with the highest ratio may refer to directly using the TA with the highest ratio as the target TA, or using the TA with the highest ratio and a TA that is similar in size to the TA with the highest ratio as the target TA.
  • the TA integer value with the highest TA integer value in each grid may be taken as TAmax, and TAmax-1 and TAmax+, which are similar in size to TAmax, may be determined according to TAmax. 1 respectively as the target TA, and excluding the other MR samples in each grid except the corresponding TAmax, TAmax-1 and TAmax+1. Taking Grid1 in FIG.
  • the target TA is determined by taking the TA with the highest proportion of the corresponding TAs in the same grid to realize the use of the MR sample with the highest TA reported by the UE within a close range as the MR sample with the highest position confidence, and the ratio with the TA
  • the MR sample corresponding to the highest neighboring TA is regarded as the MR sample with a higher position confidence, and the MR samples except the highest confidence and the higher confidence are eliminated, so that the solitary point features that do not show obvious and carry A class of MR samples with inaccurate GNSS information are identified and eliminated to avoid the impact of introducing inaccurate latitude and longitude MR on the prediction results of the actual location of the cell.
  • step 103 determining the MR sample to be located according to the location information corresponding to the MR sample of the measurement report and the time advance TA, further including:
  • the set number can be adjusted according to the actual situation, and the size of the set number should ensure that the number of MR samples can meet the sampling accuracy.
  • the setting number is 30.
  • the current prediction of the latitude and longitude of the target cell can be exited to avoid that the number of valid target MR samples that should be collected is insufficient to meet the prediction of the latitude and longitude Precision requirements.
  • the target area including the target cell is determined according to the MR sample to be located, including:
  • determining the initial position information according to the TA corresponding to the MR sample to be located may be calculating the longitude mean Longitude_center and the latitude mean Latitude_center separately from the position information carried in the MR sample to be located, and using the longitude mean Longitude_center and the latitude mean Latitude_cente as the initial position information.
  • Determining the reference MR sample according to the TA corresponding to the MR sample to be located may refer to selecting the MR with the smallest TA among the MR samples to be located as the reference MR sample, and recording the number n of reference MR samples.
  • the reference position information is determined according to the distance between the reference MR sample and the initial position information, which may be calculated as the distance between the MR sample with the smallest TA and the longitude mean Longitude_center and latitude mean Latitude_center, sorted according to the distance from small to large Then, find the latitude and longitude of the MR sample point corresponding to the median in the distance set, and record them as Longitude and Latitude, and use Longitude and Latitude as reference position information, respectively.
  • Obtaining the latitude and longitude of the MR sample point corresponding to the median in the distance set may include: if n is an odd number, find the (n+1)/2th MR sample in the distance set sorted from small to large distance, and divide the The latitude and longitude of (n+1)/2 MR samples are used as the reference position information; if n is an even number, find the n/2th MR sample in the distance set sorted from small to large and set the n/2th MR sample The latitude and longitude of the MR sample is used as reference position information.
  • the first longitude distance and the first latitude distance extended to the surroundings are obtained, and the target area including the target cell is obtained according to the extended position, and the actual position and area including the target cell Closed area as small as possible.
  • the distance represented by the TA corresponding to the reference MR sample is converted to longitude and latitude, which may refer to the minimum distance Distance_min grouping the reference MR sample to the target cell is converted to the degree of longitude and latitude, and the first longitude distance and the first A latitude distance is recorded as Longitude_Distance and Latitude_Distance.
  • the specific conversion formula for the first longitude distance and the first latitude distance can be as follows:
  • the first longitude distance and the first latitude distance respectively extended to the surroundings refer to the first longitude distance and the first latitude distance corresponding to the east, west, south, north, respectively, centered on the reference position information, so that The actual location of the target cell must be able to fall within the target area after expansion.
  • the first longitude distance is extended outwards from east to west with the longitude in the reference position information as the center, and the north and south directions are respectively centered with the latitude in the reference position information as the center Expand Latitude_Distance, the specific calculation formula can be as follows:
  • the latitude and longitude of the four corners of the expanded target area are A1: (Longitude_Min, Latitude_Min), B1: (Longitude_Max, Latitude_Min), C1: (Longitude_Min, Latitude_Max), D1: (Longitude_Max, Latitude_Max).
  • the to-be-determined location is selected according to the target area, and the location information of the to-be-located MR sample and the location information of the to-be-determined location are predicted based on the distance represented by the corresponding TA
  • the longitude and latitude of the target cell including:
  • the predicted latitude and longitude of the target cell are determined.
  • the size of the step can be set according to actual needs, and converted to the degree of longitude and latitude according to the size of the step, which is recorded as the longitude step length Longitude_Y and the latitude step length Latitude_Y.
  • the longitude step length Longitude_Y As an example, the longitude step length Longitude_Y
  • the conversion formula of Latitude_Y and latitude step can be as follows:
  • the antenna Iteratively calculates the association relationship that must be satisfied between the horizontal distance and the UE to determine the predicted distance between the base station corresponding to each MR sample to be located and the target cell, and obtains the X value of each MR sample.
  • the predicted distance set is recorded as ⁇ X1..Xm ⁇ [i][j].
  • variables i and j can be set to represent the current iteration round corresponding to longitude and latitude respectively, then the longitude of the current iteration round can be recorded as Longitude_Min+i*Longitude_Y, the latitude of the current iteration round It can be written as Latitude_Min+j*Latitude_Y.
  • the horizontal distance Distance_Horizon between the antenna and the UE is calculated according to the latitude and longitude of the to-be-determined position obtained by the current iteration round and the latitude and longitude information carried in the MR to be located respectively.
  • the predicted latitude and longitude of the target cell is determined according to the predicted distance set, which may be calculated as the mean square deviation of the predicted distance set for each iteration round, which is recorded as STDEV(X1..Xm)[i][j], and select all A plurality of rounds with the smallest mean square deviation among the iteration rounds, such as the latitude and longitude of the three rounds, and calculating the average of the latitude and longitude of the multiple rounds respectively, and using the average as the predicted latitude and longitude of the target cell.
  • the number of multiple rounds with the smallest average variance among all iteration rounds may be other numbers of multiple rounds, and the prediction of the target cell is obtained according to the latitude and longitude of the to-be-determined position corresponding to the multiple rounds
  • the latitude and longitude are not limited to calculating the average value, but can also be determined by taking an intermediate value, or taking a part of the latitude and longitude of the position to be determined.
  • the cell longitude and latitude prediction method further includes:
  • the predicted latitude and longitude corresponding to the target cell and the predicted latitude and longitude corresponding to other cells are determined.
  • the set formed by the corresponding predicted latitude and longitude results obtained by the set of cells located at the same position is subjected to secondary processing, such as averaging the predicted latitude and longitude of the cells located at the same position, ie The predicted latitudes and longitudes of multiple cells located at the same location can be obtained.
  • secondary processing such as averaging the predicted latitude and longitude of the cells located at the same position, ie The predicted latitudes and longitudes of multiple cells located at the same location can be obtained.
  • the cell latitude and longitude prediction method includes the following steps:
  • Step S11 Collect MR samples carrying GNSS information corresponding to the cell to be located; the cell to be located is the target cell.
  • step S12 the multipath MRs with the same or similar positions are eliminated: the MR sets with the same or similar positions but different timing advances in the MR data of the cell to be located containing the user reported latitude and longitude information are performed according to the method of taking the minimum timing advance MR Deduplication processing.
  • Step S13 Recognize and remove the MR reported by the MR with inaccurate latitude and longitude and for covering the isolated point MR: According to the preset first threshold distance around the MR, the density of other MRs belonging to the same cell is lower than the preset The two-threshold method is used to identify and eliminate the coverage solitary point MR.
  • Step S14 Identify and eliminate the MR of the cell with inaccurate latitude and longitude and non-covering outliers: After rasterizing the MR data of the cell, the cell has the highest proportion of MR sample points in each grid. The corresponding MR is set to the MR with the highest latitude and longitude confidence, and the MR belonging to the same grid of the cell is selected. The MR with the TA closest to the MR with the highest latitude and longitude confidence is selected as the MR with the higher latitude and longitude confidence. , And excluding other MRs with non-highest and non-high confidence in latitude and longitude of the cell in each grid.
  • step S15 multipath MR sample points with large TA and high probability of multipath propagation are eliminated: the MR set with a small TA is preferred and the MR set to be located is generated, and the number of sample points of the MR set to be located in the cell is satisfied A preset third threshold is used to remove the remaining MR sample points.
  • the MR set to be located with accurate latitude and longitude of the cell and a direct path is obtained.
  • Step S16 Obtain a closed area including the actual location of the cell according to the data of the MR set to be located with accurate latitude and longitude of the cell and a direct path; determine the closed area based on the data of the MR set to be located with accurate longitude and latitude of the cell and the direct path , So that the actual location of the cell must fall within the enclosed area, and the enclosed area is as small as possible.
  • step S17 an objective function is set.
  • the objective function is as follows:
  • X calculated by each to-be-located MR is equal and a fixed value;
  • Distance_TA is the distance represented by the time advance;
  • Distance_Horizon is the horizontal distance between the UE and the cell antenna, where, The latitude and longitude of the UE can be obtained directly in the MR.
  • Distance_Horizon can be obtained according to the calculation formula of the ground horizontal distance between two points; Antenna Height is the antenna height; UE_Height is the UE height, which is 1.5 meters; Distance_AntennatoUE is The linear distance between the antenna and the UE can be obtained according to the Pythagorean theorem using the horizontal distance between the antenna and the UE as the hook and the height difference between the antenna and the UE as the stock. Measurement Error is the system measurement error; Fiber Length is the fiber length; Feeder Length is the feeder length.
  • each X and L is accurate and is the set of X calculated by the MR to be located for the direct path. It is close to equal and should be a fixed value.
  • Step S18 Find a point in the enclosed area by traversing iteratively, so that when the predicted position of the cell is located at this point, the MR set of the MR set to be located in which the longitude and latitude of the cell is accurate and is the direct path is calculated by each MR The X set is closest to equal, and the latitude and longitude of this point is used as the predicted latitude and longitude result of the cell to be located.
  • Step S19 when it is known that several cells belong to the same position, secondary processing is performed according to the cell prediction latitude and longitude result set obtained by the cell set belonging to the same location to obtain a predicted latitude and longitude result.
  • the cell longitude and latitude prediction method includes the following steps:
  • step S21 the target cell is selected, and the start time and end time of data collection are set. It is recommended to collect data for more than 3 consecutive days for macro stations and more than 5 days for micro-stations and room sub-stations to ensure that the amount of data is sufficient to avoid errors in calculation results caused by insufficient sample points.
  • Step S22 Collect MR data carrying the GNSS information within the set start time and end time of the target cell selected in step S21.
  • the MR data needs to include the time advance information of the base station number, cell number, longitude, latitude, and slot level.
  • Step S23 Rasterize the MR data of the cell carrying GNSS information, and perform deduplication processing on sample points belonging to the same grid; where the grid size can be 10 meters, the Take the MR sample point with the smallest TA in the MR set, and remove the MR sample points other than the MR sample point with the smallest TA;
  • the mapping relationship between each MR of the cell and the home grid may be established as shown in Table 1 in the foregoing embodiment, and will not be repeated here.
  • Step S25 Calculate the density of the grid containing the cell MR within a certain distance around the grid containing the cell MR, and identify and eliminate the inaccurate latitude and longitude according to the density and cover the isolated point MR; where If the number of neighboring grids containing the MR of the cell exists within a preset distance range around the grid containing the MR of the cell ⁇ a preset threshold, the MR points in the grid can be identified as covering isolated points, And remove these MR points from Table 1.
  • step S26 after the processing in step S25, the TA of the MR set of the cell in each grid of the cell in Table 1 is taken as an integer.
  • the latitude and longitude are not accurate and are not Recognition and elimination of covered solitary points MR; among them, please refer to FIG. 8 again, the TA integer value with the highest proportion of TA integer values in each grid can be taken as TAmax.
  • Table 1 the corresponding TA integer values in the MR sample points of the cells in each grid are excluded from the MR sample points except TAmax-1, TAmax, and TAmax+1.
  • Step S27 Set a threshold m of the MR sample point to be located in the cell, and select a MR sample point with a relatively small TA value reaching the sample point threshold m according to the TA value from small to large; wherein, the sample point threshold may be 30 , According to the TA value from small to large, select the MR sample point with a relatively small TA value reaching the sample point threshold m, you can start from the MR with the smallest TA value in Table 1, and select the MR to be located according to the TA value from small to large Sample points until the number of MR sample points to be located reaches the MR sample point threshold.
  • the cell does not meet the accuracy requirements for positioning latitude and longitude, and exits.
  • step S29 the MR set to be located in the cell is respectively obtained a longitude mean Longitude_center and a latitude mean Latitude_center.
  • the calculation method to obtain the MR sample points corresponding to the median of the distance set can be: if n is an odd number, find the (n+1)/2th MR record in the set sorted from small to large distance, if n is an even number Find the n/2th MR record in the set sorted from small to large distance.
  • Step S31 converting the minimum distance Distance_min corresponding to TA_min in the MR set to be located obtained in step S28 into longitude and latitude degrees; the longitude and latitude degrees can be recorded as Longitude_Distance and Latitude_Distance, then:
  • Step S32 According to the Longitude obtained in step S30, extend Longitude_Distance outward from east to west, and Latitude expand Latitude_Distance outward from north to south, respectively, to obtain the target area; please refer to FIG. 9 again, where the actual location of the cell must be able to fall in latitude and longitude Within the scope of the target area formed after the respective expansion, namely:
  • the latitude and longitude of the four corners of the expanded rectangle are as follows:
  • Step S33 Set Y meters as the step length and convert them to the corresponding longitude and latitude degrees, and record them as Longitude_Y and Latitude_Y; where Y can be selected as 5 meters, then:
  • Step S34 starting from setting the origin in the target area, the longitude uses Longitude_Y as the step size, and the latitude uses the Latitude_Y as the step size to perform a binary nested iteration, and each iteration obtains a latitude and longitude as i*Longitude_Min+j*Longitude_Y; where, The origin can start with (Longitude_Min, Latitude_Min), longitude takes Longitude_Y as step, and latitude takes Latitude_Y as step to select the position to be determined in turn until it reaches (Longitude_Max, Latitude_Max).
  • Step S35 calculating the horizontal distance Distance_Horizon from the latitude and longitude of the to-be-determined position obtained in the current iteration round and each latitude and longitude in the MR set to be located respectively.
  • the linear distance between the antenna and the UE is calculated using the distance between the antenna height and the UE height and the horizontal distance Distance_Horizon between the antenna and the UE.
  • the formula is:
  • step S38 the mean square error of the set of predicted distances for each iteration round is calculated, and recorded as STDEV(X1..Xm)[i][j].
  • Step S39 after the iteration is completed, the latitude and longitude of the multiple rounds with the smallest STDEV(X1..Xm)[i][j] in all iteration rounds are taken, and the cell is determined according to the average of the latitude and longitude of the multiple rounds Predicted longitude and latitude; where, the longitude and latitude of the three rounds with the smallest average variance among all iteration rounds can be taken, and the 3 longitudes and 3 latitudes are averaged respectively to obtain the predicted longitude and latitude of the cell.
  • Step S40 When it is known that several cells are located at the same location, the predicted latitude and longitude of the cells located at the same location are averaged to obtain the predicted latitude and longitude of the cells located at the same location.
  • the method for predicting cell latitude and longitude can make predictions through the mobile communication network system side, and can obtain relatively accurate cell longitude and latitude prediction results from the system side without going to the station for measurement. Since the longitude and latitude prediction results of the cell are achieved by collecting the MR data of the target cell, there is no special requirement for the cell type of the cell, and it is applicable to the omnidirectional station, directional station, micro station, remote station, and room substation.
  • the average precision of the obtained latitude and longitude of the cell can reach about 20-40 meters, and the proportion of cells within 50 meters can reach 80%-90%.
  • the distance between the predicted longitude and latitude of the cell and the longitude and latitude of the engineering parameter of the cell may be used
  • the confidence threshold for predicting the accuracy of the latitude and longitude provided by the embodiment of the present application is exceeded, it can be confirmed that the latitude and longitude of the engineering parameter of the cell is inaccurate.
  • the range of the cell that needs to be measured by the upper station in the operator's network can be greatly reduced, thereby greatly improving the efficiency of the calibration of the latitude and longitude of the engineering parameter. Operation and maintenance laid a good foundation.
  • the cell latitude and longitude prediction device includes: a sample selection module 13 configured to position information and/or time advance TA corresponding to MR samples according to a measurement report , Determine the MR sample to be located, the MR sample is the MR sample carrying the GNSS information of the global navigation satellite system within the set period of the target cell; the position determination module 15 is configured to determine that the target cell is included according to the MR sample to be located The target area included; the prediction module 17 is configured to select a to-be-determined position according to the target area, according to the position information and the position information of the to-be-determined position carried in the MR sample to be located, and the corresponding TA representative The distance predicts the latitude and longitude of the target cell.
  • the sample screening module 13 is specifically configured to determine that MR samples whose positions meet the set conditions and whose TAs are different according to the measurement report corresponding to the location information and time advance TA of the MR samples in the measurement report form an MR set; according to the MR Whether the TA in the set meets the setting requirements for deduplication processing.
  • the sample screening module 13 is specifically configured to use the measurement report MR samples as MR samples to be identified, and determine the presence of the MR samples to be identified based on the position information corresponding to the MR samples to be identified.
  • the distribution density of the MR samples contained within the first threshold distance; according to whether the distribution density meets the set condition, the MR samples to be identified are eliminated.
  • the sample screening module 13 is specifically configured to rasterize the measurement report MR samples and establish a correspondence between each of the MR samples and the attribution grid; according to each of the grids The ratio of the number of MR samples in the grid determines the target TA, determines the target MR sample with confidence according to the target TA, and excludes other MR samples in the grid except the target MR sample.
  • the sample screening module 13 is specifically configured to select a set number of target MR samples with a relatively small TA in the TA ranking according to the time advance TA corresponding to the measurement report MR sample, and select MR samples other than MR samples are eliminated.
  • the sample screening module 13 is specifically configured to rasterize the measurement report MR samples, and form MR sets of the MR samples belonging to the same grid; for each of the MR The MR samples other than the MR sample with the smallest TA in the set are deduplicated.
  • the sample screening module 13 is further configured to select any grid as the initial grid to determine the position of the initial grid; determine the neighboring grid included in the first threshold distance relative to the position of the initial grid When it is determined that the number of adjacent grids is less than a preset value, the MR samples contained in the initial grid are eliminated.
  • the sample screening module 13 is further configured to count the proportion of the corresponding TA in the MR samples contained in each of the grids; determine the target TA according to the TA with the highest proportion, and for each of the grids Other MR samples in the grid except the MR samples corresponding to the target TA are excluded.
  • the sample screening module 13 is further configured to sort the MR samples according to TA; determine a set number of target MR samples in which TA is sorted from small to large in the sorting, and determine other than the target MR samples MR samples are rejected.
  • the position determination module 15 is specifically configured to determine the initial position information and the reference MR sample according to the TA corresponding to the MR sample to be located; according to the relationship between the reference MR sample and the initial position information Determine the reference position information based on the distance; expand the first longitude distance and the first latitude distance that are set to the surroundings according to the reference position information, and obtain the target area including the target cell according to the expanded position.
  • the position determination module 15 is further configured to convert the distance represented by the TA corresponding to the reference MR sample into longitude and latitude, and determine the first longitude distance and the first latitude distance according to the conversion result.
  • the prediction module 17 is specifically configured to determine a to-be-determined position with a point in the target area as an origin and a set distance as a step; according to the position information carried in the MR sample to be located And the position information of the to-be-determined position, respectively determine the distance between the antenna corresponding to each MR sample point to be located and the terminal; determine the distance represented by the TA corresponding to the MR sample to be located; according to each of the to-be-determined Determine the predicted distance between the base station and the target cell corresponding to each MR sample to be located, and the distance between the TA represented by the corresponding MR sample and the corresponding distance between the antenna and the terminal A set of predicted distances corresponding to the to-be-determined location; according to the set of predicted distances, the predicted latitude and longitude of the target cell are determined.
  • the cell latitude and longitude prediction device When the cell latitude and longitude prediction device provided by the above embodiment performs cell latitude and longitude prediction, only the above division of each program module is used as an example for illustration. In practical applications, the above steps may be allocated by different program modules according to needs, that is, Divide the internal structure of the device into different program modules to complete all or part of the processing described above.
  • the device for predicting the longitude and latitude of the cell provided in the above embodiment belongs to the same concept as the embodiment of the method for predicting the longitude and latitude of the cell. For the specific implementation process, refer to the method embodiment, which is not repeated here.
  • the server includes a processor 201 and a storage medium 202 for storing a computer program that can be run on the processor 201, where the processor 201 is configured to execute any task of this application when it is configured to run the computer program
  • the steps of the cell longitude and latitude prediction method provided by an embodiment.
  • the server also includes a memory 203, a network interface 204, and a system bus 205 connecting the processor 201, the memory 203, the network interface 204, and the storage medium 202.
  • the storage medium 202 stores an operating system and a cell longitude and latitude prediction device for implementing the memory cache management method provided by the embodiments of the present application.
  • the processor 201 is used to improve calculation and control capabilities and support the operation of the entire server.
  • the memory 203 is used to provide an environment for the operation of the memory buffer management method in the storage medium 202.
  • the network interface 204 is configured to perform network communication with the base station, receive or send data, for example, obtain MR sample data collected by the base station, and return the latitude and longitude of the cell Forecast results, etc.
  • a base station analyzes the collected MR sample data to perform the cell longitude and latitude prediction method provided in the embodiment of the present application to predict the cell latitude and longitude based on the MR sample data.
  • the base station includes a processor and a storage medium for storing a computer program that can run on the processor, where the processor is configured to execute the cell latitude and longitude prediction provided by any embodiment of the present application when the processor is configured to run the computer program Method steps.
  • An embodiment of the present application further provides a storage medium, for example, including a memory storing a computer program, and the computer program can be executed by a processor to complete the steps of the dispersion estimation method provided by any embodiment of the present application.
  • the storage medium may be a memory such as FRAM, ROM, PROM, EPROM, EEPROM, Flash, magnetic surface memory, optical disk, or CD-ROM; it may also be a variety of devices including one or any combination of the above memories.

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Abstract

本申请公开一种小区经纬度确定方法、装置、服务器、基站及存储介质，该方法包括：根据测量报告MR样本对应的位置信息和/或时间提前量TA，确定待定位MR样本，所述MR样本为目标小区在设置周期内的携带全球导航卫星系统GNSS信息的MR样本；根据所述待定位MR样本确定包含所述目标小区在内的目标区域；根据所述目标区域选取待定位置，根据所述待定位MR样本中携带的所述位置信息和所述待定位置的位置信息、以及对应的TA代表的距离预测所述目标小区的经纬度。

Description

小区经纬度预测方法、装置、服务器、基站及存储介质
相关申请的交叉引用
本申请基于申请号为201811476242.2、申请日为2018年12月04日的中国专利申请提出，并要求该中国专利申请的优先权，该中国专利申请的全部内容在此引入本申请作为参考。
技术领域
本申请涉及对移动通信网络小区定位的技术，尤其涉及一种小区经纬度预测方法、装置、服务器、基站及存储介质。
背景技术
工程参数，也称为基站信息表，主要包含基站和小区的经纬度、天线高度、方位角、下倾角等信息。
在无线网络运维领域中，无论是网络仿真、加站、扩容规划，还是无线网络覆盖分析，邻区、扰码、PCI等组网参数规划与优化，天线方位角下倾角优化等工作，其准确性严重依赖于工程参数的准确性。如果工程参数出现较大的偏差，会导致网络优化方案的效果不佳，甚至带来网络性能的恶化。此外基站维护工程师需要获取到准确的小区经纬度信息才能找到RRU和天线的准确位置，以便于进行设备维护工作。因此获取准确的小区经纬度信息是无线网络运维工作的基础。
相关技术中，小区经纬度信息通常是在基站安装和开通时，由工程师利用GPS手持终端在实地测量得到并记录的。由于工程师在测量和记录的过程中全部为手工操作，难免出现错误。
为了更好的对网络进行运维，获得准确的小区经纬度信息，运营商需要进行基站基础信息的普查工作，不仅需要耗费大量的人力物力，而且需要耗费大量的时间成本，效率低下，且由于仍需手动测量和记录，仍存在出错的可能。
发明内容
本申请实施例提供一种小区经纬度预测方法、装置、服务器、基站及存储介质。
本申请实施例的技术方案是这样实现的：
一种小区经纬度预测方法，包括：根据测量报告MR样本对应的位置信息和/或时间提前量TA，确定待定位MR样本，所述MR样本为目标小区在设置周期内的携带全球导航卫星系统GNSS信息的MR样本；根据所述待定位MR样本确定包含所述目标小区在内的目标区域；根据所述目标区域选取待定位置，根据所述待定位MR样本中携带的所述位置信息和所述待定位置的位置信息、以及对应的TA代表的距离预测所述目标小区的经纬度。
一种小区经纬度预测装置，包括：样本筛选模块，配置为根据测量报告MR样本对应的位置信息和/或时间提前量TA，确定待定位MR样本，所述MR样本为目标小区在设置周期内的携带全球导航卫星系统GNSS信息的MR样本；位置确定模块，配置为根据所述待定位MR样本确定包含所述目标小区在内的目标区域；预测模块，配置为根据所述目标区域选取待定位置，根据所述待定位MR样本中携带的所述位置信息和所述待定位置的位置信息、以及对应的TA代表的距离预测所述目标小区的经纬度。
一种服务器，包括处理器和用于存储能够在处理器上运行的计算机程序的存储器；其中，所述处理器配置为运行所述计算机程序时，执行本申请实施例所述的小区经纬度预测方法。
一种基站，包括处理器和用于存储能够在处理器上运行的计算机程序的存储器；其中，所述处理器配置为运行所述计算机程序时，执行本申请实施例所述的小区经纬度预测方法。
一种存储介质，所述存储介质中存储有可执行指令，所述可执行指令被处理器执行时实现本申请实施例所述的小区经纬度预测方法。
上述实施例所提供的小区经纬度预测方法、装置、服务器、基站及存储介质，通过根据目标小区在设置周期内的携带全球导航卫星系统GNSS信息的MR样本对应的位置信息和/或时间提前量，对MR样本进行筛选以确定待定位MR样本，并根据待定位MR样本确定的目标区域中包含的待定位置，预测所 述目标小区的经纬度，如此，通过根据待定位MR样本中携带的位置信息与对应的目标小区的实际位置之间将满足的距离关系，可以实现对目标小区的实际位置的准确预测，无需上站测量即可进行小区的经纬度预测；待定位MR样本是通过采集到的MR样本对应的位置信息和/或TA进行筛选确定的，从而该对小区的经纬度进行预测的方法对该小区的站型没有特殊要求，对全向站、定向站、拉远站、微站、室分站等站型均适合，测量精度高，提升了工程参数中小区经纬度校准的效率，为网络运维打下良好的基础，大大减小测量成本。
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附图用来提供对本申请技术方案的进一步理解，并且构成说明书的一部分，与本申请的实施例一起用于解释本申请的技术方案，并不构成对本申请技术方案的限制。
图1为本申请一实施例中小区经纬度预测方法应用场景示意图；
图2为本申请一实施例中小区经纬度预测方法的流程示意图；
图3为本申请一实施例中剔除多径MR样本的示意图；
图4为本申请一实施例中剔除携带不准确GNSS信息且呈现明显孤点特征的MR样本的示意图；
图5为本申请一实施例中剔除携带不准确GNSS信息且呈现非明显孤点特征的MR样本的示意图；
图6为本申请另一实施例中剔除多径MR样本的示意图；
图7为本申请一实施例中识别和剔除携带不准确GNSS信息且呈现明显孤点特征的MR样本的示意图；
图8为本申请一实施例中识别和剔除携带不准确GNSS信息且呈现非明显孤点特征的MR样本的示意图；
图9为本申请一实施例中根据待定位MR样本确定包含目标小区在内的目标区域的示意图；
图10为本申请另一实施例中小区经纬度预测方法的流程示意图；
图11为本申请又一实施例中小区经纬度预测方法的流程示意图；
图12为本申请一实施例中小区经纬度预测装置的结构示意图；
图13为本申请一实施例中服务器的结构示意图。
具体实施方式
以下结合说明书附图及具体实施例对本申请技术方案做进一步的详细阐述。除非另有定义，本文所使用的所有的技术和科学术语与属于本申请的技术领域的技术人员通常理解的含义相同。本文中在本申请的说明书中所使用的术语只是为了描述具体的实施例的目的，不是旨在于限制本申请。本文所使用的术语“和/或”包括一个或多个相关的所列项目的任意的和所有的组合。
在以下的描述中，涉及到“一些实施例”的表述，其描述了所有可能实施例的子集，但是应当理解，“一些实施例”可以是所有可能实施例的相同子集或不同子集，并且可以在不冲突的情况下相互结合。
对本申请进行进一步详细说明之前，对本申请实施例中涉及的名词和术语进行说明，本申请实施例中涉及的名词和术语适用于如下的解释。
1)、基站(Base Station)，基站即公用移动通信基站是无线电台站的一种形式，是指在一定的无线电覆盖区中，通过移动通信交换中心，与移动电话终端之间进行信息传递的无线电收发信电台。在WCDMA网络中，通常用NodeBID标识相同RNC(Radio Network Controller，无线网络控制器)下的不同基站，在LTE网络中，通常用EnodeBID标识相同本地网的不同基站。
2)、小区(Cell)，为用户提供服务的无线逻辑区域，在系统侧通常用CellID标识同基站下的唯一小区。
3)、测量报告(Measurement Report，MR)，是在移动通信系统中网络侧获得终端无线信息的主要手段，主要包含两个部分：上行测量以及下行测量信息。其中下行测量信息由终端测量采集并通过空口的Measurement Report信令上报给基站，上行测量信息由基站测量并采集。MR又分为事件型和周期型，其中周期型MR通常用于评估网络的覆盖、干扰、质量、性能、业务量等指标，并以评估结果为基础，应用于无线网络规划和优化工作。
4)、时间提前量(Timing Advanced，TA)，是指移动台信号到达基站的实际时间和假设该移动台与基站距离为0时移动台信号到达基站的时间的差值。基站对移动台信号到达的时间始终进行监控，根据到达时间的变化对移动台发送TA命令，使移动台提前发送上行信息，以便于移动台发送的上行信息 能够被基站正确接收。
在LTE制式中，1个TA单位代表移动台到基站的距离约为78米，1个TA包含16个TS(Time Slot，时隙)，每个TS的距离约为4.9米。
5)、栅格(Grid)：是指根据位置数据确定的区域，如将地球表面划分为大小均匀紧密相邻的单元格阵列，每个单元格做为一个象元或象素由行、列定义，并包含一个代码标识该象素的属性类型或量值。因此栅格结构是以规则的阵列来表示空间地物或现象分布的数据组织，组织中的每个数据表示地物或现象的非集合属性特征。在移动通信领域，对MR等数据进行栅格化处理是无线网数据工具的常用手段，通常被用于地理化描述一个单元格内的网络覆盖、质量、干扰、性能、业务量等特征。
在移动通信系统中，小区经纬度通常用于描述射频拉远单元(Radio Remote Unit，RRU)的经纬度，RRU是在远端将基带光信号转成射频信号放大并传送给天线的设备。RRU是一种新型的分布式网络覆盖模式，它将大容量宏蜂窝基站集中放置在可获得的中心机房内，基带部分集中处理，采用光纤将基站中的射频模块拉远到远端射频单元，分置于网络规划确定的站点上。
本申请发明人在研究中发现，在实际工程安装时，为了降低因馈线传输带来的信号损耗，RRU和天线之间的馈线长度应越短越好，因此RRU通常安装在天线旁边或较近的位置，故小区经纬度可以认为等同于RRU经纬度和天线经纬度。对于1个小区对应多RRU的场景，如多RRU室分站、超级小区及高铁站，在该多RRU的场景下，需要定位的实际是所述小区每个RRU的位置，针对多RRU的场景则默认为需要对每个RRU的定位。当基站为非RRU拉远站时，基站经纬度和当前基站每个小区的经纬度相等或近似相等，即小区经纬度为当前小区所属基站BBU的经纬度；而当基站为RRU拉远站时，例如集中式无线接入网(Centralized Radio Access Network，CRAN)场景，小区经纬度与基站基带处理单元(Base band Unit，BBU)经纬度不同，在宽带码分多址(Wideband Code Division Multiple Access，WCDMA)网络和全双工通信技术长期演进(Frequency Division Duplexing-Long Term Evolution，FDD-LTE)网络中，基站BBU侧一般不安装GPS模块，从而无法获得BBU的位置，更无法获得RRU的位置。
另外，在时分双工(Time Division Long Term Evolution，TD-LTE)系统中对时钟同步的要求很高，因此大多数基站在BBU侧安装了GPS模块，主要用于系统的时钟同步，同时也能够测量并获得BBU的位置信息。但由于移动通信网络中存在大量的RRU拉远站，因此BBU的位置信息并不能等同于RRU的位置信息，因此BBU经纬度与小区经纬度并非直接的等同关系，也无法直接应用于实际的网络运维工作中。
基于此，本申请通过获取携带全球导航卫星系统(Global Navigation Satellite System，GNSS)信息的周期性测量报告(Measurement Report，MR)数据，根据小区的实际位置对应的MR数据所确定的时间提前量TA代表的距离与天线和终端(UE)之间直线距离、天线和UE之间水平距离之间必然会满足的关联关系，可以通过获取待预测位置的目标小区的有效MR数据与对应的待定位置之间是否满足对应的所述关联关系，以判断所述待定位置是否归属于所述目标小区的实际位置，以进一步根据所述待定位置预测所述目标小区的经纬度。
其中，请参阅图1，小区的实际位置对应的MR数据所确定的时间提前量TA代表的距离与天线和终端(UE)之间直线距离、天线和UE之间水平距离之间必然会满足的关联关系如下公式1表示：
Figure PCTCN2019078444-appb-000001
其中，Distance_TA：时间提前量代表的距离。可以获取MR中的时间提前量，并换算为对应的距离；时间提前量可以由BBU测量，其测量参考点实际代表了从BBU到UE的距离，包括BBU到RRU的光纤长度、RRU到天线的馈线长度、以及天线到UE的传播距离。
Distance_Horizon：天线和UE间水平距离。可以获取MR中的UE经纬度以及小区经纬度后，利用两点间距离公式得到。
Distance_AntennatoUE：天线和UE间直线距离。天线高度和UE高度之间存在高度差异，故天线和UE之间的直线距离(Distance_AntennatoUE)与天线和UE间水平距离(Distance_Horizon)并不完全相等，需要考虑天线和UE的高度差。每个小区的天线高度(Antenna Height)不同，在获取到每个小区较准 确的天线高度后，利用勾股定理公式获得Distance_AntennatoUE与Distance_Horizon的关联关系，公式为
Figure PCTCN2019078444-appb-000002
Antenna Height：天线高度。
UE Height：UE高度，当UE可以上报含GNSS信息的MR数据时，认为UE的高度为1.5米。
MultiPath Error：多径误差，即UE在某位置点由于信号反射、折射等多径传播MR(简称多径MR)的TA测量值相对于UE在该点的直射径传播MR(简称直射径MR)的TA测量值之间的误差。TA越小，则意味着UE距离RRU越近，无线信号传播过程中直射径的概率越高，即TA换算出来的UE和RRU之间的信号传播距离与UE和RRU之间的实际距离误差越小；反之，TA越大则意味着UE距离RRU越远，无线信号传播过程中多径的概率越高，即TA换算出来的UE和RRU之间的信号传播距离与UE和RRU之间的实际距离误差越大。通常，多径误差无法完全避免，且无线信号传播环境过于复杂，无法准确预测，但可通过一些方法对部分多径MR进行识别与剔除。
Measurement Error：测量误差，即基站在测量TA时产生的误差。受到系统元器件的灵敏度和软件算法的影响，系统测量TA本身存在一些误差。对指定BBU而言测量误差可以近似为固定值。
Measurement Interval Error：测量粒度误差，在LTE系统中，一个TA约78米，一个TS约4.9米，故若相同小区测量到的两个UE的TA相等，则此两个UE与小区间距离的最大误差有约78米；若相同小区测量到的两个UE的时隙粒度的TA相等，则此两个UE与小区间距离的最大误差有约4.9米。测量粒度误差无法避免，本申请实施例中，进行小区经纬度预测时可以获取时隙粒度的TA，通过时隙粒度的测量粒度误差较小，以避免测量粒度误差对预测结果的影响。
Fiber Length：光纤长度，BBU至RRU的光纤长度。对于RRU拉远站，相同BBU下每个RRU的光纤长度差异可能较大且无法准确获取。但对相同小区而言，光纤长度是固定的。
Feeder Length：馈线长度，RRU至天线的馈线长度。由于每个小区的馈线长度不同，且在安装基站时并未准确记录每个小区的馈线长度，因此每个小区的馈线长度无法准确获取。但对于相同小区而言，馈线长度是固定的。
其中，由于Measurement Error、Fiber Length和Feeder length在一个小区中可视为固定值，故对于指定小区，当只选取直射径MR，当且仅当所述小区预测经纬度位于所述小区的实际位置时，该小区MR集合中每条MR按照公式2中计算出的X结果理论上应相等且为固定值，也即，小区的实际位置对应的MR数据所确定的时间提前量TA代表的距离与天线和终端(UE)之间直线距离、天线和UE之间水平距离之间必然会满足的关联关系还可以用如下公式2表示。
Figure PCTCN2019078444-appb-000003
其中，关于1米对应的径度和纬度的度数的计算方式：地球半径为R米，则在任意经度上的地球周长为2*Π*R米。假设当前纬度为Latitude，则在纬度Latitude的地球周长为R*cos(Latitude)。假设R＝6371000米，则任意经度上的地球周长为2*Π*6371000＝40030174米，故：
1米的实际纬度为360°/40030173＝0.00000899322°。
1米的实际经度为：360°/(40030173*cos(Latitude/180*Π))＝0.00000899322°/cos(Latitude/180*Π)。
关于已知两点的经纬度，计算两点之间的距离的计算方式：以点1和点2为例，根据点1和点2的经纬度计算点1和点2之间的距离可以如下公式三所示：
Distance(Point1Point2)＝arccos(sin(Lat1*Π/180)*sin(Lat2*Π/180)+cos(Lat1*Π/180)*cos(Lat2*Π/180)*cos(Lon2*Π/180-Lon1*Π/180))*6371000(公式3)
其中：Point1代表点1，Point2代表点2；Lon1和Lon1分别代表点1、点2经度；Lat1和Lat2分别代表点1、点2纬度；6371000为地球的近似半径，单位为米。
请参阅图2，为本申请一实施例提供的一种小区经纬度预测方法，包括如下步骤：
步骤103，根据测量报告MR样本对应的位置信息和/或时间提前量TA，确定待定位MR样本，所述MR样本为目标小区在设置周期内的携带全球导航卫星系统GNSS信息的MR样本。
其中，获得携带GNSS信息的MR数据，可以是终端UE上报GNSS信息或者是基站下发RRC消息中配置要求UE在MR中携带GNSS信息的请求。以在3GPPT S36.331、36.355协议中描述的UE上报GNSS信息为例，当UE接入网络时，在UE上报的UE CapabilityInformation消息中，在UE-BasedNetwPerfMeasParameters-r10结构体中标识为standaloneGNSS-Location-r10 supported，则说明UE支持GNSS位置信息的上报。基站可以通过在下发RRC Connection Reconfiguration消息中的measConfig中配置为：includeLocationInfo-r10true来要求UE在MR中携带GNSS信息。UE在MR中上报了GNSS信息后，基站可以通过后处理将每条携带GNSS信息的MR进行记录和保存。
设置周期的长度可以根据实际情况进行设定，主要目的是确保采集到的MR样本的数据量足够。对于宏基站，设置周期通常是3天及以上，对于微站或者室分站，设置周期通常是5天及以上，以确保样本量充分而提升计算结果的准确性。
MR样本对应的位置信息是指UE上报的GNSS信息。MR样本中包含基站号和小区号，针对小区为多RRU场景，则MR中所述小区号可以是指每个RRU编号，预测的小区经纬度相应是指小区下每个RRU的位置。时间提前量TA是指时隙粒度的TA。这里，根据所述MR样本对应的位置信息和/或时间提前量TA，确定待定位MR样本，主要是指根据MR样本对应的位置信息和/或TA，对多径MR进行识别和剔除以及对经纬度不准确的MR进行识别和剔除，得到经纬度准确且为直射径MR的MR样本作为待定位MR样本。对多径MR进行识别和剔除以及对经纬度不准确的MR数据进行剔除可以包括如下几种方式：
第一、根据所述MR样本对应的位置信息和时间提前量TA，对位置相同或相近的多径MR剔除，将目标小区包含的UE上报了GNSS信息的MR数据中位置相同或相近、但TA不同的MR集合按照取最小TA对应的MR的方案， 对所述MR集合中的其它MR进行去重处理。
第二、根据所述MR样本对应的位置信息，对MR上报GNSS信息不准确且为覆盖孤点MR的识别与剔除，根据MR周边预设的第一阈值距离内归属于相同小区的其它MR的密集程度低于预设的第二阈值的方法，确定该对应的MR是否为覆盖孤点MR进行识别和剔除。
第三、根据所述MR样本对应的位置信息和时间提前量TA，对小区经纬度不准确且非覆盖孤点的MR的识别与剔除，将目标小区的MR数据栅格化后，将目标小区在每个栅格内MR样本点占比最高的TA对应的MR设置为置信度最高的MR，并将归属于所述目标小区相同栅格内的MR中，选择TA值最邻近于置信度最高的MR设置为置信度较高的MR，并将所述目标小区在每个栅格内置信度非最高和非较高的MR进行识别和剔除。
第四、根据所述MR样本对应的时间提前量TA，对较高概率存在的多径MR进行剔除，由于TA越大，较高概率存在多径MR，从而选取TA相对较小的MR集合，确保所述MR集合中MR样本数量满足预设的第三阈值，将剩余的MR进行剔除。
根据MR样本对应的位置信息和/或TA，对多径MR进行识别和剔除以及对经纬度不准确的MR数据进行剔除，可以减小多径MR引入的计算误差和携带GNSS信息不准确MR引入的计算误差，提升计算准确性。通过根据MR样本对应的位置信息和/或TA确定待定位MR样本，当目标小区的预测经纬度位于所述目标小区的实际位置时，目标小区的每条上报准确经纬度且为直射径的MR中的TA代表的距离减去MR中天线和UE间的直线距离所获得的结果集合最接近相等且为固定值。
步骤105，根据所述待定位MR样本确定包含所述目标小区在内的目标区域。
通过从采集到的MR样本中确定待定位MR样本，并根据待定位MR样本确定包含所述目标小区在内的目标区域，可以获得包含目标小区实际位置且面积尽可能小的封闭区域，可以减少于该目标区域对目标小区的实际位置进行预测的计算量，并提升预测结果的准确性。
步骤107，根据所述目标区域选取待定位置，根据所述待定位MR样本中携带的所述位置信息和所述待定位置的位置信息、以及对应的TA代表的距离 预测所述目标小区的经纬度。
根据目标区域选取待定位置，将该待定位置作为目标小区的假设实际位置，利用小区的实际位置对应的MR数据所确定的TA代表的距离与天线和UE之间直线距离、天线和UE之间水平距离之间必然会满足的关联关系，将该待定位置作为目标小区的假设实际位置计算目标小区的预测经纬度。该待定位置可以根据设置规则选取多个，在该目标区域可以通过遍历迭代的方法找到一待定位置点，将该待定位置点作为目标小区的假设实际位置时，对应的待定位MR中每条MR根据关联关系计算出的X集合最接近相等，从而通过该待定位置点可以预测目标小区的经纬度。
上述实施例中，通过根据目标小区在设置周期内的携带GNSS信息的MR样本对应的位置信息和/或时间提前量，对MR样本进行筛选以确定待定位MR样本，并根据待定位MR样本确定的目标区域中包含的待定位置，预测所述目标小区的经纬度，如此，通过根据待定位MR确定目标区域，利用根据待定位MR样本中携带的位置信息与目标小区的实际位置之间将满足的关联关系，在目标区域内实现对目标小区的实际位置的准确预测，无需上站测量即可进行小区的经纬度预测；待定位MR样本是通过采集到的MR样本对应的位置信息和/或TA进行筛选确定，从而对小区的经纬度进行预测的方法对该小区的站型没有特殊要求，对全向站、定向站、拉远站、微站、室分站等站型均适合，测量精度高，提升了工程参数中小区经纬度校准的效率，为网络运维打下良好的基础，大大减小测量成本。
在一些实施例中，所述步骤103，根据测量报告MR样本对应的位置信息和/或时间提前量TA，确定待定位MR样本之前，还包括：
采集所述目标小区在设置周期内的携带全球导航卫星系统GNSS信息的测量报告MR样本。
这里，采集可以是指根据UE上报的GNSS信息对应的MR样本数据，或者，UE基于基站下发RRC消息中配置要求UE在MR中携带GNSS信息的请求而上报的GNSS信息对应的MR样本数据。
在一些实施例中，步骤103，根据测量报告MR样本对应的位置信息和/或 时间提前量TA，确定待定位MR样本，包括：
根据测量报告MR样本对应的位置信息和时间提前量TA，确定位置符合设置条件且TA不同的MR样本形成MR集合；
根据所述MR集合中所述TA是否符合设置要求进行去重处理。
这里，位置符合设置条件是指根据MR样本对应的位置相同或者相近，可以根据MR样本中携带的GNSS信息进行判断。TA是否符合设置要求可以是指TA排序中相对较小的一个或者多个。请参阅图3，在同一小区下，UE在相同位置处与天线间直射径传播的信号TA最小，通过针对位置相同或者相近的MR样本所形成的MR集合中，选取最小TA的MR并对MR集合中其它的MR进行剔除的去重处理，可以对位置相同或相近的多径MR进行剔除。这里，根据所述MR集合中所述TA是否符合设置要求进行去重处理是指将针对位置相同或者相近的同一MR集合，将TA最小之外的其它MR进行剔除，如针对第一位置Position1对应取TA最小的TA＝0的MR，针对第二位置Position2对应取TA最小的TA＝1的MR。
在一些实施例中，步骤103，根据测量报告MR样本对应的位置信息和/或时间提前量TA，确定待定位MR样本，包括：
将测量报告MR样本分别作为待识别MR样本，根据所述待识别MR样本对应的位置信息，确定相对于所述待识别MR样本在第一阈值距离内包含的MR样本的分布密度；
根据所述分布密度是否符合设置条件，对所述待识别MR样本进行剔除。
这里，分布密度是否符合设置条件可以是指分布密度小于预设的分布密度阈值。通过确定某一MR样本在第一阈值距离内包含的MR样本的分布密度小于预设的分布密度阈值时，可以认为该对应的MR样本呈现出孤点特征，并对该对应的MR样本进行剔除。
针对呈现出孤点特征的MR样本，请参阅图4，在同一小区的MR中，针对个别UR上报的MR中携带的GNSS信息与小区间的实际距离非常远，且所述MR上报的TA代表的距离相差非常大，如图4中所示的TA＝0(GNSS error and stand-alone MR，携带不准确GNSS信息且呈现明显的孤点特征)的用白色 圆点表示的MR样本点，不符合MR携带的位置信息与小区的实际位置之间将满足的关联关系的规律，这些MR即为呈现出明显的孤点特征的MR。通过确定MR样本在第一阈值距离内包含的MR样本的分布密度而对呈现出孤点特征的MR样本进行识别和剔除，可以将实际包含了不准确的经纬度信息的MR进行识别和剔除，以避免引入经纬度不准确的MR对小区实际位置的预测结果的影响。
在一些实施例中，步骤103，根据测量报告MR样本对应的位置信息和/或时间提前量TA，确定待定位MR样本，包括：
将测量报告MR样本进行栅格化，并建立每一所述MR样本与归属栅格之间的对应关系；
根据每一所述栅格内所述MR样本数占比确定目标TA，根据所述目标TA确定置信度符合要求的目标MR样本，对所述栅格内除所述目标MR样本之外的其它MR样本进行剔除。
这里，置信度符合要求的目标MR样本可以是指每一栅格内位置信息置信度相对较高的一个或者多个MR样本。作为一个可选的实施例，根据每一栅格内MR样本数占比确定目标TA，根据所述目标TA确定置信度符合要求的目标MR样本可以包括：将每个栅格内对应的MR样本数占比最高的TA作为目标TA，并将与所述目标TA对应的MR样本作为置信度最高的MR，并将归属于同一栅格内的MR样本中TA与所述目标TA最邻近的相邻TA所对应的MR样本作为置信度较高的MR。
针对同一栅格内置信度较低的MR样本，请参阅图5，在同一小区的MR中，针对个别UE上报的MR中携带的GNSS信息与小区间的实际距离与所述MR上报的TA代表的距离相差较大，如图5中所示的TA＝0(GNSS error and not stand-alone MR，携带不准确GNSS信息且不呈现明显的孤点特征)的用白色圆点表示的MR样本，不符合MR携带的位置信息与小区的实际位置之间将满足的关联关系的规律，且这些MR样本的周边区域内包含对应小区较多的其它MR样本，并不会呈现出明显的孤点特征。基于此，通过利用相近距离范围内UE上报的TA占比最高的MR样本作为位置置信度最高的MR样本，与TA占比最高的相邻TA对应的MR样本作为位置置信度较高的MR样本，对除置信 度最高和置信度较高之外的其它MR样本进行剔除，从而可以对不呈现明显的孤点特征且携带不准确GNSS信息的一类MR样本进行识别和剔除，以避免引入经纬度不准确的MR对小区实际位置的预测结果的影响。
在一些实施例中，步骤103，根据测量报告MR样本对应的位置信息和/或时间提前量TA，确定待定位MR样本，包括：
根据测量报告MR样本对应的时间提前量TA，选取所述TA排序中TA相对较小的设置数量的目标MR样本，对所述目标MR样本之外的其它MR样本进行剔除。
这里，设置数量可以根据实际情况进行调整，设置数量的大小应确保MR样本的数量能够满足采样精度。请参阅图6，通常，TA越大，越较高概率会存在多径MR样本，如图6所示的TA>4的用白色圆点表示的MR样本，通过选取TA排序中TA相对较小的设置数量的目标MR样本，从所述MR样本中选取TA较小的MR作为目标MR样本，并使目标小区对应的目标MR样本的数量满足预设的数量阈值，并将剩余的MR样本剔除，以将相对较高概率为多径MR样本的MR样本进行剔除。如此，在MR样本数可以满足计算小区位置的精度的条件下，优先选择TA小的MR数据，以减少多径MR引起的误差。
在一些实施例中，步骤103，根据测量报告MR样本对应的位置信息和/或时间提前量TA，确定待定位MR样本，包括：
对测量报告MR样本进行栅格化，将归属于同一所述栅格内的所述MR样本形成MR集合；
对每一所述MR集合中除TA最小的MR样本之外的其它MR样本进行去重处理。
这里，通过对MR样本进行栅格化，栅格的大小可以为设置大小，如10米。归属于同一栅格内的MR样本对应的位置信息相同或者相近，由于在同一小区下，UE在相同位置处与天线间直射径传播的信号TA最小，通过针对位置相同或者相近的MR样本所形成的MR集合中，也即位于同一栅格内的MR所形成的MR集合中，选取最小TA的MR并对MR集合中其它的MR进行剔除 的去重处理，可以对位置相同或相近的多径MR进行剔除。
对目标小区的MR数据栅格化，其中栅格可以即为GridSize，且栅格的大小可以根据TA进行设置，如可以设置在1TA至2TA之间，并建立每个MR与归属栅格之间的对应关系。该对应关系可以用表格进行记录，如下表一所示：
表一
Figure PCTCN2019078444-appb-000004
其中，步骤103，根据测量报告MR样本对应的位置信息和时间提前量TA，确定待定位MR样本，还包括：
选取任一栅格为初始栅格，确定所述初始栅格的位置；
确定相对所述初始栅格的位置在第一阈值距离内包含的邻近栅格的数量，确定所述邻近栅格的数量小于预设值时，将所述初始栅格内包含的MR样本剔除。
通过选取任一栅格作为初始栅格，并对初始栅格周边的第一阈值距离内包含的邻近栅格的数量进行统计，通过邻近栅格的数量确定对应的初始栅格周边的MR样本的分布密度，当邻近栅格的数量小于预设值时，也即该初始栅格周边的MR样本的分布密度较小时，可以识别该初始栅格内的MR点呈现出孤点特征，从而将该初始栅格内的MR样本进行剔除。如此，通过基于建立栅格并统计每一栅格的邻近栅格的数量，对呈现出孤点特征的MR样本进行识别和剔除，可以将实际包含了不准确的经纬度信息的MR进行识别和剔除，以避免引入经纬度不准确的MR对小区实际位置的预测结果的影响。
请参阅图7，确定初始栅格的位置，可以是取对应栅格的中心点经纬度作为该初始栅格的位置。对于一个栅格，以图7中Grid1为例，其周边一圈栅格范围内共有8个邻近栅格，其中4个栅格的中心点与该栅格的中心点距离为GridSize，另4个栅格的中心点与该栅格的中心点距离为
Figure PCTCN2019078444-appb-000005
第一阈值距离可以设为
Figure PCTCN2019078444-appb-000006
和GirdSize*2之间，可认为该栅格周围有8个邻近栅格。作为一个可选的实施例，第一阈值距离可以为GridSize*1.5，预设值为 4。以图7中Grid2为例，Gird2在第一阈值距离内的邻近栅格的数量小于预设值，呈现出明显的孤点特征，可识别为携带不准确经纬度信息的MR并进行剔除。
其中，步骤103，根据测量报告MR样本对应的位置信息和时间提前量TA，确定待定位MR样本，还包括：
统计每一所述栅格内包含的MR样本中对应的TA的占比；
根据所述占比最高的TA确定目标TA，对每一所述栅格内除所述目标TA对应的MR样本之外的其它MR样本进行剔除。
这里，统计每一栅格内包含的MR样本中对应的TA的占比，可以是指将每一栅格内包含的MR样本对应的TA进行取整数，比如通过去掉对应的TA的小数部分、或者根据四省五入的规则对TA的小数部分进行处理得到对应的TA整数值，并统计取整数后的TA各自的占比。作为另一可选的实施例，当所述栅格的尺寸较小时，统计每一栅格内包含的MR样本中对应的TA的占比，也可以是指不对TA进行取整数，而直接统计每一栅格内包含的MR样本中对应的TA的占比。根据所述占比最高的TA确定目标TA，可以是指直接将占比最高的TA作为目标TA、或者将占比最高的TA以及与所述占比最高的TA大小相近的TA作为目标TA。
请参阅图8，作为一个可选的实施例，可以取每个栅格内TA整数值占比最高的TA整数值，记为TAmax，并根据TAmax确定大小与TAmax相近的TAmax-1和TAmax+1分别作为目标TA，并对每个栅格中除对应的TAmax、TAmax-1和TAmax+1之外的其它MR样本进行剔除。以图8中Grid1为例，TAmax为占比最高的50％的TA＝0，将TA＝0和TA＝1分别选取作为目标TA；再以Grid2为例，TAmax为占比最高的50％的TA＝1，将TA＝0、TA＝1和TA＝2分别选取作为目标TA；再以Grid3为例，TAmax为占比最高的40％的TA＝2，将TA＝1、TA＝2和TA＝3分别选取作为目标TA；再以Grid4为例，TAmax为占比最高的40％的TA＝3，将TA＝2、TA＝3和TA＝4分别选取作为目标TA。
通过对同一栅格内对应的TA取整数的占比最高的TA确定目标TA，实现利用相近距离范围内UE上报的TA占比最高的MR样本作为位置置信度最高的MR样本，与TA占比最高的相邻TA对应的MR样本作为位置置信度较高 的MR样本，对除置信度最高和置信度较高之外的其它MR样本进行剔除，从而可以对不呈现明显的孤点特征且携带不准确GNSS信息的一类MR样本进行识别和剔除，以避免引入经纬度不准确的MR对小区实际位置的预测结果的影响。
其中，步骤103，根据测量报告MR样本对应的位置信息和时间提前量TA，确定待定位MR样本，还包括：
对所述MR样本按照TA进行排序；
确定所述排序中TA由小到大的设置数量的目标MR样本，对所述目标MR样本之外的其它MR样本进行剔除。
这里，设置数量可以根据实际情况进行调整，设置数量的大小应确保MR样本的数量能够满足采样精度。作为一个可选的实施例，设置数量为30。通过对MR样本按照TA进行排序，按照TA值由小到大选择MR样本，直至选择到的MR样本的数量达到设置数量为止。通过选取排序中TA由小到大的设置数量的MR样本作为目标MR样本，对所述目标MR样本之外的其它MR样本进行剔除，可以将相对较高概率为多径MR样本的MR样本进行剔除。
其中，当目标小区对应的目标MR样本数量不满足设置数量的要求时，可以退出当前的对目标小区的经纬度的预测，以避免应采集到的有效的目标MR样本数量不足而不能满足经纬度预测的精度要求。
在一些实施例中，步骤105，根据所述待定位MR样本确定包含所述目标小区在内的目标区域，包括：
根据所述待定位MR样本对应的TA确定初始位置信息及参考MR样本；
根据所述参考MR样本与所述初始位置信息之间的距离，确定参考位置信息；
根据所述参考位置信息分别向周围扩展设置的第一经度距离和第一纬度距离，根据扩展后的位置得到包含所述目标小区在内的目标区域。
这里，根据待定位MR样本对应的TA确定初始位置信息，可以是根据待定位MR样本中携带的位置信息分别计算经度均值Longitude_center和纬度均 值Latitude_center，将经度均值Longitude_center和纬度均值Latitude_cente作为初始位置信息。根据待定位MR样本对应的TA确定参考MR样本，可以是指将待定位MR样本中选取TA最小的MR作为参考MR样本，并记录参考MR样本数量n。根据所述参考MR样本与所述初始位置信息之间的距离，确定参考位置信息，可以是分别计算TA最小的MR样本与经度均值Longitude_center和纬度均值Latitude_center之间的距离，按照距离从小到大排序后，找出距离集合中中位数对应的MR样本点的经纬度，记为Longitude、Latitude，将该Longitude、Latitude分别作为参考位置信息。
获取距离集合中中位数对应的MR样本点的经纬度可以包括：若n为奇数则找出距离由小到大排序的距离集合中第(n+1)/2个MR样本，并将该第(n+1)/2个MR样本的经纬度作为参考位置信息；若n为偶数则找出距离由小到大排序的距离集合中第n/2个MR样本，并将该第n/2个MR样本的经纬度作为参考位置信息。
根据所述参考位置信息分别向周围扩展设置的第一经度距离和第一纬度距离，根据扩展后的位置得到包含所述目标小区在内的目标区域，可以获得包含目标小区的实际位置且面积尽可能小的封闭区域。
其中，所述根据所述参考位置信息分别向周围扩展设置的第一经度距离和第一纬度距离之前，包括：
将所述参考MR样本对应的TA代表的距离换算为经度和纬度，根据换算结果确定第一经度距离和第一纬度距离。
根据参考MR样本对应的TA代表的距离换算为经度和纬度，可以是指将参考MR样本分组到目标小区的最小距离Distance_min换算为经度和纬度的度数，根据换算结果确定第一经度距离和第一纬度距离分别记为Longitude_Distance和Latitude_Distance。第一经度距离和第一纬度距离的具体换算公式可以如下所示：
Longitude_Distance＝Distance_min*0.00000899322/cos(Latitude)
Latitude_Distance＝Distance_min*0.00000899322
根据所述参考位置信息分别向周围扩展设置的第一经度距离和第一纬度距 离，是指以参考位置信息为中心分别向东西南北对应外扩第一经度距离和第一纬度距离，使得目标小区的实际位置一定能够落在外扩后形成目标区域内。请参阅图9，作为一可选的实施例，以参考位置信息中的经度Longitude为中心分别向东西各外扩第一经度距离Longitude_Distance、以参考位置信息中的纬度Latitude为中心分别向南北各外扩Latitude_Distance，具体计算公式可以如下所示：
Longitude_Max＝Longitude+Longitude_Distance
Longitude_Min＝Longitude-Longitude_Distance
Latitude_Max＝Latitude+Latitude_Distance
Latitude_Min＝Latitude-Latitude_Distance
其中，外扩后的目标区域的四个角的经纬度分别为A1：(Longitude_Min,Latitude_Min)、B1：(Longitude_Max,Latitude_Min)、C1：(Longitude_Min,Latitude_Max)、D1：(Longitude_Max,Latitude_Max)。
在一些实施例中，所述根据所述目标区域选取待定位置，根据所述待定位MR样本中携带的所述位置信息和所述待定位置的位置信息、以及对应的TA代表的距离预测所述目标小区的经纬度，包括：
以所述目标区域中一点为原点、以设置距离为步长分别确定待定位置；
根据所述待定位MR样本中携带的所述位置信息和所述待定位置的位置信息，分别确定每一所述待定位MR样本点对应的天线与终端之间的距离；
确定所述待定位MR样本对应的TA代表的距离；
根据每一所述待定位MR样本对应的TA代表的距离、以及对应的所述天线与终端之间的距离，确定每一所述待定位MR样本对应的基站与所述目标小区之间的预测距离，形成与所述待定位置对应的预测距离集合；
根据所述预测距离集合，确定所述目标小区的预测经纬度。
这里，步长的大小可以根据实际需要进行设置，根据步长的大小相应换算为经度和纬度的度数，记为经度步长Longitude_Y、纬度步长Latitude_Y，以步长Y为例，经度步长Longitude_Y和纬度步长Latitude_Y的换算公式可以如下所示：
Longitude_Y＝Y*0.000000899322/cos(Latitude)
Latitude_Y＝Y*0.00000899322
以目标区域中一点为原点，可以是选择目标区域对应的其中一角，如A1：(Longitude_Min,Latitude_Min)为原点，经度以经度步长Longitude_Y为步长，纬度以纬度步长Latitude_Y为步长依次分别确定待定位置，分别以所述待定位置作为目标小区的假设实际位置，根据小区的实际位置对应的MR数据所确定的时间提前量TA代表的距离与天线和终端(UE)之间直线距离、天线和UE之间水平距离之间必然会满足的关联关系进行迭代计算，确定每一所述待定位MR样本对应的基站与所述目标小区之间的预测距离，得到每个MR样本的X值形成的预测距离集合，记为{X1..Xm}[i][j]。
其中，以一次迭代计算为例，可以设置变量i和j，分别代表经度和纬度对应的当前迭代轮次，则当前迭代轮次的经度可以记为Longitude_Min+i*Longitude_Y，当前迭代轮次的纬度可记为Latitude_Min+j*Latitude_Y。根据当前迭代轮次获得的待定位置的经纬度分别与待定位MR中携带的经纬度信息进行天线和UE之间的水平距离Distance_Horizon的计算。对于待定位MR中的每个MR样本，都按照勾股定理，以天线高度和UE高度差为勾，以天线和UE之间的水平距离Distance_Horizon为股，计算出天线到UE之间的直线距离Distance_AntennatoUE，公式为：
Figure PCTCN2019078444-appb-000007
将待定位MR样本中每个MR样本对应的TA转换为距离Distance_TA，公式为：Distance TA＝TA*78；根据每个MR样本对应的TA代表的距离以及对应的天线到UE之间的距离Distance_AntennatoUE，计算每个MR的X＝Distance_TA-Distance_AntennatoUE，得到每个MR样本的X值形成的预测距离集合，记为{X1..Xm}[i][j]。
根据所述预测距离集合，确定所述目标小区的预测经纬度，可以是计算每个迭代轮次的预测距离集合的均方差，记为STDEV(X1..Xm)[i][j]，选取所有迭代轮次中均方差最小的多个轮次，如3个轮次的经纬度，并将该多个轮次的经纬度分别计算平均值，将所述平均值作为目标小区的预测经纬度。需要说明的 是，这里选取所有迭代轮次中均方差最小的多个轮次的数量可以是其它数量的多个轮次，且根据该多个轮次对应的待定位置的经纬度获得目标小区的预测经纬度并不局限于计算平均值，也可以通过取中间值、或者取其中一部分待定位置的经纬度进行确定。
在一些实施例中，所述小区经纬度预测方法还包括：
确定与所述目标小区归属于相同位置的其它小区；
根据所述目标小区对应的预测经纬度和其它小区对应的预测经纬度，确定归属于所述相同位置的多个小区对应的预测经纬度。
这里，当已知若干小区位于相同位置，将所述位于相同位置的小区集合已获得的对应的预测经纬度结果形成的集合进行二次处理，如将位于相同位置的小区的预测经纬度求平均，即可获得位于所述相同位置的多个小区的预测经纬度。如此，通过归属于相同位置的多个小区对应的预测经纬度结果进一步对目标小区的预测经纬度结果进行调整，可以进一步提升目标小区的经纬度预测结果的精度。
为了能够对本申请实施例所提供的小区经纬度预测方法的实现流程能够进一步具体的了解，下面结合两个可选的具体实施例进行说明，请参阅图10，小区经纬度预测方法包括如下步骤：
步骤S11，采集待定位小区对应的携带有GNSS信息的MR样本；该待定位小区即为目标小区。
步骤S12，对位置相同或相近的多径MR剔除：将待定位小区包含用户上报了经纬度信息的MR数据中位置相同或相近但时间提前量不同的MR集合按照取最小时间提前量MR的方法进行去重处理。
步骤S13，对MR上报经纬度不准确且为覆盖孤点MR的识别与剔除：根据所述MR周边预设的第一阈值距离内，归属于相同小区的其它MR的密集程度低于预设的第二阈值的方法进行覆盖孤点MR的识别与剔除。
步骤S14，对小区经纬度不准确且非覆盖孤点的MR的识别与剔除：将所述小区的MR数据栅格化后，将所述小区在每个栅格内MR样本点占比最高的TA对应的MR设置为经纬度置信度最高的MR，并将归属于所述小区相同栅格 内的MR中，选择TA最邻近于该经纬度置信度最高的MR的MR设置为经纬度置信度较高的MR，并剔除掉所述小区在每个栅格内经纬度置信度非最高和非较高的其它MR。
步骤S15，对TA较大、较高概率存在多径传播的多径MR样本点剔除：优选TA较小的MR集合并生成待定位MR集合，并使所述小区待定位MR集合的样本点数满足预设的第三阈值，将剩余的MR样本点进行剔除。
根据步骤S12～S15处理后，得到小区经纬度准确且为直射径的待定位的MR集合。
步骤S16，根据小区经纬度准确且为直射径的待定位的MR集合的数据获得包含该小区实际位置在内的封闭区域；根据小区经纬度准确且为直射径的待定位的MR集合的数据确定封闭区域，可以使得所述小区的实际位置一定可以落入该封闭区域内，且该封闭区域尽可能小。
步骤S17，设置目标函数，该目标函数如下：
Figure PCTCN2019078444-appb-000008
当待定位置经纬度位于小区实际位置时，每一待定位MR计算出的X相等且为固定值；其中Distance_TA为时间提前量代表的距离；Distance_Horizon为UE和所述小区天线间的水平距离，其中，UE的经纬度可直接在MR中获取，当所述小区经纬度已知时，根据两点间地面水平距离的计算公式可以获得Distance_Horizon；Antenna Height为天线高度；UE_Height为UE高度，取1.5米；Distance_AntennatoUE为天线到UE的直线距离，根据以天线和UE间水平距离为勾，天线和UE高度差为股的勾股定理可得到。Measurement Error为系统测量误差；Fiber Length为光纤长度；Feeder Length为馈线长度。由于系统测量误差、光纤长度、馈线长度对于指定小区而言为固定值，故当小区预测经纬度位于小区实际位置时，每条经纬度准确且为直射径的待定位的MR计算出的X的集合最接近相等且应为固定值。
步骤S18，在所述封闭区域内通过遍历迭代的方法找到一点，使得当小区的预测位置位于此点时，所述小区经纬度准确且为直射径的待定位的MR集合中每条MR计算出的X集合最接近相等，将此点的经纬度做为待定位小区的预 测经纬度结果。
步骤S19，当已知若干小区归属于相同位置时，根据归属于相同位置的小区集合已获得的小区预测经纬度结果集合进行二次处理，得到预测经纬度结果。
在另一实施例中，请参阅图11，小区经纬度预测方法包括如下步骤：
步骤S21，选择目标小区，并设定采集数据的起始时间和结束时间。对于宏站推荐采集连续3天以上的数据，对于微站和室分站推荐采集连续5天以上的数据，保证数据量足够，避免因样本点数不足引起的计算结果误差。
步骤S22，采集在步骤S21中选择的目标小区在设定的起始时间和结束时间内的携带GNSS信息的MR数据。MR数据中需要包含基站号、小区号、经度、纬度、时隙级别的时间提前量信息。
步骤S23，将所述小区的携带GNSS信息的MR数据进行栅格化处理，将归属于相同栅格的样本点进行去重处理；其中，栅格大小可以为10米，将相同栅格内的MR集合中取TA最小的MR样本点，对TA最小的MR样本点之外的其它MR样本点剔除；
步骤S24，建立所述小区每个MR与归属栅格之间的映射关系；其中，将所述小区的MR数据栅格化，栅格记为GridSize，可以在1TA到2TA之间，具体可选为：GridSize＝90米。建立所述小区每个MR与归属栅格的映射关系可以如前述实施例中的表1所示，在此不再赘述。
步骤S25，计算每个包含所述小区MR的栅格周边一定距离范围内包含所述小区MR的栅格的密集程度，根据密集程度对经纬度不准确且为覆盖孤点MR的识别与剔除；其中，若包含所述小区MR的栅格周边预设的距离范围内存在包含所述小区的MR的邻近栅格数<预设的阈值，则可识别该栅格内的MR点为覆盖孤点，并将这些MR点从表1中剔除。
请再次参阅图7，可以取每个栅格在对应的栅格图中的中心点经纬度，代表每个栅格的位置。对于一个栅格，其周边一圈栅格范围内共有8个邻近栅格，其中4个栅格的中心点与该栅格的中心点距离为GridSize，另4个栅格的中心点与该栅格的中心点距离为
Figure PCTCN2019078444-appb-000009
故当距离范围阈值设为
Figure PCTCN2019078444-appb-000010
和GirdSize*2之间时可认为该栅格周围有8个邻近栅格。因此距离范围阈值建议为GridSize*1.5，邻近栅格数阈值建议为4。
步骤S26，统计经过了步骤S25处理后表1中所述小区的每个栅格中所述 小区MR集合的TA取整数的占比，根据取整数占比最高的TA对经纬度不准确且为非覆盖孤点MR的识别与剔除；其中，请再次参阅图8，可以取每个栅格内TA整数值占比最高的TA整数值，记为TAmax。在表1中剔除掉每个栅格中所述小区的MR样本点中对应的TA整数值为除TAmax-1、TAmax和TAmax+1之外的MR样本点。
步骤S27，设置所述小区待定位的MR样本点阈值m，按照TA值由小到大选取TA值相对较小达到该样本点阈值m的MR样本点；其中，该样本点阈值可选为30，按照TA值由小到大选取TA值相对较小达到该样本点阈值m的MR样本点，可以从表1中从TA值最小的MR开始，按照TA值由小到大选择待定位的MR样本点，直到待定位的MR样本点数达到MR样本点阈值为止。
当所述小区的待定位的MR样本点数不满足阈值m的要求，则所述小区不满足定位经纬度的精度要求，退出。
步骤S28，获得所述小区待定位MR集合的最小时间提前量TA_min，并换算为距离Distance_min＝TA_min*78。
步骤S29，将所述小区待定位MR集合分别求经度均值Longitude_center和纬度均值Latitude_center。
步骤S30，取出所述小区待定位MR集合中时间提前量＝TA_min的MR样本点，并记录样本点数n，将时间提前量＝TA_min的所有MR样本点分别与(Longitude_Center,Latitude_Center)点计算距离，得到参考位置信息；该参考位置可以是按照距离从小到大排序后，并找出距离集合中位数对应的MR样本点的经纬度，记为Longitude、Latitude。
获得距离集合中位数对应的MR样本点的计算方法可以为：若n为奇数则找出距离由小到大排序的集合中第(n+1)/2个MR记录，若n为偶数则找出距离由小到大排序的集合中第n/2个MR记录。
步骤S31，将步骤S28中获得的待定位MR集合中TA_min对应的最小距离Distance_min换算为经度和纬度的度数；该经度和纬度的度数可以记为Longitude_Distance和Latitude_Distance，则：
Longitude_Distance＝Distance_min*0.00000899322/cos(Latitude)
Latitude_Distance＝Distance_min*0.00000899322
步骤S32，根据步骤S30中获得的Longitude分别向东西各外扩 Longitude_Distance，Latitude分别向南北各外扩Latitude_Distance，获得目标区域；请再次参阅图9，其中，所述小区的实际位置一定能够落在经纬度分别外扩后形成的目标区域范围内，即：
Longitude_Max＝Longitude+Longitude_Distance
Longitude_Min＝Longitude-Longitude_Distance
Latitude_Max＝Latitude+Latitude_Distance
Latitude_Min＝Latitude-Latitude_Distance
外扩后的矩形四个角的经纬度分别为：
A1：(Longitude_Min,Latitude_Min)
B1：(Longitude_Max,Latitude_Min)
C1：(Longitude_Min,Latitude_Max)
D1：(Longitude_Max,Latitude_Max)
步骤S33，设置Y米为步长，并换算为相应的经度和纬度的度数，记为Longitude_Y和Latitude_Y；其中Y具体可选为5米，则：
Longitude_Y＝Y*0.000000899322/cos(Latitude)
Latitude_Y＝Y*0.00000899322
步骤S34，从目标区域中设置原点开始，经度以Longitude_Y为步长，纬度以Latitude_Y为步长进行二元嵌套迭代，每次迭代都获得一个经纬度记为i*Longitude_Min+j*Longitude_Y；其中，原点可以为(Longitude_Min,Latitude_Min)开始，经度以Longitude_Y为步长，纬度以Latitude_Y为步长依次选取待定位置，直至到(Longitude_Max,Latitude_Max)为止。设置变量i和j，分别代表经度和纬度的当前迭代轮次，则一次迭代轮次的经度可记为Longitude_Min+i*Longitude_Y，一次迭代轮次的纬度可记为Latitude_Min+j*Latitude_Y。
步骤S35，将当前迭代轮次获得的待定位置的经纬度分别与待定位MR集合中每个经纬度进行水平距离Distance_Horizon的计算。
对于待定位MR集合中每个MR，都按照勾股定理，以天线高度和UE高度差为勾，以天线和UE水平距离Distance_Horizon为股，计算出天线到UE的 直线距离。公式为：
Figure PCTCN2019078444-appb-000011
步骤S36，将待定位MR集合中每个MR的TA转换为距离；换算公式可以为：Distance_TA＝TA*78。
步骤S37，计算每个MR的X＝Distance_TA-Distance_AntennatoUE，形成一个预测距离集合；该预测距离集合可以记为{X1..Xm}[i][j]。
步骤S38，计算每个迭代轮次的预测距离集合的均方差，记为STDEV(X1..Xm)[i][j]。
步骤S39，当迭代完成后，取所有迭代轮次中的STDEV(X1..Xm)[i][j]最小的多个轮次的经纬度，根据多个轮次的经纬度的均值确定所述小区的预测经纬度；其中，可以取所有迭代轮次中均方差最小的3个轮次的经纬度，并将3个经度和3个纬度分别求平均，获得所述小区的预测经纬度。
步骤S40，当已知若干小区位于相同位置，将所述位于相同位置的小区的预测经纬度求平均，即可获得所述位于相同位置的若干小区的预测经纬度。
本申请上述实施例提供的小区经纬度预测方法，可以通过移动通信网络系统侧进行预测，无需上站测量就可以从系统侧获取较为准确的小区经纬度预测结果。由于是通过采集目标小区的MR数据实现小区经纬度预测结果，从而对小区的站型没有特殊要求，对全向站、定向站、微站、拉远站、室分站等站型均适用。
经过外场实测证明，当所述小区经纬度准确且为直射径的待定位的MR样本点数满足预设的阈值要求，可获取到所述小区准确的天线高度时，利用本申请所提供的技术方案所获得的小区经纬度平均精度可达到20～40米左右，精度在50米以内的小区占比可达80％～90％左右。
作为采用本申请实施例提供的小区经纬度预测方法得到的对应小区的预测经纬度结果的一种可选的应用场景，可以当所述小区的预测经纬度和所述小区的工程参数的经纬度之间的距离，超过了利用本申请实施例提供的预测经纬度精度的置信度门限时，可确认所述小区的工程参数的经纬度不准确。即使需要 进行上站测量所述工程参数不准确的小区的实际经纬度，也可以大大减小了运营商网络内需要上站测量的小区范围，从而大幅提升了工程参数的经纬度校准的效率，为网络运维打下良好的基础。
本申请实施例另一方面提供一种小区经纬度预测装置，请参阅图12，该小区经纬度预测装置包括：样本筛选模块13，配置为根据测量报告MR样本对应的位置信息和/或时间提前量TA，确定待定位MR样本，所述MR样本为目标小区在设置周期内的携带全球导航卫星系统GNSS信息的MR样本；位置确定模块15，配置为根据所述待定位MR样本确定包含所述目标小区在内的目标区域；预测模块17，配置为根据所述目标区域选取待定位置，根据所述待定位MR样本中携带的所述位置信息和所述待定位置的位置信息、以及对应的TA代表的距离预测所述目标小区的经纬度。
在一些实施例中，所述样本筛选模块13，具体配置为根据测量报告MR样本对应的位置信息和时间提前量TA，确定位置符合设置条件且TA不同的MR样本形成MR集合；根据所述MR集合中所述TA是否符合设置要求进行去重处理。
在一些实施例中，所述样本筛选模块13，具体配置为将测量报告MR样本分别作为待识别MR样本，根据所述待识别MR样本对应的位置信息，确定相对于所述待识别MR样本在第一阈值距离内包含的MR样本的分布密度；根据所述分布密度是否符合设置条件，对所述待识别MR样本进行剔除。
在一些实施例中，所述样本筛选模块13，具体配置为将测量报告MR样本进行栅格化，并建立每一所述MR样本与归属栅格之间的对应关系；根据每一所述栅格内所述MR样本数占比确定目标TA，根据所述目标TA确定置信度符合要求的目标MR样本，对所述栅格内除所述目标MR样本之外的其它MR样本进行剔除。
在一些实施例中，所述样本筛选模块13，具体配置为根据测量报告MR样本对应的时间提前量TA，选取所述TA排序中TA相对较小的设置数量的目标MR样本，对所述目标MR样本之外的其它MR样本进行剔除。
在一些实施例中，所述样本筛选模块13，具体配置为对测量报告MR样本进行栅格化，将归属于同一所述栅格内的所述MR样本形成MR集合；对每一 所述MR集合中除TA最小的MR样本之外的其它MR样本进行去重处理。
其中，所述样本筛选模块13，还配置为选取任一栅格为初始栅格，确定所述初始栅格的位置；确定相对所述初始栅格的位置在第一阈值距离内包含的邻近栅格的数量，确定所述邻近栅格的数量小于预设值时，将所述初始栅格内包含的MR样本剔除。
其中，所述样本筛选模块13，还配置为统计每一所述栅格内包含的MR样本中对应的TA的占比；根据所述占比最高的TA确定目标TA，对每一所述栅格内除所述目标TA对应的MR样本之外的其它MR样本进行剔除。
其中，所述样本筛选模块13，还配置为对所述MR样本按照TA进行排序；确定所述排序中TA由小到大的设置数量的目标MR样本，对所述目标MR样本之外的其它MR样本进行剔除。
在一些实施例中，所述位置确定模块15，具体配置为根据所述待定位MR样本对应的TA确定初始位置信息及参考MR样本；根据所述参考MR样本与所述初始位置信息之间的距离，确定参考位置信息；根据所述参考位置信息分别向周围扩展设置的第一经度距离和第一纬度距离，根据扩展后的位置得到包含所述目标小区在内的目标区域。
在一些实施例中，所述位置确定模块15，还配置为将所述参考MR样本对应的TA代表的距离换算为经度和纬度，根据换算结果确定第一经度距离和第一纬度距离。
在一些实施例中，所述预测模块17，具体配置为以所述目标区域中一点为原点、以设置距离为步长分别确定待定位置；根据所述待定位MR样本中携带的所述位置信息和所述待定位置的位置信息，分别确定每一所述待定位MR样本点对应的天线与终端之间的距离；确定所述待定位MR样本对应的TA代表的距离；根据每一所述待定位MR样本对应的TA代表的距离、以及对应的所述天线与终端之间的距离，确定每一所述待定位MR样本对应的基站与所述目标小区之间的预测距离，形成与所述待定位置对应的预测距离集合；根据所述预测距离集合，确定所述目标小区的预测经纬度。
上述实施例提供的小区经纬度预测装置在进行小区经纬度预测时，仅以上述各程序模块的划分进行举例说明，在实际应用中，可以根据需要而将上述步骤分配由不同的程序模块完成，即可以将装置的内部结构划分成不同的程序模 块，以完成以上描述的全部或者部分处理。另外，上述实施例提供的小区经纬度预测装置与小区经纬度预测方法实施例属于同一构思，其具体实现过程详见方法实施例，这里不再赘述。
本申请实施例另一方面还提供一种服务器，该服务器可以与移动通信网络系统侧进行网络通信，如与移动通信网络系统侧的基站进行网络通信，接收移动通信网络系统侧采集到的MR样本数据，以基于所述MR样本数据执行本申请实施例提供的小区经纬度预测方法对小区经纬度进行预测。请参阅图13，该服务器包括处理器201以及用于存储能够在处理器201上运行的计算机程序的存储介质202，其中，所述处理器201配置为运行所述计算机程序时，执行本申请任一实施例所提供的小区经纬度预测方法的步骤。这里，处理器201和存储介质202并非指代对应的数量为一个，而是可以为一个或者多个。其中，该服务器还包括内存203、网络接口204以及将处理器201、内存203、网络接口204及存储介质202连接的系统总线205。存储介质202中存储有操作系统和用于实现本申请实施例所提供的内存缓存管理方法的小区经纬度预测装置，该处理器201用于提高计算和控制能力，支撑整个服务器的运行。该内存203用于为存储介质202中的内存缓存管理方法的运行提供环境，网络接口204配置为与基站进行网络通信，接收或发送数据，如，获取基站采集到的MR样本数据、返回小区经纬度预测结果等。
本申请实施例又一方面，还提供一种基站，该基站通过对采集到的MR样本数据进行分析，以基于所述MR样本数据执行本申请实施例提供的小区经纬度预测方法对小区经纬度进行预测。该基站包括处理器以及用于存储能够在处理器上运行的计算机程序的存储介质，其中，所述处理器配置为运行所述计算机程序时，执行本申请任一实施例所提供的小区经纬度预测方法的步骤。
本申请实施例还提供了一种存储介质，例如包括存储有计算机程序的存储器，该计算机程序可以由处理器执行，以完成本申请任一实施例所提供的色散估计方法的步骤。该存储介质可以是FRAM、ROM、PROM、EPROM、EEPROM、Flash Memory、磁表面存储器、光盘、或CD-ROM等存储器；也可以是包括上述存储器之一或任意组合的各种设备。
以上所述，仅为本申请的具体实施方式，但本发明的保护范围并不局限于此，任何熟悉本技术领域的技术人员在本申请揭露的技术范围内，可轻易想到 变化或替换，都应涵盖在本发明的保护范围之内。本发明的保护范围应以所述权利要求的保护范围以准。

Claims (17)

  1. 一种小区经纬度预测方法，其中，包括：
    根据测量报告MR样本对应的位置信息和/或时间提前量TA，确定待定位MR样本，所述MR样本为目标小区在设置周期内的携带全球导航卫星系统GNSS信息的MR样本；
    根据所述待定位MR样本确定包含所述目标小区在内的目标区域；
    根据所述目标区域选取待定位置，根据所述待定位MR样本中携带的所述位置信息和所述待定位置的位置信息、以及对应的TA代表的距离预测所述目标小区的经纬度。
  2. 如权利要求1所述的方法，其中，所述根据测量报告MR样本对应的位置信息和/或时间提前量TA，确定待定位MR样本，包括：
    根据测量报告MR样本对应的位置信息和时间提前量TA，确定位置符合设置条件且TA不同的MR样本形成MR集合；
    根据所述MR集合中所述TA是否符合设置要求进行去重处理。
  3. 如权利要求1所述的方法，其中，所述根据测量报告MR样本对应的位置信息和/或时间提前量TA，确定待定位MR样本，包括：
    将测量报告MR样本分别作为待识别MR样本，根据所述待识别MR样本对应的位置信息，确定相对于所述待识别MR样本在第一阈值距离内包含的MR样本的分布密度；
    根据所述分布密度是否符合设置条件，对所述待识别MR样本进行剔除。
  4. 如权利要求1所述的方法，其中，所述根据测量报告MR样本对应的位置信息和/或时间提前量TA，确定待定位MR样本，包括：
    将测量报告MR样本进行栅格化，并建立每一所述MR样本与归属栅格之间的对应关系；
    根据每一所述栅格内所述MR样本数占比确定目标TA，根据所述目标TA确定置信度符合要求的目标MR样本，对所述栅格内除所述目标MR样本之外的其它MR样本进行剔除。
  5. 如权利要求1所述的方法，其中，所述根据测量报告MR样本对应的位 置信息和/或时间提前量TA，确定待定位MR样本，包括：
    根据测量报告MR样本对应的时间提前量TA，选取所述TA排序中TA相对较小的设置数量的目标MR样本，对所述目标MR样本之外的其它MR样本进行剔除。
  6. 如权利要求1所述的方法，其中，所述根据测量报告MR样本对应的位置信息和时间提前量TA，确定待定位MR样本，包括：
    对测量报告MR样本进行栅格化，将归属于同一所述栅格内的所述MR样本形成MR集合；
    对每一所述MR集合中除TA最小的MR样本之外的其它MR样本进行去重处理。
  7. 如权利要求6所述的方法，其中，所述根据测量报告MR样本对应的位置信息和时间提前量TA，确定待定位MR样本，还包括：
    选取任一栅格为初始栅格，确定所述初始栅格的位置；
    确定相对所述初始栅格的位置在第一阈值距离内包含的邻近栅格的数量，确定所述邻近栅格的数量小于预设值时，将所述初始栅格内包含的MR样本剔除。
  8. 如权利要求7所述的方法，其中，所述根据测量报告MR样本对应的位置信息和时间提前量TA，确定待定位MR样本，还包括：
    统计每一所述栅格内包含的MR样本中对应的TA的占比；
    根据所述占比最高的TA确定目标TA，对每一所述栅格内除所述目标TA对应的MR样本之外的其它MR样本进行剔除。
  9. 如权利要求8所述的方法，其中，所述根据测量报告MR样本对应的位置信息和时间提前量TA，确定待定位MR样本，还包括：
    对所述MR样本按照TA进行排序；
    确定所述排序中TA由小到大的设置数量的目标MR样本，对所述目标MR样本之外的其它MR样本进行剔除。
  10. 如权利要求1至9中任一项所述的方法，其中，所述根据所述待定位MR样本确定包含所述目标小区在内的目标区域，包括：
    根据所述待定位MR样本对应的TA确定初始位置信息及参考MR样本；
    根据所述参考MR样本与所述初始位置信息之间的距离，确定参考位置信息；
    根据所述参考位置信息分别向周围扩展设置的第一经度距离和第一纬度距离，根据扩展后的位置得到包含所述目标小区在内的目标区域。
  11. 如权利要求10所述的方法，其中，所述根据所述参考位置信息分别向周围扩展设置的第一经度距离和第一纬度距离之前，包括：
    将所述参考MR样本对应的TA代表的距离换算为经度和纬度，根据换算结果确定第一经度距离和第一纬度距离。
  12. 如权利要求1至9中任一项所述的方法，其中，所述根据所述目标区域选取待定位置，根据所述待定位MR样本中携带的所述位置信息和所述待定位置的位置信息、以及对应的TA代表的距离预测所述目标小区的经纬度，包括：
    以所述目标区域中一点为原点、以设置距离为步长分别确定待定位置；
    根据所述待定位MR样本中携带的所述位置信息和所述待定位置的位置信息，分别确定每一所述待定位MR样本点对应的天线与终端之间的距离；
    确定所述待定位MR样本对应的TA代表的距离；
    根据每一所述待定位MR样本对应的TA代表的距离、以及对应的所述天线与终端之间的距离，确定每一所述待定位MR样本对应的基站与所述目标小区之间的预测距离，形成与所述待定位置对应的预测距离集合；
    根据所述预测距离集合，确定所述目标小区的预测经纬度。
  13. 如权利要求1至9中任一项所述的方法，其中，还包括：
    确定与所述目标小区归属于相同位置的其它小区；
    根据所述目标小区对应的预测经纬度和所述其它小区对应的预测经纬度，确定归属于所述相同位置的多个小区对应的预测经纬度。
  14. 一种小区经纬度预测装置，其中，包括：
    样本筛选模块，配置为根据测量报告MR样本对应的位置信息和/或时间提前量TA，确定待定位MR样本，所述MR样本为目标小区在设置周期内的携 带全球导航卫星系统GNSS信息的MR样本；
    位置确定模块，配置为根据所述待定位MR样本确定包含所述目标小区在内的目标区域；
    预测模块，配置为根据所述目标区域选取待定位置，根据所述待定位MR样本中携带的所述位置信息和所述待定位置的位置信息、以及对应的TA代表的距离预测所述目标小区的经纬度。
  15. 一种服务器，其中，包括处理器和用于存储能够在处理器上运行的计算机程序的存储器；其中，
    所述处理器配置为运行所述计算机程序时，执行权利要求1至13中任一项所述小区经纬度预测方法。
  16. 一种基站，其中，包括处理器和用于存储能够在处理器上运行的计算机程序的存储器；其中，
    所述处理器配置为运行所述计算机程序时，执行权利要求1至13中任一项所述小区经纬度预测方法。
  17. 一种存储介质，其中，所述存储介质中存储有可执行指令，所述可执行指令被处理器执行时实现权利要求1至13中任一项所述的小区经纬度预测方法。
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Cited By (8)

* Cited by examiner, † Cited by third party
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CN113301646A (zh) * 2021-05-21 2021-08-24 恒安嘉新（北京）科技股份公司 一种定位方法、装置、电子设备及存储介质
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Families Citing this family (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
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CN114374990B (zh) * 2021-12-23 2024-03-22 北京红山信息科技研究院有限公司 一种提升基于ta计算mr与基站间距离精度方法
CN115278702B (zh) * 2022-07-27 2023-04-14 四川通信科研规划设计有限责任公司 一种基于移动用户mr数据的基站经纬度纠偏方法、系统、存储介质及终端

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103858462A (zh) * 2013-11-22 2014-06-11 华为技术有限公司 一种确定位置的方法及基站
CN106507411A (zh) * 2016-12-02 2017-03-15 浪潮通信信息系统有限公司 一种基于mr的lte工参自动核查方法
CN108260202A (zh) * 2016-12-27 2018-07-06 中国移动通信集团广东有限公司 一种测量报告采样点的定位方法及装置

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105611568B (zh) * 2015-11-11 2019-01-01 北京电旗通讯技术股份有限公司 一种基于mro测量报告对lte终端位置进行准确定位的方法
CN105357675B (zh) * 2015-12-08 2019-01-08 广东海格怡创科技有限公司 Ue小区重叠覆盖情况检测方法与系统
CN107466103B (zh) * 2016-04-29 2020-06-02 华为技术有限公司 一种终端定位方法及网络设备

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103858462A (zh) * 2013-11-22 2014-06-11 华为技术有限公司 一种确定位置的方法及基站
CN106507411A (zh) * 2016-12-02 2017-03-15 浪潮通信信息系统有限公司 一种基于mr的lte工参自动核查方法
CN108260202A (zh) * 2016-12-27 2018-07-06 中国移动通信集团广东有限公司 一种测量报告采样点的定位方法及装置

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
See also references of EP3890361A4 *

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* Cited by examiner, † Cited by third party
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CN114205733B (zh) * 2020-08-31 2023-12-26 中国移动通信集团浙江有限公司 一种高速道路用户的异常感知事件定位方法
CN113301646B (zh) * 2021-05-21 2023-04-07 恒安嘉新（北京）科技股份公司 一种定位方法、装置、电子设备及存储介质
CN113301646A (zh) * 2021-05-21 2021-08-24 恒安嘉新（北京）科技股份公司 一种定位方法、装置、电子设备及存储介质
CN113382359A (zh) * 2021-07-05 2021-09-10 中国电信股份有限公司 定位方法、定位装置、计算机可读介质及电子设备
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CN114491307A (zh) * 2022-01-14 2022-05-13 青岛海信网络科技股份有限公司 一种设备聚合方法及装置
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CN115379479A (zh) * 2022-08-23 2022-11-22 中国联合网络通信集团有限公司 小区覆盖类型确定方法、装置、设备及存储介质
CN115379479B (zh) * 2022-08-23 2024-04-19 中国联合网络通信集团有限公司 小区覆盖类型确定方法、装置、设备及存储介质
WO2024051799A1 (zh) * 2022-09-09 2024-03-14 华为技术有限公司 射频单元位置的测量方法及通信装置
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CN117613909B (zh) * 2024-01-24 2024-04-12 国能日新科技股份有限公司 分布式光伏区域气象建模方法及装置

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