Base station direction angle correction method based on grid rsrp data, storage medium and device

Abstract

The application discloses a base station direction angle correction method based on grid RSRP data, a storage medium and a device, and the method comprises the following steps: taking the base station position as the original point of the direction angle, setting the lobe angle size, the calculation radius and the initial position of the direction angle, and the angle movement degree of the direction angle; moving the direction angle according to the angle movement degree, and calculating the fitting degree of each direction angle; taking the direction angle with the highest fitting degree as the base station direction angle; the calculation of the fitting degree of each direction angle comprises: calculating the actual rsrp value of the MR data uploaded by all user positions in the direction angle, and calculating the corresponding fitting rsrp value through a coverage model; if the actual rsrp value and the fitting rsrp value are within a certain error range, the fitting degree is increased. The application only needs to input the MR data containing the latitude and longitude information and the rsrp value information and the base station latitude and longitude data, and generates the coverage direction angle of the cell through automatic calculation.

Description

Technical Field

[0001] This invention relates to a base station azimuth angle correction method, storage medium, and apparatus based on grid RSRP data. Background Technology

[0002] Base station azimuth information is fundamental to base station management. However, currently, most of the base station cell azimuth data uploaded to the big data system by network management is manually entered, lacking a stable acquisition method. This leads to discrepancies between the network management azimuth and the actual azimuth of some base stations, posing challenges to subsequent daily maintenance and optimization management. To address these issues, a method is established to determine the base station azimuth based on user-uploaded MR data. Based on this method, base station azimuth correction can be performed. Summary of the Invention

[0003] The purpose of this invention is to overcome the shortcomings of the prior art and provide a base station azimuth angle correction method, storage medium and device based on grid RSRP data.

[0004] The objective of this invention is achieved through the following technical solution:

[0005] A first aspect of the present invention provides a base station azimuth angle correction method based on grid RSRP data, comprising the following steps:

[0006] Using the base station location as the origin of the azimuth angle, set the beam angle size, calculation radius, initial position, and angular movement degree of the azimuth angle;

[0007] The direction angle is moved according to the degree of angular movement, and the fit of each direction angle is calculated;

[0008] The orientation angle with the highest fitting degree is used as the base station orientation angle;

[0009] The calculation of the fit degree for each direction angle includes:

[0010] Calculate the actual RSRP values ​​of all user location-uploaded MR data within the orientation angle, and obtain the corresponding fitted RSRP values ​​through the coverage model;

[0011] If the actual rsrp value and the fitted rsrp value are within a certain error range, then increase the fit.

[0012] Furthermore, the beam angle is 65°, the calculation radius is 1km, and the angle shift is 5°.

[0013] Furthermore, the method for calculating the fitted rsrp value is as follows:

[0014] Base station transmit power + base station antenna gain - noise floor - path loss - penetration loss - human body loss - interference margin + mobile phone antenna gain - mobile phone noise figure.

[0015] Furthermore, the base station transmit power = 10 * LOG 10 (Total base station power / Total number of PRBs / 12 * 1000); The base station antenna gain is set to a fixed value according to different equipment models; The noise floor = -174 + 10lg (subcarrier spacing).

[0016] Furthermore, the path loss is divided into urban scenario path loss and rural scenario path loss; the calculation method for the urban scenario path loss includes:

[0017]

[0018]

[0019]

[0020]

[0021]

[0022] Among them, PL Uma-NLOS PL represents the path loss in non-line-of-sight scenarios for urban macrocells. Uma-LOS PL' represents the path loss in line-of-sight scenarios for urban macrocells. Uma-LOS This represents the path loss in non-line-of-sight scenarios for actual urban macrocells;

[0023] d' represents the planar distance between the user's location and the base station cell. BP The distance to the dividing point is 4h'. BS *h' UT *f c / c,h' BS =h BS – h E ,h' UT = h UT – h E h BS h represents the height of the base station antenna. UT h represents the user's height. E The length is 0.8m-1.2m. It is the center frequency of the base station, c = 3.0 × 10 8 m / s, The 3D distance between this point and the base station cell antenna is calculated using the following formula: ;

[0024] The calculation method for path loss in rural scenarios includes:

[0025] exist When within 10m to 5km, use As PL path loss, when When the distance is greater than 5km, use As the PL path loss, where:

[0026]

[0027]

[0028]

[0029]

[0030]

[0031] Among them, PL Rma-NLOS This represents the path loss in non-line-of-sight scenarios for rural macrocells, PL Rma-LOS PL' represents the path loss in line-of-sight scenarios for rural macrocells. Rma-LOS This represents the path loss in non-line-of-sight scenarios for rural macrocells, as actually calculated.

[0032] W represents the street width, and h represents the average building height. .

[0033] Furthermore, the penetration loss refers to the loss of electromagnetic waves of different frequencies penetrating through different scene paths.

[0034] Furthermore, the human body loss, mobile phone antenna gain, and mobile phone noise figure are all fixed values; the interference margin is set to different values ​​depending on the scenario.

[0035] Furthermore, the actual RSRP values ​​of the MR data uploaded by all users within the calculated orientation angle include:

[0036] The acquired MR data includes an MR latitude and longitude field and an MR signal strength rsrp field. The MR latitude and longitude field is used to calculate the fitted rsrp value, and the MR signal strength rsrp field is used as the actual rsrp value.

[0037] In a second aspect, the present invention provides a storage medium storing computer instructions thereon, wherein the computer instructions, when executed, perform the steps of the base station azimuth angle correction method based on grid RSRP data.

[0038] A third aspect of the present invention provides an apparatus comprising a memory and a processor, wherein the memory stores computer instructions executable on the processor, and the processor executes the steps of the base station azimuth angle correction method based on grid RSRP data when executing the computer instructions.

[0039] The beneficial effects of this invention are:

[0040] (1) In an exemplary embodiment of the present invention, only MR data containing latitude and longitude information and rsrp value information and the latitude and longitude data of the base station itself need to be input, and the coverage direction angle of the cell is automatically calculated.

[0041] (2) In an exemplary embodiment of the present invention, a specific calculation method for fitting the rsrp value is disclosed, which makes the calculation accurate and reliable. Attached Figure Description

[0042] Figure 1 This is a flowchart of a method in an exemplary embodiment of the present invention;

[0043] Figure 2 This is a schematic diagram of the initial position in an exemplary embodiment of the present invention;

[0044] Figure 3 This is a schematic diagram illustrating the movement of the direction angle to one of the directions in an exemplary embodiment of the present invention;

[0045] Figure 4 This is a schematic diagram of the user location and base station cell in an exemplary embodiment of the present invention;

[0046] Figure 5 This is an antenna gain pattern in an exemplary embodiment of the present invention. Detailed Implementation

[0047] The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0048] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0049] It should be understood that although the terms first, second, third, etc., may be used in this application to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to determination."

[0050] Furthermore, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0051] See Figure 1 , Figure 1 This invention illustrates a base station azimuth angle correction method based on grid RSRP data, provided by an exemplary embodiment of the present invention. MR (Measurement Report) refers to data that is sent every 480ms on the traffic channel (470ms on the signaling channel), and this data can be used for network evaluation and optimization.

[0052] The method includes the following steps:

[0053] S1: Using the base station location as the origin of the azimuth angle, set the beam angle size, calculation radius, initial position, and angular movement degree of the azimuth angle.

[0054] In one exemplary embodiment, the beam angle is 65°, the calculation radius is 1 km, the angular movement is 5°, and the initial position is due north. Figure 2 As shown in the figure, the dots represent the user's location.

[0055] S3: Move the direction angle according to the angle movement degree, and calculate the fitting degree of each direction angle.

[0056] like Figure 3 As shown, Figure 3 This is a schematic diagram of the direction angle moving to one of the directions.

[0057] S5: Use the orientation angle with the highest fitting degree as the base station orientation angle.

[0058] Wherein: the calculation of the fitting degree for each direction angle includes:

[0059] S31: Calculate the actual rsrp values ​​of all user location-uploaded MR data within the orientation angle, and obtain the corresponding fitted rsrp values ​​through the coverage model;

[0060] S33: If the actual rsrp value and the fitted rsrp value are within a certain error range, then increase the fit.

[0061] RSRP (Reference Signal Receiving Power) is a key parameter in LTE networks that represents the strength of wireless signals and is one of the physical layer measurement requirements. It is the average signal power received on all REs (resource particles) carrying the reference signal within a certain symbol.

[0062] That is, while keeping the total number of MR lines constant, we finally find the orientation angle with the highest fit.

[0063] rsrp mr From MR messages, which are automatically reported by all user terminals and automatically obtained by the network management system. In actual calculations, the area within a sector's azimuth angle can be drawn as a 10*10m (a certain area) grid. The RSRP within the same grid... mr Take the arithmetic mean and accumulate it over a certain period of time to ensure that each grid cell contains rsrp. mr Data is sufficient.

[0064] In one exemplary embodiment, the initial value of the goodness of fit is 0, and the goodness of fit is increased by 1 each time; while in another exemplary embodiment, the initial value of the goodness of fit is 0, and the goodness of fit is increased by a factor of 1 based on the actual error ratio, that is: if rsrp mr =rsrp 拟合 The fit increases by 1; if rsrp mr =X*rsrp 拟合 , or X*rsrp mr =rsrp 拟合 If X is less than 1 and greater than 0.9 (optional), the fit will increase by X accordingly; otherwise, the fit will not increase.

[0065] More preferably, in an exemplary embodiment, the calculation of the actual rsrp value of the MR data uploaded by all users within the orientation angle in step S31 includes:

[0066] The acquired MR data includes an MR latitude and longitude field and an MR signal strength rsrp field. The MR latitude and longitude field is used to calculate the fitted rsrp value, and the MR signal strength rsrp field is used as the actual rsrp value.

[0067] The MR latitude and longitude field indicates the user's location when reporting the MR. This field is unaffected by other factors and is highly accurate. The MR signal strength (rsrp) field reflects the reference signal received by the UE from the serving cell. This field allows us to determine the signal strength of the user during service usage.

[0068] That is, rsrp mr The MR messages are automatically reported by all user terminals and automatically acquired by the network management system. Based on actual network experience, RSRP is accumulated over a certain period of time. mr The data can be traversed across all locations on the map.

[0069] More preferably, in an exemplary embodiment, the calculation method for the fitted rsrp value in step S31 is as follows:

[0070] Base station transmit power + base station antenna gain - noise floor - path loss - penetration loss - human body loss - interference margin + mobile phone antenna gain - mobile phone noise figure.

[0071] The following explains the method for fitting the nine calculated values ​​in the rsrp value:

[0072] More preferably, in an exemplary embodiment, the base station transmit power = 10*LOG 10 (Total base station power / Total number of PRBs / 12 * 1000). PRB stands for Physical Resource Block, which is the smallest unit of downlink resource allocation. Its size is 25 subcarriers, or 375kHz.

[0073] The base station antenna gain is set to a fixed value depending on the equipment model, and the antenna gain pattern is as follows. Figure 5 As shown.

[0074] The noise floor is -174 + 10lg (subcarrier spacing), and in a preferred exemplary embodiment, the subcarrier spacing is 30k.

[0075] More preferably, in an exemplary embodiment, the path loss is divided into urban scene path loss and rural scene path loss; the calculation method for the urban scene path loss includes:

[0076]

[0077]

[0078]

[0079]

[0080]

[0081] Among them, PL Uma-NLOS PL represents the path loss in non-line-of-sight scenarios for urban macrocells. Uma-LOS PL' represents the path loss in line-of-sight scenarios for urban macrocells. Uma-LOS This represents the path loss in non-line-of-sight scenarios for actual urban macrocells;

[0082] like Figure 4 As shown, d' represents the planar distance between the user's location and the base station cell. BP The BreakPoint Distance is 4h'. BS *h' UT *f c / c,h' BS =h BS – h E ,h' UT = h UT – h E h BS h represents the height of the base station antenna. UT h represents the user's height. E 0.8m-1.2m (preferably 1m), It is the center frequency of the base station, c = 3.0 × 10 8 m / s, The 3D distance between this point and the base station cell antenna is calculated using the following formula: ;

[0083] The calculation method for path loss in rural scenarios includes:

[0084] exist When within 10m to 5km, use As PL path loss, when When the distance is greater than 5km, use As the PL path loss, where:

[0085]

[0086]

[0087]

[0088]

[0089]

[0090] Among them, PL Rma-NLOSThis represents the path loss in non-line-of-sight scenarios for rural macrocells, PL Rma-LOS PL' represents the path loss in line-of-sight scenarios for rural macrocells. Rma-LOS This represents the path loss in non-line-of-sight scenarios for rural macrocells, as actually calculated.

[0091] W represents the street width, and h represents the average building height. .

[0092] More preferably, in an exemplary embodiment, the penetration loss is the loss of electromagnetic waves of different frequencies penetrating along different scene paths.

[0093] Specifically, the empirical values ​​are shown in the table below:

[0094] Frequency band (GHz) 0.8 1.8 2.1 2.6 3.5 4.5 Dense urban areas 18 21 22 23 26 28 urban area 14 17 18 19 22 24 suburbs 10 13 14 15 18 20 rural areas 7 10 11 12 15 17

[0095] More preferably, in an exemplary embodiment, the human body loss, mobile phone antenna gain, and mobile phone noise figure are all fixed values; the interference margin is set to different values ​​depending on the scenario.

[0096] Among them, the human body loss is a fixed value, which is 0 under 3.5G; the mobile phone antenna gain is generally 3dB; the mobile phone noise figure is a fixed value of 7dB.

[0097] Interference margin varies depending on the scenario. For example, when covering an outdoor scenario, the margin is 17dB in densely populated urban areas, 15dB in urban areas, 13dB in suburban areas, and 10dB in rural areas. When covering an indoor scenario, the margin is 7dB in densely populated urban areas, 6dB in urban areas, 4dB in suburban areas, and 2dB in rural areas.

[0098] Furthermore, the orientation angle calculated by this invention can better help present the coverage direction and actual coverage of cells on GIS maps, i.e. Figure 2 and Figure 3 As shown.

[0099] Another exemplary embodiment of the present invention provides a storage medium storing computer instructions thereon, which, when executed, perform the steps of the base station azimuth angle correction method based on grid RSRP data.

[0100] Another exemplary embodiment of the present invention provides an apparatus including a memory and a processor, wherein the memory stores computer instructions executable on the processor, and the processor executes the steps of the base station azimuth angle correction method based on grid RSRP data when executing the computer instructions.

[0101] Based on this understanding, the technical solution of this embodiment, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause the device to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0102] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art can make other variations or modifications based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A base station azimuth angle correction method based on grid RSRP data, characterized in that: Includes the following steps: Using the base station location as the origin of the azimuth angle, set the beam angle size, calculation radius, initial position, and angular movement degree of the azimuth angle; The direction angle is moved according to the degree of angular movement, and the fit of each direction angle is calculated; The orientation angle with the highest fitting degree is used as the base station orientation angle; The calculation of the fit degree for each direction angle includes: Calculate the actual RSRP values ​​of all user location-uploaded MR data within the orientation angle, and obtain the corresponding fitted RSRP values ​​through the coverage model; If the actual RSRP value and the fitted RSRP value are within the error range, then increase the fit. Among them, rsrp mr The MR message is automatically reported by all user terminals and automatically obtained by the network management system. The initial value of the goodness of fit is 0, and the goodness of fit is increased by 1 each time; or: the initial value of the goodness of fit is 0, and the goodness of fit is increased by a factor of 1 based on the actual error ratio, that is: if rsrp mr =rsrp 拟合 The fit increases by 1; if rsrp mr =X*rsrp 拟合 , or X*rsrp mr =rsrp 拟合 If X is less than 1 and greater than 0.9, the goodness of fit is increased by X accordingly; otherwise, the goodness of fit is not increased. 拟合 That is, fitting the rsrp value; The method for calculating the fitted rsrp value is as follows: Base station transmit power + base station antenna gain - noise floor - path loss - penetration loss - human body loss - interference margin + mobile phone antenna gain - mobile phone noise figure; The base station's transmit power = 10 * log 10 ((Total base station power / Total number of PRBs) / (12 * 1000)); The noise floor = -174 + 10lg(subcarrier spacing); The path loss is divided into urban scenario path loss and rural scenario path loss; the calculation method for the urban scenario path loss includes: ； ； ； ； ； in, This represents the path loss in non-line-of-sight scenarios for urban macrocells. This represents the path loss in line-of-sight scenarios for urban macrocells. This represents the path loss in non-line-of-sight scenarios for actual urban macrocells; The planar distance between the user's location and the base station cell. The distance to the dividing point is represented by a value. , , , Indicates the height of the base station antenna. Indicates user height, The length is 0.8m-1.2m. It is the center frequency of the base station, c = 3.0 × 10 8 m / s, The 3D distance between the user's location and the base station cell antenna is calculated using the following formula: ; The calculation method for path loss in rural scenarios includes: exist When within 10m to 5km, use As PL path loss, when When the distance is greater than 5km, use As the PL path loss, where: ； ； ； ； ； in, This represents the path loss in non-line-of-sight scenarios for rural macrocells. This represents the path loss in a line-of-sight scenario for rural macrocells. This represents the path loss in non-line-of-sight scenarios for rural macrocells, as actually calculated. Indicates the width of the street. Indicates the average building height. .

2. The base station azimuth angle correction method based on grid RSRP data according to claim 1, characterized in that: The beam angle is 65°, the calculation radius is 1km, and the angle shift is 5°.

3. The base station azimuth angle correction method based on grid RSRP data according to claim 1, characterized in that: The penetration loss refers to the loss of electromagnetic waves of different frequencies as they penetrate different scene paths, including dense urban areas, urban areas, suburbs, and rural areas.

4. The base station azimuth angle correction method based on grid RSRP data according to claim 1, characterized in that: The human body loss, mobile phone antenna gain, and mobile phone noise figure are all fixed values; the interference margin is set to different values ​​according to different scenarios. When it is an outdoor coverage of an outdoor scenario, it is 17dB in dense urban areas, 15dB in urban areas, 13dB in suburbs, and 10dB in rural areas; when it is an outdoor coverage of an indoor scenario, it is 7dB in dense urban areas, 6dB in urban areas, 4dB in suburbs, and 2dB in rural areas.

5. The base station azimuth angle correction method based on grid RSRP data according to claim 1, characterized in that: The actual RSRP values ​​of the MR data uploaded by all users within the calculated orientation angle include: The acquired MR data includes an MR latitude and longitude field and an MR signal strength rsrp field. The MR latitude and longitude field is used to calculate the fitted rsrp value, and the MR signal strength rsrp field is used as the actual rsrp value.

6. The base station azimuth angle correction method based on grid RSRP data according to claim 1, characterized in that: In the actual calculation process, the area within the sector direction angle is drawn as a grid of a certain area. The rsrpmr of the same grid is taken as the arithmetic mean. After accumulating for a certain period of time, it is ensured that there is rsrpmr data in each grid. rsrpmr is the actual rsrp value. The certain area is 10*10m.

7. The base station azimuth angle correction method based on grid RSRP data according to claim 1, characterized in that: The base station antenna gain is set to a fixed value depending on the equipment model.

8. A storage medium storing computer instructions thereon, characterized in that: When the computer instructions are executed, they perform the steps of the base station azimuth angle correction method based on grid RSRP data as described in any one of claims 1 to 7.

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