Cell azimuth estimation method, apparatus, electronic device, and storage medium

Abstract

The present disclosure provides a cell azimuth angle estimation method, apparatus, electronic device, and storage medium, belonging to the field of wireless communication. The method includes the steps of: performing a gridding process on MR sampling points of a cell to obtain a first grid of MR sampling points; extracting the top N second grids with the highest signal strengths from a target grid, where the target grid is the first grid whose distance from the cell is greater than a predetermined distance; and calculating a cell azimuth angle based on the latitude and longitude of the second grid. Implementing the technical solution of the present disclosure enables fast and accurate estimation of cell azimuth angles, reducing the difficulty of wireless network optimization.

Description

Cross-reference to Related Applications , , , ,

[0004] , , ,

[0003] , , , , ,

[0005]

[0001] This disclosure is filed based on a Chinese patent application with the application number "202310921043.2" and the filing date of July 25, 2023, claims the priority of the Chinese patent application, and all the contents of the Chinese patent application are incorporated herein by reference.

Technical Field

[0002] This disclosure relates to the field of wireless communication, and particularly to a cell azimuth estimation method, apparatus, electronic device, and storage medium.

Background Art

[0003] In wireless communication, antennas play a role in transmitting and receiving signals. For a base station cell, the azimuth angle of the antenna is an important parameter, which determines the main coverage direction of the cell and further affects the signal quality of the terminals in this coverage area. Obtaining the actual azimuth angle of each cell plays an important role in optimizing the coverage of the wireless network. Currently, the azimuth angle data of the cell mainly comes from the cell's working parameter table. However, since the azimuth angle of the cell antenna always needs to be adjusted, the azimuth angle recorded in the working parameter table may not actually match, increasing the difficulty of optimizing the wireless network and requiring a lot of manpower and time for inspection.

Summary of the Invention

[0004] To solve at least one technical problem in the prior art, this disclosure provides a cell azimuth estimation method, apparatus, electronic device, and storage medium.

[0005] A first aspect of this disclosure is a cell azimuth angle estimation method comprising the steps of: performing a grid processing on the MR sampling points of a cell to obtain a first grid of the MR sampling points; and extracting the top N second grids with large signal strengths from the target grid, wherein the target grid is the first grid whose distance from the cell is greater than a preset distance. Furthermore, if N is smaller than the number of grid cells A cell azimuth estimation method comprising the steps of: 1) calculating the cell azimuth angle based on the latitude and longitude of the second grid; and 2)

[0006] Selectively, before performing grid processing on the MR sampling points of the cell, the method further includes: calculating a first distance between the MR sampling point and the cell site based on the RSRP value of the MR sampling point of the cell; calculating a second distance between the MR sampling point and the cell based on the latitude and longitude of the MR sampling point of the cell and the latitude and longitude of the cell; and filtering out MR sampling points where the difference between the first distance and the second distance is greater than a preset threshold.

[0007] Selectively, the step of calculating a first distance between the MR sampling point and the site of the cell based on the RSRP value of the MR sampling point of the cell is:

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[0008] Selectively, the step of performing a gridding process on the MR sampling points of the cell to obtain a first grid of the MR sampling points includes the step of mapping the MR sampling points to corresponding grids on a gridded map based on the location information of the MR sampling points, and designating the corresponding grids as the first grid of the MR sampling points.

[0009] Selectively, the signal intensity is the RSRP value, and / or, N is an integer from 1 to M, where M is half the number of target grids.

[0010] Selectively, the preset distance is before Note cell The median distance between the MR sampling point and the cell, or the preset distance. is before Note cell This is the average value of the distance between the MR sampling point and the cell.

[0011] Optionally, the step of calculating the cell azimuth angle based on the latitude and longitude of the second grid includes the steps of calculating the relative position angle between the second grid and the cell based on the latitude and longitude of the second grid and the latitude and longitude of the cell, and obtaining the cell azimuth angle based on the relative position angle.

[0012] A second aspect of the present disclosure is a cell azimuth estimation device comprising a grid processing module for performing grid processing on MR sampling points of a cell to obtain a first grid of the MR sampling points, and a grid extraction module for extracting the top N second grids with large signal strengths from a target grid, wherein the target grid is the first grid whose distance from the cell is greater than a preset distance. Furthermore, if N is smaller than the number of grid cells This is a cell azimuth estimation device that includes a grid extraction module and a calculation module for calculating the cell azimuth angle based on the latitude and longitude of the second grid.

[0013] A third aspect of the present disclosure is an electronic device including a processor and memory for storing a program, wherein the program, when executed by the processor, includes instructions causing the processor to perform a method according to any of the first aspects of the present disclosure.

[0014] A fourth aspect of the present disclosure is a non-temporary computer-readable storage medium in which computer instructions are stored, the computer instructions causing the computer to perform the method described in any of the first aspects of the present disclosure.

[0015] One or more technical solutions provided in the embodiments of this application can reduce the difficulty of wireless network optimization by rapidly and accurately estimating the cell azimuth angle.

[0016] In one or more technical solutions provided in the embodiments of this application, a first grid of MR sampling points is obtained by a gridding process, the top N second grids with high signal strength are extracted from the first grids whose distance from the cell is greater than a preset distance, and the cell azimuth angle is calculated based on the latitude and longitude of the second grids. The main coverage area can be accurately represented and the cell azimuth angle can be calculated using a small number of second grids, the adverse effect of the horizontal offset of the second grid on the estimation of the cell azimuth angle can be reduced, and both the estimation speed and estimation accuracy of the cell azimuth angle can be achieved when estimating the cell azimuth angle based on the latitude and longitude of this second grid. [Brief explanation of the drawing]

[0017] The drawings illustrate exemplary embodiments of the present disclosure and are used in conjunction with the description to illustrate the principles of the present disclosure, including these drawings, in order to provide a further understanding of the present disclosure. The drawings are incorporated herein and constitute part of this specification. [Figure 1] A flowchart 1 of a cell azimuth angle estimation method according to an exemplary embodiment of this disclosure is shown. [Figure 2] FIG. 2 is a flowchart of a cell azimuth estimation method according to an exemplary embodiment of the present disclosure. [Figure 3] FIG. 5 is a schematic diagram of an angle estimation deviation at the same horizontal deviation according to an exemplary embodiment of the present disclosure. [Figure 4] FIG. 3 is a flowchart of a cell azimuth estimation method according to an exemplary embodiment of the present disclosure. [Figure 5] FIG. 11 is a schematic block diagram of a cell azimuth estimation apparatus according to an exemplary embodiment of the present disclosure. [Figure 6] FIG. 14 is a configuration block diagram of an exemplary electronic device that can be used to implement an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0018] Hereinafter, embodiments of the present disclosure will be described in more detail with reference to the accompanying drawings. Although several embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as limited to the embodiments provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are for illustrative purposes only and are not used to limit the protection scope of the present disclosure.

[0019] It should be understood that each step described in the embodiments of the method of the present disclosure can be executed in a different order and / or in parallel. Furthermore, the method embodiments can include additional steps and / or omit the execution of the illustrated steps. The scope of the present disclosure is not limited in this regard.

[0020] As used herein, the term "comprising" and its variations are open-ended inclusion, i.e., "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment", the term "another embodiment" means "at least one another embodiment", and the term "some embodiments" means "at least some embodiments". Related definitions of other terms are given in the following description. Note that the concepts such as "first", "second", etc. referred to in the present disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions executed by these devices, modules or units.

[0021] The modifiers "one" and "a plurality" referred to in the present disclosure are not limiting but general, and those skilled in the art should understand that, unless otherwise explicitly specified in the context, it is understood as "one or a plurality".

[0022] The names of messages or information that interact between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0023] Hereinafter, the invention of the present disclosure will be described with reference to the drawings.

[0024] Referring to FIG. 1, the cell azimuth estimation method provided by the present disclosure includes the following steps S101 to S103.

[0025] In step S101, grid processing is performed on the MR (Measurement Report) sampling points of the cell to obtain the first grid of the MR sampling points.

[0026] An MR sampling point represents the location of a terminal device or a dedicated test device. The cell in which an MR sampling point is located represents the serving cell in which the terminal device or dedicated test device corresponding to this MR sampling point is located. MR sampling points can be obtained from MR data; for example, MR data is obtained from a network management system, and the location information of the MR sampling point and the cell in which it is located are obtained from the MR data. MR data includes fields such as eNodeBID, PCI, alfncn, longitude, latitude, and RSRP, where eNodeBID represents the base station identifier, PCI represents the physical cell identifier, alfncn represents the absolute radio frequency channel number, longitude represents the longitude of the user sampling point in this cell, latitude represents the latitude of the user sampling point in the cell, and RSRP represents the reference signal received power, which is used to describe the received strength of the reference signal at the corresponding user sampling point. Using the eNodeBID, PCI, and alfncn fields, a single cell can be uniquely determined. On the other hand, the latitude and longitude of a cell can be obtained by directly querying the cell work parameter table, or it can be estimated from the distribution characteristics of the MR data.

[0027] In one embodiment, step S101 can map the MR sampling points to corresponding grids on a gridded map based on the location information of the MR sampling points, and the corresponding grids can be designated as the first grids for the MR sampling points. Specifically, the MR sampling points can be mapped to corresponding grids on a gridded map based on the location information of the MR sampling points and the location information of the grids on the gridded map. Exemplarily, based on the geographical location distribution, the map can be divided into square grids with specific side lengths, and each MR sampling point can be mapped to a specific grid, the side lengths of the grids can be set as needed, for example, to 20 meters. To obtain the first grids for the MR sampling points, the method of this embodiment can also employ other gridding processing methods.

[0028] After grid processing, the main coverage area can be extracted by replacing the MR sampling points with the first grid as the granularity.

[0029] Before executing step S101, you can also perform data preprocessing and filtering steps.

[0030] In one embodiment, referring to Figure 2, before performing step S101, the method includes the following steps S201 to S203.

[0031] In step S201, a first distance is calculated between the MR sampling point and the cell site based on the RSRP value of the cell's MR sampling point.

[0032] In this step, this first distance can be calculated using a correlated method that calculates the distance using the RSRP values ​​of the MR sampling points.

[0033] For example, the first distance is calculated based on a simplified SPM (Standard Propagation Model), where RSRP = a × lg(D) + b, and D is the distance from the MR sampling point to the cell site, and a and b are the parameters to be estimated, which can be obtained by fitting calculations using existing historical data. The SPM calculates the first distance based on the RSRP value.

[0034] Specifically, equation (1) is obtained based on SPM, and the first distance is calculated based on equation (1).

number

[0035] In step S202, a second distance is calculated between the MR sampling point and the cell based on the latitude and longitude of the cell's MR sampling point and the latitude and longitude of the cell.

[0036] In this step, a second distance can be calculated using a correlated calculation method that calculates the horizontal distance based on latitude and longitude.

[0037] For example, the half-vector equation (2) is used to calculate the second distance based on the latitude and longitude (x,y) of the MR sampling point and the latitude and longitude (x0,y0) of the cell:

number

[0038] In step S203, MR sampling points where the difference between the first distance and the second distance is greater than a preset threshold are filtered and removed.

[0039] The difference between the first distance and the second distance may be a forward difference or a reverse difference. Specifically, when calculating, the absolute value of the subtraction between the first distance and the second distance can be used as the difference. A preset threshold is set according to specific needs and may be set to, for example, 150 meters. If the difference between the first distance and the second distance of an MR sampling point is greater than this preset threshold, the MR sampling point is filtered and removed, removing anomalous MR sampling points and further conforming to radio propagation rules. This makes the signal strength distribution clearer and reduces the impact of anomalous data on azimuth estimation.

[0040] In step S102, the top N second grids with the greatest signal strength from the target grid are extracted, and the target grid is the first grid whose distance from the cell is greater than a predetermined distance. Furthermore, if N is smaller than the number of grid cells .

[0041] The second grid is used as the grid for the main coverage area (i.e., the grid for azimuth) for cell azimuth calculation. However, since the extracted second grid is not necessarily perfectly aligned with the normal of the cell azimuth, a certain horizontal offset may exist. To mitigate the negative impact of this horizontal offset on cell azimuth calculation, this step extracts the second grid from the first grid whose distance from the cell is greater than a preset distance, ensuring that the second grid is farther from the cell. By extracting the top N grids with the highest signal strength from the target grid and designating them as the second grid, the second grid is positioned within the main coverage area. The preset distance can be set according to actual needs, for example, cell It can be set to the median distance between the MR sampling point and the cell, or cellThe value can be set to the average distance between the MR sampling point and the cell. N can be set according to the actual needs; for example, N is an integer from 1 to M, and M may be half the number of target grids, or 1 / 10 of the number of target grids, etc. Specifically, N can be set to 2, 3, 4, etc. The signal strength can be the RSRP value, etc. Illustratively, the distance r from the cell of the grid is calculated based on the half-vector formula using the grid mean latitude and longitude and the cell latitude and longitude. Grids where r is greater than a predetermined distance are selected, and the three grids with the largest RSRP values ​​are selected to be the grids of the extracted main coverage area (i.e., the second grid above). If there are multiple MR sampling points within a grid, the average value of the MR sampling points can be used as the grid value.

[0042] Referring to Figure 3, the latitude and longitude of the cell are taken as the center, and the arc length formula

number

[0043] In this embodiment, in order to calculate the azimuth angle, a grid in the main coverage area (i.e., a grid based on azimuth angle) is extracted from the grid in the cell based on signal intensity features such as RSRP, and the angle is calculated based on the average latitude and longitude of the MR sampling points within this grid and the latitude and longitude of the cell.

[0044] In step S103, the cell azimuth angle is calculated based on the latitude and longitude of the second grid.

[0045] In one embodiment, referring to Figure 4, step S103 includes the following steps S401 to S402.

[0046] In step S401, the relative position angle between the second grid and the cell is calculated based on the latitude and longitude of the second grid and the latitude and longitude of the cell.

[0047] For two points on a map with latitude and longitude coordinates (x0, y0) and (x, y), their relative position angle can be determined based on the inverse trigonometric relationship between the horizontal distance and the longitude distance between them. The horizontal distance r is calculated and obtained in step 4, and only the longitude distance L needs to be determined, and its calculation formula (3) is as follows:

number

[0048] For example, for the three extracted grids, their horizontal and longitude distances are added together, and the relative position angle α is calculated using the inverse trigonometric function (4):

number

[0049] In step S402, the cell azimuth angle is obtained based on the relative position angle.

[0050] The cell azimuth angle is defined as the clockwise angle between the true north direction and the cell azimuth normal. Since the relative position angle α in the previous step is the angle between the azimuth normal and the meridian, the cell azimuth angle can be obtained by performing angle correction based on the relative position angle between the grid and the cell, and correcting this relative position angle. The average latitude and longitude of the grid is

number

number

[0051] The corrected θ is the estimated cell azimuth angle. After obtaining the estimated cell azimuth angle, it can be compared with the azimuth angle in the cell work parameter table, and for cells with large deviations, the azimuth angle can be investigated on-site, allowing for timely calibration of abnormal cell azimuth angles.

[0052] This step can also be performed by employing a different latitude and longitude-based cell azimuth calculation method to calculate the cell azimuth based on the latitude and longitude of the second grid.

[0053] The proposed technology in this disclosure reduces the impact of anomalous data on azimuth estimation by filtering data in combination with a radio propagation model. In contrast, conventional technologies are relatively simple in terms of data filtering, merely filtering by a single threshold in combination with the number of users or signal strength, but such thresholds often differ depending on the base station, making it difficult to select a common threshold. The method in this disclosure combines SPM and the fitting results of actual data to inversely estimate the distance from the site based on the RSRP value of the sample point, compares this distance with the latitude and longitude distance from the site, and filters out sample points with large difference values. This allows the filtered data to better conform to radio propagation rules, clarify the signal strength distribution, retain sample points for some sight distance scenarios, reduce the impact of anomalous data on azimuth estimation, and reduce the influence of complex radio environments on azimuth. By filtering data in combination with a radio propagation model, the impact of anomalous data on azimuth estimation is reduced.

[0054] This published technical proposal can improve the efficiency and accuracy of azimuth angle estimation by combining grid processing and RSRP distribution. Typically, user data in a single cell can reach thousands, tens of thousands, or even more. Calculating latitude and longitude based on sampling points would undoubtedly significantly reduce the efficiency of the algorithm. After employing grid processing, the main coverage area is extracted directly using the grid as the granularity, improving estimation efficiency if accuracy does not decrease. Simultaneously, the main coverage area can be extracted by combining RSRP indicators within the grid, and the accuracy of azimuth angle estimation can be improved by combining this with the characteristic of signal strength.

[0055] The proposed technology in this disclosure extracts the main coverage area by selecting a grid far from the site, reducing the impact of the main coverage area extraction deviation on the azimuth angle, improving algorithm stability, and increasing accuracy. When the main coverage area is close to the base station, the horizontal deviation extracted in that area often results in a large angular deviation. As can be seen when combined with the arc length formula, for the same arc length deviation, a larger radius results in a smaller angular deviation. Therefore, the extraction of the main coverage area should be performed by selecting a distant area or grid, which is advantageous in improving the stability and accuracy of the algorithm.

[0056] Referring to Figure 5, the embodiments of this disclosure further provide a cell azimuth angle estimation device, A grid processing module 501 performs grid processing on the MR sampling points of a cell to obtain the first grid of MR sampling points, A grid extraction module 502 extracts the top N second grids with the greatest signal strength from a target grid, wherein the target grid is a first grid whose distance from the cell is greater than a preset distance. Furthermore, if N is smaller than the number of grid cells Grid extraction module 502, Includes a calculation module 503 for calculating the cell azimuth angle based on the latitude and longitude of the second grid.

[0057] In one embodiment, the device further includes a filtering and removal module for calculating a first distance between an MR sampling point and the cell site based on the RSRP value of the cell's MR sampling point, calculating a second distance between the MR sampling point and the cell based on the latitude and longitude of the cell's MR sampling point and the cell's latitude and longitude, and filtering and removing MR sampling points where the difference between the first distance and the second distance is greater than a preset threshold.

[0058] In one embodiment, when calculating a first distance between an MR sampling point and a cell site based on the RSRP value of the cell's MR sampling point, the filtering and removal module specifically:

number

[0059] In one embodiment, when performing grid processing on the MR sampling points of a cell to obtain a first grid of MR sampling points, the filtering and removal module specifically maps the MR sampling points to the corresponding grids in the grid map based on the location information of the MR sampling points, and sets the corresponding grids as the first grid of MR sampling points.

[0060] In one embodiment, the signal intensity is the RSRP value.

[0061] In one embodiment, the predetermined distance is cell This is the median or mean distance between the MR sampling point and the cell.

[0062] In one embodiment, N is an integer from 1 to M, where M is half the number of target grids.

[0063] In one embodiment, when calculating the cell azimuth angle based on the latitude and longitude of the second grid, the calculation module 503 specifically calculates the relative position angle between the second grid and the cell based on the latitude and longitude of the second grid and the latitude and longitude of the cell, and obtains the cell azimuth angle based on the relative position angle.

[0064] Exemplary embodiments of the present disclosure further provide an electronic device including at least one processor and memory communicably connected to the at least one processor. The memory stores a computer program executable by the at least one processor, and the computer program, when executed by the at least one processor, is used to cause the electronic device to perform a method according to an embodiment of the present disclosure.

[0065] Exemplary embodiments of the present disclosure further provide a non-temporary computer-readable storage medium in which a computer program is stored causing the computer to perform the method according to the embodiments of the present disclosure, when executed by a computer processor.

[0066] The exemplary embodiments of the present disclosure further provide a computer program product that includes a computer program for causing a computer to perform the methods according to the embodiments of the present disclosure when executed by a computer processor.

[0067] Referring to Figure 6, a block diagram of an electronic device 600 that may be a server or client of this disclosure is described, which may be an example of a hardware device applicable to various aspects of this disclosure. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, mobile phones, smartphones, wearable devices, and other similar computing devices. The components, their connections and relationships, and their functions shown herein are merely examples and are not intended to limit the description herein and / or the implementation of the disclosure as requested.

[0068] As shown in Figure 6, the electronic device 600 includes a computing unit 601 capable of performing various appropriate operations and processes according to a computer program stored in a read-only memory (ROM) 602 or a computer program loaded from a storage unit 608 into a random access memory (RAM) 603. The RAM 603 may also store various programs and data necessary for the operation of the electronic device 600. The computing unit 601, ROM 602, and RAM 603 are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0069] Multiple components of the electronic device 600 are connected to an I / O interface 605 which includes an input unit 606, an output unit 607, a storage unit 608, and a communication unit 609. The input unit 606 may be any type of device capable of inputting information into the electronic device 600, and may receive input numeric or character information and generate key signal inputs related to user settings and / or function control of the electronic device. The output unit 607 may be any type of device capable of presenting information and may include, but is not limited to, a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. The storage unit 604 may include, but is not limited to, a magnetic disk or an optical disk. The communication unit 609 enables the electronic device 600 to exchange information / data with other devices via computer networks such as the Internet and / or various telecommunications networks, and may include, but is not limited to, modems, network cards, infrared communication devices, wireless communication transceivers and / or chipsets, such as Bluetooth®™ devices, WiFi devices, WiMAX devices, cellular communication devices and / or similar.

[0070] The computing unit 601 may be a variety of general-purpose and / or dedicated processing components having processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various machine driving learning model algorithm computing units, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 601 performs each of the methods and processes described in the preamble. For example, in some embodiments, the methods of the embodiments of the disclosure may be implemented as a computer software program tangibly contained in a machine-readable medium such as a storage unit 608. In some embodiments, part or all of the computer program may be loaded and / or installed into the electronic device 600 via a ROM 602 and / or a communication unit 609. In some embodiments, the computing unit 601 may be configured to perform the methods of the embodiments of the disclosure by any other suitable means (e.g., using firmware).

[0071] Program code for performing the methods of this disclosure can be written in any combination of one or more programming languages. When executed by a processor or controller, this program code may be provided to a processor or controller of a general-purpose computer, a dedicated computer, or other programmable data processing device so that the functions / operations defined in the flowcharts and / or block diagrams are performed. The program code may run entirely on a machine, partially on a machine, or, as a standalone software package, partially on a machine, partially on a remote machine, or entirely on a remote machine or server.

[0072] In the context of this disclosure, a machine-readable medium may be a tangible medium that contains or can store a program for use by, or in combination with, an instruction execution system, apparatus, or device. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination thereof. More specific examples of machine-readable storage media include one or more line-based electrical connections, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0073] As used in this disclosure, the terms “machine-readable medium” and “computer-readable medium” refer to any computer program product, device, and / or apparatus (e.g., magnetic disks, optical disks, memory, programmable logic devices (PLDs)) used to provide machine instructions and / or data to a programmable processor, and include machine-readable mediums that receive machine instructions, which are machine-readable signals. The term “machine-readable signal” refers to any signal for providing machine instructions and / or data to a programmable processor.

[0074] To provide user interaction, the systems and technologies described herein can be implemented on a computer, which may have a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) and a keyboard and pointing device (e.g., a mouse or trackball), and the user may provide input to the computer using the keyboard and pointing device. Other types of devices may also provide user interaction, for example, the feedback provided to the user may be any form of sensing feedback (e.g., vision feedback, auditory feedback, or haptic feedback), and may receive input from the user in any form (including acoustic input, voice input, or haptic input).

[0075] The systems and technologies described herein can be run on computing systems including backend components (e.g., data servers), computing systems including middleware components (e.g., application servers), computing systems including frontend components (e.g., user computers having a graphical user interface or web browser, through which users can interact with embodiments of the systems and technologies described herein), or any combination of such backend components, middleware components, and frontend components. The components of the system can be interconnected by digital data communication in any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0076] A computer system can include clients and servers. Clients and servers are generally geographically separated and typically interact via a communication network. The client-server relationship is generated by computer programs running on corresponding computers that have a client-server relationship with each other.

Claims

1. A method for estimating cell azimuth, A step of calculating a first distance between the MR sampling point and the site of the cell based on the reference signal received power (RSRP) value of the MR sampling point of the cell, A step of calculating a second distance between the MR sampling point and the cell based on the latitude and longitude of the MR sampling point of the cell and the latitude and longitude of the cell, The steps include filtering out and removing MR sampling points where the difference between the first distance and the second distance is greater than a preset threshold, The steps include: performing a gridding process on the measurement report (MR) sampling points of the cell to obtain a first grid of the MR sampling points; A step of extracting the top N second grids with the greatest signal strength from the target grid, wherein the target grid is a first grid whose distance from the cell is greater than a predetermined distance, and N is less than the number of grids of the cell. The steps include: calculating the cell azimuth angle based on the latitude and longitude of the second grid; A cell azimuth estimation method, including the above.

2. The step of calculating a first distance between the MR sampling point and the site of the cell based on the RSRP value of the MR sampling point of the cell is: [Math 1] A cell azimuth angle estimation method according to claim 1, comprising the step of calculating the first distance D based on the formula, wherein a and b are preset parameters.

3. The step of performing a gridding process on the MR sampling points of the cell to obtain a first grid of the MR sampling points is: A cell azimuth estimation method according to claim 1, comprising the steps of mapping the MR sampling point to a corresponding grid on a grid map based on the location information of the MR sampling point, and making the corresponding grid the first grid of the MR sampling point.

4. The cell azimuth angle estimation method according to claim 1, wherein the signal intensity is an RSRP value, and / or N is an integer from 1 to M, where M is half the number of target grids.

5. The cell azimuth angle estimation method according to claim 1, wherein the preset distance is the median of the distances between the MR sampling point of the cell and the cell, or the average value of the distances between the MR sampling point of the cell and the cell.

6. The step of calculating the cell azimuth angle based on the latitude and longitude of the second grid is: A step of calculating the relative position angle between the second grid and the cell based on the latitude and longitude of the second grid and the latitude and longitude of the cell, The steps include obtaining the cell azimuth angle based on the relative position angle, A cell azimuth estimation method according to claim 1, including the method described in claim 1.

7. A cell azimuth angle estimation device, A filtering and removal module for filtering out MR sampling points where the difference between the first and second distances is greater than a preset threshold, by calculating a first distance between the MR sampling point and the cell site based on the reference signal received power (RSRP) value of the cell's MR sampling point, and a second distance between the MR sampling point and the cell based on the latitude and longitude of the cell's MR sampling point and the latitude and longitude of the cell, and for calculating a first distance between the MR sampling point and the cell site based on the latitude and longitude of the cell's MR sampling point. A grid processing module for performing grid processing on the MR sampling points of the aforementioned cells and obtaining a first grid of the MR sampling points, A grid extraction module for extracting the top N second grids with the largest signal strengths from a target grid, wherein the target grid is a first grid whose distance from the cell is greater than a preset distance, and the N is less than the number of grids in the cell. A calculation module for calculating the cell azimuth angle based on the latitude and longitude of the second grid, A cell azimuth angle estimation device, including one.

8. It is an electronic device, Processor and Includes memory for storing the program, An electronic device wherein the program includes instructions, and when the instructions are executed by the processor, the processor causes the processor to perform the method according to any one of claims 1 to 6.

9. A non-temporary, computer-readable storage medium in which computer instructions are stored, The computer instruction is a non-temporary computer-readable storage medium that causes a computer to perform the method according to any one of claims 1 to 6.

10. A computer program, when executed by a processor, that causes the processor to perform the method according to any one of claims 1 to 6.

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