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

Abstract

The present disclosure belongs to the field of wireless communication. Provided are a cell azimuth angle estimation method and apparatus, an electronic device, and a storage medium. The method comprises: performing rasterization processing on MR sampling points of a cell to obtain first grids of the MR sampling points; extracting from amongst target grids second grids having first N greatest signal intensities, wherein the target grids are first grids of which the distance to the cell is greater than a preset distance; and calculating a cell azimuth angle on the basis of the longitudes and latitudes of the second grids. The technical solution of the present disclosure can quickly and accurately estimate the cell azimuth angle, thus reducing the difficulty of wireless network optimization.

Description

Method, device, electronic device and storage medium for estimating cell azimuth angle

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This disclosure is based on and claims the priority of Chinese patent application with application number 202310921043.2 and application date of July 25, 2023. The entire content of the Chinese patent application is hereby incorporated into this disclosure by reference. Technical Field

[0003] The present disclosure relates to the field of wireless communications, and in particular to a method, device, electronic device, and storage medium for estimating a cell azimuth angle. Background Art

[0004] In wireless communications, antennas transmit and receive signals. For base station cells, the antenna's azimuth is a crucial parameter, determining the cell's primary coverage direction and, in turn, affecting the signal quality of terminals within that coverage area. Obtaining the actual azimuth of each cell plays a crucial role in optimizing wireless network coverage. Currently, cell azimuth data primarily comes from the cell's operating parameter table. However, because cell antenna azimuths frequently require adjustment, the azimuths recorded in the table may not match the actual azimuths. This complicates wireless network optimization and often requires significant manpower and time for verification.

[0005] Summary of the Invention

[0006] In order to solve at least one technical problem in the prior art, the present disclosure provides a method, device, electronic device and storage medium for estimating a cell azimuth angle.

[0007] According to a first aspect of the present disclosure, a method for estimating a cell azimuth angle includes: rasterizing MR sampling points of a cell to obtain a first grid of the MR sampling points; extracting second grids with the top N largest signal strengths in a target grid, where the target grid is the first grid whose distance from the cell is greater than a preset distance; and calculating the cell azimuth angle based on the latitude and longitude of the second grid.

[0008] Optionally, before performing rasterization 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 an 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 for which the difference between the first distance and the second distance is greater than a preset threshold.

[0009] Optionally, calculating the first distance between the MR sampling point and the cell site based on the RSRP value of the MR sampling point of the cell includes: calculating the first distance D according to the following formula: Where a and b are preset parameters.

[0010] Optionally, the rasterizing the MR sampling points of the cell to obtain the first grid of the MR sampling points includes: mapping the MR sampling points to a grid corresponding to a rasterized map according to position information of the MR sampling points, and using the corresponding grid as the first grid of the MR sampling points.

[0011] Optionally, the signal strength is an RSRP value, and / or N is any integer between 1 and M, and M is half of the number of target grids.

[0012] Optionally, the preset distance is a median of distances between each of the MR sampling points and the cell, or the preset distance is an average of distances between each of the MR sampling points and the cell.

[0013] Optionally, calculating the cell azimuth based on the longitude and latitude of the second grid includes: calculating the relative position angle between the second grid and the cell according to the longitude and latitude of the second grid and the longitude and latitude of the cell; and obtaining the cell azimuth according to the relative position angle.

[0014] According to a second aspect of the present disclosure, a cell azimuth angle estimation device includes: a rasterization processing module for performing rasterization processing on MR sampling points of a cell to obtain a first grid of the MR sampling points; a raster extraction module for extracting second grids with the top N largest signal strengths in a target grid, where the target grid is the first grid whose distance from the cell is greater than a preset distance; and a calculation module for calculating the cell azimuth angle based on the latitude and longitude of the second grid.

[0015] According to a third aspect of the present disclosure, an electronic device includes: a processor; and a memory storing a program, wherein the program includes instructions that, when executed by the processor, cause the processor to execute any one of the methods described in the first aspect of the present disclosure.

[0016] According to a fourth aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to cause the computer to execute the method according to any one of the first aspects of the present disclosure.

[0017] One or more technical solutions provided in the embodiments of the present application can quickly and accurately estimate the cell azimuth angle to reduce the difficulty of wireless network optimization.

[0018] One or more technical solutions provided in the embodiments of the present application utilize rasterization to obtain a first grid of MR sampling points, extract the top N second grids with the highest signal strengths from the first grid whose distance to the cell is greater than a preset distance, and calculate the cell azimuth based on the longitude and latitude of the second grids. This allows a small number of second grids to accurately represent the primary coverage area for calculating the cell azimuth, reduces the negative impact of horizontal offsets of the second grids on the cell azimuth estimation, and balances the estimation speed and accuracy of the cell azimuth estimation based on the longitude and latitude of the second grids. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings illustrate exemplary embodiments of the present disclosure and together with the description serve to explain the principles of the present disclosure. These drawings are included to provide a further understanding of the present disclosure and are incorporated in and constitute a part of this specification.

[0020] FIG1 shows a flowchart 1 of a method for estimating a cell azimuth angle according to an exemplary embodiment of the present disclosure.

[0021] FIG2 shows a second flowchart of a method for estimating a cell azimuth angle according to an exemplary embodiment of the present disclosure.

[0022] FIG3 is a schematic diagram showing angle estimation deviation under the same horizontal deviation according to an exemplary embodiment of the present disclosure.

[0023] FIG4 shows a third flowchart of a method for estimating a cell azimuth angle according to an exemplary embodiment of the present disclosure.

[0024] FIG5 shows a schematic block diagram of a cell azimuth angle estimation device according to an exemplary embodiment of the present disclosure.

[0025] FIG6 shows a structural block diagram of an exemplary electronic device that can be used to implement the embodiments of the present disclosure. DETAILED DESCRIPTION

[0026] The following describes embodiments of the present disclosure in more detail with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying 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 described herein. Rather, these embodiments are provided to provide 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 intended to limit the scope of protection of the present disclosure.

[0027] It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0028] The term "including" and its variations used in this document are open inclusions, that is, "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one other embodiment"; the term "some embodiments" means "at least some embodiments". The relevant definitions of other terms will be given in the description below. It should be noted that the concepts of "first", "second", etc. mentioned in this disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0029] It should be noted that the modifications of "one" and "multiple" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, they should be understood as "one or more".

[0030] The names of the messages or information exchanged 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.

[0031] Aspects of the present disclosure are described below with reference to the accompanying drawings.

[0032] Referring to FIG1 , the present disclosure provides a method for estimating a cell azimuth angle, including the following steps S101 - S103 .

[0033] In step S101, MR (Measurement Report) sampling points of a cell are rasterized to obtain a first grid of MR sampling points.

[0034] The MR sampling point represents the location of the terminal device or specialized test equipment. The cell where the MR sampling point is located represents the serving cell where the terminal device or specialized test equipment corresponding to the MR sampling point is located. The MR sampling point can be obtained from MR data. For example, the MR data is obtained from a network management system, and the location information and the cell where the MR sampling point is located are obtained from the MR data. The MR data contains fields such as eNodeBID, PCI, arfcn, longitude, latitude, and RSRP, where eNodeBID represents the base station identifier, PCI represents the physical cell identifier, arfcn represents the cell antenna frequency (absolute radio frequency channel number), longitude represents the longitude of the user sampling point in the 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 reference signal received strength of the corresponding user sampling point. For the above fields, the eNodeBID, PCI, and arfcn fields can be used to uniquely identify a cell. As for obtaining the longitude and latitude of the cell, it can be directly queried from the cell engineering parameter table, or its longitude and latitude can be estimated based on the distribution characteristics of the MR data.

[0035] In one embodiment, step S101 may map the MR sampling points to a grid corresponding to the rasterized map based on the location information of the MR sampling points, with the corresponding grid serving as the first grid for the MR sampling points. Specifically, the MR sampling points may be mapped to the grid corresponding to the rasterized map based on the location information of the MR sampling points and the location information of the grids in the rasterized map. For example, based on geographic location distribution, a square grid with a specific side length is created on the map, and each MR sampling point is mapped to a specific grid. The grid side length is set based on actual needs, for example, 20 meters. The method of this embodiment may also employ other rasterization processing methods to obtain the first grid for the MR sampling points.

[0036] After the rasterization process, the MR sampling points can be replaced with the first grid as the granularity to extract the main coverage area.

[0037] Before executing step S101 , data preprocessing and filtering steps may also be performed.

[0038] In one embodiment, referring to FIG. 2 , before executing step S101 , the method includes the following steps S201 - S203 .

[0039] In step S201 , a first distance between the MR sampling point and the cell site is calculated based on the RSRP value of the MR sampling point of the cell.

[0040] In this step, the first distance may be calculated by using a method for calculating the distance using the RSRP value of the MR sampling point.

[0041] Exemplarily, the first distance is calculated based on a simplified SPM (Standard Propagation Model), where the simplified SPM is: RSRP = a × lg(D) + b, where D is the distance from the MR sampling point to the cell site, and a and b are parameters to be estimated, which can be calculated by fitting existing historical data. Based on the SPM, the first distance is calculated according to the RSRP value.

[0042] Specifically, formula (1) is obtained according to SPM, and the first distance is calculated according to formula (1).

[0043] In step S202 , a second distance between the MR sampling point and the cell is calculated based on the longitude and latitude of the MR sampling point of the cell and the longitude and latitude of the cell.

[0044] In this step, the second distance may be calculated using a method for calculating horizontal distance based on longitude and latitude.

[0045] Exemplarily, the second distance is calculated using the haversine formula (2) based on the longitude and latitude (x, y) of the MR sampling point and the longitude and latitude (x0, y0) of the cell:

[0046] Where R is the radius of the Earth and d is the second distance.

[0047] In step S203 , MR sampling points whose difference between the first distance and the second distance is greater than a preset threshold are filtered out.

[0048] The difference between the first distance and the second distance can be a forward difference or a reverse difference. Specifically, the difference can be calculated by calculating the absolute value of the difference between the first and second distances. A preset threshold can be set based on specific needs, such as 150 meters. When the difference between the first and second distances of an MR sampling point exceeds the preset threshold, the MR sampling point is filtered out. This not only removes anomalous MR sampling points, but also makes the point more consistent with wireless propagation laws, resulting in a more pronounced signal strength distribution and reducing the impact of anomalous data on azimuth estimation.

[0049] In step S102, the second grids with the top N largest signal strengths in the target grid are extracted, wherein the target grid is the first grid whose distance to the cell is greater than a preset distance.

[0050] The second grid, serving as the grid in the primary coverage area (i.e., the grid in azimuth), is used to calculate the cell azimuth. However, since the extracted second grid is not necessarily completely on the normal to the cell azimuth, there may be a certain horizontal offset. To reduce the negative impact of horizontal offset on the cell azimuth calculation, this step extracts the second grid from the first grid that is farther from the cell than a preset distance, so that the second grid is farther from the cell. Furthermore, the grid with the N highest signal strengths in the target grid is extracted as the second grid, so that the second grid is in the primary coverage area. The preset distance can be set according to actual needs. For example, it can be set to the median of the distances between each MR sampling point and the cell, or it can be set to the average of the distances between each MR sampling point and the cell. N can be set according to actual needs. For example, N can be any integer between 1 and M. M can be half the number of target grids, M can be 1 / 10 of the number of target grids, etc. Specifically, N can be set to 2, 3, 4, etc. The signal strength can be measured using the RSRP value, etc. For example, the distance r between the grid and the cell is calculated using the grid mean longitude and latitude and the cell longitude and latitude according to the haversine formula. Grids with r greater than the preset distance are selected, and the three grids with the largest RSRP values ​​within the grid are selected as the extracted primary coverage area grids (i.e., the second grid described above). If there are multiple MR sampling points within the grid, the mean of the MR sampling points can be used as the grid value.

[0051] Refer to Figure 3. Taking the longitude and latitude of the cell as the center of the circle, according to the arc length formula Under the same horizontal deviation Δs, the greater the distance r from the cell, the smaller the angle estimation deviation Δθ, and the more accurate the azimuth estimation. Therefore, when calculating the cell azimuth angle based on the second grid obtained in this embodiment, the problem of inaccurate cell azimuth angle caused by horizontal offset can be overcome to a certain extent, thereby improving the estimation accuracy of the cell azimuth angle.

[0052] In this embodiment, in order to calculate the azimuth angle, the grid under the main coverage area (i.e., the grid on the azimuth angle) is extracted from the grid under the cell based on signal strength characteristics such as RSRP, and the angle is calculated based on the average longitude and latitude of the MR sampling points in the grid and the longitude and latitude of the cell.

[0053] In step S103, the cell azimuth is calculated based on the longitude and latitude of the second grid.

[0054] In one embodiment, referring to FIG. 4 , step S103 includes the following steps S401 - S402 .

[0055] In step S401, the relative position angle between the second grid and the cell is calculated based on the longitude and latitude of the second grid and the longitude and latitude of the cell.

[0056] For two points on a map with latitude and longitude (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. The horizontal distance r has been calculated in the fourth step, so only the longitude distance L needs to be determined. The calculation formula (3) is: L = R arccos [cos 2 (y0)cos(x0-x)+sin 2 (y0)]·π / 180 (3)

[0057] For example, for the three extracted grids, their horizontal distances and longitude distances are accumulated respectively, and then the relative position angle α is calculated by the inverse trigonometric function (4):

[0058] In step S402, the cell azimuth angle is obtained according to the relative position angle.

[0059] Since the cell azimuth is defined as the clockwise angle between the true north direction and the cell azimuth normal, the relative position angle α in the previous step is the angle between the azimuth normal and the longitude. Therefore, the angle correction can be performed based on the relative position angle between the grid and the cell, and the relative position angle can be corrected to obtain the cell azimuth. Assume that the mean latitude and longitude of the grid is The longitude and latitude of the cell are (x0, y0), then the modified formula (5) is as follows:

[0060] The corrected θ is the estimated cell azimuth. Once the estimated cell azimuth is obtained, it can be compared with the azimuth in the cell engineering parameter table. For cells with large deviations, the azimuth can be verified on-site and any abnormal cell azimuths can be calibrated as appropriate.

[0061] This step may also adopt other cell azimuth calculation methods based on longitude and latitude to calculate the cell azimuth according to the longitude and latitude of the second grid.

[0062] The technical solution disclosed in the present invention reduces the impact of abnormal data on azimuth estimation by combining data filtering with a wireless propagation model. In comparison, the existing technical solutions are relatively simple in terms of data filtering, and only use a threshold filter based on the number of users or signal strength. However, such a threshold often varies from base station to base station, making it difficult to select a universal threshold. The method disclosed in the present invention combines SPM with the actual data fitting results, and reversely infers the distance from the station site based on the RSRP value of the sample point. This distance is compared with the longitude and latitude distance from the station site, and the sample points with large differences are filtered out, so that the filtered data is more in line with the law of wireless propagation, the signal strength distribution is more obvious, and the sample points of some line-of-sight scenarios are retained, reducing the impact of abnormal data on azimuth estimation and reducing the impact of complex wireless environments on azimuth estimation. By combining data filtering with a wireless propagation model, the impact of abnormal data on azimuth estimation is reduced.

[0063] The technical solution disclosed in the present invention combines rasterization processing with RSRP distribution to estimate the azimuth angle, which can improve the efficiency and accuracy of azimuth angle estimation. Usually, the user data in a cell can reach thousands to tens of thousands, or even more. If the longitude and latitude are calculated based on the sampling points, the efficiency of the algorithm will inevitably be greatly reduced. After adopting rasterization processing, the main coverage area is directly extracted with the grid as the granularity, which improves the estimation efficiency without reducing the accuracy. At the same time, the main coverage area can also be extracted by combining the RSRP indicator within the grid, and the signal strength feature can be combined to improve the accuracy of azimuth angle estimation.

[0064] The technical solution disclosed herein selects a grid farther from the base station to extract the primary coverage area, reducing the impact of the extraction deviation of the primary coverage area on the azimuth, improving the stability of the algorithm, and achieving higher accuracy. If the primary coverage area is closer to the base station, the horizontal deviation of the extraction in this area often leads to a larger angular deviation. Combined with the arc length formula, it can be seen that under the same arc length deviation, the larger the radius, the smaller the angular deviation. Therefore, the extraction of the primary coverage area should be performed in a distant area or grid, which is beneficial to improving the stability and accuracy of the algorithm.

[0065] Referring to FIG5 , an embodiment of the present disclosure further provides a cell azimuth angle estimation device, including:

[0066] The rasterization processing module 501 is configured to perform rasterization processing on the MR sampling points of the cell to obtain a first grid of MR sampling points.

[0067] The grid extraction module 502 is configured to extract the second grids with the top N largest 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.

[0068] The calculation module 503 is configured to calculate the cell azimuth based on the longitude and latitude of the second grid.

[0069] In one embodiment, the apparatus further includes: a filtering module configured to calculate 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; calculate 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 filter out MR sampling points for which the difference between the first distance and the second distance is greater than a preset threshold.

[0070] In one embodiment, when calculating the first distance between the MR sampling point and the cell site based on the RSRP value of the MR sampling point of the cell, the filtering module is specifically configured to calculate the first distance D according to the following formula:

[0071] Among them, a and b are preset parameters.

[0072] In one embodiment, when rasterizing the MR sampling points of a cell to obtain a first grid of MR sampling points, the filtering module is specifically configured to: map the MR sampling points to a grid corresponding to the rasterized map based on the position information of the MR sampling points, and use the corresponding grid as the first grid of the MR sampling points.

[0073] In one embodiment, the signal strength is an RSRP value.

[0074] In one embodiment, the preset distance is the median or average of the distances between each MR sampling point and the cell.

[0075] In one embodiment, N is any integer between 1 and M, and M is half of the number of target grids.

[0076] In one embodiment, when calculating the cell azimuth based on the longitude and latitude of the second grid, the calculation module 503 is specifically used to: calculate the relative position angle between the second grid and the cell based on the longitude and latitude of the second grid and the longitude and latitude of the cell; and obtain the cell azimuth based on the relative position angle.

[0077] The exemplary embodiments of the present disclosure further provide an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor. The memory stores a computer program executable by the at least one processor, the computer program being configured to cause the electronic device to perform a method according to an exemplary embodiment of the present disclosure when executed by the at least one processor.

[0078] Exemplary embodiments of the present disclosure further provide a non-transitory computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor of a computer, is used to cause the computer to perform a method according to an embodiment of the present disclosure.

[0079] Exemplary embodiments of the present disclosure further provide a computer program product, including a computer program, wherein when the computer program is executed by a processor of a computer, it is used to cause the computer to perform the method according to the embodiment of the present disclosure.

[0080] With reference to Figure 6, a block diagram of an electronic device 600 that can serve as a server or client of the present disclosure will now be described, which is an example of a hardware device that can be applied to various aspects of the present disclosure. The electronic device is intended to represent various forms of digital electronic computer equipment, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0081] As shown in Figure 6, electronic device 600 includes a computing unit 601, which can perform various appropriate actions 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. In RAM 603, various programs and data required for the operation of device 600 can also be stored. 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 bus 604.

[0082] Multiple components within electronic device 600 are connected to I / O interface 605, including an input unit 606, an output unit 607, a storage unit 608, and a communication unit 609. Input unit 606 can be any type of device capable of inputting information into electronic device 600. Input unit 606 can receive input numeric or character information and generate key input signals related to user settings and / or function control of the electronic device. Output unit 607 can 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. Storage unit 604 may include, but is not limited to, a magnetic disk or an optical disk. Communication unit 609 allows electronic device 600 to exchange information / data with other devices via computer networks such as the Internet and / or various telecommunication networks and may include, but is not limited to, a modem, a network card, an infrared communication device, a wireless communication transceiver and / or a chipset, such as a Bluetooth™ device, a WiFi device, a WiMax device, a cellular communication device, and / or the like.

[0083] The computing unit 601 may be a variety of general and / or special processing components with 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 computing units that run machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 601 performs the various methods and processes described above. For example, in some embodiments, the method of the embodiment of the present disclosure may be implemented as a computer software program, which is 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 on the electronic device 600 via the ROM 602 and / or the communication unit 609. In some embodiments, the computing unit 601 may be configured to perform the method of the embodiment of the present disclosure in any other appropriate manner (e.g., by means of firmware).

[0084] The program code for implementing the method of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0085] In the context of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in conjunction with an instruction execution system, device or equipment. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0086] As used in this disclosure, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and / or device (e.g., a magnetic disk, an optical disk, a memory, a programmable logic device (PLD)) for providing machine instructions and / or data to a programmable processor, including machine-readable media that receive machine instructions as machine-readable signals. The term "machine-readable signal" refers to any signal used to provide machine instructions and / or data to a programmable processor.

[0087] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0088] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0089] Computer systems may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The client and server relationship arises through computer programs running on the respective computers and having a client-server relationship to each other.

Claims

1. A method for estimating a cell azimuth angle, comprising: Performing rasterization processing on the MR sampling points of the cell to obtain a first grid of the MR sampling points; Extracting the second grids with the top N largest signal strengths in the target grid, wherein the target grid is the first grid whose distance from the cell is greater than a preset distance; and The cell azimuth is calculated based on the longitude and latitude of the second grid.

2. The method according to claim 1, wherein: Before the rasterization processing is performed 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 an 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 longitude and latitude of the MR sampling point of the cell and the longitude and latitude of the cell; and MR sampling points whose difference between the first distance and the second distance is greater than a preset threshold are filtered out.

3. The method according to claim 2, wherein: The calculating, based on the RSRP value of the MR sampling point of the cell, a first distance between the MR sampling point and the cell site comprises: The first distance D is calculated according to the following formula: Where a and b are preset parameters.

4. The method according to claim 1, wherein: The step of performing rasterization processing on the MR sampling points of the cell to obtain a first grid of the MR sampling points includes: According to the position information of the MR sampling point, the MR sampling point is mapped to a grid corresponding to the rasterized map, and the corresponding grid is used as a first grid of the MR sampling point.

5. The method according to claim 1, wherein: The signal strength is an RSRP value, and / or N is any integer between 1 and M, and M is half of the number of target grids.

6. The method according to claim 1, wherein: The preset distance is a median of distances between each of the MR sampling points and the cell, or the preset distance is an average of distances between each of the MR sampling points and the cell.

7. The method according to claim 1, wherein: The calculating the cell azimuth angle based on the longitude and latitude of the second grid includes: Calculating a relative position angle between the second grid and the cell according to the longitude and latitude of the second grid and the longitude and latitude of the cell; and The cell azimuth angle is obtained according to the relative position angle.

8. A cell azimuth estimation device, comprising: A rasterization processing module, used for performing rasterization processing on the MR sampling points of the cell to obtain a first grid of the MR sampling points; A grid extraction module, used to extract the second grids with the top N largest signal strengths in the target grid, wherein the target grid is the first grid whose distance from the cell is greater than a preset distance; as well as A calculation module is used to calculate the cell azimuth based on the longitude and latitude of the second grid.

9. An electronic device, comprising: processor; as well as Memory for storing programs, The program includes instructions, which, when executed by the processor, cause the processor to perform the method according to any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-7.

11. A computer program product comprising instructions, wherein when the instructions are executed by a processor, the processor is caused to perform the method according to any one of claims 1 to 7.

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