CN111988813A - Method, device and computer equipment for determining weak coverage cell in mobile communication network - Google Patents
Method, device and computer equipment for determining weak coverage cell in mobile communication network Download PDFInfo
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- CN111988813A CN111988813A CN201910430811.8A CN201910430811A CN111988813A CN 111988813 A CN111988813 A CN 111988813A CN 201910430811 A CN201910430811 A CN 201910430811A CN 111988813 A CN111988813 A CN 111988813A
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Abstract
The embodiment of the application provides a method, a device and computer equipment for determining a weak coverage cell in a mobile communication network, wherein the method comprises the steps of obtaining a predicted value of network coverage of each grid area in a second time interval after a first time interval according to a trained network coverage prediction model; then calculating relative errors of the network coverage rate predicted value and a network coverage rate actual value corresponding to the network coverage rate predicted value, and screening out a key grid area according to the relative errors; acquiring a first data file of the associated cell in the first time interval and a second data file of the associated cell in the second time interval; and finally, positioning a weak coverage cell according to a comparison result of fluctuation parameters in the first data file and the second data file, and determining the weak coverage cell as a cell to be optimized, so that the judgment accuracy is greatly improved. Further, the weak coverage cell can be determined according to the comparison result of the fluctuation parameters in the adjacent cells.
Description
Technical Field
The present application relates to the field of artificial intelligence technologies, and in particular, to a method, an apparatus, and a computer device for determining a weak coverage cell in a mobile communication network.
Background
Currently, the coverage capability of a Long Term Evolution (Long Term Evolution; hereinafter referred to as LTE) network is evaluated mainly by a coverage index, and is measured from the level of the whole network, an area or a base station. The evaluation method comprises the steps of extracting the coverage rate of a specific time period, comparing the coverage rate with a preset unified qualified threshold, and judging the quality of the coverage performance. But has the following problems:
(1) a problem identification stage: each base station has respective coverage fluctuation characteristics including geographic environment difference, user behavior fluctuation, seasonal time period influence and the like, the coverage rate absolute threshold is used for judging the area, the coverage abnormity of the base station does not take the factors into consideration, and the judgment error is large.
(2) A problem analysis stage: although the traditional method considers the influence of the serving cell and the adjacent cells on the coverage rate, the importance of the influence of the adjacent cells cannot be effectively measured. In other words, the existing network is an overlay network to a large extent, and the lack and performance degradation of some neighboring cells do not bring substantial coverage influence to the local area. Therefore, the traditional method cannot identify the criticality of evaluating the neighbor cell at present.
Disclosure of Invention
The embodiment of the application provides a method, a device and computer equipment for determining a weak coverage cell in a mobile communication network.
In a first aspect, an embodiment of the present application provides a method for determining a weak coverage cell in a mobile communication network, including:
acquiring a network coverage rate predicted value of each grid area in a second time period after the first time period according to the trained network coverage rate prediction model, wherein the grid area is a grid area pre-divided according to a geographic area covered by the network;
calculating a relative error between the network coverage rate predicted value and a network coverage rate actual value corresponding to the network coverage rate predicted value, and screening out a key grid area according to the relative error, wherein a cell corresponding to a network coverage signal of the key grid area is a related cell of the key grid area;
obtaining a first data file of the associated cell during the first time period and a second data file of the associated cell during the second time period, and,
And positioning the weak coverage cell according to the comparison result of the fluctuation parameters in the first data file and the second data file.
In a possible implementation manner, the obtaining, according to the trained network coverage prediction model, a network coverage prediction value of each grid region in a second time period after the first time period, further includes:
acquiring a network coverage rate historical value and historical time corresponding to the network coverage rate historical value;
performing exponential smoothing processing on the historical value of the network coverage rate and the historical time to obtain a smoothing factor;
and obtaining a trained network coverage rate prediction model according to the smoothing factor.
In a possible implementation manner, the calculating a relative error between the predicted network coverage and an actual network coverage corresponding to the predicted network coverage, and screening out a key grid region according to the relative error includes:
and when the relative error is greater than or equal to a preset discrete threshold corresponding to the grid region, determining the grid region as the key grid region.
In a possible implementation manner, the locating a weak coverage cell according to a comparison result of fluctuation parameters in the first data file and the second data file includes:
Acquiring a first fluctuation parameter in the first data file and a second fluctuation parameter in the second data file, wherein the first fluctuation parameter comprises a first time advance and a first signal arrival angle, and the second fluctuation parameter comprises a second time advance and a second signal arrival angle;
when the second time advance is greater than the first time advance or the second signal arrival angle is smaller than the first signal arrival angle, determining that the corresponding associated cell is the weak coverage cell; and the number of the first and second groups,
and positioning the weak coverage cell according to the second time advance and the second signal arrival angle.
In a possible implementation manner, after the calculating a relative error between the predicted network coverage value and an actual network coverage value corresponding to the predicted network coverage value, and screening out a key grid area according to the relative error, the method further includes:
obtaining a third data file in the first time period and a fourth data file in the second time period of neighboring cells of the associated cell, and,
and positioning the weak coverage cell according to the comparison result of the fluctuation parameters in the third data file and the fourth data file.
In a possible implementation manner, the positioning the weak coverage cell according to the comparison result of the fluctuation parameters in the third data file and the fourth data file includes:
acquiring a third fluctuation parameter in the third data file and a fourth fluctuation parameter in the fourth data file, wherein the third fluctuation parameter comprises a third time advance and a third signal arrival angle, and the fourth fluctuation parameter comprises a fourth time advance and a fourth signal arrival angle;
when the fourth time advance is greater than the third time advance or the fourth signal arrival angle is smaller than the third signal arrival angle, determining that the corresponding associated cell is the weak coverage cell; and the number of the first and second groups,
and positioning the weak coverage cell according to the fourth time advance and the fourth signal arrival angle.
In one possible implementation, the predicted dispersion threshold may be calculated by the following formula:
presetting a discrete threshold value A + 2S
Wherein, a represents an average value of each corresponding relative error in a specified time period of the grid region, and S represents a standard deviation of each corresponding relative error in the specified time period of the grid region, and the relative error is a ratio of an absolute error of the predicted network coverage rate value to the actual network coverage rate value to the predicted network coverage rate value.
In a second aspect, an embodiment of the present application further provides an apparatus for determining a weak coverage cell in a mobile communication network, including:
the first acquisition module is used for acquiring a network coverage prediction value of each grid area in a second time period after the first time period according to the trained network coverage prediction model, wherein the grid area is a grid area pre-divided according to a network-covered geographic area;
the calculation module is connected with the first acquisition module and used for calculating the relative error between the predicted network coverage rate value and the actual network coverage rate value corresponding to the predicted network coverage rate value;
the screening module is connected with the computing module and used for screening out a key grid area according to the relative error, and a cell corresponding to a network coverage signal of the key grid area is a related cell of the key grid area;
a second obtaining module, connected to the screening module, configured to obtain a first data file of the associated cell in the first time period and a second data file of the associated cell in the second time period;
and the positioning module is connected with the second acquisition module and used for positioning the weak coverage cell according to the comparison result of the fluctuation parameters in the first data file and the second data file.
In a third aspect, an embodiment of the present application further provides a computer device, including:
at least one processor; and
at least one memory communicatively coupled to the processor;
the memory stores program instructions executable by the processor, and the processor calls the program instructions to execute the method for determining the weak coverage cell in the mobile communication network.
In a fourth aspect, an embodiment of the present application further provides a non-transitory computer-readable storage medium storing computer instructions, where the computer instructions cause the computer to execute the method for determining a weak coverage cell in the mobile communication network.
In the above technical scheme, aiming at the technical problem that coverage rate judgment is inaccurate due to the fact that each base station has respective coverage fluctuation characteristics in the related technical scheme, the method obtains a predicted network coverage rate value of each grid area in a second time period after the first time period according to a trained network coverage rate prediction model; then calculating relative errors of the network coverage rate predicted value and a network coverage rate actual value corresponding to the network coverage rate predicted value, and screening out a key grid area according to the relative errors; acquiring a first data file of the associated cell in the first time interval and a second data file of the associated cell in the second time interval; and finally, positioning a weak coverage cell according to a comparison result of fluctuation parameters in the first data file and the second data file, and determining the weak coverage cell as a cell to be optimized, so that the judgment accuracy is greatly improved. Further, the weak coverage cell can be determined according to the comparison result of the fluctuation parameters in the adjacent cells.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to these drawings without inventive exercise.
Fig. 1 is a flowchart illustrating an embodiment of a method for determining a weak coverage cell in a mobile communication network according to the present invention;
FIG. 2 is a flowchart illustrating a method for determining a weak coverage cell in a mobile communication network according to another embodiment of the present invention;
FIG. 3 is a flowchart illustrating a method for determining a weak coverage cell in a mobile communication network according to another embodiment of the present invention;
FIG. 4 is a flowchart illustrating a method for determining a weak coverage cell in a mobile communication network according to another embodiment of the present invention;
fig. 5 is a schematic connection structure diagram of a device for determining a weak coverage cell in a mobile communication network according to the present application;
FIG. 6 is a schematic structural diagram of an embodiment of a computer apparatus according to the present application.
Detailed Description
For better understanding of the technical solutions of the present application, the following detailed descriptions of the embodiments of the present application are provided with reference to the accompanying drawings.
It should be understood that the embodiments described are only a few embodiments of the present application, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The terminology used in the embodiments of the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in the examples of this application and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
Fig. 1 is a flowchart of an embodiment of a method for determining a weak coverage cell in a mobile communication network according to the present application, as shown in fig. 1, the method includes:
step 101: and acquiring a predicted network coverage value of each grid area in a second time period after the first time period according to the trained network coverage prediction model, wherein the grid area is a grid area pre-divided according to a geographic area covered by the network.
The first time interval is the last historical time of inputting the network coverage prediction model, the second time interval is the time corresponding to the network coverage prediction value obtained by the network coverage prediction model, and the network coverage prediction model is obtained by training and may include:
(1) Acquiring a network coverage rate historical value and historical time corresponding to the network coverage rate historical value;
(2) performing exponential smoothing processing on the historical value of the network coverage rate and the historical time to obtain a smoothing factor;
(3) and obtaining a trained network coverage rate prediction model according to the smoothing factor.
In a specific implementation, the network coverage prediction model may be a HOLT-WINTER model (HOLT-WINTER) as the network coverage prediction model in this application. In practical application, the network coverage rate and the historical time have seasonal characteristics because the network coverage rate and the historical time related by the application are influenced by certain natural conditions and regularly change along with the change of time sequence in one year or less, and the three-time exponential smoothing method can predict the time sequence with the seasonal characteristics. Therefore, in the step (2), the network coverage history value and the history time can be subjected to three times of exponential smoothing processing. The HOLT-WINTER model adopted by the application is a very important and very flowing model in the current machine learning, so that the HOLT-WINTER model has good performance.
Step 102: and calculating a relative error between the network coverage rate predicted value and a network coverage rate actual value corresponding to the network coverage rate predicted value, and screening out a key grid area according to the relative error, wherein a cell corresponding to a network coverage signal of the key grid area is a related cell of the key grid area.
In a specific embodiment, the network coverage actual value can be obtained by calculating according to formula (1):
whereinWhere Cov denotes the actual network coverage value of the grid area, k denotes the number of cells corresponding to the network coverage signal of the grid area,
indicating the number of levels in the k-th cell that fall in the i-th coverage level.
For example, when the coverage level is 7, the number of level values between-110 dbm to-105 dbm can be regarded as the number of levels falling in the 7 th coverage level. The value range of the level value corresponding to the coverage grade can be automatically set according to actual requirements, and the application does not limit the value range.
In one embodiment, screening out the critical grid region according to the relative error comprises:
and when the relative error is greater than or equal to a preset discrete threshold corresponding to the grid region, determining the grid region as the key grid region.
Preset discrete threshold value a + 2S formula (2)
Wherein, a represents an average value of each corresponding relative error in a specified time period of the grid region, and S represents a standard deviation of each corresponding relative error in the specified time period of the grid region, and the relative error is a ratio of an absolute error of the predicted network coverage rate value to the actual network coverage rate value to the predicted network coverage rate value.
In practical application, the geographic environment and the user behavior of each grid region are not fixed, so that the method based on probability density is adopted to set the preset discrete threshold value in each grid region. Specifically, firstly, the method counts relative errors between a predicted network coverage rate value and an actual network coverage rate value corresponding to the predicted network coverage rate value in each grid area, calculates two key statistics of an average value of the relative errors and a standard deviation of the relative errors, and finally obtains a preset discrete threshold corresponding to each grid area by referring to a formula (2). Thus, according to the statistical properties of the normal distribution, when the relative error occurs in the region outside the two standard deviations on both sides of the mean, the probability is 4.55%, and the event is regarded as a small probability event. That is, when the relative error is greater than or equal to a preset discrete threshold corresponding to the grid region, the grid region is determined to be the key grid region.
Step 103: and acquiring a first data file of the associated cell in the first time interval and a second data file of the associated cell in the second time interval.
Step 104: and positioning the weak coverage cell according to the comparison result of the fluctuation parameters in the first data file and the second data file.
Specifically, referring to fig. 2, the step 104 may include:
step 201: and acquiring a first fluctuation parameter in the first data file and a second fluctuation parameter in the second data file. The first fluctuation parameter comprises a first time advance and a first signal arrival angle, and the second fluctuation parameter comprises a second time advance and a second signal arrival angle;
step 202: judging whether the second time lead is larger than the first time lead, if so, entering the step 203, otherwise, ending the whole process;
step 203: determining the corresponding associated cell as a weak coverage cell;
step 204: and positioning the weak coverage cell according to the second time advance and the second signal arrival angle.
The comparison of the timing advance in step 202 may also be obtained by comparing the arrival angles of the signals. Specifically, when the second signal arrival angle is smaller than the first signal arrival angle, it may also be determined that the corresponding associated cell is a weak coverage cell.
Specifically, the first data file and the second data file respectively refer to a measurement report sample data file MRO in a first period/a second period, and the measurement report sample data file MRO is a Long Term Evolution (LTE) measurement report sample data file MRO. The step 204 specifically includes:
Firstly, determining the distance range from a base station to the weak coverage cell according to a second time advance, and determining the range of the horizontal distance from the base station to the weak coverage cell according to the distance range and the height of the base station antenna from the ground;
secondly, converting the longitude and latitude of the base station into a Gaussian plane rectangular coordinate value according to a Gaussian projection forward calculation formula;
then, determining the projected plane coordinate range of the weak coverage cell according to the plane rectangular coordinate value of the base station, the range of the horizontal distance and the arrival angle of the second signal;
specifically, the second signal arrival angle is used to define an estimated angle of a cell with respect to a measurement reference direction, which is a north direction of the base station.
Preferably, as shown in fig. 3 to 4, after step 102, the method further includes:
step 303: acquiring a fourth data file of a third data file of a neighboring cell of the associated cell in the first time interval in the second time interval;
step 304: and positioning a weak coverage cell according to the comparison result of the fluctuation parameters in the third data file and the fourth data file, and determining the weak coverage cell as a cell to be optimized.
Specifically, referring to fig. 4, the step 304 may include:
step 401: and acquiring a third fluctuation parameter in the third data file and a fourth fluctuation parameter in the fourth data file. The third fluctuation parameter comprises a third time advance and a third signal arrival angle, and the fourth fluctuation parameter comprises a fourth time advance and a fourth signal arrival angle;
step 402: judging whether the fourth time lead is greater than the third time lead, if so, entering a step 403, otherwise, ending the whole process;
step 403: determining the corresponding associated cell as a weak coverage cell;
step 404: and positioning the weak coverage cell according to the fourth time advance and the arrival angle of the fourth signal.
The comparison of the timing advance in step 402 may also be obtained by comparing the arrival angles of the signals. Specifically, when the fourth signal arrival angle is smaller than the third signal arrival angle, it may also be determined that the corresponding associated cell is a weak coverage cell.
Specifically, the third data file and the fourth data file respectively refer to a measurement report sample data file MRO in a first time period/a second time period, where the measurement report sample data file MRO is a Long Term Evolution (LTE) measurement report sample data file MRO. The step 504 specifically includes:
Firstly, determining the distance range from a base station to the weak coverage cell according to a fourth time advance, and determining the range of the horizontal distance from the base station to the weak coverage cell according to the distance range and the height of the base station antenna from the ground;
secondly, converting the longitude and latitude of the base station into a Gaussian plane rectangular coordinate value according to a Gaussian projection forward calculation formula;
then, determining the projected plane coordinate range of the weak coverage cell according to the plane rectangular coordinate value of the base station, the range of the horizontal distance and the arrival angle of the fourth signal;
specifically, the fourth angle of arrival is used to define an estimated angle of a cell with respect to a measurement reference direction, which is a north direction of the base station.
Fig. 5 is a schematic connection structure diagram of a device for determining a weak coverage cell in a mobile communication network according to the present application, and as shown in fig. 5, the device includes:
a first obtaining module 501, configured to obtain, according to a trained network coverage prediction model, a network coverage prediction value of each grid area in a second time period after the first time period, where the grid area is a rasterized area pre-divided according to a geographic area covered by a network;
A calculating module 502, connected to the first obtaining module 501, for calculating a relative error between the predicted network coverage and an actual network coverage corresponding to the predicted network coverage;
a screening module 503, connected to the calculating module 502, configured to screen out a key grid area according to the relative error, where a cell corresponding to a network coverage signal of the key grid area is a cell associated with the key grid area;
a second obtaining module 504, connected to the screening module 503, configured to obtain a first data file of the associated cell in the first time period and a second data file of the associated cell in the second time period;
a positioning module 505, connected to the second obtaining module 504, for positioning the weak coverage cell according to a comparison result of the fluctuation parameters in the first data file and the second data file.
The network coverage prediction model is obtained through training, and may include:
(1) acquiring a network coverage rate historical value and historical time corresponding to the network coverage rate historical value;
(2) performing exponential smoothing processing on the historical value of the network coverage rate and the historical time to obtain a smoothing factor;
(3) And obtaining a trained network coverage rate prediction model according to the smoothing factor.
In a specific implementation, the network coverage prediction model may be a HOLT-WINTER model (HOLT-WINTER) as the network coverage prediction model in this application. In practical application, the network coverage rate and the historical time have seasonal characteristics because the network coverage rate and the historical time related by the application are influenced by certain natural conditions and regularly change along with the change of time sequence in one year or less, and the three-time exponential smoothing method can predict the time sequence with the seasonal characteristics. Therefore, in the step (2), the network coverage history value and the history time can be subjected to three times of exponential smoothing processing. The HOLT-WINTER model adopted by the application is a very important and very flowing model in the current machine learning, so that the HOLT-WINTER model has good performance.
In a specific embodiment, the network coverage actual value can be obtained by calculating according to formula (1):
wherein Cov represents the actual network coverage value of the grid area, k represents the number of cells corresponding to the network coverage signal of the grid area, 
Indicating the number of levels in the k-th cell that fall in the i-th coverage level.
For example, when the coverage level is 7, the number of level values between-110 dbm to-105 dbm can be regarded as the number of levels falling in the 7 th coverage level. The value range of the level value corresponding to the coverage grade can be automatically set according to actual requirements, and the application does not limit the value range.
In one embodiment, screening out the critical grid region according to the relative error comprises:
and when the relative error is greater than or equal to a preset discrete threshold corresponding to the grid region, determining the grid region as the key grid region.
Preset discrete threshold value a + 2S formula (2)
Wherein, a represents an average value of each corresponding relative error in a specified time period of the grid region, and S represents a standard deviation of each corresponding relative error in the specified time period of the grid region, and the relative error is a ratio of an absolute error of the predicted network coverage rate value to the actual network coverage rate value to the predicted network coverage rate value.
In practical application, the geographic environment and the user behavior of each grid region are not fixed, so that the method based on probability density is adopted to set the preset discrete threshold value in each grid region. Specifically, firstly, the method counts relative errors between a predicted network coverage rate value and an actual network coverage rate value corresponding to the predicted network coverage rate value in each grid area, calculates two key statistics of an average value of the relative errors and a standard deviation of the relative errors, and finally obtains a preset discrete threshold corresponding to each grid area by referring to a formula (2). Thus, according to the statistical properties of the normal distribution, when the relative error occurs in the region outside the two standard deviations on both sides of the mean, the probability is 4.55%, and the event is regarded as a small probability event. That is, when the relative error is greater than or equal to a preset discrete threshold corresponding to the grid region, the grid region is determined to be the key grid region.
Specifically, the positioning module 505 is specifically configured to obtain a first fluctuation parameter in the first data file and a second fluctuation parameter in the second data file. The first fluctuation parameter comprises a first time advance and a first signal arrival angle, and the second fluctuation parameter comprises a second time advance and a second signal arrival angle; then judging whether the second time lead is larger than the first time lead, if so, determining that the corresponding associated cell is a weak coverage cell, and positioning the weak coverage cell according to the second time lead and a second signal arrival angle; otherwise, the whole process is ended.
The comparison of the time advance can also be obtained by comparing the arrival angles of the signals. Specifically, when the second signal arrival angle is smaller than the first signal arrival angle, it may also be determined that the corresponding associated cell is a weak coverage cell.
Specifically, the first data file and the second data file respectively refer to a measurement report MR in a first period/a second period, and the measurement report MR is a Long Term Evolution (LTE) measurement report MR. The positioning process specifically includes:
firstly, determining the distance range from a base station to the weak coverage cell according to a second time advance, and determining the range of the horizontal distance from the base station to the weak coverage cell according to the distance range and the height of the base station antenna from the ground;
Secondly, converting the longitude and latitude of the base station into a Gaussian plane rectangular coordinate value according to a Gaussian projection forward calculation formula;
then, determining the projected plane coordinate range of the weak coverage cell according to the plane rectangular coordinate value of the base station, the range of the horizontal distance and the arrival angle of the second signal;
specifically, the second signal arrival angle is used to define an estimated angle of a cell with respect to a measurement reference direction, which is a north direction of the base station.
FIG. 6 is a schematic block diagram of an embodiment of a computer device, which may include at least one processor; and at least one memory communicatively coupled to the processor; the memory stores program instructions executable by the processor, and the processor calls the program instructions to perform the method for determining the weak coverage cell in the mobile communication network, so that the method for determining the weak coverage cell in the mobile communication network provided by the embodiment of the present application can be implemented.
The computer device may be a server, for example: the cloud server, or the computer device may also be a computer device, for example: the present invention relates to a smart device, and more particularly, to a smart device such as a smart phone, a smart watch, a Personal Computer (PC), a notebook Computer, or a tablet Computer.
FIG. 6 illustrates a block diagram of an exemplary computer device 52 suitable for use in implementing embodiments of the present application. The computer device 52 shown in fig. 6 is only an example and should not bring any limitation to the function and scope of use of the embodiments of the present application.
As shown in FIG. 6, computer device 52 is in the form of a general purpose computing device. The components of computer device 52 may include, but are not limited to: one or more processors or processing units 56, a system memory 78, and a bus 58 that couples various system components including the system memory 78 and the processing unit 56.
Computer device 52 typically includes a variety of computer system readable media. Such media may be any available media that is accessible by computer device 52 and includes both volatile and nonvolatile media, removable and non-removable media.
The system Memory 78 may include computer system readable media in the form of volatile Memory, such as Random Access Memory (RAM) 70 and/or cache Memory 72. The computer device 52 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, the storage system 74 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 6, commonly referred to as a "hard drive"). Although not shown in FIG. 6, a disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a Compact disk Read Only Memory (CD-ROM), a Digital versatile disk Read Only Memory (DVD-ROM), or other optical media) may be provided. In these cases, each drive may be connected to the bus 58 by one or more data media interfaces. Memory 78 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the application.
A program/utility 80 having a set (at least one) of program modules 82 may be stored, for example, in memory 78, such program modules 82 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may comprise an implementation of a network environment. Program modules 82 generally perform the functions and/or methodologies of the embodiments described herein.
The computer device 52 may also communicate with one or more external devices 54 (e.g., keyboard, pointing device, display 64, etc.), with one or more devices that enable a user to interact with the computer device 52, and/or with any devices (e.g., network card, modem, etc.) that enable the computer device 52 to communicate with one or more other computing devices. Such communication may be through an input/output (I/O) interface 62. Also, computer device 52 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public Network such as the Internet) via Network adapter 60. As shown in FIG. 6, the network adapter 60 communicates with the other modules of the computer device 52 via the bus 58. It should be appreciated that although not shown in FIG. 6, other hardware and/or software modules may be used in conjunction with the computer device 52, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
The processing unit 56 executes programs stored in the system memory 78 to execute various functional applications and data processing, for example, to implement the method for determining the weak coverage cell in the mobile communication network provided by the embodiment of the present application.
An embodiment of the present application further provides a non-transitory computer-readable storage medium, which stores computer instructions, where the computer instructions cause the computer to execute the method for determining a weak coverage cell in the mobile communication network.
The non-transitory computer readable storage medium described above may take any combination of one or more computer readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a Read Only Memory (ROM), an Erasable Programmable Read Only Memory (EPROM), a flash Memory, an optical fiber, a portable compact disc Read Only Memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of Network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
In the description herein, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present application, "plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing steps of a custom logic function or process, and alternate implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present application.
The word "if" as used herein may be interpreted as "at … …" or "when … …" or "in response to a determination" or "in response to a detection", depending on the context. Similarly, the phrases "if determined" or "if detected (a stated condition or event)" may be interpreted as "when determined" or "in response to a determination" or "when detected (a stated condition or event)" or "in response to a detection (a stated condition or event)", depending on the context.
The above description is only exemplary of the present application and should not be taken as limiting the present application, as any modification, equivalent replacement, or improvement made within the spirit and principle of the present application should be included in the scope of protection of the present application.
Claims (10)
1. A method for determining a weak coverage cell in a mobile communication network, the method comprising:
acquiring a network coverage rate predicted value of each grid area in a second time period after the first time period according to the trained network coverage rate prediction model, wherein the grid area is a grid area pre-divided according to a geographic area covered by the network;
calculating a relative error between the network coverage rate predicted value and a network coverage rate actual value corresponding to the network coverage rate predicted value, and screening out a key grid area according to the relative error, wherein a cell corresponding to a network coverage signal of the key grid area is a related cell of the key grid area;
obtaining a first data file of the associated cell during the first time period and a second data file of the associated cell during the second time period, and,
and positioning the weak coverage cell according to the comparison result of the fluctuation parameters in the first data file and the second data file.
2. The method of claim 1, wherein obtaining the predicted network coverage value of each grid area in a second time period after the first time period according to the trained network coverage prediction model further comprises:
Acquiring a network coverage rate historical value and historical time corresponding to the network coverage rate historical value;
performing exponential smoothing processing on the historical value of the network coverage rate and the historical time to obtain a smoothing factor;
and obtaining a trained network coverage rate prediction model according to the smoothing factor.
3. The method of claim 1, wherein calculating a relative error between the predicted network coverage value and an actual network coverage value corresponding to the predicted network coverage value, and screening out a critical grid area according to the relative error comprises:
and when the relative error is greater than or equal to a preset discrete threshold corresponding to the grid region, determining the grid region as the key grid region.
4. The method of claim 1, wherein the locating a weak coverage cell according to the comparison result of the fluctuation parameters in the first data file and the second data file comprises:
acquiring a first fluctuation parameter in the first data file and a second fluctuation parameter in the second data file, wherein the first fluctuation parameter comprises a first time advance and a first signal arrival angle, and the second fluctuation parameter comprises a second time advance and a second signal arrival angle;
When the second time advance is greater than the first time advance or the second signal arrival angle is smaller than the first signal arrival angle, determining that the corresponding associated cell is the weak coverage cell; and the number of the first and second groups,
and positioning the weak coverage cell according to the second time advance and the second signal arrival angle.
5. The method of claim 1, wherein after calculating a relative error between the predicted network coverage value and an actual network coverage value corresponding to the predicted network coverage value, and screening out a critical grid area according to the relative error, the method further comprises:
obtaining a third data file in the first time period and a fourth data file in the second time period of neighboring cells of the associated cell, and,
and positioning the weak coverage cell according to the comparison result of the fluctuation parameters in the third data file and the fourth data file.
6. The method of claim 5, wherein the locating a weak coverage cell according to the comparison result of the fluctuation parameters in the third data file and the fourth data file comprises:
acquiring a third fluctuation parameter in the third data file and a fourth fluctuation parameter in the fourth data file, wherein the third fluctuation parameter comprises a third time advance and a third signal arrival angle, and the fourth fluctuation parameter comprises a fourth time advance and a fourth signal arrival angle;
When the fourth time advance is greater than the third time advance or the fourth signal arrival angle is smaller than the third signal arrival angle, determining that the corresponding associated cell is the weak coverage cell; and the number of the first and second groups,
and positioning the weak coverage cell according to the fourth time advance and the fourth signal arrival angle.
7. The method of claim 3, wherein the predicted dispersion threshold is calculated by the following formula:
presetting a discrete threshold value A + 2S
Wherein, a represents an average value of each corresponding relative error in a specified time period of the grid region, and S represents a standard deviation of each corresponding relative error in the specified time period of the grid region, and the relative error is a ratio of an absolute error of the predicted network coverage rate value to the actual network coverage rate value to the predicted network coverage rate value.
8. An apparatus for determining a weak coverage cell in a mobile communication network, the apparatus comprising:
the first acquisition module is used for acquiring a network coverage prediction value of each grid area in a second time period after the first time period according to the trained network coverage prediction model, wherein the grid area is a grid area pre-divided according to a network-covered geographic area;
The calculation module is connected with the first acquisition module and used for calculating the relative error between the predicted network coverage rate value and the actual network coverage rate value corresponding to the predicted network coverage rate value;
the screening module is connected with the computing module and used for screening out a key grid area according to the relative error, and a cell corresponding to a network coverage signal of the key grid area is a related cell of the key grid area;
a second obtaining module, connected to the screening module, configured to obtain a first data file of the associated cell in the first time period and a second data file of the associated cell in the second time period;
and the positioning module is connected with the second acquisition module and used for positioning the weak coverage cell according to the comparison result of the fluctuation parameters in the first data file and the second data file.
9. A computer device, comprising:
at least one processor; and
at least one memory communicatively coupled to the processor;
the memory stores program instructions executable by the processor, the processor invoking the program instructions to perform the method of any of claims 1 to 7.
10. A non-transitory computer-readable storage medium storing computer instructions that cause a computer to perform the method of any one of claims 1 to 7.
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