CN110602741A - Network weak coverage identification method, device, equipment and storage medium - Google Patents
Network weak coverage identification method, device, equipment and storage medium Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W24/00—Supervisory, monitoring or testing arrangements
- H04W24/02—Arrangements for optimising operational condition
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W24/00—Supervisory, monitoring or testing arrangements
- H04W24/08—Testing, supervising or monitoring using real traffic
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
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- H04W24/10—Scheduling measurement reports ; Arrangements for measurement reports
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Abstract
The embodiment of the invention provides a method, a device, equipment and a storage medium for identifying network weak coverage. The method comprises the following steps: acquiring network coverage information; identifying a network weak coverage area according to the network coverage information, and determining coverage area information of the network weak coverage area; wherein the network coverage information comprises: traffic statistics, OTT, measurement report MR, user history complaint information, and network black point library. The embodiment of the invention can accurately determine the position of the weak coverage area, and further determine the severity of the problem of the weak coverage area and the range of the weak coverage area with the problem.
Description
Technical Field
The present invention relates to the field of wireless network technologies, and in particular, to a method, an apparatus, a device, and a storage medium for identifying network weak coverage.
Background
The rural network overall coverage evaluation and the network weak coverage village identification are the basis for the rural network wide coverage improvement. After the accurate rural network coverage evaluation result is obtained, an optimization scheme of the rural network coverage is determined according to the rural network coverage evaluation result.
Currently, the 4th Generation mobile communication technology (4G) mobile network rural area wireless network coverage quality assessment and weak coverage area identification mainly rely on Measurement Report (MR) data, user feedback and field test methods. Whether a weak coverage area exists around a base station sector can be simply judged through MR data, the specific position of the weak coverage area cannot be determined, and a solution for the weak coverage area is difficult to put forward. The location of the weak coverage area can only be roughly determined through user feedback, which results in that the severity of the problem occurring in the weak coverage area and the size of the range of the weak coverage area in which the problem occurs cannot be determined.
Disclosure of Invention
The embodiment of the invention provides a method, a device, equipment and a storage medium for identifying network weak coverage, which can solve the technical problems that the position of a weak coverage area cannot be determined, the severity of problems in the weak coverage area cannot be determined, and the range of the weak coverage area with the problems cannot be determined.
In a first aspect, an embodiment of the present invention provides a method for optimizing network weak coverage, where the method includes:
acquiring network coverage information;
identifying a network weak coverage area according to the network coverage information, and determining the coverage area information of the network weak coverage area; wherein the network coverage information comprises: traffic statistics, OTT, Measurement Report (MR), user history complaint information, and network black point library.
In one possible implementation, the method further comprises:
acquiring the resource utilization rate, signal transmitting power, OTT sampling points and parameters of a base station;
determining the weak coverage scene type of the network weak coverage area according to the coverage area information, the configuration information and the parameters of the base station;
and determining a scheme for optimizing the weak coverage of the network according to the type of the weak coverage scene.
In one possible implementation, identifying a network weak coverage area based on the network coverage information includes:
respectively calculating the telephone traffic statistics, the OTT, the MR, the historical complaint information of the user and the evaluation scores corresponding to the network black point library by using a network weak coverage algorithm;
calculating a network coverage score according to the evaluation score and the weight corresponding to each item of information in the network coverage information;
and determining the area with the network coverage score meeting the preset threshold value as a network weak coverage area.
In one possible implementation, determining a weak coverage scenario type of a network weak coverage area according to coverage information, configuration information of a base station, and parameters includes:
acquiring user position information in an MR coverage area;
determining the inclination angle and the azimuth angle of the base station antenna in the network weak coverage area according to the parameters;
determining the power of a reference signal CRS of the base station according to the configuration information of the base station;
determining the distance between a network weak coverage area and a coverage area of a base station main lobe according to the coverage area information, wherein the base station main lobe is the maximum radiation beam of a base station;
and determining the weak coverage scene type of the network weak coverage area according to the inclination angle and the azimuth angle of the base station antenna and the power and the distance of the CRS.
In one possible implementation, the weak coverage scenario type includes whether a base station antenna tilt angle of a network weak coverage area meets a first preset condition;
determining whether the inclination angle of a base station antenna in the network weak coverage area meets a first preset condition or not according to the parameters and the user position information in the network weak coverage area;
wherein, include:
acquiring position information of a base station;
determining the distance between the user and the base station according to the user position information and the position information of the base station;
determining the base station antenna inclination angle of the network weak coverage area according to the pitch angle of the base station in the parameters;
generating a Thiessen polygon according to a Thiessen polygon algorithm by taking the base station as a central point, and calculating the average value from the central point to each edge in the Thiessen polygon as the distance between the Thiessen polygons;
the size of the antenna inclination angle meets a preset inclination angle threshold value, the distance between a user and a base station meets a preset distance condition, the Thiessen polygon interval meets a preset interval threshold value, and the base station antenna inclination angle of the network weak coverage area meets a first preset condition.
In one possible implementation, the weak coverage scenario type includes whether a base station antenna azimuth angle of the network weak coverage area satisfies a second preset condition;
the type determining module is specifically used for determining whether the azimuth angle of the base station antenna in the network weak coverage area meets a second preset condition according to the parameters and the user position information in the network weak coverage area;
wherein, include:
acquiring azimuth angles and position information of base station antennas in parameters of a base station;
determining the distance between the user and the base station according to the user position information and the position information of the base station;
determining the azimuth information of user distribution and the distribution density of users in the coverage azimuth of the base station based on the user position information;
determining the average distribution azimuth angle of the users according to the azimuth information of the user distribution, the distance between the users and the base station and the distribution density of the users;
and determining that the azimuth angle of the base station antenna in the network weak coverage area meets a second preset condition when the difference value between the azimuth angle of the base station antenna and the user average distribution azimuth angle is smaller than a preset azimuth angle threshold value.
In a second aspect, an embodiment of the present invention provides an apparatus for optimizing network weak coverage, where the apparatus includes:
the information acquisition module is used for acquiring network coverage information;
the information determining module is used for identifying the network weak coverage area according to the network coverage information and determining the coverage area information of the network weak coverage area; wherein the network coverage information comprises: traffic statistics, OTT, measurement report MR, user history complaint information, and network black point library.
In one possible implementation, the apparatus further comprises:
the information acquisition module is also used for acquiring the resource utilization rate, the signal transmitting power, the OTT sampling point and the parameters of the base station;
the type determining module is used for determining the weak coverage scene type of the network weak coverage area according to the coverage area information, the configuration information and the parameters of the base station;
and the scheme determining module is used for determining a scheme for optimizing the network weak coverage according to the weak coverage scene type.
In one possible implementation, the information determining module is configured to identify a network weak coverage area according to network coverage information, and includes:
respectively calculating the telephone traffic statistics, the OTT, the MR, the historical complaint information of the user and the evaluation scores corresponding to the network black point library by using a network weak coverage algorithm;
calculating a network coverage score according to the evaluation score and the weight corresponding to each item of information in the network coverage information;
and determining the area with the network coverage score meeting the preset threshold value as a network weak coverage area.
In one possible implementation, the type determining module is configured to determine a weak coverage scene type of a network weak coverage area according to coverage information, resource utilization of a base station, signal transmission power, an OTT sampling point, and parameters, and includes:
acquiring user position information in an MR coverage area;
determining the inclination angle and the azimuth angle of the base station antenna in the network weak coverage area according to the parameters;
determining the power of a reference signal CRS of the base station according to the resource utilization rate, the signal transmitting power, the OTT sampling point and the parameters of the base station;
determining the distance between a network weak coverage area and a coverage area of a base station main lobe according to the coverage area information, wherein the base station main lobe is the maximum radiation beam of a base station;
and determining the weak coverage scene type of the network weak coverage area according to the inclination angle and the azimuth angle of the base station antenna and the power and the distance of the CRS.
In one possible implementation, the weak coverage scenario type includes whether a base station antenna tilt angle of a network weak coverage area meets a first preset condition;
the type determining module is specifically used for determining whether the inclination angle of the base station antenna in the network weak coverage area meets a first preset condition according to the parameters and the user position information in the network weak coverage area;
wherein, include:
acquiring position information of a base station;
determining the distance between the user and the base station according to the user position information and the position information of the base station;
determining the base station antenna inclination angle of the network weak coverage area according to the pitch angle of the base station in the parameters;
generating a Thiessen polygon according to a Thiessen polygon algorithm by taking the base station as a central point, and calculating the average value from the central point to each edge in the Thiessen polygon as the distance between the Thiessen polygons;
the size of the antenna inclination angle meets a preset inclination angle threshold value, the distance between a user and a base station meets a preset distance condition, the Thiessen polygon interval meets a preset interval threshold value, and the base station antenna inclination angle of the network weak coverage area meets a first preset condition.
In one possible implementation, the weak coverage scenario type includes whether a base station antenna azimuth angle of the network weak coverage area satisfies a second preset condition;
the type determining module is specifically used for determining whether the azimuth angle of the base station antenna in the network weak coverage area meets a second preset condition according to the parameters and the user position information in the network weak coverage area;
wherein, include:
acquiring the azimuth angle of a base station antenna and the position information of a base station in the parameters of the base station;
determining the distance between the user and the base station according to the user position information and the position information of the base station;
determining the azimuth information of user distribution and the distribution density of users in the coverage azimuth of the base station based on the user position information;
calculating the average distribution azimuth angle of the users according to the azimuth information of the user distribution, the distance between the users and the base station and the distribution density of the users;
and determining that the azimuth angle of the base station antenna in the network weak coverage area meets a second preset condition when the difference value between the azimuth angle of the base station antenna and the user average distribution azimuth angle is smaller than a preset azimuth angle threshold value.
In a third aspect, an embodiment of the present invention provides a computing device, including: at least one processor, at least one memory, and computer program instructions stored in the memory, which when executed by the processor, implement the method of the first aspect or any of the possible implementations of the first aspect as described in the embodiments above.
In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, on which computer program instructions are stored, which, when executed by a processor, implement the method according to the first aspect of the foregoing embodiments or any possible implementation manner of the first aspect.
The embodiment of the invention provides a method, a device, equipment and a storage medium for identifying network weak coverage. The method comprises the following steps: acquiring network coverage information; identifying a network weak coverage area according to the network coverage information, and determining the coverage area information of the network weak coverage area; wherein the network coverage information comprises: traffic statistics, OTT, measurement report MR, user history complaint information, and network black point library. The embodiment of the invention can accurately determine the position of the weak coverage area, and further determine the severity of the problem of the weak coverage area and the range of the weak coverage area with the problem.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required to be used in the embodiments of the present invention will be briefly described below, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 illustrates a flow chart of a method for identifying network weak coverage according to some embodiments of the present invention;
fig. 2 is a flowchart illustrating a method for determining a weak coverage scene type of a network weak coverage area in an identification method of network weak coverage according to some embodiments of the present invention;
FIG. 3 illustrates a schematic diagram of a weak coverage user position azimuth algorithm provided in accordance with some embodiments of the present invention;
FIG. 4 illustrates a schematic diagram of an antenna downtilt angle and coverage area diagram provided in accordance with some embodiments of the present invention;
FIG. 5 illustrates a block diagram of an apparatus for identifying network weak coverage according to some embodiments of the present invention;
FIG. 6 illustrates a schematic structural diagram of a computing device provided in accordance with some embodiments of the present invention.
Detailed Description
Features and exemplary embodiments of various aspects of the present invention will be described in detail below, and in order to make objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not to be construed as limiting the invention. It will be apparent to one skilled in the art that the present invention may be practiced without some of these specific details. The following description of the embodiments is merely intended to provide a better understanding of the present invention by illustrating examples of the present invention.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
Rural overall coverage assessment and weak coverage village identification are the basis for rural wide coverage promotion. After an accurate rural coverage evaluation result is obtained, how to apply a reasonable, efficient and economic coverage improvement scheme corresponding to different scenes is a difficult point and a key point for solving a rural weak coverage area. Currently, the 4th Generation mobile communication technology (4G) mobile network rural area wireless coverage quality assessment, weak coverage area identification mainly depends on MR data, user feedback and field test methods. Whether a weak coverage area exists around a base station sector can be simply judged through MR data, but a specific position cannot be determined, and a solution is difficult to put forward. The user feedback also only enables the specific location to be roughly determined, and the degree of the weak coverage problem, the size of the problem area range and the best solution measure cannot be determined. The optimal solution can be determined only by determining the weak coverage area, the range and the degree of the weak coverage problem through detailed field tests, but the method has large input of manpower, material resources and time and cannot be implemented in a large range.
In conclusion, in the identification and optimization process of the existing network weak coverage area, a data source is relatively single, mutual correlation analysis is lacked, the problem identification of the weak coverage area can only be qualitative analysis, and a series of quantitative indexes such as specific positions (specific villages, longitude and latitude), size of the weak coverage area, specific coverage strength and the like cannot be accurately identified; the rural weak coverage area of the whole network cannot be identified quickly, efficiently and accurately, certain difficulty is brought to optimization adjustment and optimal solution determination, the optimization difficulty is increased, a large amount of manpower, vehicles and time are required to be invested for troubleshooting and testing, and the method is time-consuming, labor-consuming and low in efficiency. The identification and judgment result of the weak coverage problem in the rural area and the optimization solution have no accurate matching model and algorithm, the one-to-one correspondence between the weak coverage problem in different scenes and different types and the optimal solution cannot be realized, and the problem solving and optimizing efficiency is low.
Therefore, the method, the device, the equipment and the storage medium for identifying the network weak coverage provided by the embodiment of the invention can reduce the purchase cost and improve the product utilization rate.
For the convenience of understanding the embodiment, first, a detailed description is given to an identification method of network weak coverage disclosed in the embodiment of the present invention.
Referring to fig. 1, an embodiment of the present invention provides a method for identifying network weak coverage, including: S101-S102.
S101: and acquiring network coverage information.
In one embodiment of the invention, in order to accurately identify the network weak coverage area, the whole coverage evaluation of the rural area is taken as an entry point, the network weak coverage area is defined by two dimensions of coverage and value, and the quintuple method for evaluating the current 4G coverage status of all villages is realized by utilizing five key dimension information of telephone traffic statistics, OTT, MR, user historical complaint information and a network black point library. Therefore, network coverage information needs to be acquired before determining the network weak coverage area, wherein the network coverage information includes traffic statistics, OTT, MR, user history complaint information and a network black point library, and further data diversification for evaluating the network weak coverage is increased.
S102: and identifying the network weak coverage area according to the network coverage information, and determining the coverage area information of the network weak coverage area.
In an embodiment of the invention, a network weak coverage area can be identified by using five pieces of key dimension information, namely traffic statistics, OTT, MR, user history complaint information and a network black spot library, for example, five pieces of key dimension information in the network weak coverage information occupy different weights when whether the area is the network weak coverage area is evaluated, and the area or village covered by the network weak coverage area can be accurately identified according to the weights of the five pieces of key dimension information.
Specifically, referring to fig. 2, an embodiment of the present invention provides a method for identifying a network weak coverage area according to network coverage information, including:
respectively calculating the telephone traffic statistics, the OTT, the MR, the historical complaint information of the user and the evaluation scores corresponding to the network black point library by using a network weak coverage algorithm;
calculating a network coverage score according to the evaluation score and the weight corresponding to each item of information in the network coverage information;
and determining the area with the network coverage score meeting the preset threshold value as a network weak coverage area.
In one embodiment of the invention, first, an evaluation score of each penta-toxicity information is determined according to a network weak coverage algorithm, and the coverage rate of the MR in the identified network weak coverage area can be determined according to the MR, the coverage rate of different MRs can be determined when the MR is in the evaluation of the network weak coverage area, the corresponding evaluation scores are different, for example, when the coverage of the MR is 90% -100%, the evaluation score of the MR in the weak coverage area of the evaluation network is 50, when the coverage rate of the MR is 75% -89%, the evaluation score of the MR in the weak coverage area of the evaluation network is 60 points, when the coverage rate of the MR is 50% -74%, the evaluation score of the MR in the weak coverage area of the evaluation network is 80 points, when the coverage rate of the MR is 0% -49%, the evaluation score of the MR in the weak coverage area of the evaluation network is 100.
And calculating the network coverage score according to the evaluation score corresponding to each network coverage information and the weight corresponding to each item of information in the network coverage information. For example, the evaluation score of the traffic statistics is 68 points, and the weight proportion is 10%; the evaluation score of the MR is 56 points, and the weight proportion is 30 percent; the evaluation score of the OTT is 53 points, and the weight proportion is 50 percent; the evaluation score of the historical complaint information of the user is 100, and the weight accounts for 10%; the evaluation score of the network black point library is 0, and the network black point library has no weight ratio. The network black point library is an information library of an area with high probability of no coverage or weak coverage. According to the evaluation score and the weight of each dimension information in the network coverage information, the obtained network coverage score F is as follows: f ═ 68 × 10% +56 × 30% +53 × 50% +100 × 10% +0 ═ 57.1.
Assuming that the preset threshold is 0-50 and the network coverage score F of 57.1 cannot satisfy the preset threshold, the area is a network weak coverage area.
After the network weak coverage area is identified and determined, the network weak coverage area needs to be optimized, so that the problems existing in the network weak coverage area need to be determined, the weak coverage scene type is further determined, and the scheme for optimizing the network weak coverage is obtained.
Specifically, the method for identifying network weak coverage provided by the embodiment of the present invention further includes:
acquiring the resource utilization rate, signal transmitting power, OTT sampling points and parameters of a base station;
determining the weak coverage scene type of the network weak coverage area according to the coverage area information, the resource utilization rate of the base station, the signal transmitting power, the OTT sampling point and the parameters;
and determining a scheme for optimizing the weak coverage of the network according to the type of the weak coverage scene.
In an embodiment of the present invention, in order to quickly determine a scheme for optimizing network weak coverage, a weak coverage scenario type needs to be determined in advance according to a parameter of a base station, an MR, an OTT sampling point, a resource utilization rate of an area covered by the base station, and a power of the base station, where a resource of the area covered by the base station may be a configuration of software and hardware in the area covered by the base station, and a License configuration permission. The OTT may refer to various application services provided to the user through the internet, for example, application services such as WeChat, Paibao, and Didi, and the OTT sampling point refers to a sampling point corresponding to the OTT where the user has a network weak coverage condition.
Referring to fig. 2, in the method for identifying network weak coverage provided in the embodiment of the present invention, determining a weak coverage scene type of a network weak coverage area according to coverage information, resource utilization of a base station, signal transmission power, OTT sampling points, and parameters includes:
s201: acquiring user position information in an MR coverage area;
s202: determining the inclination angle and the azimuth angle of the base station antenna in the network weak coverage area according to the parameters;
s203: determining the power of the CRS of the base station according to the resource utilization rate, the signal transmitting power, the OTT sampling point and the parameters of the base station;
s204: determining the distance between a network weak coverage area and a coverage area of a base station main lobe according to the coverage area information, wherein the base station main lobe is the maximum radiation beam of a base station;
s205: and determining the weak coverage scene type of the network weak coverage area according to the inclination angle and the azimuth angle of the base station antenna and the power and the distance of the CRS.
In one embodiment of the invention, the coverage information refers to the specific coverage range of the network weak coverage. Determining the type of the network weak coverage scenario corresponding to the network weak coverage area requires first evaluating the base station in the network weak coverage area, for example, evaluating whether various parameter configurations of the base station are reasonable. The weak coverage scene type is shown as the following table one:
watch 1
The scheme for optimizing the network weak coverage is shown in table two corresponding to each type of weak coverage scene.
Watch two
First, in S201, the tilt angle and the azimuth angle of the base station antenna need to be determined according to the obtained base station parameters, for example, the pitch angle of the base station antenna corresponds to the actual total tilt angle of each cell, which includes an electronic tilt angle and a mechanical tilt angle, where a condition that the tilt angle is too small is a necessary condition for determining that the tilt angle is too small when the pitch angle is less than 6 degrees, and a condition that the pitch angle is too large when the pitch angle is greater than 10 degrees is a primary condition.
Specifically, the weak coverage scene type includes whether a base station antenna inclination angle of a network weak coverage area meets a first preset condition;
determining whether the inclination angle of a base station antenna in the network weak coverage area meets a first preset condition or not according to the parameters and the user position information in the network weak coverage area;
wherein, include:
acquiring position information of a base station;
determining the distance between the user and the base station according to the user position information and the position information of the base station;
determining the base station antenna inclination angle of the network weak coverage area according to the pitch angle of the base station in the parameters;
generating a Thiessen polygon according to a Thiessen polygon algorithm by taking the base station as a central point, and calculating the average value from the central point to each edge in the Thiessen polygon as the distance between the Thiessen polygons;
the size of the antenna inclination angle meets a preset inclination angle threshold value, the distance between a user and a base station meets a preset distance condition, the Thiessen polygon interval meets a preset interval threshold value, and the base station antenna inclination angle of the network weak coverage area meets a first preset condition.
In an embodiment of the present invention, in order to determine whether the tilt angle of the base station antenna is a factor affecting the network weak coverage, it is necessary to obtain the user location information and the base station location information in the network weak coverage area, and the distance between the user and the base station can be determined according to the base station location information and the user location information. In addition, whether the inclination angle of the base station antenna is a factor influencing the weak coverage of the network needs to be determined by combining the Thiessen polygon distance and the distance between the user and the base station. That is, the rationality of the antenna tilt angle satisfies the following algorithm (1):
Trational inclination＝f(TInclination angle，MRTADistribution of，DTailin polygon inter-station distance) (1)
Wherein, TInclination angleIndicating the tilt angle, MRTA, of the base station antennaDistribution ofRepresenting the position information of the user in the coverage area of the cell MR, DTailin polygon inter-station distanceRepresenting the pitch of the Thiessen polygon.
And if the antenna inclination angle is reasonable, the antenna inclination angle is not a factor influencing the weak coverage of the network.
Wherein the Thiessen polygon pitch is established by the following method: and generating a Thiessen polygon by taking the base station as a central point according to a Thiessen polygon algorithm, wherein the average value of the distance from the central point to each edge of the Thiessen polygon is defined as the station spacing of the Thiessen polygon. In addition, whether the users are distributed in a network weak coverage area in a balanced mode or whether the users exceed the coverage area or not is judged according to the Thiessen polygon inter-station distance and the distribution situation of the user sampling points.
According to the algorithm (1), the size of the inclination angle of the base station antenna, the position information of the user in the coverage area of the cell MR, the Thiessen polygon distance and the reasonability of the inclination angle of the base station antenna can be determined, for example, the reasonability of the antenna inclination angle comprises the following three conditions:
first, the tilt angle is too small.
When the inclination angle of the base station antenna is smaller than 6 degrees, and the distance between the 40% of user positions in the MRTA sampling points and the base station position is larger than 1.5 times of the Thiessen polygonal inter-station distance, determining that the inclination angle of the base station antenna is too small, and readjusting the inclination angle of the base station antenna is needed.
Secondly, the inclination angle is too large.
And when the Thiessen polygonal inter-station distance is larger than 2Km and smaller than 10Km, the inclination angle of the base station antenna is larger than 10 degrees, and the distance between the 90% user position in the MRTA sampling point and the base station position is smaller than 0.2 times of the Thiessen polygonal inter-station distance, determining that the inclination angle of the base station antenna is too large, and needing to readjust the inclination angle of the base station antenna.
Thirdly, the inclination angle is reasonable, namely the inclination angle of the antenna of the base station meets the first preset condition.
Except for the judgment conditions of overlarge inclination angle and undersize inclination angle, the inclination angle of the base station antenna is considered to be reasonable.
In addition to the tilt angle of the base station antenna can affect the network weak coverage, the azimuth angle of the base station antenna is also one of the factors affecting the network weak coverage.
Specifically, in the method for identifying network weak coverage provided in the embodiment of the present invention, the weak coverage scene type includes whether the azimuth of the base station antenna in the network weak coverage area satisfies a second preset condition, which includes:
acquiring the azimuth angle of a base station antenna and the position information of a base station in the parameters of the base station;
determining the distance between the user and the base station according to the user position information and the position information of the base station;
determining the azimuth information of user distribution and the distribution density of users in the coverage azimuth of the base station based on the user position information;
determining the average distribution azimuth angle of the users according to the azimuth information of the user distribution, the distance between the users and the base station and the distribution density of the users;
and determining that the azimuth angle of the base station antenna in the network weak coverage area meets a second preset condition when the difference value between the azimuth angle of the base station antenna and the user average distribution azimuth angle is smaller than a preset azimuth angle threshold value.
In an embodiment of the present invention, if the azimuth angle of the base station antenna satisfies the second predetermined condition, that is, the azimuth angle of the base station antenna satisfies the condition: and if the difference value between the azimuth angle of the base station antenna and the user average distribution azimuth angle is smaller than a preset azimuth angle threshold value, the azimuth angle of the base station antenna is not a factor influencing the weak coverage of the network. Then, firstly, we need to determine the direction information of user distribution and the distribution density of users according to the position information of users, and synthesize the average distribution direction of users and the distribution distance between users according to the direction information of user distribution, the distance between users and the base station and the distribution density of users. And calculating the difference between the azimuth angle of the base station antenna and the average distribution azimuth angle of the user, wherein if the difference between the azimuth angle of the base station antenna and the average distribution azimuth angle of the user is smaller than a preset azimuth angle threshold value, the azimuth angle of the base station antenna is not a factor influencing the weak coverage area of the network. For example, if the azimuth angle threshold is 30 degrees, and the difference between the azimuth angle of the base station antenna and the average distribution azimuth angle of the users is greater than 30 degrees, the azimuth angle of the base station antenna is not reasonable, and the azimuth angle of the antenna needs to be adjusted.
In addition, the power of the CRS of the base station and the distance between the network weak coverage area and the coverage area of the main lobe of the base station are also important factors affecting the weak coverage of the network, and therefore, the power of the CRS of the base station and the distance between the network weak coverage area and the coverage area of the main lobe of the base station need to be determined.
According to the MRTA sampling point analysis of the MR cell coverage area, outputting the sampling point proportion of the cell coverage area, and segmenting the MRTA counted by the base station cell MR into all sampling points between fields 00 and 47 according to the following distance: within 500 m [0, 500], 500 to 1000 m [501, 1000], 1Km to 2Km [1001, 2000], 2Km or more [2001, + ∞ ], the distribution of the current main user group of the cell and the main lobe coverage are judged by the distance segment interval, and the distance segment interval with the most distributed group of users is used as the coverage center point to obtain the main lobe coverage center point LA (grid area).
Evaluating the power configuration rationality of a cell CRS reference signal according to the software and hardware configuration and License configuration permission of a base station cell, and judging whether a network weak coverage area can be reduced by improving the power configuration, wherein the power License configuration value is greater than a power License use value, namely the power of the CRS reference signal has a room for improvement; if the RRU number of the F frequency band cell is less than 2 or the RRU number of the D frequency band is less than 2, the CRS power configuration margin can be increased.
And evaluating the resource utilization rate and the load condition of the cell according to the PRB resource utilization rate of the cell, the number of RRC users and the like. MAX (uplink PUSCH utilization, downlink PDSCH utilization, downlink PDCCH utilization) > 50% or RRC active connection user number >200 is a high resource utilization and high load cell.
Through OTT coverage sampling point evaluation, the OTT sampling point proportion in the cell main lobe coverage is calculated, the reasonability of the cell parameter data is determined, and a specific evaluation algorithm is as follows: judging the proportion of OTT sampling points smaller than-110 dBm by using the RSRP coverage level of the accurate OTT sampling points counted by the cell, and evaluating the coverage rate index of the cell, wherein if the coverage rate is larger than or equal to 90%, the network coverage is good, and the configuration of the working parameters is reasonable; if the coverage rate is 90% of rain, the cell is a network weak coverage cell, and the working parameter is possibly unreasonable, further judgment is carried out, the distance between the OTT sampling point position (longitude and latitude data) with the coverage rate less than 90% and the base station is judged, if the average distance of the weak coverage sampling point is more than 2Km, the working parameter is unreasonable, otherwise, the working parameter is reasonable.
On the other hand, weak coverage sampling points are screened through OTT sampling point evaluation, specifically, a weak coverage grid and a weak coverage center accurate coordinate L1 are determined by combining and clustering and utilizing a self-defined function mean (U), and then the weak coverage center coordinate L is obtained1(X1,Y1) With base station coordinates L2(X2,Y2) Using a custom weak coverage orientation decision function f _ Dir (L)1,L2) And outputting the azimuth angle of the weak coverage in the main service cell, then evaluating the distance relationship between the weak coverage area and the coverage range of the main lobe, and judging which coverage improvement measure is adopted.
Specifically, the weak coverage center precise coordinate L1 satisfies the following formula (2):
L1＝Mean(U) (2)
wherein, U represents the position information of the weak coverage sampling point in the OTT user.
Wherein the content of the first and second substances,
and the azimuth angle of the weak coverage area of the network is determined according to the position information of the weak coverage sampling point in the OTT user and the position information of the base station.
For example, as shown in fig. 3, the schematic diagram of the location azimuth algorithm of the weak coverage user is shown, where D1 is the distance between the weak coverage sampling point and the base station in the OTT user, and the azimuth of the primary coverage cell of the base station is 90 ° ± ^ a, and ^ a can be calculated according to the trigonometric function principle.
See FIG. 4 for the antenna downtilt angleCoverage range diagram, according to the diagram, the main lobe coverage distance is AB and AC respectively; the main lobe coverage width is BC; horizontal half-power angle: θ e. According toThe cell main lobe coverage distance can be calculated.
Where θ represents a downtilt angle of the antenna, h represents a height of the antenna, R represents a coverage radius of a cell, and a represents a vertical plane half-power angle of the antenna. Main lobe coverage boundary width range:
after the weak coverage scene type is determined, a scheme for optimizing the network weak coverage is determined from the table two according to the determined weak coverage scene type.
According to the identification method of the network weak coverage provided by the embodiment of the invention, the problem positioning is faster and more accurate through the multi-dimensional data source joint analysis; accurate position identification of a rural area weak coverage village and a weak coverage area is realized, longitude and latitude level weak coverage problem position positioning is realized, and quantitative identification is realized. And through the engineering participation rational evaluation, whether the parameter setting of the existing network base station is reasonable can be judged, and an important basis is provided for optimizing the network quality.
Secondly, 7 scene model classifications of all the weak coverage problem types in the rural area are realized, the problem cause identification is more accurate, and an important basis is provided for the selection of an optimization solution.
Finally, the automatic processing process of accurate identification, reason positioning and optimal scheme selection of the rural weak coverage problem is realized, the network optimization efficiency is greatly improved, the investment of manpower, material resources and time is saved, and the economic efficiency is remarkable.
Referring to fig. 5, an embodiment of the present invention provides an apparatus for identifying network weak coverage, where the apparatus includes:
an information obtaining module 501, configured to obtain network coverage information;
an information determining module 502, configured to identify a network weak coverage area according to the network coverage information, and determine coverage area information of the network weak coverage area; wherein the network coverage information comprises: traffic statistics, OTT, measurement report MR, user history complaint information, and network black point library.
In one possible implementation, the apparatus further comprises:
the information obtaining module 501 is further configured to obtain a resource utilization rate, a signal transmitting power, an OTT sampling point, and a parameter of the base station;
a type determining module 503, configured to determine a weak coverage scene type of a network weak coverage area according to the coverage area information, configuration information of a base station, and parameters;
and a scheme determining module 504, configured to determine a scheme for optimizing the network weak coverage according to the weak coverage scene type.
In one possible implementation, the information determining module 502 is configured to identify a network weak coverage area according to network coverage information, and includes:
respectively calculating the telephone traffic statistics, the OTT, the MR, the historical complaint information of the user and the evaluation scores corresponding to the network black point library by using a network weak coverage algorithm;
calculating a network coverage score according to the evaluation score and the weight corresponding to each item of information in the network coverage information;
and determining the area with the network coverage score meeting the preset threshold value as a network weak coverage area.
In a possible implementation, the type determining module 503 is configured to determine a weak coverage scene type of a network weak coverage area according to coverage information, resource utilization of a base station, signal transmission power, OTT sampling points, and parameters, and includes:
acquiring user position information in an MR coverage area;
determining the inclination angle and the azimuth angle of the base station antenna in the network weak coverage area according to the parameters;
determining the power of a reference signal CRS of the base station according to the resource utilization rate, the signal transmitting power, the OTT sampling point and the parameters of the base station;
determining the distance between a network weak coverage area and a coverage area of a base station main lobe according to the coverage area information, wherein the base station main lobe is the maximum radiation beam of a base station;
and determining the weak coverage scene type of the network weak coverage area according to the inclination angle and the azimuth angle of the base station antenna and the power and the distance of the CRS.
In one possible implementation, the weak coverage scenario type includes whether a base station antenna tilt angle of a network weak coverage area meets a first preset condition;
the type determining module 503 is specifically configured to determine whether a base station antenna tilt angle of the network weak coverage area meets a first preset condition according to the parameter and the user location information in the network weak coverage area;
wherein, include:
acquiring position information of a base station;
determining the distance between the user and the base station according to the user position information and the position information of the base station;
determining the base station antenna inclination angle of the network weak coverage area according to the pitch angle of the base station in the parameters;
generating a Thiessen polygon according to a Thiessen polygon algorithm by taking the base station as a central point, and calculating the average value from the central point to each edge in the Thiessen polygon as the distance between the Thiessen polygons;
the size of the antenna inclination angle meets a preset inclination angle threshold value, the distance between a user and a base station meets a preset distance condition, the Thiessen polygon interval meets a preset interval threshold value, and the base station antenna inclination angle of the network weak coverage area meets a first preset condition.
In one possible implementation, the weak coverage scenario type includes whether a base station antenna azimuth angle of the network weak coverage area satisfies a second preset condition;
the type determining module 503 is specifically configured to determine whether the azimuth angle of the base station antenna in the network weak coverage area meets a second preset condition according to the parameter and the user location information in the network weak coverage area;
wherein, include:
acquiring the azimuth angle of a base station antenna and the position information of a base station in the parameters of the base station;
determining the distance between the user and the base station according to the user position information and the position information of the base station;
determining the azimuth information of user distribution and the distribution density of users in the coverage azimuth of the base station based on the user position information;
calculating the average distribution azimuth angle of the users according to the azimuth information of the user distribution, the distance between the users and the base station and the distribution density of the users;
and determining that the azimuth angle of the base station antenna in the network weak coverage area meets a second preset condition when the difference value between the azimuth angle of the base station antenna and the user average distribution azimuth angle is smaller than a preset azimuth angle threshold value.
Additionally, the method of embodiments of the present invention may be implemented by a computing device. Fig. 6 is a schematic diagram illustrating a hardware structure of a computing device according to an embodiment of the present invention.
The computing device may include a processor 601 and memory 602 that stores computer program instructions.
Specifically, the processor 601 may include a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or may be configured as one or more Integrated circuits implementing embodiments of the present invention.
Memory 602 may include mass storage for data or instructions. By way of example, and not limitation, memory 602 may include a Hard Disk Drive (HDD), floppy Disk Drive, flash memory, optical Disk, magneto-optical Disk, tape, or Universal Serial Bus (USB) Drive or a combination of two or more of these. Memory 602 may include removable or non-removable (or fixed) media, where appropriate. The memory 602 may be internal or external to the data processing apparatus, where appropriate. In a particular embodiment, the memory 602 is a non-volatile solid-state memory. In a particular embodiment, the memory 602 includes Read Only Memory (ROM). Where appropriate, the ROM may be mask-programmed ROM, Programmable ROM (PROM), Erasable PROM (EPROM), Electrically Erasable PROM (EEPROM), electrically rewritable ROM (EAROM), or flash memory or a combination of two or more of these.
The processor 601 reads and executes the computer program instructions stored in the memory 602 to implement any one of the above-described network weak coverage identification methods.
In one example, the computing device may also include a communication interface 603 and a bus 610. As shown in fig. 6, the processor 601, the memory 602, and the communication interface 603 are connected via a bus 610 to complete communication therebetween.
The communication interface 603 is mainly used for implementing communication between modules, apparatuses, units and/or devices in the embodiments of the present invention.
Bus 610 includes hardware, software, or both to couple components of the computing device to one another. By way of example, and not limitation, a bus may comprise an Enhanced Industry Standard Architecture (EISA) bus, a front-side bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an infiniband interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a video electronics standards association local (VLB) bus, or other suitable bus or a combination of two or more of these. Bus 610 may include one or more buses, where appropriate. Although specific buses have been described and shown in the embodiments of the invention, any suitable buses or interconnects are contemplated by the invention.
In addition, in combination with the method for identifying network weak coverage in the foregoing embodiments, embodiments of the present invention may provide a computer-readable storage medium to implement. The computer readable storage medium having stored thereon computer program instructions; the computer program instructions, when executed by a processor, implement any one of the above embodiments of the method for identifying network weak coverage.
It is to be understood that the invention is not limited to the specific arrangements and instrumentality described above and shown in the drawings. A detailed description of known methods is omitted herein for the sake of brevity. In the above embodiments, several specific steps are described and shown as examples. However, the method processes of the present invention are not limited to the specific steps described and illustrated, and those skilled in the art can make various changes, modifications and additions or change the order between the steps after comprehending the spirit of the present invention.
The functional blocks shown in the above structural block diagrams may be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, it may be, for example, an electronic circuit, an Application Specific Integrated Circuit (ASIC), suitable firmware, plug-in, function card, or the like. When implemented in software, the elements of the invention are the programs or code segments used to perform the required tasks. The program or code segments may be stored in a machine-readable medium or transmitted by a data signal carried in a carrier wave over a transmission medium or a communication link. A "machine-readable medium" may include any medium that can store or transfer information. Examples of a machine-readable medium include electronic circuits, semiconductor memory devices, ROM, flash memory, Erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, Radio Frequency (RF) links, and so forth. The code segments may be downloaded via computer networks such as the internet, intranet, etc.
It should also be noted that the exemplary embodiments mentioned in this patent describe some methods or systems based on a series of steps or devices. However, the present invention is not limited to the order of the above-described steps, that is, the steps may be performed in the order mentioned in the embodiments, may be performed in an order different from the order in the embodiments, or may be performed simultaneously.
As will be apparent to those skilled in the art, for convenience and brevity of description, the specific working processes of the systems, modules and units described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again. It should be understood that the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive various equivalent modifications or substitutions within the technical scope of the present invention, and these modifications or substitutions should be covered within the scope of the present invention.
Claims (10)
1. A method for identifying network weak coverage, the method comprising:
acquiring network coverage information;
identifying a network weak coverage area according to the network coverage information, and determining coverage area information of the network weak coverage area; wherein the network coverage information comprises: traffic statistics, OTT, measurement report MR, user history complaint information, and network black point library.
2. The method of claim 1, further comprising:
acquiring the resource utilization rate, signal transmitting power, OTT sampling points and parameters of a base station;
determining the weak coverage scene type of the network weak coverage area according to the coverage area information, the resource utilization rate of the base station, the signal transmitting power, the OTT sampling point and the parameters;
and determining a scheme for optimizing the network weak coverage according to the weak coverage scene type.
3. The method of claim 1, wherein the identifying a network weak coverage area according to the network coverage information comprises:
respectively calculating the telephone traffic statistics, the OTT, the MR, the user historical complaint information and the evaluation scores corresponding to the network black spot library by using a network weak coverage algorithm;
calculating a network coverage score according to the evaluation score and the weight corresponding to each item of information in the network coverage information;
and determining the area with the network coverage score meeting a preset threshold as a network weak coverage area.
4. The method according to claim 2, wherein the determining the weak coverage scene type of the network weak coverage area according to the coverage area information, the resource utilization of the base station, the signal transmission power, the OTT sampling point, and the parameters comprises:
acquiring user position information in the MR coverage area;
determining the inclination angle and the azimuth angle of the base station antenna in the network weak coverage area according to the parameters;
determining the power of a reference signal CRS of the base station according to the resource utilization rate, the signal transmitting power, the OTT sampling point and the parameters of the base station;
determining the distance between the network weak coverage area and a coverage area of a base station main lobe according to the coverage area information, wherein the base station main lobe is the maximum radiation beam of the base station;
and determining the weak coverage scene type of the network weak coverage area according to the inclination angle and the azimuth angle of the base station antenna, the power of the CRS and the distance.
5. The method according to claim 4, wherein the weak coverage scenario type includes whether a base station antenna tilt angle of the network weak coverage area satisfies a first preset condition;
determining whether the inclination angle of the base station antenna in the network weak coverage area meets a first preset condition or not according to the parameters and the user position information in the network weak coverage area;
wherein, include:
acquiring the position information of the base station;
determining the distance between the user and the base station according to the user position information and the position information of the base station;
determining the inclination angle of the base station antenna in the network weak coverage area according to the pitch angle of the base station in the parameters;
generating a Thiessen polygon according to a Thiessen polygon algorithm by taking the base station as a central point, and calculating the average value from the central point to each edge in the Thiessen polygon as the distance between the Thiessen polygons;
and determining that the base station antenna inclination angle of the network weak coverage area meets a first preset condition, wherein the size of the antenna inclination angle meets a preset inclination angle threshold value, the distance between the user and the base station meets a preset distance condition, and the Thiessen polygon interval meets a preset interval threshold value.
6. The method according to claim 4, wherein the weak coverage scenario type includes whether a base station antenna azimuth angle of the network weak coverage area satisfies a second preset condition;
the type determining module is specifically used for determining whether the azimuth angle of the base station antenna in the network weak coverage area meets a second preset condition according to the parameters and the user position information in the network weak coverage area;
wherein, include:
acquiring the azimuth angle of a base station antenna and the position information of the base station in the parameters of the base station;
determining the distance between the user and the base station according to the user position information and the position information of the base station;
determining the position information of user distribution and the distribution density of the users in the coverage position of the base station based on the user position information;
calculating the average distribution azimuth angle of the users according to the azimuth information of the user distribution, the distance between the users and the base station and the distribution density of the users;
and determining that the azimuth angle of the base station antenna in the network weak coverage area meets a second preset condition when the difference value between the azimuth angle of the base station antenna and the user average distribution azimuth angle is smaller than a preset azimuth angle threshold value.
7. An apparatus for identifying weak coverage of a network, the apparatus comprising:
the information acquisition module is used for acquiring network coverage information;
the information determining module is used for identifying a network weak coverage area according to the network coverage information and determining coverage area information of the network weak coverage area; wherein the network coverage information comprises: traffic statistics, OTT, MR, user historical complaint information, and network black point library.
8. The apparatus of claim 7, further comprising:
the information acquisition module is also used for acquiring the resource utilization rate, the signal transmitting power, the OTT sampling point and the parameters of the base station;
a type determining module, configured to determine a weak coverage scene type of the network weak coverage area according to the coverage area information, the configuration information of the base station, and the parameter;
and the scheme determining module is used for determining a scheme for optimizing the network weak coverage according to the weak coverage scene type.
9. A computing device, comprising: at least one processor, at least one memory, and computer program instructions stored in the memory that, when executed by the processor, implement the method of any of claims 1-6.
10. A computer-readable storage medium having computer program instructions stored thereon, which when executed by a processor implement the method of any one of claims 1-6.
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| CN114641015B (en) * | 2020-12-16 | 2024-03-12 | 中国联合网络通信集团有限公司 | Network evaluation method, device, electronic equipment and storage medium |
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| WO2022175588A1 (en) * | 2021-02-17 | 2022-08-25 | Elisa Oyj | A method for analysing antenna directions in a communications network |
| CN113133058A (en) * | 2021-04-07 | 2021-07-16 | 中国移动通信集团陕西有限公司 | Load balancing method, device and system |
| CN113179521A (en) * | 2021-05-11 | 2021-07-27 | 中国联合网络通信集团有限公司 | Method and device for determining 5G network coverage area |
| CN116033434A (en) * | 2021-10-25 | 2023-04-28 | 中国联合网络通信集团有限公司 | Network coverage management method, device, equipment and readable storage medium |
| CN116033434B (en) * | 2021-10-25 | 2024-04-02 | 中国联合网络通信集团有限公司 | Network coverage management method, device, equipment and readable storage medium |
| CN114422955A (en) * | 2021-12-21 | 2022-04-29 | 北京东土拓明科技有限公司 | Method, device, equipment and storage medium for positioning 5G indoor weak coverage area |
| WO2024001525A1 (en) * | 2022-06-30 | 2024-01-04 | 中兴通讯股份有限公司 | Wireless scene identification method and system, electronic device and storage medium |
| CN115988511A (en) * | 2022-12-22 | 2023-04-18 | 浪潮通信信息系统有限公司 | Village network coverage optimization method based on big data |
| CN116405944A (en) * | 2023-03-03 | 2023-07-07 | 中山大学 | Network coverage capacity optimization method, system, device and storage medium |
| CN116405944B (en) * | 2023-03-03 | 2024-02-06 | 中山大学 | Network coverage capacity optimization method, system, device and storage medium |
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