CN114339855A - Wireless network coverage rate evaluation method and device and computing equipment - Google Patents
Wireless network coverage rate evaluation method and device and computing equipment Download PDFInfo
- Publication number
- CN114339855A CN114339855A CN202011032719.5A CN202011032719A CN114339855A CN 114339855 A CN114339855 A CN 114339855A CN 202011032719 A CN202011032719 A CN 202011032719A CN 114339855 A CN114339855 A CN 114339855A
- Authority
- CN
- China
- Prior art keywords
- wireless network
- coverage rate
- network coverage
- feature
- tested
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Landscapes
- Mobile Radio Communication Systems (AREA)
Abstract
The embodiment of the invention relates to the technical field of artificial intelligence, and discloses a wireless network coverage rate evaluation method, a wireless network coverage rate evaluation device and computing equipment. The method comprises the following steps: acquiring measured data related to wireless network coverage in a plurality of areas; selecting a training set and a verification set from the measured data; determining a characteristic item for a wireless network coverage rate evaluation model based on data of a training set, and establishing an algorithm model to be tested; inputting characteristic values corresponding to characteristic items in the data of the verification set into the algorithm model to be tested to obtain the coverage rate of the wireless network to be tested, and determining the algorithm model to be tested as a wireless network coverage rate evaluation model if the difference value between the coverage rate of the wireless network to be tested and the coverage rate of the real wireless network meets the preset requirement; and inputting the characteristic value corresponding to the characteristic item of the area to be evaluated into a wireless network coverage rate evaluation model to obtain the wireless network coverage rate of the area to be evaluated. Through the mode, the embodiment of the invention realizes the intelligent evaluation of the wireless network coverage rate.
Description
Technical Field
The embodiment of the invention relates to the technical field of artificial intelligence, in particular to a wireless network coverage rate evaluation method, a wireless network coverage rate evaluation device and computing equipment.
Background
With the development of wireless network technology, the use requirements of users on the wireless network technology are more and more extensive. To accommodate technological advances and user needs, wireless network coverage needs to be adjusted.
In the process of implementing the embodiment of the present invention, the inventors found that: in the related art, the wireless network coverage rate of a specific area is adjusted mainly by adjusting power, adjusting an antenna azimuth angle, newly building a station and the like. In order to obtain the actual effect of the wireless network coverage rate adjustment, personnel need to go to the site to perform actual test, obtain data related to the wireless network coverage rate adjustment, analyze and process the related data, and finally obtain the actual numerical value of the wireless network coverage rate. It can be seen that the related art has a large cost and low efficiency for evaluating the coverage of the wireless network.
Disclosure of Invention
In view of the foregoing problems, embodiments of the present invention provide a method, an apparatus, and a computing device for evaluating wireless network coverage, which are used to solve the problems in the prior art that the cost for evaluating wireless network coverage is high and the efficiency is low.
According to an aspect of the embodiments of the present invention, there is provided a wireless network coverage assessment method, including:
acquiring measured data related to wireless network coverage in a plurality of areas;
selecting a training set and a verification set from the measured data according to a preset distribution proportion;
determining a characteristic item for a wireless network coverage rate evaluation model based on the data of the training set, and establishing an algorithm model to be tested;
inputting the characteristic value corresponding to the characteristic item in the data of the verification set into the algorithm model to be tested to obtain the coverage rate of the wireless network to be tested, and determining the algorithm model to be tested as a wireless network coverage rate evaluation model if the difference value between the coverage rate of the wireless network to be tested and the coverage rate of the real wireless network meets the preset requirement;
and inputting the characteristic value corresponding to the characteristic item of the area to be evaluated into the wireless network coverage rate evaluation model to obtain the wireless network coverage rate of the area to be evaluated.
In an optional manner, the measured data includes tentative test data of the plurality of areas before wireless network optimization and retest data after wireless network optimization.
In an optional manner, after the selecting a training set and a verification set from the measured data according to a preset allocation ratio, the method further includes:
and performing hyper-parameter tuning in the training set by using K-fold cross validation.
In an optional manner, the determining feature items for the wireless network coverage assessment model based on the data of the training set further comprises:
screening out a first characteristic item set influencing the wireless network coverage rate based on the training set;
removing the characteristic items with the association degree lower than a preset threshold value with the wireless network coverage rate from the first characteristic item set according to a preset association degree analysis mode to obtain a second characteristic item set;
performing distribution conversion on the feature items in the second feature item set according to a preset conversion mode, and removing at least one feature item from the converted second feature item set, wherein the at least one feature item is associated with each other and is associated with the wireless network coverage rate, so as to obtain a third feature item set;
and performing feature screening of a machine learning model from the third feature item set by using recursive feature elimination and cross validation to obtain a fourth feature item set, and determining the feature items in the fourth feature item set as feature items for a wireless network coverage rate evaluation model.
In an optional manner, the method further comprises:
performing data processing on the feature items in the first feature item set, wherein the data processing mode comprises the following steps: log operation, squaring operation and addition and subtraction polynomial operation among characteristic items.
In an optional manner, the algorithm model under test includes: one or more of a random forest regression tree algorithm, a gradient boosting regression tree algorithm, and a ridge regression algorithm.
In an optional manner, before inputting the feature values corresponding to the feature items in the data of the verification set into the algorithm model to be tested, the method further includes:
and optimizing the algorithm model to be tested by utilizing a grid search technology, a learning curve and a verification curve.
According to another aspect of the embodiments of the present invention, there is provided a wireless network coverage evaluation apparatus, including:
the system comprises an actual measurement data acquisition module, a wireless network coverage rate acquisition module and a data processing module, wherein the actual measurement data acquisition module is used for acquiring actual measurement data related to the wireless network coverage rates of a plurality of areas;
the data selection module is used for selecting a training set and a verification set from the measured data according to a preset distribution proportion;
the algorithm model establishing module is used for determining a characteristic item for a wireless network coverage rate evaluation model based on the data of the training set and establishing an algorithm model to be tested;
the evaluation model determining module is used for inputting the characteristic value corresponding to the characteristic item in the data of the verification set into the algorithm model to be tested to obtain the coverage rate of the wireless network to be tested, and if the difference value between the coverage rate of the wireless network to be tested and the coverage rate of the real wireless network meets the preset requirement, determining the algorithm model to be tested as the wireless network coverage rate evaluation model;
and the coverage rate obtaining module is used for inputting the characteristic value corresponding to the characteristic item of the area to be evaluated into the wireless network coverage rate evaluation model to obtain the wireless network coverage rate of the area to be evaluated.
According to another aspect of embodiments of the present invention, there is provided a computing device including: the system comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface complete mutual communication through the communication bus;
the memory is configured to store at least one executable instruction that causes the processor to perform the operations of the wireless network coverage assessment method as described above.
According to a further aspect of the embodiments of the present invention, there is provided a computer-readable storage medium, having at least one executable instruction stored therein, which when executed on a wireless network coverage evaluation device, causes the wireless network coverage evaluation device to perform the operations of the wireless network coverage evaluation method as described above.
The embodiment of the invention obtains the measured data of a plurality of areas related to the wireless network coverage rate, selects a training set and a verification set from the measured data according to a preset distribution proportion, further determines the characteristic items for the wireless network coverage rate evaluation model based on the data of the training set, establishes the algorithm model to be tested, further determines the wireless network coverage rate evaluation model by inputting the data of the verification set into the algorithm model to be tested, and obtains the wireless network coverage rate of the area to be evaluated according to the characteristic values corresponding to the characteristic items of the area to be evaluated through the wireless network coverage rate evaluation model. Therefore, the embodiment of the invention can carry out on-site drive test without manpower, reduces the evaluation cost of the wireless network coverage rate and has high efficiency.
The foregoing description is only an overview of the technical solutions of the embodiments of the present invention, and the embodiments of the present invention can be implemented according to the content of the description in order to make the technical means of the embodiments of the present invention more clearly understood, and the detailed description of the present invention is provided below in order to make the foregoing and other objects, features, and advantages of the embodiments of the present invention more clearly understandable.
Drawings
The drawings are only for purposes of illustrating embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
fig. 1 is a flowchart illustrating a wireless network coverage assessment method according to an embodiment of the present invention;
fig. 2 is a flowchart illustrating a wireless network coverage assessment method according to another embodiment of the present invention;
fig. 3 is a schematic structural diagram of a wireless network coverage assessment apparatus provided in an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a computing device provided in an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be embodied in various forms and should not be limited to the embodiments set forth herein.
Fig. 1 illustrates a flow chart of an embodiment of a wireless network coverage assessment method of the present invention, which is performed by a computing device. In embodiments of the present invention, the memory space of the computing device has stored therein executable instructions that may cause the processor to perform a wireless network coverage assessment method. As shown in fig. 1, the method comprises the steps of:
step 120: and acquiring measured data of a plurality of areas related to the coverage rate of the wireless network.
The multiple areas are areas subjected to wireless network coverage optimization. The wireless network optimization of the plurality of areas can be realized by adjusting the power, the azimuth angle of the antenna, newly building a station and the like. The wireless network may be a multi-band wireless network, such as an FDD900 band.
In an optional manner of the embodiment of the present invention, the actual measurement data is manual test data, which includes tentative test data of the plurality of areas before the optimization of the wireless network and retest data after the optimization of the wireless network. The empirical test data may include data affecting regional wireless network coverage prior to wireless network optimization and the resulting regional wireless network coverage. The retest data may include data affecting the regional wireless network coverage after wireless network optimization and the resulting regional wireless network coverage.
Step 140: and selecting a training set and a verification set from the measured data according to a preset distribution proportion.
The predetermined distribution ratio may be, for example, 80%/20%, 70%/30%, or other ratios. For example, 80% of the measured data may be selected for the training set and 20% may be selected for the validation set. Generally, training set data as much as possible can be selected, and the accuracy of the algorithm is improved.
Step 160: and determining a characteristic item for a wireless network coverage rate evaluation model based on the data of the training set, and establishing an algorithm model to be tested.
In an optional manner of the embodiment of the present invention, step 160 may further include the following steps:
step 161: and screening out a first characteristic item set influencing the wireless network coverage rate based on the training set.
Wherein a region may be divided into several regularly distributed grids. The feature items may include, for example, an area total coverage, a grid attribute (road or natural village), a covered grid number (RSRP > -110dBm & SINR > -3dB), a grid total number, an average RSRP (Reference Signal Receiving Power), an average SINR (Signal to Interference plus Noise Ratio), an RSRP > -110 sampling point number, an SINR > -3 sampling point number, a nearest site distance, an azimuth of a site at a grid center point, a 1Km raised Power cell number, a 1Km raised Power amplitude (sum), a 1Km raised Power amplitude (mean), a 2Km raised Power cell number, a 2Km raised Power amplitude (sum), a 2Km raised Power amplitude (mean), a RSRP > -105 sampling point number, an RSRP > -100 sampling point number, an RSRP > -90 sampling point number, a-90 sampling point number, SINR > is the number of sampling points of 0.
Step 162: and removing the characteristic items with the association degree lower than a preset threshold value with the wireless network coverage rate from the first characteristic item set according to a preset association degree analysis mode to obtain a second characteristic item set.
The preset association degree analysis method may be one or more of distribution analysis, box diagram analysis, joint probability density analysis, and the like. In one embodiment, before removing the feature items with the association degree with the wireless network coverage rate lower than a preset threshold from the first feature item set according to a preset association degree analysis manner, the abnormal data in the first feature item set may be removed first. The anomalous data in the first set of feature terms may be identified based on empirical analysis.
Step 163: and performing distribution conversion on the feature items in the second feature item set according to a preset conversion mode, and removing at least one feature item from the converted second feature item set, wherein the at least one feature item is associated with each other and is associated with the wireless network coverage rate, so as to obtain a third feature item set.
The preset conversion mode may be, for example, a box-cox technique, or a log1p (x) function formula. Before at least one of the feature items which are associated with each other and are each associated with the coverage of the wireless network is removed from the converted second feature item set, Correlation between the feature items and the coverage of the wireless network and between the feature items can be analyzed by using a Pearson Correlation Coefficient method (Pearson Correlation Coefficient) and a Spearman Correlation Coefficient method (Spearman's rank Correlation Coefficient) in statistics. The weight of the feature items under the linear model can be more accurate by removing at least one feature item from the converted second feature item set, wherein the at least one feature item is related to each other and is related to the wireless network coverage rate.
Step 164: and performing feature screening of a machine learning model from the third feature item set by using recursive feature elimination and cross validation to obtain a fourth feature item set, and determining the feature items in the fourth feature item set as feature items for a wireless network coverage rate evaluation model.
Wherein, Recursive feature elimination (Recursive feature elimination) is to select the best feature item by repeatedly constructing the model, and then continue to select the best feature item on the remaining feature items until all feature items are traversed. Cross Validation (Cross Validation) is a model obtained by dividing original data into training data and Validation data, then training a classifier through the training data, and testing and training the Validation data.
In an optional manner of the embodiment of the present invention, data processing may be performed on the feature items in the first feature item set, and then step 162 is performed. The data processing mode comprises the following steps: log operation, squaring operation and addition and subtraction polynomial operation among characteristic items. By carrying out data processing on the feature items in the first feature item set, the consistency and the robustness of the feature items in the first feature item set can be improved. The data processing may employ, for example, the following standardized processing formula:
The method comprises the steps of establishing an algorithm model to be tested according to data of a training set and determined characteristic items for a wireless network coverage rate evaluation model, and outputting the wireless network coverage rate of the area to be evaluated according to the characteristic items of the area to be evaluated by the algorithm model to be tested.
The algorithm model to be tested can be a machine learning algorithm model and can be selected according to the data scale, for example, in the embodiment of the invention, because the training set and the verification set have smaller data scale, one or more of a random forest regression tree algorithm, a gradient lifting regression tree algorithm and a ridge regression algorithm with better performance and accuracy under small data scale are selected. It is understood that the random forest regression tree algorithm, the gradient boosting regression tree algorithm, and the ridge regression algorithm are all common machine learning algorithms, and are not described herein again.
Step 180: and inputting the characteristic value corresponding to the characteristic item in the data of the verification set into the algorithm model to be tested to obtain the coverage rate of the wireless network to be tested, and determining the algorithm model to be tested as a wireless network coverage rate evaluation model if the difference value between the coverage rate of the wireless network to be tested and the coverage rate of the real wireless network meets the preset requirement.
The characteristic values corresponding to the characteristic items in the data of the verification set can be respectively input into the multiple machine learning algorithm models, and the coverage rate of the multiple wireless networks to be tested is obtained. And determining the algorithm model to be tested corresponding to the wireless network coverage rate to be tested which is closest to the real wireless network coverage rate as a wireless network coverage rate evaluation model.
Step 200: and inputting the characteristic value corresponding to the characteristic item of the area to be evaluated into the wireless network coverage rate evaluation model to obtain the wireless network coverage rate of the area to be evaluated.
The characteristic items of the area to be evaluated and the corresponding characteristic values thereof can be obtained according to the result of the background testing of the area to be evaluated. The characteristic values corresponding to the characteristic items of the area to be evaluated and the related parameters for wireless network optimization of the area to be evaluated can be input into the wireless network coverage rate evaluation model together, the wireless network coverage rate of the area to be evaluated output by the model is obtained, and further the actual effect of wireless network optimization of the area to be evaluated is analyzed.
In the embodiment of the invention, a training set and a verification set are selected from measured data by acquiring measured data of a plurality of areas related to wireless network coverage according to a preset distribution proportion, then a characteristic item for a wireless network coverage evaluation model is determined based on the data of the training set, an algorithm model to be tested is established, the data of the verification set is input into the algorithm model to be tested, the wireless network coverage evaluation model can be further determined, and the wireless network coverage of the area to be evaluated can be obtained through the wireless network coverage evaluation model according to a characteristic value corresponding to the characteristic item of the area to be evaluated. Therefore, the embodiment of the invention can carry out on-site drive test without manpower, reduces the evaluation cost of the wireless network coverage rate and has high efficiency.
Fig. 2 is a flow diagram illustrating another embodiment of a wireless network coverage assessment method of the present invention, as performed by a computing device. In embodiments of the present invention, the memory space of the computing device has stored therein executable instructions that may cause the processor to perform a wireless network coverage assessment method. As shown in fig. 2, the difference between this embodiment and the above embodiment is that before the step 180 inputs the feature values corresponding to the feature items in the data of the verification set into the algorithm model to be tested, and obtains the wireless network coverage to be tested, the wireless network coverage assessment method further includes:
step 171: and performing hyper-parameter tuning in the training set by using K-fold cross validation.
The K-fold cross validation may be, for example, ten-fold cross validation, the ten-fold cross validation divides the training set into ten parts, and takes 9 parts of data in the training set as training data and 1 part of data as test data in turn. Through K-fold cross validation, under-fitting and over-fitting of the algorithm model to be tested can be reduced, so that the hyper-parameters of the algorithm model to be tested are optimized.
Step 172: and optimizing the algorithm model to be tested by utilizing a grid search technology, a learning curve and a verification curve.
The grid search technology, the learning curve and the verification curve are all conventional means for optimizing an algorithm model in the field of machine learning, and are not described herein again. In an optional manner of the embodiment of the present invention, the optimizing the algorithm model to be tested may further include pre-pruning and post-pruning the random forest regression tree algorithm, pre-pruning and post-pruning the gradient boosting regression tree algorithm, regularizing the ridge regression algorithm, and optimizing the algorithm model to be tested using new retest data.
In the embodiment of the invention, the K-fold cross validation is used for carrying out hyper-parameter tuning in the training set, and the algorithm model to be tested is optimized by utilizing the grid search technology, the learning curve and the validation curve, so that the algorithm model to be tested can be more accurate, under-fitting and over-fitting are reduced, and the optimal algorithm model can be selected as the wireless network coverage rate evaluation model through actual test of the algorithm model to be tested.
Fig. 3 is a schematic structural diagram of an embodiment of the wireless network coverage assessment apparatus according to the present invention. As shown in fig. 3, the apparatus 300 includes: the measured data acquisition module 310, the data selection module 320, the algorithm model establishment module 330, the evaluation model determination module 340, and the coverage rate acquisition module 350.
An actual measurement data obtaining module 310, configured to obtain actual measurement data of multiple areas related to a coverage rate of a wireless network;
a data selecting module 320, configured to select a training set and a verification set from the measured data according to a preset distribution ratio;
an algorithm model establishing module 330, configured to determine, based on the data of the training set, a feature item for a wireless network coverage evaluation model, and establish an algorithm model to be tested;
the evaluation model determining module 340 is configured to input a feature value corresponding to the feature item in the data of the verification set into the algorithm model to be tested, so as to obtain a coverage rate of the wireless network to be tested, and determine the algorithm model to be tested as the wireless network coverage rate evaluation model if a difference between the coverage rate of the wireless network to be tested and a coverage rate of a real wireless network meets a preset requirement;
a coverage obtaining module 350, configured to input a feature value corresponding to the feature item in the area to be evaluated into the wireless network coverage evaluation model, so as to obtain a wireless network coverage of the area to be evaluated.
In an optional manner, the measured data obtained by the measured data obtaining module 310 includes background test data of the plurality of areas before the wireless network optimization and retest data after the wireless network optimization.
In an alternative manner, the algorithm model building module 330 is further configured to:
screening out a first characteristic item set influencing the wireless network coverage rate based on the training set;
removing the characteristic items with the association degree lower than a preset threshold value with the wireless network coverage rate from the first characteristic item set according to a preset association degree analysis mode to obtain a second characteristic item set;
performing distribution conversion on the feature items in the second feature item set according to a preset conversion mode, and removing at least one feature item from the converted second feature item set, wherein the at least one feature item is associated with each other and is associated with the wireless network coverage rate, so as to obtain a third feature item set;
and performing feature screening of a machine learning model from the third feature item set by using recursive feature elimination and cross validation to obtain a fourth feature item set, and determining the feature items in the fourth feature item set as feature items for a wireless network coverage rate evaluation model.
In an alternative manner, the algorithm model building module 330 is further configured to:
performing data processing on the feature items in the first feature item set, wherein the data processing mode comprises the following steps: log operation, squaring operation and addition and subtraction polynomial operation among characteristic items.
In an optional manner, the algorithm model to be tested established by the algorithm model establishing module 330 includes: one or more of a random forest regression tree algorithm, a gradient boosting regression tree algorithm, and a ridge regression algorithm.
In an optional manner, the apparatus 300 further includes a model optimization module 360 for performing hyper-parameter tuning using K-fold cross validation within the training set.
In an optional manner, the model optimization module 360 is further configured to optimize the algorithm model to be tested by using a grid search technique, a learning curve, and a verification curve.
In the embodiment of the invention, the measured data related to the wireless network coverage in a plurality of areas can be obtained through the measured data obtaining module, the data selecting module can select a training set and a verification set from the measured data according to a preset distribution proportion, the algorithm model establishing module can determine the characteristic items used for the wireless network coverage evaluation model based on the data of the training set and establish the algorithm model to be evaluated, the evaluation model determining module can determine the wireless network coverage evaluation model used for the wireless network coverage evaluation of the area to be evaluated, and the coverage obtaining module can input the characteristic values corresponding to the characteristic items of the area to be evaluated into the wireless network coverage evaluation model and obtain the wireless network coverage of the area to be evaluated. The wireless network coverage rate evaluation device can establish a wireless network coverage rate evaluation model and output the wireless network coverage rate of the area to be evaluated according to the established wireless network coverage rate evaluation model, and compared with manual field drive test, the wireless network coverage rate evaluation device can improve the evaluation efficiency of the wireless network coverage rate and reduce the evaluation cost.
Fig. 4 is a schematic structural diagram of an embodiment of a computing device according to the present invention, and the specific embodiment of the present invention does not limit the specific implementation of the computing device.
As shown in fig. 4, the computing device may include: a processor (processor)402, a Communications Interface 404, a memory 406, and a Communications bus 408.
Wherein: the processor 402, communication interface 404, and memory 406 communicate with each other via a communication bus 408. A communication interface 404 for communicating with network elements of other devices, such as clients or other servers. The processor 402 is configured to execute the program 410, and may specifically perform the relevant steps in the embodiment of the wireless network coverage evaluation method described above.
In particular, program 410 may include program code comprising computer-executable instructions.
The processor 402 may be a central processing unit CPU or an application Specific Integrated circuit asic or one or more Integrated circuits configured to implement embodiments of the present invention. The computing device includes one or more processors, which may be the same type of processor, such as one or more CPUs; or may be different types of processors such as one or more CPUs and one or more ASICs.
And a memory 406 for storing a program 410. Memory 406 may comprise high-speed RAM memory, and may also include non-volatile memory (non-volatile memory), such as at least one disk memory.
The program 410 may be specifically invoked by the processor 402 to cause the computing device to perform the following operations:
acquiring measured data related to wireless network coverage in a plurality of areas;
selecting a training set and a verification set from the measured data according to a preset distribution proportion;
determining a characteristic item for a wireless network coverage rate evaluation model based on the data of the training set, and establishing an algorithm model to be tested;
inputting the characteristic value corresponding to the characteristic item in the data of the verification set into the algorithm model to be tested to obtain the coverage rate of the wireless network to be tested, and determining the algorithm model to be tested as a wireless network coverage rate evaluation model if the difference value between the coverage rate of the wireless network to be tested and the coverage rate of the real wireless network meets the preset requirement;
and inputting the characteristic value corresponding to the characteristic item of the area to be evaluated into the wireless network coverage rate evaluation model to obtain the wireless network coverage rate of the area to be evaluated.
In an optional manner, the measured data includes tentative test data of the plurality of areas before wireless network optimization and retest data after wireless network optimization.
In an optional manner, the determining feature items for the wireless network coverage assessment model based on the data of the training set further comprises:
screening out a first characteristic item set influencing the wireless network coverage rate based on the training set;
removing the characteristic items with the association degree lower than a preset threshold value with the wireless network coverage rate from the first characteristic item set according to a preset association degree analysis mode to obtain a second characteristic item set;
performing distribution conversion on the feature items in the second feature item set according to a preset conversion mode, and removing at least one feature item from the converted second feature item set, wherein the at least one feature item is associated with each other and is associated with the wireless network coverage rate, so as to obtain a third feature item set;
and performing feature screening of a machine learning model from the third feature item set by using recursive feature elimination and cross validation to obtain a fourth feature item set, and determining the feature items in the fourth feature item set as feature items for a wireless network coverage rate evaluation model.
In an optional manner, after the selecting a training set and a verification set from the measured data according to a preset allocation ratio, the method further includes:
and performing hyper-parameter tuning in the training set by using K-fold cross validation.
In an alternative approach, the program 410 is invoked by the processor 402 to cause the computing device to:
performing data processing on the feature items in the first feature item set, wherein the data processing mode comprises the following steps: log operation, squaring operation and addition and subtraction polynomial operation among characteristic items.
In an optional manner, the algorithm model under test includes: one or more of a random forest regression tree algorithm, a gradient boosting regression tree algorithm, and a ridge regression algorithm.
In an alternative approach, the program 410 is invoked by the processor 402 to cause the computing device to:
and optimizing the algorithm model to be tested by utilizing a grid search technology, a learning curve and a verification curve before inputting the characteristic values corresponding to the characteristic items in the data of the verification set into the algorithm model to be tested.
In the embodiment of the present invention, a program on the computing device may be called by the processor to enable the computing device to execute the wireless network coverage assessment method in any of the above method embodiments, so that the network coverage of the area to be assessed may be automatically assessed, the accuracy of network coverage assessment is improved, and the cost of network coverage assessment is reduced.
An embodiment of the present invention provides a computer-readable storage medium, where the storage medium stores at least one executable instruction, and when the executable instruction is executed on a processor, the processor is enabled to execute the wireless network coverage assessment method in any of the above method embodiments.
The executable instructions may be specifically configured to cause the processor to:
acquiring measured data related to wireless network coverage in a plurality of areas;
selecting a training set and a verification set from the measured data according to a preset distribution proportion;
determining a characteristic item for a wireless network coverage rate evaluation model based on the data of the training set, and establishing an algorithm model to be tested;
inputting the characteristic value corresponding to the characteristic item in the data of the verification set into the algorithm model to be tested to obtain the coverage rate of the wireless network to be tested, and determining the algorithm model to be tested as a wireless network coverage rate evaluation model if the difference value between the coverage rate of the wireless network to be tested and the coverage rate of the real wireless network meets the preset requirement;
and inputting the characteristic value corresponding to the characteristic item of the area to be evaluated into the wireless network coverage rate evaluation model to obtain the wireless network coverage rate of the area to be evaluated.
In an optional manner, the measured data includes tentative test data of the plurality of areas before wireless network optimization and retest data after wireless network optimization.
In an optional manner, after the selecting a training set and a verification set from the measured data according to a preset allocation ratio, the method further includes:
and performing hyper-parameter tuning in the training set by using K-fold cross validation.
In an optional manner, the determining feature items for the wireless network coverage assessment model based on the data of the training set further comprises:
screening out a first characteristic item set influencing the wireless network coverage rate based on the training set;
removing the characteristic items with the association degree lower than a preset threshold value with the wireless network coverage rate from the first characteristic item set according to a preset association degree analysis mode to obtain a second characteristic item set;
performing distribution conversion on the feature items in the second feature item set according to a preset conversion mode, and removing at least one feature item from the converted second feature item set, wherein the at least one feature item is associated with each other and is associated with the wireless network coverage rate, so as to obtain a third feature item set;
and performing feature screening of a machine learning model from the third feature item set by using recursive feature elimination and cross validation to obtain a fourth feature item set, and determining the feature items in the fourth feature item set as feature items for a wireless network coverage rate evaluation model.
In an optional manner, the algorithm model under test includes: one or more of a random forest regression tree algorithm, a gradient boosting regression tree algorithm, and a ridge regression algorithm.
In an alternative form, the executable instructions cause the computing device to: performing data processing on the feature items in the first feature item set, wherein the data processing mode comprises the following steps: log operation, squaring operation and addition and subtraction polynomial operation among characteristic items.
In an alternative form, the executable instructions cause the computing device to:
and optimizing the algorithm model to be tested by utilizing a grid search technology, a learning curve and a verification curve before inputting the characteristic values corresponding to the characteristic items in the data of the verification set into the algorithm model to be tested.
In the embodiment of the present invention, at least one executable instruction is stored in a computer-readable storage medium, and when the executable instruction runs on a processor, the processor executes the wireless network coverage assessment method in any of the above method embodiments, so that the network coverage of an area to be assessed can be automatically assessed, the accuracy of network coverage assessment is improved, and the cost of network coverage assessment is reduced.
The algorithms or displays presented herein are not inherently related to any particular computer, virtual system, or other apparatus. Various general purpose systems may also be used with the teachings herein. The required structure for constructing such a system will be apparent from the description above. In addition, embodiments of the present invention are not directed to any particular programming language. It is appreciated that a variety of programming languages may be used to implement the teachings of the present invention as described herein, and any descriptions of specific languages are provided above to disclose the best mode of the invention.
In the description provided herein, numerous specific details are set forth. It is understood, however, that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the embodiments of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the invention and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: that the invention as claimed requires more features than are expressly recited in each claim.
Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names. The steps in the above embodiments should not be construed as limiting the order of execution unless specified otherwise.
Claims (10)
1. A wireless network coverage assessment method, the method comprising:
acquiring measured data related to wireless network coverage in a plurality of areas;
selecting a training set and a verification set from the measured data according to a preset distribution proportion;
determining a characteristic item for a wireless network coverage rate evaluation model based on the data of the training set, and establishing an algorithm model to be tested;
inputting the characteristic value corresponding to the characteristic item in the data of the verification set into the algorithm model to be tested to obtain the coverage rate of the wireless network to be tested, and determining the algorithm model to be tested as a wireless network coverage rate evaluation model if the difference value between the coverage rate of the wireless network to be tested and the coverage rate of the real wireless network meets the preset requirement;
and inputting the characteristic value corresponding to the characteristic item of the area to be evaluated into the wireless network coverage rate evaluation model to obtain the wireless network coverage rate of the area to be evaluated.
2. The method of claim 1, wherein the measured data comprises blinded test data of the plurality of regions before wireless network optimization and retest data after wireless network optimization.
3. The method according to claim 1 or 2, wherein after selecting a training set and a validation set from the measured data according to a preset allocation ratio, the method further comprises:
and performing hyper-parameter tuning in the training set by using K-fold cross validation.
4. The method of claim 1, wherein determining feature terms for a wireless network coverage assessment model based on the training set of data further comprises:
screening out a first characteristic item set influencing the wireless network coverage rate based on the training set;
removing the characteristic items with the association degree lower than a preset threshold value with the wireless network coverage rate from the first characteristic item set according to a preset association degree analysis mode to obtain a second characteristic item set;
performing distribution conversion on the feature items in the second feature item set according to a preset conversion mode, and removing at least one feature item from the converted second feature item set, wherein the at least one feature item is associated with each other and is associated with the wireless network coverage rate, so as to obtain a third feature item set;
and performing feature screening of a machine learning model from the third feature item set by using recursive feature elimination and cross validation to obtain a fourth feature item set, and determining the feature items in the fourth feature item set as feature items for a wireless network coverage rate evaluation model.
5. The method of claim 4, further comprising:
performing data processing on the feature items in the first feature item set, wherein the data processing mode comprises the following steps: log operation, squaring operation and addition and subtraction polynomial operation among characteristic items.
6. The method of claim 1, wherein the algorithm model under test comprises: one or more of a random forest regression tree algorithm, a gradient boosting regression tree algorithm, and a ridge regression algorithm.
7. The method according to claim 1, wherein before inputting the feature values corresponding to the feature items in the data of the verification set into the algorithm model to be tested, the method further comprises:
and optimizing the algorithm model to be tested by utilizing a grid search technology, a learning curve and a verification curve.
8. An apparatus for wireless network coverage assessment, the apparatus comprising:
the system comprises an actual measurement data acquisition module, a wireless network coverage rate acquisition module and a data processing module, wherein the actual measurement data acquisition module is used for acquiring actual measurement data related to the wireless network coverage rates of a plurality of areas;
the data selection module is used for selecting a training set and a verification set from the measured data according to a preset distribution proportion;
the algorithm model establishing module is used for determining a characteristic item for a wireless network coverage rate evaluation model based on the data of the training set and establishing an algorithm model to be tested;
the evaluation model determining module is used for inputting the characteristic value corresponding to the characteristic item in the data of the verification set into the algorithm model to be tested to obtain the coverage rate of the wireless network to be tested, and if the difference value between the coverage rate of the wireless network to be tested and the coverage rate of the real wireless network meets the preset requirement, determining the algorithm model to be tested as the wireless network coverage rate evaluation model;
and the coverage rate obtaining module is used for inputting the characteristic value corresponding to the characteristic item of the area to be evaluated into the wireless network coverage rate evaluation model to obtain the wireless network coverage rate of the area to be evaluated.
9. A computing device, comprising: the system comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface complete mutual communication through the communication bus;
the memory is configured to store at least one executable instruction that causes the processor to perform the operations of the wireless network coverage assessment method according to any one of claims 1-7.
10. A computer storage medium having stored therein at least one executable instruction that, when run on a wireless network coverage assessment apparatus, causes the wireless network coverage assessment apparatus to perform the operations of the wireless network coverage assessment method according to any one of claims 1-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011032719.5A CN114339855B (en) | 2020-09-27 | 2020-09-27 | Wireless network coverage rate evaluation method and device and computing equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011032719.5A CN114339855B (en) | 2020-09-27 | 2020-09-27 | Wireless network coverage rate evaluation method and device and computing equipment |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114339855A true CN114339855A (en) | 2022-04-12 |
| CN114339855B CN114339855B (en) | 2023-08-15 |
Family
ID=81011728
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011032719.5A Active CN114339855B (en) | 2020-09-27 | 2020-09-27 | Wireless network coverage rate evaluation method and device and computing equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114339855B (en) |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170061655A1 (en) * | 2012-06-05 | 2017-03-02 | Apple Inc. | System and Method for Generating Signal Coverage Information from Client Metrics |
| US20180184300A1 (en) * | 2015-10-23 | 2018-06-28 | Telefonaktiebolaget Lm Ericsson (Publ) | Cell operation in a wireless communications network |
| CN109495898A (en) * | 2017-09-12 | 2019-03-19 | 中国移动通信集团设计院有限公司 | A kind of the index quantification prediction technique and equipment of wireless network covering |
| CN109587721A (en) * | 2017-09-28 | 2019-04-05 | 中国移动通信集团浙江有限公司 | A kind of subzone network coverage evaluating method and device |
| CN110418354A (en) * | 2019-08-06 | 2019-11-05 | 北京邮电大学 | It is a kind of that propagation model wireless network planning method is exempted from based on machine learning |
| US10701566B1 (en) * | 2019-06-28 | 2020-06-30 | T-Mobile Usa, Inc. | Multidimensional analysis and network response |
-
2020
- 2020-09-27 CN CN202011032719.5A patent/CN114339855B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170061655A1 (en) * | 2012-06-05 | 2017-03-02 | Apple Inc. | System and Method for Generating Signal Coverage Information from Client Metrics |
| US20180184300A1 (en) * | 2015-10-23 | 2018-06-28 | Telefonaktiebolaget Lm Ericsson (Publ) | Cell operation in a wireless communications network |
| CN109495898A (en) * | 2017-09-12 | 2019-03-19 | 中国移动通信集团设计院有限公司 | A kind of the index quantification prediction technique and equipment of wireless network covering |
| CN109587721A (en) * | 2017-09-28 | 2019-04-05 | 中国移动通信集团浙江有限公司 | A kind of subzone network coverage evaluating method and device |
| US10701566B1 (en) * | 2019-06-28 | 2020-06-30 | T-Mobile Usa, Inc. | Multidimensional analysis and network response |
| CN110418354A (en) * | 2019-08-06 | 2019-11-05 | 北京邮电大学 | It is a kind of that propagation model wireless network planning method is exempted from based on machine learning |
Non-Patent Citations (1)
| Title |
|---|
| 吕非彼 等: "图像识别技术在5G网络覆盖率评估中的应用探讨", 《邮电设计技术》 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114339855B (en) | 2023-08-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US10606862B2 (en) | Method and apparatus for data processing in data modeling | |
| CN109886388A (en) | A kind of training sample data extending method and device based on variation self-encoding encoder | |
| CN112566196B (en) | Heterogeneous network access selection method based on smart grid and related equipment | |
| CN102938071B (en) | Fuzzy clustering analysis method for detecting synthetic aperture radar (SAR) image changes based on non-local means | |
| CN113329437B (en) | Wireless network signal propagation path loss prediction method and electronic equipment | |
| CN103308463A (en) | Characteristic spectrum area selection method for near infrared spectrum | |
| CN108375363B (en) | Antenna azimuth deflection checking method, device, equipment and medium | |
| CN106612511B (en) | Wireless network throughput evaluation method and device based on support vector machine | |
| CN112243249A (en) | LTE new access anchor point cell parameter configuration method and device under 5G NSA networking | |
| JP6696859B2 (en) | Quality estimation device and quality estimation method | |
| CN116223962B (en) | Method, device, equipment and medium for predicting electromagnetic compatibility of wire harness | |
| CN111178633A (en) | Method and device for predicting scenic spot passenger flow based on random forest algorithm | |
| CN102487516B (en) | Method and device for performing automatic plot planning optimization by utilizing drive test data | |
| CN113420722B (en) | Emergency linkage method and system for airport security management platform | |
| CN113569345A (en) | Numerical control system reliability modeling method and device based on multi-source information fusion | |
| CN110913407A (en) | Method and device for analyzing overlapping coverage | |
| CN114339855B (en) | Wireless network coverage rate evaluation method and device and computing equipment | |
| Xue et al. | A novel intelligent antenna synthesis system using hybrid machine learning algorithms | |
| CN113890833B (en) | Network coverage prediction method, device, equipment and storage medium | |
| Nagao et al. | Geographical clustering of path loss modeling for wireless emulation in various environments | |
| CN116706992A (en) | Self-adaptive power prediction method, device and equipment for distributed photovoltaic cluster | |
| CN116719714A (en) | Training method and corresponding device for screening model of test case | |
| CN115278706B (en) | Network structure evaluation method, device, equipment and computer storage medium | |
| CN110489797B (en) | Electromagnetic environment identification method suitable for external radiation source radar | |
| CN114157374A (en) | Method and device for predicting cell wireless signal strength and computer readable storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |