CN108243435B - Parameter optimization method and device in LTE cell scene division - Google Patents
Parameter optimization method and device in LTE cell scene division Download PDFInfo
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- CN108243435B CN108243435B CN201611215983.6A CN201611215983A CN108243435B CN 108243435 B CN108243435 B CN 108243435B CN 201611215983 A CN201611215983 A CN 201611215983A CN 108243435 B CN108243435 B CN 108243435B
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Abstract
The embodiment of the invention discloses a parameter optimization method and a device in LTE cell scene division, wherein the method comprises the following steps: screening physical parameters of an LTE cell, and carrying out correlation analysis on the physical parameters to obtain a first dimension parameter; acquiring a second dimension parameter of the check cell; and obtaining optimized recommended parameter configuration according to the first dimension parameter and the second dimension parameter. The device comprises: the device comprises a correlation analysis module, a second dimension parameter acquisition module and a parameter optimization module. According to the embodiment of the invention, the first dimension parameter is obtained by screening and correlation analysis of the physical parameters of the LTE cell, the optimized recommended parameter configuration is obtained by combining the second dimension parameter of the verification cell, the method is not limited to the geographical scene of the coverage area and the user type, the optimization accuracy and efficiency can be improved by adopting an automatic classification mode, excellent optimization experience can be shared, and the requirements on optimization engineers are reduced.
Description
Technical Field
The embodiment of the invention relates to the technical field of communication, in particular to a parameter optimization method and device in LTE cell scene division.
Background
LTE (Long Term Evolution) cell optimization mainly relates to the optimization setting of parameters in a system and between systems in a planning stage and a subsequent optimization stage, the planning stage plans initial cell station height, dip angle, frequency band and various soft and hard parameter configurations according to the wireless environment, road and MR deep coverage requirements, coverage scene types and capacity requirements nearby a cell, and the subsequent engineering coverage optimizes cell switching, access and wireless resource scheduling configuration parameters according to road test analysis, speech system and KPI (Key Performance Indicator) analysis, MR (Measurement Report) analysis and the like. With the advance of large-scale construction of LTE for two years, LTE network users and traffic volume increase rapidly at present, the network scale and the number of base stations are basically beyond 2/3G networks, the construction of LTE fine networks provides higher requirements for site refinement and optimization, the difficulty of network optimization is increased, and the challenge is increased.
At present, different parameter configuration strategies are mainly formulated according to different scenes of sites in the traditional network parameter optimization, and the scene classification is mainly divided into tens of scenes such as dense urban areas, town centers, general urban areas, suburban rural areas and the like according to wireless environment and geographic features, and is divided into tens of scenes such as residential areas, business buildings, universities, traffic hubs and the like according to the use of covered areas and land areas and user types. Then, under the traditional site scene classification, after different network planning optimization strategies and parameter configuration strategies are formulated, optimization work is respectively carried out from drive test data, MR, session and KPI analysis. For the conditions that the current network scale is large, 2/3/4G networks jointly cover, the urban wireless environment is complex, and the requirement of users on 4G services is increasing, the traditional scene-based optimization can not meet the requirements of LTE fine network refinement and high-efficiency optimization gradually, so that research is necessary for an LTE cell feature classification parameter automatic optimization method based on a clustering algorithm for a TDL network.
In the process of implementing the embodiment of the invention, the inventor finds that the cell characteristics of the existing network in the existing method are limited to the geographical scene of the coverage area and the user type, the classification is not accurate enough by adopting a manual classification mode, a unified parameter strategy is configured for all the cells classified into the same type when the optimization is implemented, then the cell-level personalized parameter optimization is performed, the optimization is divided into two stages of the area and a single station, although the fine optimization can be achieved, the whole optimization period is longer, and the optimization experience requirement on an optimization engineer is higher.
Disclosure of Invention
The embodiment of the invention provides a parameter optimization method and device in LTE cell scene division, and solves the problems that in the existing method, the cell features are limited to geographical scenes and user types of coverage areas, the manual classification mode is adopted, the classification is not accurate enough, the whole optimization period is long, and the requirement on optimization experience of optimization engineers is high.
In a first aspect, an embodiment of the present invention provides a method for optimizing parameters in LTE cell scene division, including:
screening physical parameters of an LTE cell, and carrying out correlation analysis on the physical parameters to obtain a first dimension parameter;
acquiring a second dimension parameter of the check cell;
and obtaining optimized recommended parameter configuration according to the first dimension parameter and the second dimension parameter.
Optionally, the performing correlation analysis on the physical parameter to obtain a first dimension parameter specifically includes:
and performing correlation analysis on the physical parameters to obtain correlation analysis results, screening the correlation analysis results according to a correlation threshold value, and performing normalization processing on the screened physical parameters to obtain first dimension parameters.
Optionally, the obtaining the optimized recommended parameter configuration according to the first dimension parameter and the second dimension parameter specifically includes:
calculating the Euclidean distance between the first dimension parameter and the second dimension parameter according to a K-means clustering algorithm;
screening according to a distance threshold to obtain a target dimension parameter, and acquiring a target verification cell corresponding to the target dimension parameter;
and comparing the set values of the target dimension parameters of the target verification cells, and selecting the target dimension parameter corresponding to the maximum set value as the optimized recommended parameter configuration.
Optionally, the first dimension parameter and the second dimension parameter each include: the antenna hanging height of the cell, the total downward inclination angle of the cell, the average value of the reference signal receiving power of the adjacent cell, the RRC connection request times of the adjacent cell, the average single-user switching times of the adjacent cell, the downlink average MCS level of the adjacent cell and the downlink average TM3/8 ratio of the adjacent cell.
Optionally, the method further comprises:
comparing the recommended parameter configuration with the original parameter configuration, and determining whether to issue the recommended parameter configuration according to a comparison result;
and performing performance evaluation on the verification cell which is configured and operated by using the recommended parameters, and if the performance evaluation result meets a preset evaluation threshold value, determining that the recommended parameters are configured to be target parameter configuration.
In a second aspect, an embodiment of the present invention further provides a device for optimizing parameters in LTE cell scene division, where the device includes:
the correlation analysis module is used for screening physical parameters of the LTE cell and performing correlation analysis on the physical parameters to obtain a first dimension parameter;
the dimension parameter acquisition module is used for acquiring a second dimension parameter of the check cell;
and the parameter optimization module is used for obtaining optimized recommended parameter configuration according to the first dimension parameter and the second dimension parameter.
Optionally, the correlation analysis module is specifically configured to perform correlation analysis on the physical parameters to obtain correlation analysis results, screen the correlation analysis results according to a correlation threshold, and perform normalization processing on the screened physical parameters to obtain the first dimension parameters.
Optionally, the parameter optimization module specifically includes:
the distance calculation unit is used for calculating the Euclidean distance between the first dimension parameter and the second dimension parameter according to a K-means clustering algorithm;
the parameter screening unit is used for screening according to the distance threshold to obtain a target dimension parameter and acquiring a target verification cell corresponding to the target dimension parameter;
and the parameter comparison unit is used for comparing the set values of the target dimension parameters of the target verification cells, and selecting the target dimension parameter corresponding to the maximum set value as the optimized recommended parameter configuration.
Optionally, the first dimension parameter and the second dimension parameter in the correlation analysis module each include: the antenna hanging height of the cell, the total downward inclination angle of the cell, the average value of the reference signal receiving power of the adjacent cell, the RRC connection request times of the adjacent cell, the average single-user switching times of the adjacent cell, the downlink average MCS level of the adjacent cell and the downlink average TM3/8 ratio of the adjacent cell.
Optionally, the method further comprises:
the result auditing unit is used for comparing the recommended parameter configuration with the original parameter configuration and determining to issue the recommended parameter configuration according to the comparison result;
and the performance evaluation unit is used for evaluating the performance of the check cell which is configured and operated by using the recommended parameters, and if the performance evaluation result meets a preset evaluation threshold value, determining that the recommended parameters are configured as target parameters.
According to the technical scheme, the physical parameters of the LTE cell are screened and subjected to correlation analysis to obtain the first dimension parameter, the second dimension parameter of the verification cell is combined to obtain the optimized recommended parameter configuration, the optimized recommended parameter configuration is not limited to the geographical scene of the coverage area and the user type, the automatic classification mode is adopted, the optimization accuracy and efficiency can be improved, excellent optimization experience can be shared, and the requirements on optimization engineers are reduced.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a schematic flowchart of a parameter optimization method in LTE cell scene division according to an embodiment of the present invention;
fig. 2 is a data flow diagram of a parameter optimization method in LTE cell scene division according to an embodiment of the present invention;
fig. 3 is a schematic flowchart of parameter optimization verification in LTE cell scene division according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a parameter optimization apparatus in LTE cell scene division according to an embodiment of the present invention;
FIG. 5 is a logic block diagram of an electronic device in one embodiment of the invention.
Detailed Description
The following further describes embodiments of the present invention with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
Fig. 1 shows a flowchart of a method for optimizing parameters in LTE cell scene division provided in this embodiment, where the method includes:
s101, screening physical parameters of the LTE cell, and carrying out correlation analysis on the physical parameters to obtain a first dimension parameter.
The correlation analysis refers to analyzing two or more variable elements with correlation, so as to measure the degree of closeness of correlation of the two variable elements.
The performing correlation analysis on the physical parameters to obtain a first dimension parameter specifically includes:
and performing correlation analysis on the physical parameters to obtain correlation analysis results, screening the correlation analysis results according to a correlation threshold value, and performing normalization processing on the screened physical parameters to obtain first dimension parameters.
Specifically, fig. 2 shows a data flow diagram of a parameter optimization method in LTE cell scenario division according to an embodiment, and first, excellent parameter configuration verification and collection are achieved based on correlation analysis.
And (3) acquiring the quantitative characteristics of the cell (selecting the wireless parameter data of the LTE cell and seven-dimensional data of nearly 3 days) from an LTE cell engineering parameter database, a background speech system or a planning database (for a new station) of the current network.
The parameter screening needs to select cell physical parameters, namely independent variables which can be output before the cell is started to access the network, and the existing network new access sites have the following types of parameters which can be extracted and have analysis values: the method comprises the following steps of high hanging of an antenna of the cell, total downward inclination of the cell, average value of reference signal receiving power of a neighbor cell, times of RRC connection request of the neighbor cell, average single-user switching times of the neighbor cell, downlink average MCS level of the neighbor cell, downlink average TM3/8 ratio of the neighbor cell, MR coverage rate of the neighbor cell, average number of users in the neighbor cell and downlink PRB utilization rate of the neighbor cell.
And (5) carrying out correlation analysis on the parameters of 10 dimensions. The strong correlation among the parameter dimensions causes data clustering distortion and needs to be eliminated.
The 3 parameters have strong correlation (the correlation between the average value of the reference signal received power and the MR coverage rate is 0.87, the correlation between the times of the RRC connection request and the average number of users in the cell is 0.93, and the correlation between the times of the RRC connection request and the PRB utilization rate is 0.91), so that the MR coverage rate, the average number of users in the cell and the data of the downlink PRB utilization rate are removed, and only 7 dimensions are reserved.
Since the weights of the 7 dimensions are not completely consistent, the influence degree of some dimensions is larger under the condition of not processing data. Therefore, normalization needs to be performed on 7 parameter dimensions, so that the weights of the parameter dimensions are consistent when clustering analysis is performed subsequently.
Wherein the Normalization method is Min-Max Normalization (Min-Max Normalization). This method, also known as dispersion normalization, is a linear transformation of the raw data, with the resulting values mapped between [0-1 ]. The transfer function is as follows:
and S102, acquiring a second dimension parameter of the check cell.
The verification cell is a cell outside the LTE cell and is used for verifying the LTE cell.
The first dimension parameter and the second dimension parameter are the same parameter and correspond to different parameter values.
Specifically, as shown in the lower left box of fig. 2, the current network is first selected to check the non-default parameter configuration of the cell.
Cell dimension parameters (the first dimension parameter and the second dimension parameter) data template samples (54 items in total):
and then inputting the second dimension parameter configuration of the check cell into the system.
S103, obtaining optimized recommended parameter configuration according to the first dimension parameter and the second dimension parameter.
Further, S103 specifically includes:
s1031, calculating Euclidean distances between the first dimension parameters and the second dimension parameters according to a K-means clustering algorithm;
s1032, screening according to the distance threshold to obtain a target dimension parameter, and acquiring a target verification cell corresponding to the target dimension parameter;
s1033, comparing the setting values of the target dimension parameters of the target verification cells, and selecting the target dimension parameter corresponding to the maximum setting value as the optimized recommended parameter configuration.
Specifically, as shown in the lower middle block diagram in fig. 2, the cell matching parameters are configured by a clustering algorithm and recommended parameter configuration is output.
Firstly, the quantitative characteristics of the new station cell of the current network are extracted and input into the system.
Then, clustering is carried out according to the quantitative characteristics of the cells, and recommended parameter configuration is matched for the cells.
The method comprises the following steps of adopting a K-means clustering algorithm, calculating the Euclidean distance between a cell and a verification cell series to obtain recommended parameter configuration, wherein the specific algorithm is described as follows:
a1, calculating Euclidean distances between a new station cell and 7 dimension quantization features of each check cell sequence;
a2, selecting n check cells closest to the Euclidean distance of the new station cell, and outputting a list; the default value of n is 100, and optimization can be realized based on parameter output effect in application;
a3, counting and comparing 54 same setting values of dimension parameters in the selected n verification cells, and counting 54 dimension parameter item parameter values of 100 cells output by default respectively;
and A4, selecting a parameter dimension setting value corresponding to the maximum count value of the 54 dimension parameters as a new station cell optimization setting value to be output.
In the embodiment, the target parameters are obtained by screening and correlation analysis of physical parameters of the LTE cell, the optimized recommended parameter configuration is obtained by combining dimension parameters of the verification cell, the method is not limited to geographical scenes and user types of coverage areas, an automatic classification mode is adopted, the optimization accuracy and efficiency can be improved, excellent optimization experience can be shared, and requirements on optimization engineers are reduced.
Further, on the basis of the above embodiment of the method, the method further comprises:
s104, comparing the recommended parameter configuration with the original parameter configuration, and determining whether to issue the recommended parameter configuration according to a comparison result;
and S105, performing performance evaluation on the verification cell which is configured and operated by using the recommended parameters, and if the performance evaluation result meets a preset evaluation threshold, determining that the recommended parameters are configured to be target parameter configuration.
The target parameter configuration may be imported into the original factory parameter compilation tool, and the original configuration comparison difference may be used to determine whether to issue an OMC implementation.
Specifically, firstly, the difference between the current network parameters and the recommended configuration parameters of each cell is compared, parameters with different recommended values and current network configured values are found preliminarily and checked according to actual conditions, the checking is issued without problems, and if part of the parameters pass, only the passing part of the parameters can be executed.
And then, after parameter configuration is issued, continuously evaluating the performance, coverage and perception conditions of the optimized cell and the surrounding cells. After the performance evaluation is carried out, under the condition that the ideal effect is not achieved, the value of the cell selection number n is adjusted, and C2 is carried out again until the optimization is carried out to obtain a solution closed loop.
In the embodiment, the LTE cells are finely divided through a clustering algorithm, and the optimization efficiency of the LTE network is improved through a method of outputting recommended parameters through automatic optimization software, so that the optimization accuracy is improved, and meanwhile, the optimization action has the minimum influence on the peripheral area. Two sets of model software are applied, and automatic matching output optimization parameter software is respectively used for a current network cell verification database and cell characteristics, and the current network cell verification database inputs quantization characteristics and dimension parameter settings of a current network cell and is used for matching a new station cell based on Euclidean distance; and the cell feature automatic matching output optimization parameter software realizes clustering based on the Euclidean distance and outputs recommended parameter configuration for an optimization engineer to refer to.
The embodiment also realizes verification and collection of excellent parameter configuration based on correlation analysis, inputs dimension parameters (second dimension parameters) of the current network check cell, configures cell matching parameters through a clustering algorithm and outputs recommended parameter configuration, completes optimization and evaluation work and perfects excellent cell configuration in real time. The network optimization work efficiency is effectively improved, the network optimization parameter optimization fineness is improved, and the network operation and maintenance cost is reduced.
The existing network technology mainly adopts a mode of subjectively dividing scenes and carrying out targeted parameter optimization, has great dependence on the experience of engineers, and is simultaneously influenced by the performance difference of equipment of different manufacturers with the same parameters and the private parameters of different manufacturers. The method carries out fine division on the cells through the cell quantization characteristics and the clustering algorithm, and directly matches the parameter setting of the cells of the current network, so that excellent optimization parameters are popularized more quickly and accurately.
And carrying out fine division on the cells through cell quantization characteristics and a clustering algorithm. The traditional method for dividing the user group and the coverage scene subjectively is changed, and quantifiable characteristics such as the cell network structure, the traffic volume, the user characteristics and the like are combined to divide the scene objectively and accurately. The follow-up optimization accuracy is improved, the negative influence of parameter adjustment on the whole regional network is reduced, and the optimization action of dismantling the east wall and supplementing the west wall is avoided. The method is simple to operate, has wide coverage parameters, and can be quickly realized according to the requirements of engineers. The relevant cell parameter configuration is optimized and updated along with the LTE network construction, and the use is more mature. The network structure does not need to be changed, the operation is simple, and the network risk does not exist. Only the relevant data is exported and analyzed by software, and the parameters of the relevant cells are modified based on the analysis result.
The following describes a parameter optimization method in LTE cell scenario division by using specific examples, see fig. 3:
b1, extracting and normalizing the quantization characteristics of the current network check cell 7, and inputting the quantization characteristics and the dimension parameters related to the cell into the software system.
And B2, selecting 15 cells to be tested, wherein the devices are in a network access state and a non-engineering state, and the cells are newly built after the cluster optimization stage is completed or is in progress. And (3) extracting the quantitative characteristics of the cells, inputting the quantitative characteristics into a software system, and giving a recommended dimension parameter configuration scheme for each cell by software through a K-means clustering algorithm under default value configuration. And comparing the difference between the current network parameters of each cell and the parameters of the recommended configuration, preliminarily finding out parameters with different recommended values from the current network configuration, submitting the parameters to review by a regional optimization engineer, and executing parameter modification operation after the final parameter scheme is confirmed by the review.
B3, confirming that the site has no fault, and making the original parameter backup.
And B4, inputting the parameter scheme into a factory parameter script compiling tool, and issuing parameter modification through a factory OMC network element.
B5, after the parameter modification, continuously monitoring the cell performance index for at least 3 days, and outputting the network management performance statistical index. And respectively carrying out optimization operation aiming at the adjustment of the value n of the cell selection number to be 50, 80, 120 and 150 to obtain the optimization effect under the configuration of each value n.
The traffic under each configuration basically keeps a steady state, and from the evaluation of verification effect, the value of N is respectively 100>50>80>120>150 from good to bad.
From the comparison of various KPI indexes, the value of N is respectively 50-100 >120>80>150 from good to bad.
Based on the verification result of the N-value optimization, when the N value takes the default value of 100, the effect is relatively optimal. And subsequently, reserving the application with N being 100 in daily optimization application. The whole optimization process is closed loop.
Therefore, the method provided by the embodiment effectively enables the LTE cell parameter optimization to be more accurate and efficient. The LTE cells are finely divided by using a clustering algorithm, and compared with the traditional subjective scene division, the method is more quantitative and more accurate, and the accuracy of subsequent optimization is improved. The method has the advantages that the cell parameter configuration with excellent local performance of the existing network is directly verified and collected, the problems that wireless equipment manufacturers are multiple, private parameters are multiple, and parameters are limited in optimization and attention parameters at ordinary times are solved, and meanwhile popularization and application of excellent parameter samples are rapidly achieved. The network structure does not need to be changed, the operation is simple, and the network risk does not exist. Only relevant data are exported and analyzed by software, and relevant cells are modified based on the analysis result and the experience of an optimization engineer. In conclusion, under the method, the efficiency and the effect of optimizing the LTE network in the area can be accurately, flexibly and effectively improved, the risk in the whole process is small, the cost is low, the operation and maintenance cost is effectively reduced, the network optimization efficiency is improved, and the economic benefit of a company is improved.
The method can effectively improve the degree and efficiency of planning and optimization refinement, reduce the optimization period of operators, and rapidly popularize and implement the excellent optimization example of sharing, and can avoid the difference of the performance of equipment of different manufacturers under the same parameter configuration under the condition that the wireless side equipment of the existing network has a plurality of manufacturers and the private parameters of the manufacturers are numerous, and various wireless environment scenes of the existing network are complex, thereby providing the method for finely distinguishing the setting of the cell parameters through the clustering algorithm under the complex scenes. The method automatically identifies the cell scene matching recommendation parameters by importing the current network data and based on a clustering algorithm, and can directly issue the parameters on the OMC.
Fig. 4 shows a schematic structural diagram of a parameter optimization apparatus in LTE cell scene division provided in this embodiment, where the apparatus includes: a correlation analysis module 401, a dimension parameter acquisition module 402, and a parameter optimization module 403, wherein:
the correlation analysis module 401 is configured to screen physical parameters of an LTE cell, and perform correlation analysis on the physical parameters to obtain a first dimension parameter;
the second dimension parameter obtaining module 402 is configured to obtain a second dimension parameter of the verification cell;
the parameter optimization module 403 is configured to obtain an optimized recommended parameter configuration according to the first dimension parameter and the second dimension parameter.
Specifically, the correlation analysis module 401 filters physical parameters of the LTE cell, and performs correlation analysis on the physical parameters to obtain a first dimension parameter; the second dimension parameter obtaining module 402 obtains a second dimension parameter of the verification cell; the parameter optimization module 403 obtains an optimized recommended parameter configuration according to the first dimension parameter and the second dimension parameter.
In the embodiment, the first dimension parameter is obtained by screening and analyzing the physical parameters of the LTE cell, the optimized recommended parameter configuration is obtained by combining the second dimension parameter of the verification cell, the configuration is not limited to the geographical scene of the coverage area and the user type, an automatic classification mode is adopted, the optimization accuracy and efficiency can be improved, excellent optimization experience can be shared, and the requirements on optimization engineers are reduced.
Further, on the basis of the above device embodiment, the correlation analysis module 401 is specifically configured to perform correlation analysis on the physical parameters to obtain correlation analysis results, screen the correlation analysis results according to a correlation threshold, and perform normalization processing on the screened physical parameters to obtain the first dimension parameters.
Further, on the basis of the above device embodiment, the parameter optimization module 403 specifically includes:
the distance calculation unit is used for calculating the Euclidean distance between the first dimension parameter and the second dimension parameter according to a K-means clustering algorithm;
the parameter screening unit is used for screening according to the distance threshold to obtain a target dimension parameter and acquiring a target verification cell corresponding to the target dimension parameter;
and the parameter comparison unit is used for comparing the set values of the target dimension parameters of the target verification cells, and selecting the target dimension parameter corresponding to the maximum set value as the optimized recommended parameter configuration.
Further, on the basis of the above apparatus embodiment, the first dimension parameter and the second dimension parameter in the correlation analysis module each include: the antenna hanging height of the cell, the total downward inclination angle of the cell, the average value of the reference signal receiving power of the adjacent cell, the RRC connection request times of the adjacent cell, the average single-user switching times of the adjacent cell, the downlink average MCS level of the adjacent cell and the downlink average TM3/8 ratio of the adjacent cell.
Further, on the basis of the above apparatus embodiment, the method further includes:
the result auditing unit is used for comparing the recommended parameter configuration with the original parameter configuration and determining to issue the recommended parameter configuration according to the comparison result;
and the performance evaluation unit is used for evaluating the performance of the check cell which is configured and operated by using the recommended parameters, and if the performance evaluation result meets a preset evaluation threshold value, determining that the recommended parameters are configured as target parameters.
The parameter optimization device in the LTE cell scene division according to this embodiment may be configured to execute the method embodiments, and the principle and the technical effect are similar, which are not described herein again.
Referring to fig. 5, the electronic device includes: a processor (processor)501, a memory (memory)502, and a bus 503;
wherein the content of the first and second substances,
the processor 501 and the memory 502 complete mutual communication through the bus 503;
the processor 501 is configured to call program instructions in the memory 502 to perform the methods provided by the above-mentioned method embodiments, for example, including:
screening physical parameters of an LTE cell, and carrying out correlation analysis on the physical parameters to obtain a first dimension parameter;
acquiring a second dimension parameter of the check cell;
and obtaining optimized recommended parameter configuration according to the first dimension parameter and the second dimension parameter.
The present embodiment discloses a computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, enable the computer to perform the method provided by the above-mentioned method embodiments, for example, comprising:
screening physical parameters of an LTE cell, and carrying out correlation analysis on the physical parameters to obtain a first dimension parameter;
acquiring a second dimension parameter of the check cell;
and obtaining optimized recommended parameter configuration according to the first dimension parameter and the second dimension parameter.
The present embodiments provide a non-transitory computer-readable storage medium storing computer instructions that cause the computer to perform the methods provided by the above method embodiments, for example, including:
screening physical parameters of an LTE cell, and carrying out correlation analysis on the physical parameters to obtain a first dimension parameter;
acquiring a second dimension parameter of the check cell;
and obtaining optimized recommended parameter configuration according to the first dimension parameter and the second dimension parameter.
Those of ordinary skill in the art will understand that: all or part of the steps for implementing the method embodiments may be implemented by hardware related to program instructions, and the program may be stored in a computer readable storage medium, and when executed, the program performs the steps including the method embodiments; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
It should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (6)
1. A parameter optimization method in LTE cell scene division is characterized by comprising the following steps:
screening physical parameters of an LTE cell, and carrying out correlation analysis on the physical parameters to obtain a first dimension parameter;
acquiring a second dimension parameter of the check cell;
obtaining optimized recommended parameter configuration according to the first dimension parameter and the second dimension parameter;
the performing correlation analysis on the physical parameters to obtain a first dimension parameter specifically includes:
performing correlation analysis on the physical parameters to obtain correlation analysis results, screening the correlation analysis results according to a correlation threshold value, and performing normalization processing on the screened physical parameters to obtain first dimension parameters;
obtaining the optimized recommended parameter configuration according to the first dimension parameter and the second dimension parameter, specifically including:
calculating the Euclidean distance between the first dimension parameter and the second dimension parameter according to a K-means clustering algorithm;
screening according to a distance threshold to obtain a target dimension parameter, and acquiring a target verification cell corresponding to the target dimension parameter;
comparing the set values of the target dimension parameters of the target verification cells, and selecting the target dimension parameter corresponding to the maximum set value as the optimized recommended parameter configuration;
calculating the Euclidean distance between the first dimension parameter and the second dimension parameter according to a K-means clustering algorithm; screening according to a distance threshold to obtain a target dimension parameter, specifically comprising:
calculating Euclidean distances of 7 dimension quantization characteristics of the new station cell and each check cell sequence;
selecting n check cells closest to the Euclidean distance of the new station cell, and outputting a list; the default value of n is 100, and optimization can be realized based on parameter output effect in application;
counting and comparing 54 same setting values of dimension parameters in the selected n verification cells, and counting 54 dimension parameter item parameter values of 100 cells output by default respectively;
and selecting a parameter dimension setting value corresponding to the maximum count value in the 54 dimension parameters as a new station cell optimization setting value to be output.
2. The method of claim 1, wherein the first dimension parameter and the second dimension parameter each comprise: the antenna hanging height of the cell, the total downward inclination angle of the cell, the average value of the reference signal receiving power of the adjacent cell, the RRC connection request times of the adjacent cell, the average single-user switching times of the adjacent cell, the downlink average MCS level of the adjacent cell and the downlink average TM3/8 ratio of the adjacent cell.
3. The method of claim 1, further comprising:
comparing the recommended parameter configuration with the original parameter configuration, and determining whether to issue the recommended parameter configuration according to a comparison result;
and performing performance evaluation on the verification cell which is configured and operated by using the recommended parameters, and if the performance evaluation result meets a preset evaluation threshold value, determining that the recommended parameters are configured to be target parameter configuration.
4. A parameter optimization device in LTE cell scene division is characterized by comprising:
the correlation analysis module is used for screening physical parameters of the LTE cell and performing correlation analysis on the physical parameters to obtain a first dimension parameter;
the dimension parameter acquisition module is used for acquiring a second dimension parameter of the check cell;
the parameter optimization module is used for obtaining optimized recommended parameter configuration according to the first dimension parameter and the second dimension parameter;
the correlation analysis module is specifically configured to perform correlation analysis on the physical parameters to obtain correlation analysis results, screen the correlation analysis results according to a correlation threshold, and perform normalization processing on the screened physical parameters to obtain first dimension parameters;
the parameter optimization module specifically comprises:
the distance calculation unit is used for calculating the Euclidean distance between the first dimension parameter and the second dimension parameter according to a K-means clustering algorithm;
the parameter screening unit is used for screening according to the distance threshold to obtain a target dimension parameter and acquiring a target verification cell corresponding to the target dimension parameter;
the parameter comparison unit is used for comparing the set values of the target dimension parameters of the target verification cells, and selecting the target dimension parameter corresponding to the maximum set value as the optimized recommended parameter configuration;
the distance calculation unit is used for calculating Euclidean distances between the first dimension parameter and the second dimension parameter according to a K-means clustering algorithm; the parameter screening unit is used for screening and obtaining target dimension parameters according to the distance threshold, and specifically comprises:
calculating Euclidean distances of 7 dimension quantization characteristics of the new station cell and each check cell sequence;
selecting n check cells closest to the Euclidean distance of the new station cell, and outputting a list; the default value of n is 100, and optimization can be realized based on parameter output effect in application;
counting and comparing 54 same setting values of dimension parameters in the selected n verification cells, and counting 54 dimension parameter item parameter values of 100 cells output by default respectively;
and selecting a parameter dimension setting value corresponding to the maximum count value in the 54 dimension parameters as a new station cell optimization setting value to be output.
5. The apparatus of claim 4, wherein the first dimension parameter and the second dimension parameter in the correlation analysis module each comprise: the antenna hanging height of the cell, the total downward inclination angle of the cell, the average value of the reference signal receiving power of the adjacent cell, the RRC connection request times of the adjacent cell, the average single-user switching times of the adjacent cell, the downlink average MCS level of the adjacent cell and the downlink average TM3/8 ratio of the adjacent cell.
6. The apparatus of claim 4, further comprising:
the result auditing unit is used for comparing the recommended parameter configuration with the original parameter configuration and determining to issue the recommended parameter configuration according to the comparison result;
and the performance evaluation unit is used for evaluating the performance of the check cell which is configured and operated by using the recommended parameters, and if the performance evaluation result meets a preset evaluation threshold value, determining that the recommended parameters are configured as target parameters.
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| CN108934016B (en) * | 2018-07-04 | 2021-10-29 | 广东海格怡创科技有限公司 | Method and device for dividing cell scene categories, computer equipment and storage medium |
| CN109379748B (en) * | 2018-12-29 | 2021-10-26 | 京信网络系统股份有限公司 | Cell division method and device of signal receiving and transmitting unit and computer equipment |
| CN113015180B (en) * | 2019-12-19 | 2022-07-01 | 中国移动通信集团福建有限公司 | Network parameter updating method and device, storage medium and electronic equipment |
| CN113573321B (en) * | 2021-07-23 | 2022-08-09 | 中国电信股份有限公司 | Method, device, medium and electronic device for determining network correlation of cell |
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