CN108271176B - Method and system for determining base station cell quality difference root cause - Google Patents
Method and system for determining base station cell quality difference root cause Download PDFInfo
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Abstract
A method and system for determining a root cause of cell quality difference of a base station is disclosed. The method for determining the cause of the cell quality difference of the base station comprises the following steps: acquiring the problem type of a base station cell; determining at least one preliminary quality difference root cause in a plurality of preset preliminary quality difference root causes aiming at the problem type; forming a quality difference root cause matrix according to the at least one preliminary quality difference root cause; acquiring a quality difference root cause analysis matrix corresponding to the question type; and determining the final quality difference root cause through the quality difference root cause matrix and the quality difference root cause analysis matrix.
Description
Technical Field
The present invention relates to the field of communications technologies, and in particular, to a method and a system for determining a cause of cell quality difference in a base station.
Background
In general, a base station cell may have poor quality due to various problems, which may degrade the service quality of the base station cell. In order to solve the problem of poor quality of the base station cell, the poor quality root cause needs to be analyzed, and the root cause causing the poor quality problem needs to be determined, so that the problem is solved in a targeted manner.
Conventionally, when a base station cell is poor, the most likely cause of the poor quality is generally determined by performing manual analysis on a plurality of indexes. Therefore, the manual investment is large, the pure manual calculation is easy to have errors, the judgment is related to the personal optimization level and the capability of a technician, the technician must be very familiar with a plurality of quality factors, and when the quality factors exist simultaneously, the most possible quality factor needs to be given according to personal experience.
Disclosure of Invention
The invention provides a method and a system for determining a cause of cell quality difference of a base station.
According to an aspect of the present disclosure, there is provided a method for determining a cause of cell quality difference of a base station, including: acquiring the problem type of a base station cell; determining at least one preliminary quality difference root cause in a plurality of preset preliminary quality difference root causes aiming at the problem type; forming a quality difference root cause matrix according to the at least one preliminary quality difference root cause; acquiring a quality difference root cause analysis matrix corresponding to the question type; and determining the final quality difference root cause through the quality difference root cause matrix and the quality difference root cause analysis matrix.
According to another aspect of the present disclosure, there is provided a system for determining a cause of cell quality difference of a base station, comprising: a problem type acquisition unit that acquires a problem type of a base station cell; a preliminary quality difference root cause determination unit that determines at least one preliminary quality difference root cause of a plurality of preset preliminary quality difference root causes for a problem type; the quality difference root cause matrix forming unit forms a quality difference root cause matrix according to at least one preliminary quality difference root cause; a quality difference root cause analysis matrix acquisition unit that acquires a quality difference root cause analysis matrix corresponding to the type of the problem; and a final quality difference root cause determination unit that determines a final quality difference root cause from the quality difference root cause matrix and the quality difference root cause analysis matrix.
According to yet another aspect of the present disclosure, there is provided a system for determining a cause of cell quality difference of a base station, comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to: acquiring the problem type of a base station cell; determining at least one preliminary quality difference root cause in a plurality of preset preliminary quality difference root causes aiming at the problem type; forming a quality difference root cause matrix according to the at least one preliminary quality difference root cause; acquiring a quality difference root cause analysis matrix corresponding to the question type; and determining the final quality difference root cause through the quality difference root cause matrix and the quality difference root cause analysis matrix.
According to the method, the problem type of the base station cell is obtained, at least one preliminary quality difference root cause in a plurality of preset preliminary quality difference root causes is determined according to the problem type, a quality difference root cause matrix is formed according to the at least one preliminary quality difference root cause, a quality difference root cause analysis matrix obtained by utilizing a probabilistic neural network algorithm training sample is obtained, and a quality final difference root cause is determined according to the quality difference root cause matrix and the quality difference root cause analysis matrix, so that the analysis process can be matriculated, dependence on the capability of optimization personnel is reduced, and the analysis efficiency of the quality difference root causes is improved.
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The invention may be better understood from the following description of specific embodiments thereof taken in conjunction with the accompanying drawings, in which:
FIG. 1 is a schematic diagram illustrating a probabilistic neural network algorithm;
fig. 2 is a flowchart illustrating a method of determining a root cause of cell quality of a base station according to an embodiment of the present invention;
FIG. 3 is a block diagram illustrating a system for determining a root cause of cell quality differences in a base station in accordance with an embodiment of the present invention;
fig. 4 is a block diagram illustrating an exemplary hardware architecture of a computing device capable of implementing the method and system for determining a root cause of base station cell quality according to embodiments of the present invention.
Detailed Description
Features and exemplary embodiments of various aspects of the present disclosure are described in detail below. The following description encompasses numerous specific details in order to provide a thorough understanding of the present invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced without some of these specific details. The following description of the embodiments is merely intended to provide a clearer understanding of the present invention by illustrating examples of the present invention. The present invention is by no means limited to any specific configuration set forth below, but covers any modifications, substitutions, and improvements of the relevant elements or components without departing from the spirit of the invention.
The Probabilistic Neural Network (PNN) is a feedforward type Neural Network developed from a Radial Basis Function (RBF) model. Unlike traditional RBFs, PNN is an artificial neural network dedicated to solving classification problems, the theoretical basis of which is the bayesian minimum risk criterion (i.e., Bayes decision theory). The PNN places Bayes estimation in a feedforward neural network, the essence of which is a classifier, and Bayes decision is made according to the non-parameter estimation of probability density so as to obtain a classification result. PNNs are widely used in the fields of classification and pattern recognition.
The hierarchical model of PNN includes: the basic structure of the system comprises an input layer, a mode layer, a summation layer and a decision layer, wherein the basic structure is shown in figure 1.
An input layer: the transfer function is linear, passing only the input samples completely unchanged to the nodes of the mode layer.
Mode layer: the input layer is connected with the input layer through a connecting weight, weighted summation is carried out, and the weighted summation is transmitted to the summation layer after being operated through a nonlinear operator. The number of pattern layer neurons is the same as the number of input sample vectors.
And a summation layer: the input from the mode layer with the same class in the corresponding sample (the probability of belonging to a certain class) is simply accumulated to obtain the maximum possibility that the input sample belongs to the class.
A decision layer: and receiving the probabilities output from the summation layer, wherein the neuron with the highest probability density outputs 1, namely the corresponding neuron is the mode category of the sample to be recognized, and the outputs of other neurons are all 0.
PNN has the following major advantages: (1) the training is quick, and the training time is only slightly longer than the time for reading data; (2) no matter how complex the classification problem is, as long as enough training data are available, the optimal solution under the Bayesian criterion can be obtained; (3) allowing for the addition or subtraction of training data without requiring a long training session to be repeated.
The method comprises the steps of obtaining a problem type of a base station cell, determining at least one preliminary quality difference root cause in a plurality of preset preliminary quality difference root causes aiming at the problem type, forming a quality difference root cause matrix according to the at least one preliminary root cause, obtaining a quality difference root cause analysis matrix obtained by utilizing a probabilistic neural network algorithm training sample, and determining a final quality difference root cause according to the quality difference root cause matrix and the quality difference root cause analysis matrix, so that an analysis process can be matrixed, dependence on the capability of optimization personnel is reduced, and the analysis efficiency of the quality difference root causes is improved. The following describes a method and a system for determining a cause of cell quality difference of a base station in detail according to an embodiment of the present invention with reference to the accompanying drawings.
Fig. 2 is a flowchart illustrating a method of determining a root cause of cell quality of a base station according to an embodiment of the present invention. As shown in fig. 2, in step 201, the problem type of the base station cell is acquired. In one embodiment, the problem type of the base station cell may be one of a plurality of preset problem types. The preset problem types may include test problems, low radio access rates, high radio drop rates, low handover success rates, high per traffic redirection, low Physical Resource Block (PRB) throughput rates, low Channel Quality Indication (CQI) fraction, high downlink block errors, deep coverage, zero traffic, dormancy, and complaint problems. It should be understood that the preset issue type may also include any other suitable issue type.
In one embodiment, the type of problem for the base station cell may be a low radio access rate, i.e. the base station cell is a low radio access rate cell. For example, it may be determined from the data in the LTE performance table that the base station cell is a low radio access rate cell.
In step 202, at least one preliminary poor quality root cause of a plurality of preset preliminary poor quality root causes is determined for the problem type. In one embodiment, there may be a plurality of pre-set preliminary quality root causes. For example, the plurality of predetermined preliminary poor root causes may include, but are not limited to, severe weak coverage, mild weak coverage, over coverage, overlapping coverage, cross-slot interference, device blocking interference, telecommunications Frequency Division Duplex (FDD) spur interference, telecommunications Time Division Duplex (TDD) spur interference, global system for mobile communications (GSM) intermodulation interference, Personal Handyphone System (PHS) interference, other interference, high interference ratio overrun, high interference Resource Block (RB) number overrun, utilization overrun, hard frequency point, connection number overrun, soft expansion, reference signal power, initial received target power, non-persistent scheduling desired power, cell bias, cell reselection hysteresis value, intra-system pilot a2, inter-system pilot a2 threshold (blind expansion), cell selection minimum access level, pilot/pilot system measurement start threshold, serving low priority reselection threshold, The method comprises the following steps of frequency point low-priority reselection threshold of a Universal Terrestrial Radio Access Network (UTRAN), GSM/EDGE wireless communication network (GERAN), intersystem pilot frequency A2 threshold (GSM), antenna port signal power ratio, maximum power of cell selection User Equipment (UE), Power Amplifier (PA) value when a cell Physical Downlink Shared Channel (PDSCH) adopts fixed power distribution, uplink and downlink subframe configuration, special subframe mode, cross-area coverage remote common frequency, communication TDD stray interference, common station leakage configuration, over-close leakage configuration, Measurement Report (MR) leakage configuration, adjacent cell leakage configuration, inconsistency of frequency point/physical layer cell identification (PCI) and a current network, adjacent cell common frequency common PCI, TOP adjacent cell switching difference and the like. In one embodiment, parameter values of a plurality of preset preliminary quality difference root causes may be compared with corresponding threshold conditions, and a preliminary quality difference root cause meeting the corresponding threshold conditions may be selected from the plurality of preset preliminary quality difference root causes as at least one preliminary quality difference root cause according to a comparison result. Each of the predetermined preliminary qualitative difference root causes may have more than one parameter value, and the parameter values may be compared with corresponding threshold conditions to select the preliminary qualitative difference root causes meeting the threshold conditions. For example, when the problem type of the base station cell is low radio access rate, the final quality difference root cause can be determined to be severe weak coverage and over-coverage by comparing parameter values of a plurality of preset preliminary quality difference root causes with corresponding threshold conditions.
In step 203, a quality difference root cause matrix is formed from the at least one preliminary quality difference root cause. In one embodiment, characteristic values may be set for a plurality of preset preliminary quality-difference root factors, wherein the characteristic value corresponding to at least one preliminary quality-difference root factor is set to a first value, and the characteristic values of the remaining preliminary quality-difference root factors are set to a second value, and the characteristic values are used as matrix elements to form a quality-difference root factor matrix. For example, in the case that 47 preliminary quality difference root causes are determined, the 47 preliminary quality difference root causes may be assigned with corresponding eigenvalues, and a row matrix having these eigenvalues as elements may be formed. In one embodiment, the first value may be 1 and the second value may be 2. According to the at least one preliminary poor-quality root factor determined above, the poor-quality root factor matrix may be [1, 0, 1, 0, 0,. talka.., 0], wherein two "1" correspond to severe weak coverage and over coverage, respectively.
In step 204, a quality difference root cause analysis matrix corresponding to the type of problem is obtained. In one embodiment, the quality root cause analysis matrix may be determined by training a sample quality root cause matrix for the problem type and a corresponding sample final quality root cause using a probabilistic neural network algorithm. For example, a sample quality difference root cause matrix and a corresponding sample final quality difference root cause matrix may be trained using a data training function net in a probabilistic neural network algorithm, where B is the sample quality difference root cause matrix, D is the corresponding sample final quality difference root cause matrix (i.e., a matrix composed of elements corresponding to a plurality of preset preliminary quality difference root causes, where the element corresponding to the final quality difference root cause is set to a first value (e.g., 1), the remaining elements are set to a second value (e.g., 0)), and the parameter N is a smoothing factor, e.g., 0.5. It should be understood that different N may be chosen for different samples. The sample quality difference root cause matrix and the corresponding sample final quality difference root cause are correct data obtained through manual analysis in the past, and can be continuously updated and reset according to practical results. Through the training, a quality difference root cause analysis matrix, namely net, can be obtained for subsequent use in the process of determining the root cause aiming at the quality difference problem.
In step 205, a final cause of the quality difference is determined from the cause of the quality difference matrix and the cause of the quality difference analysis matrix. In one embodiment, the final root cause of the quality may be determined by operating on the matrix of root causes of the quality and the matrix of root cause analysis of the quality using a probabilistic neural network algorithm. For example, with a function D ═ sim (net, B) ]', where net is a quality difference root cause analysis matrix, B is a quality difference root cause matrix, and D is a final quality difference root cause matrix obtained by calculation, the quality difference root cause corresponding to the element of the final quality difference root cause matrix D whose value is the first numerical value (for example, 1) is determined to be the final quality difference root cause of the base station cell. For example, in the previous example, when the final quality root cause matrix D is [0, 0, 1, 0, 0,... 0], the quality root cause is over-coverage.
In one embodiment, when the root cause of the quality difference is correct, the determined at least one preliminary root cause of the quality difference and the corresponding final root cause of the quality difference can be used as new samples of corresponding problem types, the new samples are added into the original samples, and the probabilistic neural network algorithm is used for training the new sample set so as to optimize a corresponding root cause analysis matrix of the quality difference and improve the accuracy.
According to the method for determining the quality difference root cause of the base station cell, the problem type of the base station cell is obtained, at least one preliminary quality difference root cause in a plurality of preset preliminary quality difference root causes is determined according to the problem type, the quality difference root cause matrix is formed according to the at least one preliminary quality difference root cause, the quality difference root cause analysis matrix obtained by utilizing the probabilistic neural network algorithm training sample is obtained, and the final quality difference root cause is determined according to the quality difference root cause matrix and the quality difference root cause analysis matrix, so that the analysis process can be matriculated, dependence on the capability of an optimizer is reduced, and the analysis efficiency of the quality difference root causes is improved.
A system for determining a root cause of base station cell quality in accordance with the present disclosure is described below. The system may be used to perform the method according to the present disclosure as described above. Details not disclosed for system embodiments are consistent with the disclosed method embodiments.
Fig. 3 is a block diagram illustrating a system 300 for determining a root cause of cell quality for a base station in accordance with an embodiment of the present invention. As shown in fig. 3, the system 300 for determining the cause of the cell quality of the base station may include a problem type obtaining unit 301, a preliminary cause of quality determining unit 302, a cause of quality matrix forming unit 303, a cause of quality analysis matrix obtaining unit 304, and a final cause of quality determining unit 305.
The problem type acquisition unit 301 acquires the problem type of the base station cell. In one embodiment, the problem type of the base station cell may be one of a plurality of preset problem types. The plurality of preset problem types may include test problems, low wireless turn-on rate, high wireless drop-off rate, low handover success rate, unit traffic high redirection, low Physical Resource Block (PRB) throughput rate, low Channel Quality Indication (CQI) fraction, downlink high block error, deep coverage, zero traffic, dormancy, and complaint problems. Note that the plurality of preset issue types may also include any other suitable issue types.
The preliminary quality difference root cause determination unit 302 determines at least one preliminary quality difference root cause among a plurality of preset preliminary quality difference root causes for the problem type. In one embodiment, there may be a plurality of pre-set preliminary quality root causes. The parameter values of the plurality of preset preliminary quality difference root causes may be compared with corresponding threshold conditions, and a preliminary quality difference root cause meeting the corresponding threshold conditions may be selected as the at least one preliminary quality difference root cause from the plurality of preset preliminary quality difference root causes according to the comparison result.
The quality difference root cause matrix forming unit 303 forms a quality difference root cause matrix from the at least one preliminary quality difference root cause. In one embodiment, the quality factor matrix forming unit 303 may set feature values for a plurality of preset preliminary quality factors, wherein the feature value corresponding to at least one preliminary quality factor is set to a first value, and the feature values of the remaining preliminary quality factors are set to a second value, and the feature values are used as matrix elements to form the quality factor matrix.
The quality difference root cause analysis matrix acquisition unit 304 acquires a quality difference root cause analysis matrix corresponding to the type of the problem. In one embodiment, the quality root cause analysis matrix is determined by training a sample quality root cause matrix for the problem type and a corresponding sample final quality root cause using a probabilistic neural network algorithm.
The final cause-of-deterioration determination unit 305 determines a final cause-of-deterioration from the cause-of-deterioration matrix and the cause-of-deterioration analysis matrix. In one embodiment, the final cause of deterioration determination unit 305 determines the final cause of deterioration by operating the cause of deterioration matrix and the cause of deterioration analysis matrix using a probabilistic neural network algorithm.
It should be understood that, when the system for determining a root cause of cell quality difference of a base station provided in the foregoing embodiment implements the method for determining a root cause of cell quality difference of a base station, only the above-mentioned division of each functional unit is illustrated, and in practical applications, the above-mentioned function allocation may be completed by different functional units according to actual needs, that is, the content structure of the system may be divided into different functional units to complete all or part of the above-mentioned functions. In addition, with regard to the system in the above-described embodiment, the specific manner in which each unit performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated herein.
Fig. 4 is a block diagram illustrating an exemplary hardware architecture of a computing device capable of implementing the method and system for determining a root cause of base station cell quality according to embodiments of the present invention. As shown in fig. 4, computing device 400 includes an input device 401, an input interface 402, a central processor 403, a memory 404, an output interface 405, and an output device 406. The input interface 402, the central processing unit 403, the memory 404, and the output interface 405 are connected to each other through a bus 410, and the input device 401 and the output device 406 are connected to the bus 410 through the input interface 402 and the output interface 405, respectively, and further connected to other components of the computing device 400. Specifically, the input device 401 receives input information from the outside and transmits the input information to the central processor 403 through the input interface 1302; the central processor 403 processes the input information based on computer-executable instructions stored in the memory 404 to generate output information, stores the output information temporarily or permanently in the memory 404, and then transmits the output information to the output device 406 through the output interface 405; output device 406 outputs the output information outside of computing device 400 for use by a user.
The system 300 for determining a root cause of base station cell quality as illustrated in fig. 3 may also be implemented to include: a memory storing computer-executable instructions; and a processor which, when executing the computer executable instructions, may implement the method of determining a root cause of base station cell quality described in connection with fig. 2.
It is to be understood that the invention is not limited to the specific arrangements and instrumentality described above and shown in the drawings. A detailed description of known methods is omitted herein for the sake of brevity. In the above embodiments, several specific steps are described and shown as examples. However, the method processes of the present invention are not limited to the specific steps described and illustrated, and those skilled in the art can make various changes, modifications and additions or change the order between the steps after comprehending the spirit of the present invention.
The functional blocks shown in the above-described structural block diagrams may be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, it may be, for example, an electronic circuit, an Application Specific Integrated Circuit (ASIC), suitable firmware, plug-in, function card, or the like. When implemented in software, the elements of the invention are the programs or code segments used to perform the required tasks. The program or code segments may be stored in a machine-readable medium or transmitted by a data signal carried in a carrier wave over a transmission medium or a communication link. A "machine-readable medium" may include any medium that can store or transfer information. Examples of a machine-readable medium include electronic circuits, semiconductor memory devices, ROM, flash memory, Erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, Radio Frequency (RF) links, and so forth. The code segments may be downloaded via computer networks such as the internet, intranet, etc.
The present invention may be embodied in other specific forms without departing from its spirit or essential characteristics. For example, the algorithms described in the specific embodiments may be modified without departing from the basic spirit of the invention. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.
Claims (15)
1. A method for determining a cause of cell quality difference in a base station, the method comprising:
acquiring the problem type of the base station cell;
determining at least one preliminary quality difference root cause of a plurality of preset preliminary quality difference root causes aiming at the problem type;
forming a quality difference root cause matrix according to the at least one preliminary quality difference root cause;
acquiring a quality difference root cause analysis matrix corresponding to the question type; and
determining a final quality difference root factor through the quality difference root factor matrix and the quality difference root factor analysis matrix;
the quality difference root cause analysis matrix is determined by training a sample quality difference root cause matrix aiming at the problem type and a corresponding sample final quality difference root cause by using a probabilistic neural network algorithm, wherein the quality difference root cause is added to the original sample to form a new sample, and the new sample is trained.
2. The method of claim 1, wherein the question type is one of a plurality of preset question types.
3. The method of claim 2, in which the plurality of preset problem types comprise test problems, low radio access rates, high radio drop rates, low handover success rates, high redirection per traffic, low Physical Resource Block (PRB) throughput rates, low Channel Quality Indication (CQI) fraction, high block error downlink, deep coverage, zero traffic, dormancy, and complaint problems.
4. The method of claim 1, wherein determining at least one preliminary quality difference root cause of a plurality of preset preliminary quality difference root causes for the problem type comprises:
comparing the parameter values of the plurality of preset preliminary quality difference root causes with corresponding threshold conditions; and
and selecting a preliminary quality difference root factor which meets a corresponding threshold condition from the plurality of preset preliminary quality difference root factors as the at least one preliminary quality difference root factor according to a comparison result.
5. The method of claim 1, wherein forming a matrix of quality difference root causes from the at least one preliminary quality difference root cause comprises:
setting characteristic values for the plurality of preset preliminary quality difference root causes, wherein the characteristic value corresponding to at least one preliminary quality difference root cause is set to be a first numerical value, and the characteristic values of the rest of preliminary quality difference root causes are set to be a second numerical value;
and forming the quality difference root factor matrix by taking the characteristic values as matrix elements.
6. The method of claim 5, wherein the first value is 1 and the second value is 0.
7. The method of claim 1, wherein determining a final cause of the quality difference from the matrix of the quality difference causes and the matrix of the quality difference causes analysis comprises:
and calculating the quality difference root cause matrix and the quality difference root cause analysis matrix by using a probabilistic neural network algorithm so as to determine the final quality difference root cause.
8. A system for determining a cause of cell quality difference in a base station, the system comprising:
a problem type acquisition unit that acquires a problem type of the base station cell;
a preliminary quality difference root cause determination unit that determines at least one preliminary quality difference root cause of a plurality of preset preliminary quality difference root causes for the problem type;
a quality difference root cause matrix forming unit which forms a quality difference root cause matrix according to the at least one preliminary quality difference root cause;
a quality difference root cause analysis matrix obtaining unit that obtains a quality difference root cause analysis matrix corresponding to the type of the problem; and
a final quality difference root cause determination unit that determines a final quality difference root cause from the quality difference root cause matrix and the quality difference root cause analysis matrix;
the quality difference root cause analysis matrix is determined by training a sample quality difference root cause matrix aiming at the problem type and a corresponding sample final quality difference root cause by using a probabilistic neural network algorithm, wherein the quality difference root cause is added to the original sample to form a new sample, and the new sample is trained.
9. The system of claim 8, wherein the question type is one of a plurality of preset question types.
10. The system of claim 9, wherein the plurality of preset problem types include test problems, low radio access rates, high radio drop rates, low handover success rates, high redirection per traffic, low Physical Resource Block (PRB) throughput rates, low Channel Quality Indication (CQI) fraction, high block error for downlink, deep coverage, zero traffic, dormancy, and complaint problems.
11. The system according to claim 8, wherein the preliminary qualitative difference root cause determining unit is configured to compare parameter values of the plurality of preset preliminary qualitative difference root causes with respective threshold conditions, and to select a preliminary qualitative difference root cause meeting the respective threshold conditions from the plurality of preset preliminary qualitative difference root causes as the at least one preliminary qualitative difference root cause according to a comparison result.
12. The system according to claim 8, wherein the quality difference root factor matrix forming unit is configured to set feature values for the plurality of preset preliminary quality difference roots, wherein the feature value corresponding to the at least one preliminary quality difference root is set to a first numerical value, the feature values of the remaining preliminary quality difference roots are set to a second numerical value, and form the quality difference root factor matrix with the feature values as matrix elements.
13. The system of claim 12, wherein the first value is 1 and the second value is 0.
14. The system of claim 8, wherein the final cause of merit determination unit is configured to determine the final cause of merit by operating on the cause of merit matrix and the cause of merit analysis matrix using a probabilistic neural network algorithm.
15. A system for determining a cause of cell quality difference in a base station, the system comprising:
a processor;
a memory for storing the processor-executable instructions;
wherein the processor is configured to:
acquiring the problem type of the base station cell;
determining at least one preliminary quality difference root cause in a plurality of preset preliminary quality difference root causes aiming at the problem type;
forming a quality difference root cause matrix according to the at least one preliminary quality difference root cause;
acquiring a quality difference root cause analysis matrix corresponding to the question type; and
and determining the final quality difference root cause through the quality difference root cause matrix and the quality difference root cause analysis matrix.
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| CN111212440B (en) * | 2018-11-21 | 2021-09-03 | 华为技术有限公司 | Method and network equipment for realizing quality difference root cause analysis |
| CN111246524B (en) * | 2018-11-28 | 2022-03-04 | 中国移动通信集团山东有限公司 | Quality switching processing method and device based on CQI information |
| CN111327450B (en) * | 2018-12-17 | 2022-09-27 | 中国移动通信集团北京有限公司 | Method, device, equipment and medium for determining quality difference reason |
| CN111339211B (en) * | 2018-12-19 | 2023-09-19 | 中国移动通信集团重庆有限公司 | Method, device, equipment and medium for analyzing network problems |
| CN110661648B (en) * | 2019-08-30 | 2023-01-13 | 北京拓明科技有限公司 | Method and device for determining wireless network problem root cause based on self-learning reasoning matrix |
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| CN114630423B (en) * | 2020-12-10 | 2024-10-29 | 中国联合网络通信集团有限公司 | Resource allocation method, device, equipment and medium |
| CN112637880B (en) * | 2020-12-23 | 2022-11-15 | 北京东土拓明科技有限公司 | Problematic cell processing method and device |
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