Method, device, equipment and storage medium for clustering of interfered cells

Abstract

The embodiment of the present invention provides a method, device, equipment and storage medium for clustering interfered cells, the method comprising: obtaining information of each cell in the network, and screening out multiple interfered cells according to the information; classifying multiple interfered cells to obtain different types of interfered cell sets; sorting all interfered cells in the interfered cell set from strong to weak according to the average interfered power; extracting the time-frequency characteristics of each interfered cell in the interfered cell set; and sequentially performing geographic aggregation of the interfered cells in the interfered cell set based on the time-frequency characteristics to obtain interference cluster analysis results. The embodiment of the present invention determines interference sources with strong interference power and a large number of affected cells through interference cell screening, interference cell classification, interference cell sorting, interference cell time-frequency waveform feature extraction, and interference cell time / frequency feature geographic aggregation, thereby improving the efficiency and benefits of interference positioning and optimization.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and more specifically, to a method, device, equipment and storage medium for clustering interfered cells. Background Art

[0002] External interference in wireless communication systems has a negative impact on network performance, causing a series of problems such as reduced cell throughput, reduced coverage, and deteriorated voice quality. It is an important part of 4 / 5G wireless network optimization.

[0003] Interference waveform matching is the basis of interference clustering. Currently, 4 / 5G network interference waveform matching is mainly based on frequency domain information at the PRB (Physical Resource Block) level. For example, in a 20M bandwidth LTE system, there are 100 PRBs (PRB 0 -PRB 99 ), PRBs can be extracted at a certain time granularity 0 -PRB 99 The noise floor data is then used to identify the PRB of the affected cell through the Pearson formula or image recognition technology. 0 -PRB 99 The waveform of the signal is matched with the waveform of a specific cell. If the correlation coefficient exceeds a certain threshold, it is considered that the two cells are suspected to be affected by the same interference source.

[0004] In terms of interference optimization cell selection, currently the optimization cells are selected and the priority is determined mainly according to the power strength of the interfered cells. For example, the interfered cells in the network are screened according to the threshold of the interfered power greater than -110dBm / 180kHz, and then the cell screening priority is determined according to the strength of the interfered power.

[0005] At present, there are great limitations in interference waveform matching and interference optimization cell selection: interference matching is only based on the waveform characteristics of the frequency domain PRB level, and the matching accuracy is low; the waveform matching algorithm mainly uses the Pearson formula, that is, calculating the correlation coefficient of the two waveforms, but the matching effect of the Pearson formula is not ideal when the waveform volatility is small; currently, the optimization cell and processing priority are determined according to the strength of the interference power. The stronger the interference power, the higher the priority of the cell. However, this method does not consider factors such as the impact range of the interference source. In actual application, it is found that the interference sources that this method prioritizes for positioning and troubleshooting are not necessarily interference sources with a large impact range and a large number of affected cells, resulting in low efficiency and benefits of interference optimization work. Summary of the invention

[0006] Embodiments of the present invention provide a method, apparatus, device and storage medium for clustering interfered cells that overcome the above problems or at least partially solve the above problems.

[0007] In a first aspect, an embodiment of the present invention provides a method for clustering interfered cells, including:

[0008] Obtain information of each cell in the network, and filter out multiple interfered cells according to the information, wherein the information includes cell global identifier CGI, frequency band, frequency point, bandwidth, subcarrier spacing, average interfered power, interfered power of each PRB in the frequency domain, and interfered power of each hour in the time domain;

[0009] Classify the multiple interfered cells according to the frequency band, frequency point, bandwidth and subcarrier spacing to obtain at least one type of interfered cell set;

[0010] For each type of interfered cell set, all interfered cells in the interfered cell set are sorted from strong to weak according to the average interfered power, and the interference cluster typical cell CGI corresponding to each interfered cell in the interfered cell set is set as the respective cell CGI;

[0011] Extracting the time-frequency characteristics of each interfered cell in the set of interfered cells according to the interfered power of each PRB in the frequency domain and the interfered power of each hour in the time domain of each interfered cell in the set of interfered cells;

[0012] Geographic aggregation based on the time-frequency characteristics is performed on each interfered cell in the interfered cell set in turn to obtain an interference cluster analysis result.

[0013] Optionally, according to the method for clustering of interfered cells provided by an embodiment of the present invention, the extracting the time-frequency characteristics of each interfered cell in the interfered cell set according to the interfered power of each PRB in the frequency domain and the interfered power of each hour in the time domain of each interfered cell in the interfered cell set includes:

[0014] According to the interference power of each PRB in the frequency domain of the interfered cell, the frequency domain fluctuation and frequency domain mutation point information are calculated;

[0015] According to the interfered power of the interfered cell in each hour in the time domain, calculate the time domain fluctuation and time domain mutation point information;

[0016] The frequency domain volatility is measured by the variance of the interference power of each PRB in the frequency domain, and the frequency domain mutation point information includes the frequency domain mutation point position, the frequency domain backtracking step and the frequency domain mutation characteristic value;

[0017] The time domain volatility is measured by the variance of the disturbed power in each hour of the time domain, and the time domain mutation point information includes the time domain mutation point position, the time domain backtracking step length and the time domain mutation characteristic value.

[0018] Optionally, according to the method for clustering interfered cells provided by an embodiment of the present invention, the step of sequentially performing geographical aggregation based on the time-frequency characteristics on each interfered cell in the interfered cell set to obtain an interference cluster analysis result includes:

[0019] Starting from the cell with the strongest average interfered power in the interfered cell set, the following steps are performed on each interfered cell in the interfered cell set in order of average interfered power from strong to weak:

[0020] According to the cell longitude and latitude information, a cell set whose average interfered power is less than that of the current interfered cell and whose distance to the current interfered cell is less than a preset threshold is selected from the interfered cell set;

[0021] Determine whether the cell set is empty. If the cell set is not empty, select each cell in the cell set in turn to perform time-frequency feature consistency analysis with the current interfered cell, and determine whether the selected cell and the current interfered cell meet the time-frequency feature consistency condition;

[0022] If the selected cell and the currently interfered cell meet the time-frequency characteristic consistency condition, the selected cell and the currently interfered cell are divided into the same interference cluster, and the typical cell CGI and time-frequency mutation point information of the interference cluster of the selected cell are set to the typical cell CGI and time-frequency mutation point information of the interference cluster of the currently interfered cell; or,

[0023] If the selected cell and the currently interfered cell do not meet the time-frequency characteristic consistency condition, then the next cell in the cell set is selected to start time-frequency characteristic consistency analysis with the currently interfered cell.

[0024] Optionally, according to the method for clustering interfered cells provided by an embodiment of the present invention, the sequentially selecting each cell in the cell set and the current interfered cell to perform time-frequency feature consistency analysis, includes:

[0025] In a scenario where the interference waveform in the time domain or frequency domain is highly volatile, a matching algorithm combining waveform correlation coefficient, mutation point information and volatility is used to perform consistency analysis on the time-frequency characteristics of each cell in the cell set and the time-frequency characteristics of the current interfered cell;

[0026] In a scenario where the interference waveform in the time domain or frequency domain has low volatility, a matching algorithm combining mutation point information and volatility is used to perform consistency analysis on the time-frequency characteristics of each cell in the cell set and the time-frequency characteristics of the current interfered cell.

[0027] In a second aspect, an embodiment of the present invention provides a device for clustering interfered cells, including:

[0028] An interfered cell screening module is used to obtain information of each cell in the network, and screen out multiple interfered cells according to the information, wherein the information includes cell global identifier CGI, frequency band, frequency point, bandwidth, subcarrier spacing, average interfered power, interfered power of each PRB in the frequency domain, and interfered power of each hour in the time domain;

[0029] An interfered cell classification module, used to classify the multiple interfered cells according to the four fields of the frequency band, frequency point, bandwidth and subcarrier spacing, and obtain at least one type of interfered cell set;

[0030] An interfered cell sorting module is used to sort all interfered cells in the interfered cell set from strong to weak according to the average interfered power for each type of interfered cell set, and set the interference cluster typical cell CGI corresponding to each interfered cell in the interfered cell set as the respective cell CGI;

[0031] A time-frequency feature extraction module, configured to extract the time-frequency features of each interfered cell in the interfered cell set according to the interfered power of each PRB in the frequency domain and the interfered power of each hour in the time domain of each interfered cell in the interfered cell set;

[0032] The clustering module is used to sequentially perform geographical aggregation on each interfered cell in the interfered cell set based on the time-frequency characteristics to obtain an interference cluster analysis result.

[0033] Optionally, according to the device for clustering interfered cells provided by an embodiment of the present invention, the time-frequency feature extraction module is used to:

[0034] According to the interfered power of each PRB in the frequency domain of the interfered cell, the frequency domain fluctuation and the frequency domain mutation point information are calculated;

[0035] According to the interfered power of the interfered cell in each hour in the time domain, calculate the time domain fluctuation and time domain mutation point information;

[0036] The frequency domain volatility is measured by the variance of the interference power of each PRB in the frequency domain, and the frequency domain mutation point information includes the frequency domain mutation point position, the frequency domain backtracking step length and the frequency domain mutation characteristic value;

[0037] The time domain volatility is measured by the variance of the interfered power in each hour of the time domain, and the time domain mutation point information includes the time domain mutation point position, the time domain backtracking step length and the time domain mutation characteristic value.

[0038] Optionally, according to the device for clustering interfered cells provided by an embodiment of the present invention, the clustering module is used to:

[0039] Starting from the cell with the strongest average interfered power in the interfered cell set, the following steps are performed on each interfered cell in the interfered cell set in order of average interfered power from strong to weak:

[0040] According to the cell longitude and latitude information, a cell set whose average interfered power is less than that of the current interfered cell and whose distance to the current interfered cell is less than a preset threshold is selected from the interfered cell set;

[0041] Determine whether the cell set is empty. If the cell set is not empty, select each cell in the cell set in turn to perform time-frequency feature consistency analysis with the current interfered cell, and determine whether the selected cell and the current interfered cell meet the time-frequency feature consistency condition;

[0042] If the selected cell and the currently interfered cell meet the time-frequency characteristic consistency condition, the selected cell and the currently interfered cell are divided into the same interference cluster, and the typical cell CGI and time-frequency mutation point information of the interference cluster of the selected cell are set to the typical cell CGI and time-frequency mutation point information of the interference cluster of the currently interfered cell; or,

[0043] If the selected cell and the currently interfered cell do not meet the time-frequency characteristic consistency condition, then the next cell in the cell set is selected to start time-frequency characteristic consistency analysis with the currently interfered cell.

[0044] Optionally, according to the device for clustering interfered cells provided by an embodiment of the present invention, the sequentially selecting each cell in the cell set and the current interfered cell to perform time-frequency feature consistency analysis includes:

[0045] In a scenario where the interference waveform in the time domain or frequency domain is highly volatile, a matching algorithm combining waveform correlation coefficient, mutation point information and volatility is used to perform consistency analysis on the time-frequency characteristics of each cell in the cell set and the time-frequency characteristics of the current interfered cell;

[0046] In a scenario where the interference waveform in the time domain or frequency domain has low volatility, a matching algorithm combining mutation point information and volatility is used to perform consistency analysis on the time-frequency characteristics of each cell in the cell set and the time-frequency characteristics of the current interfered cell.

[0047] In a third aspect, an embodiment of the present invention provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of the method for clustering interfered cells provided in the first aspect are implemented.

[0048] In a fourth aspect, an embodiment of the present invention provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method for clustering interfered cells provided in the first aspect.

[0049] The method, device, equipment and storage medium for clustering interfered cells provided in the embodiments of the present invention perform regional interference cluster analysis through the steps of interfered cell screening, interfered cell classification, interfered cell sorting, interfered cell time / frequency waveform feature extraction, interfered cell time / frequency feature geographical aggregation, interference cluster information confirmation, priority confirmation, etc., to determine interference sources with strong interference power and a large number of affected cells, and add matching analysis of time domain interference waveform features on the basis of the original frequency domain interference waveform matching, thereby improving the efficiency and benefits of interference positioning and optimization work. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0051] Figure 1 A schematic flow chart of a method for clustering interfered cells provided by an embodiment of the present invention;

[0052] Figure 2 A schematic diagram of a process of geographic aggregation based on time-frequency features provided by an embodiment of the present invention;

[0053] Figure 3 A schematic diagram of the structure of an apparatus for clustering interfered cells provided by an embodiment of the present invention;

[0054] Figure 4 A schematic diagram of the physical structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0056] Figure 1 A schematic flow chart of a method for clustering interfered cells provided in an embodiment of the present invention includes:

[0057] Step 100: Obtain information of each cell in the network, and select multiple interfered cells according to the information, wherein the information includes cell global identifier CGI, frequency band, frequency point, bandwidth, subcarrier spacing, average interfered power, interfered power of each PRB in the frequency domain, and interfered power of each hour in the time domain;

[0058] Specifically, the frequency band, frequency point, bandwidth, subcarrier spacing, longitude, latitude and other information of the cell are obtained from the engineering parameters, and the average interference power of the cell, the interference power of each PRB in the frequency domain, the interference power of each hour in the time domain and other information are obtained from the OMC (Operation Management Center). The obtained cell information is shown in Table 1.

[0059] Table 1 Cell information

[0060]

[0061] In Table 1, for a 20M TD-LTE system, the PRB is obtained. 0 -PRB 99 For a 100M 5G cell, if the subcarrier is configured as 30kHz, obtain the PRB 0 -PRB 272 The higher the noise floor value, the stronger the interference power to the corresponding PRB. For 4 / 5G interfered cells, the interference power T at points 0 to 23 can be obtained in the time domain. 0 , T 1 , T 2 ……T23.

[0062] In specific implementation, the interfered cells in the network may be screened according to a predetermined standard, for example, the interfered cells in the network may be screened according to a standard that the average interference power is greater than -110 dBm / 180 kHz.

[0063] Step 101: classify the multiple interfered cells according to the frequency band, frequency point, bandwidth and subcarrier spacing to obtain at least one type of interfered cell set;

[0064] Specifically, the screened interfered cells are classified according to the four fields of cell frequency band, cell frequency point, cell bandwidth, and subcarrier spacing in the engineering parameters. For example, the TD-LTE interfered cells with F frequency band, 38400 frequency point, 20M bandwidth, and 15KHz subcarrier spacing are classified into one category, and the TD-LTE interfered cells with F frequency band, 38544 frequency point, 10M bandwidth, and 15KHz subcarrier spacing are classified into another category. Finally, at least one type of interfered cell set is obtained. One interfered cell set corresponds to one type.

[0065] Step 102: for each type of interfered cell set, sort all interfered cells in the interfered cell set from strong to weak according to the average interfered power, and set the interference cluster typical cell CGI corresponding to each interfered cell in the interfered cell set as the respective cell CGI;

[0066] Specifically, for each type of interfered cell set, all interfered cells in the interfered cell set are ranked from strong to weak according to the average interfered power. It can be understood that the cell with higher interfered power is ranked higher.

[0067] For each type of interfered cell set, before performing interference cluster analysis, the default value of "interference cluster typical cell CGI (Community global identity)" in the interfered cell information is first set to the CGI of the cell, as shown in Table 2.

[0068] Table 2 Typical cell CGI settings of interference clusters

[0069]

[0070] Step 103: extracting the time-frequency characteristics of each interfered cell in the interfered cell set according to the interfered power of each PRB in the frequency domain and the interfered power of each hour in the time domain of each interfered cell in the interfered cell set;

[0071] Specifically, in an embodiment of the present application, the time domain or frequency domain characteristics of the cell interference mainly include volatility and mutation point information, wherein the mutation point information includes the mutation point position, the backtracking step length, and the mutation characteristic value. The frequency domain characteristics are extracted according to the interference power of each PRB in the frequency domain of each interfered cell in the interfered cell set, and the time domain characteristics are extracted according to the interference power of each hour in the time domain of each interfered cell in the interfered cell set.

[0072] Step 104: Geographically aggregate the interfered cells in the interfered cell set based on the time-frequency characteristics in turn to obtain interference cluster analysis results.

[0073] Specifically, among the same type of interfered cells, the interfered cells are clustered in order from strong to weak interference power, that is, geographical aggregation based on time-frequency characteristics.

[0074] After the geographical dimension aggregation of interference time / frequency characteristics is completed for all cells of the same type, the interfered cells with the same CGI as the typical cells of the interference cluster belong to the same interference cluster. Within the interference cluster, the interfered cells with the same CGI as the typical cells of the interference cluster are the typical cells in the interference cluster.

[0075] When selecting interference optimization cells and determining priorities, priority is given to interference clusters with a large number of interfered cells and strong interference power in typical cells of the interference cluster, and dispatch orders to focus on troubleshooting interference sources in the areas covered by typical cells of the interference cluster, thereby improving the efficiency and benefits of interference optimization.

[0076] The method for clustering interfered cells provided in the embodiment of the present invention performs regional interference cluster analysis through the steps of interfered cell screening, interfered cell classification, interfered cell sorting, interfered cell time / frequency waveform feature extraction, interfered cell time / frequency feature geographical aggregation, interference cluster information confirmation, priority confirmation, etc., and determines the interference source with strong interference power and a large number of affected cells. On the basis of the original frequency domain interference waveform matching, the matching analysis of the time domain interference waveform characteristics is added, thereby improving the efficiency and benefits of interference positioning and optimization work.

[0077] Further, based on the content of the above embodiment, the extracting the time-frequency characteristics of each interfered cell in the interfered cell set according to the frequency domain PRB interfered power and the time domain hour interfered power of each interfered cell in the interfered cell set includes:

[0078] According to the interfered power of each PRB in the frequency domain of the interfered cell, the frequency domain fluctuation and the frequency domain mutation point information are calculated;

[0079] According to the interfered power of the interfered cell in each hour in the time domain, calculate the time domain fluctuation and time domain mutation point information;

[0080] The frequency domain volatility is measured by the variance of the interference power of each PRB in the frequency domain, and the frequency domain mutation point information includes the frequency domain mutation point position, the frequency domain backtracking step length and the frequency domain mutation characteristic value;

[0081] The time domain volatility is measured by the variance of the interfered power in each hour of the time domain, and the time domain mutation point information includes the time domain mutation point position, the time domain backtracking step length and the time domain mutation characteristic value.

[0082] Specifically, the time domain or frequency domain characteristics of the cell interference mainly include volatility and mutation point information, wherein the mutation point information includes the mutation point position, the backtracking step length and the mutation characteristic value.

[0083] Frequency domain feature extraction: calculate frequency domain fluctuation and frequency domain mutation point information based on the interfered power of each PRB in the frequency domain of the interfered cell.

[0084] The frequency domain volatility is measured by the frequency domain variance, which is calculated according to the following formula:

[0085]

[0086] Among them, PRB i Indicates the interference power of each PRB, E 频域 Representing PRB 0 -PRB n The value of n depends on the standard and subcarrier spacing.

[0087] For example, for an LTE cell with a bandwidth of 20 MHz, the value of n is 99; for a 5G cell, if the bandwidth is 100 MHz and the subcarrier spacing is 30 kHz, the corresponding value of n is 272.

[0088] The frequency domain mutation point information includes the frequency domain mutation point position, the frequency domain backtracking step length and the frequency domain mutation characteristic value, which are calculated according to the following formula:

[0089] For PRB i , when the backtracking step length is τ, if

[0090] PRB i –PRB i-τ >δ

[0091] Then i is a positive mutation point (i ≥ τ), the backtracking step is τ, and the mutation characteristic value is δ;

[0092] On the contrary, if it satisfies:

[0093] PRB i –PRB i-τ <-δ

[0094] Then i is the reverse mutation point, the backtracking step is τ, and the mutation characteristic value is -δ.

[0095] In practical applications, τ is generally set to 2, and δ is generally set to 10 or a greater value. The frequency domain characteristics of each interfered cell can be represented by the following Table 3.

[0096] Table 3 Frequency domain feature representation

[0097]

[0098] Next, we will introduce the time domain feature extraction. According to the interfered power of the interfered cell at each hour in the time domain, we calculate the time domain volatility and time domain mutation point information.

[0099] The time domain volatility is measured by the time domain variance, which is calculated according to the following formula:

[0100]

[0101] Among them, T j Indicates the interference power at each hour, E 时域 Represents T 0 -T23 The mean of .

[0102] The time domain mutation point information includes the time domain mutation point position, the time domain backtracking step length and the time domain mutation characteristic value, which are calculated according to the following formula:

[0103] For T j , when the backtracking step length is α, if

[0104] T j –T j-α >λ

[0105] Then j is considered to be a positive mutation moment (j≥α), the backtracking step is α, and the mutation characteristic value is λ;

[0106] On the contrary, if it satisfies:

[0107] T j –T j-α <-λ

[0108] Then j is considered to be the reverse mutation moment, the backtracking step is α, and the mutation characteristic value is -λ.

[0109] In practical applications, the value of α is generally 2, and the value of λ is 6 or a greater value. The time domain characteristics of each interfered cell can be represented by the following Table 4.

[0110] Table 4 Time domain feature representation

[0111]

[0112] The method for clustering interfered cells provided in an embodiment of the present invention provides a method for automatically extracting the mutation point information of the time-frequency interference waveform of the interfered cell, including the location of the mutation point, the backtracking step, the characteristic value and other information, which can be used to carry out a joint analysis of the waveform correlation coefficient, the mutation point information and the volatility.

[0113] Further, based on the contents of the above embodiments, sequentially performing geographical aggregation based on the time-frequency characteristics on each interfered cell in the interfered cell set to obtain an interference cluster analysis result includes:

[0114] Starting from the cell with the strongest average interfered power in the interfered cell set, the following steps are performed on each interfered cell in the interfered cell set in order of average interfered power from strong to weak:

[0115] According to the cell longitude and latitude information, a cell set whose average interfered power is less than that of the current interfered cell and whose distance to the current interfered cell is less than a preset threshold is selected from the interfered cell set;

[0116] Determine whether the cell set is empty. If the cell set is not empty, select each cell in the cell set in turn to perform time-frequency feature consistency analysis with the current interfered cell, and determine whether the selected cell and the current interfered cell meet the time-frequency feature consistency condition;

[0117] If the selected cell and the currently interfered cell meet the time-frequency characteristic consistency condition, the selected cell and the currently interfered cell are divided into the same interference cluster, and the typical cell CGI and time-frequency mutation point information of the interference cluster of the selected cell are set to the typical cell CGI and time-frequency mutation point information of the interference cluster of the currently interfered cell; or,

[0118] If the selected cell and the currently interfered cell do not meet the time-frequency characteristic consistency condition, then the next cell in the cell set is selected to start time-frequency characteristic consistency analysis with the currently interfered cell.

[0119] Specifically, for the convenience of explanation, the cell with the strongest average interference power in the interfered cell set is recorded as cell A. Next, taking cell A as an example, the specific analysis process of geographic convergence based on time-frequency characteristics is described in detail. Figure 2 A schematic diagram of a process of geographic aggregation based on time-frequency features provided in an embodiment of the present invention includes:

[0120] Step 200: According to the cell longitude and latitude information, a cell set Φ is selected from the interfered cell set N, whose average interfered power is less than the current interfered cell A and whose distance to the current interfered cell A is less than a preset threshold;

[0121] Step 201, determine whether the cell set Φ is empty, if it is empty, execute step 206, otherwise, execute step 202;

[0122] Step 202: Select each cell in the cell set in turn and perform time-frequency feature consistency analysis with the currently interfered cell A;

[0123] Step 203: determine whether the selected cell and the currently interfered cell A meet the time domain feature consistency condition. If so, execute step 204; if not, return to step 202 and select the next cell in the cell set to start time-frequency feature consistency analysis with the currently interfered cell.

[0124] Step 204: determine whether the selected cell and the currently interfered cell A meet the frequency domain feature consistency condition. If so, execute step 205; if not, return to step 202 and select the next cell in the cell set to start time-frequency feature consistency analysis with the currently interfered cell.

[0125] Step 205: The selected cell and the currently interfered cell A are divided into the same interference cluster, and the typical cell CGI and time-frequency mutation point information of the interference cluster of the selected cell are set to the typical cell CGI and time-frequency mutation point information of the interference cluster of the currently interfered cell A, that is, the selected cell inherits the typical cell and time-frequency mutation point information of the interference cluster of the currently interfered cell A;

[0126] Step 206: Select the next cell in the interfered cell set N to perform interference cluster analysis.

[0127] The method for clustering interfered cells provided by the embodiment of the present invention completes the aggregation of interference clusters in the region through the steps of power ranking of similar interfered cells, setting default values ​​of cell interference clusters, screening similar interfered cells within a certain distance range of the cell, matching time / frequency waveform characteristics of the cell, and information on typical cells and time-frequency mutation points of the interference cluster, thereby realizing iterative aggregation of interference time / frequency characteristics in the geographical dimension. By analyzing the regional interference clusters, interference sources with strong interference power and a large number of affected cells are determined. By checking and optimizing typical cells of the interference cluster, interference problems of other cells in the cluster are solved, thereby improving the efficiency and benefits of interference positioning and optimization work.

[0128] Further, based on the contents of the above embodiments, the sequentially selecting each cell in the cell set and the currently interfered cell to perform time-frequency feature consistency analysis includes:

[0129] In a scenario where the interference waveform in the time domain or frequency domain is highly volatile, a matching algorithm combining waveform correlation coefficient, mutation point information and volatility is used to perform consistency analysis on the time-frequency characteristics of each cell in the cell set and the time-frequency characteristics of the current interfered cell;

[0130] In a scenario where the interference waveform in the time domain or frequency domain has low volatility, a matching algorithm combining mutation point information and volatility is used to perform consistency analysis on the time-frequency characteristics of each cell in the cell set and the time-frequency characteristics of the current interfered cell.

[0131] Specifically, in the embodiment of the present application, sequentially selecting each cell in the cell set and the current interfered cell to perform time-frequency feature consistency analysis includes the following steps:

[0132] Step 1: According to the cell longitude and latitude information, among the cells of the same type with lower interference power than cell A, select a cell set Φ whose straight-line physical distance to cell A is within a certain range. If the cell set Φ is empty, cluster the next cell of the same type;

[0133] Step 2: If the cell set Φ is not empty, determine in turn whether cell A and each cell in the cell set selected in step 1 belong to the same interference cluster:

[0134] In a scenario where the interference waveform in the time domain or frequency domain is highly volatile, a matching algorithm combining waveform correlation coefficient, mutation point information and volatility is used to perform consistency analysis on the time-frequency characteristics of each cell in the cell set and the time-frequency characteristics of the current interfered cell;

[0135] In a scenario where the interference waveform in the time domain or frequency domain has low volatility, a matching algorithm combining mutation point information and volatility is used to perform consistency analysis on the time-frequency characteristics of each cell in the cell set and the time-frequency characteristics of the current interfered cell.

[0136] Taking cell B as an example, the calculation method is as follows:

[0137] When the following formula is satisfied, it is considered that the frequency domain fluctuations of cell A and cell B are large, and the frequency domain characteristics of cell A and cell B are consistent:

[0138] If D 频域,A >Thr 频域，1 And D 频域,B >Thr 频域 , 1

[0139] PRB B,i -PRB B,i-τ >0 And 频域,A,i >0

[0140] PRB B,i -PRB B,i-τ <0 And 频域,A,i <0

[0141] When the following formula is satisfied, it is considered that the frequency domain fluctuations of cells A and B are small, but the frequency domain characteristics of cells A and B are consistent:

[0142] D 频域,A ≤Thr 频域，1

[0143] D 频域,B ≤Thr 频域，1

[0144] PRB B,i -PRB B,i-τ >0 And 频域,A,i >0

[0145] PRB B,i -PRB B,i-τ <0 And 频域,A,i <0

[0146] Among them, Cov(PRB A ,PRBB ) is the frequency domain waveform correlation coefficient between cell A and cell B, Thr 频域，1 is the first threshold value in the frequency domain, Thr 频域，2 is the second threshold value in the frequency domain;

[0147] When the following formula is satisfied, it is considered that the time domain fluctuations of cell A and cell B are large, and the time domain characteristics of cell A and cell B are consistent:

[0148] If D 时域,A >Thr 时域，1 And D 时域,B >Thr 时域 , 1

[0149] T B,j -T B,j-α >0 And 时域,A,j >0

[0150] T B,j -T B,j-α <0 And 时域,A,j <0

[0151] When the following formula is satisfied, it is considered that the time domain fluctuations of cell A and cell B are small, but the time domain characteristics of cell A and cell B are consistent:

[0152] D 时域,A ≤Thr 时域，1

[0153] D 时域,B ≤Thr 时域，1

[0154] T B,j -T B,j-α >0 And 时域,A,j >0

[0155] T B,j -T B,j-α <0 And 时域,A,j <0

[0156] Among them, Cov(T A ,T B ) is the time domain waveform correlation coefficient between cell A and cell B, Thr 时域，1 is the first threshold value in the time domain, Thr 时域，2 is the second threshold in the time domain.

[0157] When the time domain characteristics and frequency domain characteristics of cell A and cell B are consistent, it is considered that the two cells are affected by the same interference source, and cell A and cell B are divided into the same interference cluster.

[0158] Step 3: When cell B and cell A are divided into the same interference cluster, the "typical cell of the interference cluster" of cell B is set to be consistent with the "typical cell of the interference cluster" of cell A, and at the same time, the frequency domain mutation point position set, frequency domain mutation point step, frequency domain mutation point characteristic value set, time domain mutation point position set, time domain mutation point step, and time domain mutation point characteristic value set of cell B are set to be consistent with cell A, that is, cell B inherits the typical cell of the interference cluster and time-frequency mutation point information of cell A.

[0159] Step 4: Select other cells in the cell set Φ and repeat steps 2-3 until all cells in the cell set have completed the time / frequency feature similarity analysis with cell A.

[0160] After completing the geographical aggregation based on time-frequency characteristics for all cells of the same type in accordance with the above steps, the interfered cells with the same CGI as the typical cell of the interference cluster belong to the same interference cluster. In the interference cluster, the interfered cells with the same CGI as the typical cell of the interference cluster are the typical cells in the interference cluster.

[0161] The method for clustering interfered cells in an embodiment of the present invention adopts a matching algorithm that combines mutation point information, correlation coefficient and volatility in scenarios where the volatility of the interference waveform in the time domain or frequency domain is large, and adopts a matching algorithm that combines mutation point information and volatility in scenarios where the volatility of the interference waveform in the time domain or frequency domain is small. This improves the accuracy of interference time / frequency waveform matching, compensates for the problem that the correlation coefficient analysis is not effective when the volatility is small, and further expands the scope of application of the matching algorithm.

[0162] Figure 3 The schematic diagram of the structure of the device for clustering interfered cells provided by an embodiment of the present invention includes: an interfered cell screening module 310, an interfered cell classification module 320, an interfered cell sorting module 330, a time-frequency feature extraction module 340 and a clustering module 350, wherein:

[0163] The interfered cell screening module 310 is used to obtain information of each cell in the network, and screen out multiple interfered cells according to the information, wherein the information includes the cell global identifier CGI, frequency band, frequency point, bandwidth, subcarrier spacing, average interfered power, interfered power of each PRB in the frequency domain, and interfered power of each hour in the time domain;

[0164] The interfered cell classification module 320 is used to classify the multiple interfered cells according to the four fields of the frequency band, frequency point, bandwidth and subcarrier spacing to obtain at least one type of interfered cell set;

[0165] The interfered cell sorting module 330 is used to sort all the interfered cells in the interfered cell set according to the average interfered power from strong to weak for each type of interfered cell set, and set the interference cluster typical cell CGI corresponding to each interfered cell in the interfered cell set as the respective cell CGI;

[0166] The time-frequency feature extraction module 340 is used to extract the time-frequency feature of each interfered cell in the interfered cell set according to the interfered power of each PRB in the frequency domain and the interfered power of each hour in the time domain of each interfered cell in the interfered cell set;

[0167] The clustering module 350 is used to sequentially perform geographical aggregation on each interfered cell in the interfered cell set based on the time-frequency characteristics to obtain an interference cluster analysis result.

[0168] It should be noted here that the above-mentioned device for clustering interfered cells provided in an embodiment of the present invention can implement all the method steps implemented in the above-mentioned method embodiment for clustering interfered cells, and can achieve the same technical effect. The parts and beneficial effects that are the same as those in the method embodiment will not be described in detail here.

[0169] Optionally, according to the device for clustering interfered cells provided by an embodiment of the present invention, the time-frequency feature extraction module is used to:

[0170] According to the interfered power of each PRB in the frequency domain of the interfered cell, the frequency domain fluctuation and the frequency domain mutation point information are calculated;

[0171] According to the interfered power of the interfered cell in each hour in the time domain, calculate the time domain fluctuation and time domain mutation point information;

[0172] The frequency domain volatility is measured by the variance of the interference power of each PRB in the frequency domain, and the frequency domain mutation point information includes the frequency domain mutation point position, the frequency domain backtracking step length and the frequency domain mutation characteristic value;

[0173] The time domain volatility is measured by the variance of the interfered power in each hour of the time domain, and the time domain mutation point information includes the time domain mutation point position, the time domain backtracking step length and the time domain mutation characteristic value.

[0174] It should be noted here that the above-mentioned device for clustering interfered cells provided in an embodiment of the present invention can implement all the method steps implemented in the above-mentioned method embodiment for clustering interfered cells, and can achieve the same technical effect. The parts and beneficial effects that are the same as those in the method embodiment will not be described in detail here.

[0175] Optionally, according to the device for clustering interfered cells provided by an embodiment of the present invention, the clustering module is used to:

[0176] Starting from the cell with the strongest average interfered power in the interfered cell set, the following steps are performed on each interfered cell in the interfered cell set in order of average interfered power from strong to weak:

[0177] According to the cell longitude and latitude information, a cell set whose average interfered power is less than that of the current interfered cell and whose distance to the current interfered cell is less than a preset threshold is selected from the interfered cell set;

[0178] Determine whether the cell set is empty. If the cell set is not empty, select each cell in the cell set in turn to perform time-frequency feature consistency analysis with the current interfered cell, and determine whether the selected cell and the current interfered cell meet the time-frequency feature consistency condition;

[0179] If the selected cell and the currently interfered cell meet the time-frequency characteristic consistency condition, the selected cell and the currently interfered cell are divided into the same interference cluster, and the typical cell CGI and time-frequency mutation point information of the interference cluster of the selected cell are set to the typical cell CGI and time-frequency mutation point information of the interference cluster of the currently interfered cell; or,

[0180] If the selected cell and the currently interfered cell do not meet the time-frequency characteristic consistency condition, then the next cell in the cell set is selected to start time-frequency characteristic consistency analysis with the currently interfered cell.

[0181] It should be noted here that the above-mentioned device for clustering interfered cells provided in an embodiment of the present invention can implement all the method steps implemented in the above-mentioned method embodiment for clustering interfered cells, and can achieve the same technical effect. The parts and beneficial effects that are the same as those in the method embodiment will not be described in detail here.

[0182] Optionally, according to the device for clustering interfered cells provided by an embodiment of the present invention, the sequentially selecting each cell in the cell set and the current interfered cell to perform time-frequency feature consistency analysis includes:

[0183] In a scenario where the interference waveform in the time domain or frequency domain is highly volatile, a matching algorithm combining waveform correlation coefficient, mutation point information and volatility is used to perform consistency analysis on the time-frequency characteristics of each cell in the cell set and the time-frequency characteristics of the current interfered cell;

[0184] In a scenario where the interference waveform in the time domain or frequency domain has low volatility, a matching algorithm combining mutation point information and volatility is used to perform consistency analysis on the time-frequency characteristics of each cell in the cell set and the time-frequency characteristics of the current interfered cell.

[0185] It should be noted here that the above-mentioned device for clustering interfered cells provided in an embodiment of the present invention can implement all the method steps implemented in the above-mentioned method embodiment for clustering interfered cells, and can achieve the same technical effect. The parts and beneficial effects that are the same as those in the method embodiment will not be described in detail here.

[0186] Figure 4 A schematic diagram of the physical structure of an electronic device provided by an embodiment of the present invention, such as Figure 4 As shown, the electronic device may include: a processor (processor) 410, a communication interface (Communications Interface) 420, a memory (memory) 430 and a communication bus 440, wherein the processor 410, the communication interface 420, and the memory 430 complete mutual communication through the communication bus 440. The processor 410 can call a computer program stored in the memory 430 and can be run on the processor 410 to execute the method for clustering the interfered cells provided by the above-mentioned method embodiments, for example, including: obtaining information of each cell in the network, and filtering out multiple interfered cells according to the information, wherein the information includes a cell global identifier CGI, a frequency band, a frequency point, a bandwidth, a subcarrier spacing, an average interfered power, an interfered power of each PRB in the frequency domain, and an interfered power of each hour in the time domain; according to the four fields of the frequency band, the frequency point, the bandwidth, and the subcarrier spacing, the multiple interfered cells are classified to obtain at least one type of interfered cell Set; for each type of interfered cell set, sort all the interfered cells in the interfered cell set from strong to weak according to the average interfered power, and set the interference cluster typical cell CGI corresponding to each interfered cell in the interfered cell set as the respective cell CGI; extract the time-frequency characteristics of each interfered cell in the interfered cell set according to the PRB interfered power in the frequency domain and the hourly interfered power in the time domain of each interfered cell in the interfered cell set; perform geographical aggregation based on the time-frequency characteristics on each interfered cell in the interfered cell set in turn to obtain the interference cluster analysis result.

[0187] In addition, the logic instructions in the above-mentioned memory 430 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on this understanding, the technical solution of the embodiment of the present invention is essentially or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0188] The embodiment of the present invention further provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method for clustering the interfered cells provided by the above-mentioned method embodiments is implemented, for example, including: obtaining information of each cell in the network, and screening out multiple interfered cells according to the information, wherein the information includes a cell global identifier CGI, a frequency band, a frequency point, a bandwidth, a subcarrier spacing, an average interfered power, an interfered power of each PRB in the frequency domain, and an interfered power of each hour in the time domain; classifying the multiple interfered cells according to the four fields of the frequency band, the frequency point, the bandwidth, and the subcarrier spacing, and obtaining at least one type of interfered cells. Interfering cell set; for each type of interfered cell set, all interfered cells in the interfered cell set are sorted from strong to weak according to the average interfered power, and the typical cell CGI of the interference cluster corresponding to each interfered cell in the interfered cell set is set as the respective cell CGI; according to the PRB interfered power in the frequency domain and the hourly interfered power in the time domain of each interfered cell in the interfered cell set, the time-frequency characteristics of each interfered cell in the interfered cell set are extracted; each interfered cell in the interfered cell set is geographically aggregated based on the time-frequency characteristics in turn to obtain the interference cluster analysis result.

[0189] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0190] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0191] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for clustering disturbed cells, It is characterized in that include: Obtain information of each cell in the network, and filter out multiple interfered cells according to the information, wherein the information includes cell global identifier CGI, frequency band, frequency point, bandwidth, subcarrier spacing, average interfered power, interfered power of each PRB in the frequency domain, and interfered power of each hour in the time domain; Classify the multiple interfered cells according to the frequency band, frequency point, bandwidth and subcarrier spacing to obtain at least one type of interfered cell set; For each type of interfered cell set, all interfered cells in the interfered cell set are sorted from strong to weak according to the average interfered power, and the interference cluster typical cell CGI corresponding to each interfered cell in the interfered cell set is set as the respective cell CGI; Extracting the time-frequency characteristics of each interfered cell in the set of interfered cells according to the interfered power of each PRB in the frequency domain and the interfered power of each hour in the time domain of each interfered cell in the set of interfered cells; Performing geographic aggregation based on the time-frequency characteristics on each interfered cell in the interfered cell set in turn to obtain an interference cluster analysis result; The extracting the time-frequency characteristics of each interfered cell in the interfered cell set according to the interfered power of each PRB in the frequency domain and the interfered power of each hour in the time domain of each interfered cell in the interfered cell set includes: According to the interfered power of each PRB in the frequency domain of the interfered cell, the frequency domain fluctuation and the frequency domain mutation point information are calculated; According to the interfered power of the interfered cell in each hour in the time domain, calculate the time domain fluctuation and time domain mutation point information; The frequency domain volatility is measured by the variance of the interference power of each PRB in the frequency domain, and the frequency domain mutation point information includes the frequency domain mutation point position, the frequency domain backtracking step length and the frequency domain mutation characteristic value; The time domain volatility is measured by the variance of the disturbed power in each hour of the time domain, and the time domain mutation point information includes the time domain mutation point position, the time domain backtracking step length and the time domain mutation characteristic value; The sequentially performing geographical aggregation based on the time-frequency characteristics on each interfered cell in the interfered cell set to obtain an interference cluster analysis result includes: Starting from the cell with the strongest average interfered power in the interfered cell set, the following steps are performed on each interfered cell in the interfered cell set in order of average interfered power from strong to weak: According to the cell longitude and latitude information, a cell set whose average interfered power is less than that of the current interfered cell and whose distance to the current interfered cell is less than a preset threshold is selected from the interfered cell set; Determine whether the cell set is empty. If the cell set is not empty, select each cell in the cell set in turn to perform time-frequency feature consistency analysis with the current interfered cell, and determine whether the selected cell and the current interfered cell meet the time-frequency feature consistency condition; If the selected cell and the currently interfered cell meet the time-frequency characteristic consistency condition, the selected cell and the currently interfered cell are divided into the same interference cluster, and the typical cell CGI and time-frequency mutation point information of the interference cluster of the selected cell are set to the typical cell CGI and time-frequency mutation point information of the interference cluster of the currently interfered cell; or, If the selected cell and the currently interfered cell do not meet the time-frequency characteristic consistency condition, then the next cell in the cell set is selected to start time-frequency characteristic consistency analysis with the currently interfered cell.

2. The method for clustering disturbed cells according to claim 1, It is characterized in that The sequentially selecting each cell in the cell set and the currently interfered cell to perform time-frequency feature consistency analysis, includes: In a scenario where the interference waveform in the time domain or frequency domain is highly volatile, a matching algorithm combining waveform correlation coefficient, mutation point information and volatility is used to perform consistency analysis on the time-frequency characteristics of each cell in the cell set and the time-frequency characteristics of the current interfered cell; In a scenario where the interference waveform in the time domain or frequency domain has low volatility, a matching algorithm combining mutation point information and volatility is used to perform consistency analysis on the time-frequency characteristics of each cell in the cell set and the time-frequency characteristics of the current interfered cell.

3. A device for clustering disturbed cells, It is characterized in that include: An interfered cell screening module is used to obtain information of each cell in the network, and screen out multiple interfered cells according to the information, wherein the information includes cell global identifier CGI, frequency band, frequency point, bandwidth, subcarrier spacing, average interfered power, interfered power of each PRB in the frequency domain, and interfered power of each hour in the time domain; An interfered cell classification module, used to classify the multiple interfered cells according to the four fields of the frequency band, frequency point, bandwidth and subcarrier spacing, and obtain at least one type of interfered cell set; An interfered cell sorting module is used to sort all interfered cells in the interfered cell set from strong to weak according to the average interfered power for each type of interfered cell set, and set the interference cluster typical cell CGI corresponding to each interfered cell in the interfered cell set as the respective cell CGI; A time-frequency feature extraction module, configured to extract the time-frequency features of each interfered cell in the interfered cell set according to the interfered power of each PRB in the frequency domain and the interfered power of each hour in the time domain of each interfered cell in the interfered cell set; A clustering module, used for sequentially performing geographical aggregation on each interfered cell in the interfered cell set based on the time-frequency characteristics to obtain an interference cluster analysis result; The time-frequency feature extraction module is used for: According to the interfered power of each PRB in the frequency domain of the interfered cell, the frequency domain fluctuation and the frequency domain mutation point information are calculated; According to the interfered power of the interfered cell in each hour in the time domain, calculate the time domain fluctuation and time domain mutation point information; The frequency domain volatility is measured by the variance of the interference power of each PRB in the frequency domain, and the frequency domain mutation point information includes the frequency domain mutation point position, the frequency domain backtracking step length and the frequency domain mutation characteristic value; The time domain volatility is measured by the variance of the disturbed power in each hour of the time domain, and the time domain mutation point information includes the time domain mutation point position, the time domain backtracking step length and the time domain mutation characteristic value; The clustering module is used for: Starting from the cell with the strongest average interfered power in the interfered cell set, the following steps are performed on each interfered cell in the interfered cell set in order of average interfered power from strong to weak: According to the cell longitude and latitude information, a cell set whose average interfered power is less than that of the current interfered cell and whose distance to the current interfered cell is less than a preset threshold is selected from the interfered cell set; Determine whether the cell set is empty. If the cell set is not empty, select each cell in the cell set in turn to perform time-frequency feature consistency analysis with the current interfered cell, and determine whether the selected cell and the current interfered cell meet the time-frequency feature consistency condition; If the selected cell and the currently interfered cell meet the time-frequency characteristic consistency condition, the selected cell and the currently interfered cell are divided into the same interference cluster, and the typical cell CGI and time-frequency mutation point information of the interference cluster of the selected cell are set to the typical cell CGI and time-frequency mutation point information of the interference cluster of the currently interfered cell; or, If the selected cell and the currently interfered cell do not meet the time-frequency characteristic consistency condition, then the next cell in the cell set is selected to start time-frequency characteristic consistency analysis with the currently interfered cell.

4. The device for clustering interfered cells according to claim 3, It is characterized in that The sequentially selecting each cell in the cell set and the currently interfered cell to perform time-frequency feature consistency analysis, includes: In a scenario where the interference waveform in the time domain or frequency domain is highly volatile, a matching algorithm combining waveform correlation coefficient, mutation point information and volatility is used to perform consistency analysis on the time-frequency characteristics of each cell in the cell set and the time-frequency characteristics of the current interfered cell; In a scenario where the interference waveform in the time domain or frequency domain has low volatility, a matching algorithm combining mutation point information and volatility is used to perform consistency analysis on the time-frequency characteristics of each cell in the cell set and the time-frequency characteristics of the current interfered cell.

5. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, It is characterized in that When the processor executes the program, the steps of the method for clustering interfered cells as claimed in claim 1 or 2 are implemented.

6. A non-transitory computer-readable storage medium having a computer program stored thereon, It is characterized in that When the computer program is executed by a processor, the steps of the method for clustering interfered cells as claimed in claim 1 or 2 are implemented.

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