CN115515219B - Signal interference positioning method, device, terminal and computer-readable storage medium - Google Patents
Signal interference positioning method, device, terminal and computer-readable storage medium Download PDFInfo
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Abstract
本申请公开了一种信号干扰定位方法、装置、终端和计算机可读存储介质，信号干扰定位方法包括：获取各个小区的干扰数据，根据干扰数据确定聚簇小区；获取多个聚簇小区围合形成的多边形，对多边形进行栅格划分得到多个栅格；获取各个栅格中每个聚簇小区的平均电平，根据平均电平获取各个栅格中每个聚簇小区的路损值；根据各个栅格中每个聚簇小区的干扰值和路损值确定干扰源所在的目标栅格；根据目标栅格对应的位置信息确定干扰源。通过根据各个栅格中每个聚簇小区的干扰值和路损值确定干扰源所在的栅格，如此，提高了干扰定位的准确性。
The present application discloses a signal interference positioning method, device, terminal and computer-readable storage medium. The signal interference positioning method includes: obtaining interference data of each cell, determining a clustered cell according to the interference data; obtaining a polygon formed by multiple clustered cells, and grid-dividing the polygon to obtain multiple grids; obtaining the average level of each clustered cell in each grid, and obtaining the path loss value of each clustered cell in each grid according to the average level; determining the target grid where the interference source is located according to the interference value and path loss value of each clustered cell in each grid; determining the interference source according to the position information corresponding to the target grid. By determining the grid where the interference source is located according to the interference value and path loss value of each clustered cell in each grid, the accuracy of interference positioning is improved.
Description
Technical Field
The present application relates to the field of wireless technologies, and in particular, to a signal interference positioning method, a signal interference positioning device, a terminal, and a computer readable storage medium.
Background
Coverage, capacity and interference are three factors influencing network quality, and high interference leads to network quality degradation and poor user perception, for example, a recent video backhaul device interference jammer and the like frequently appear to influence peripheral user perception. The traditional external interference checking means is mainly carried out by a mode of coarse searching of a person, a car and a sweep generator, and has large investment and low accuracy. Secondly, after the interference cells are selected by the traditional interference positioning scheme, an interference source can be searched only through road traversal frequency sweeping in the area, and the method has no definite purpose, is low in efficiency and has relatively poor frequency sweeping effect. Therefore, the conventional interference positioning method has the problem of low accuracy.
Disclosure of Invention
The embodiment of the application aims to solve the problem of low accuracy of the traditional interference positioning method by providing a signal interference positioning method, a device, a terminal and a computer readable storage medium.
To achieve the above object, an aspect of the present application provides a signal interference positioning method, including:
acquiring interference data of each cell, and determining clustered cells according to the interference data;
acquiring a polygon formed by enclosing a plurality of clustered cells, and carrying out grid division on the polygon to obtain a plurality of grids;
Acquiring the average level of each clustered cell in each grid, and acquiring the path loss value of each clustered cell in each grid according to the average level;
determining a target grid where an interference source is located according to the interference value and the path loss value of each clustered cell in each grid;
and determining an interference source according to the position information corresponding to the target grid.
Optionally, the step of determining a target grid where the interference source is located according to the interference value and the path loss value of each clustered cell in each grid includes:
Obtaining the interference proportion of each grid according to the interference value of each clustered cell in each grid, and obtaining the path loss proportion of each grid according to the interference value of each clustered cell in each grid;
acquiring a ratio difference value of the interference ratio and the path loss ratio of each clustered cell in each grid;
And acquiring the number of clustered cells of which the proportion difference value is in a preset difference value range in each grid, and taking the grid with the largest number as a target grid where the interference source is located.
Optionally, the step of obtaining the interference ratio of each grid according to the interference value of each clustered cell in each grid, and the step of obtaining the path loss ratio of each grid according to the interference value of each clustered cell in each grid includes:
sorting the interference values of the clustered cells in each grid, and acquiring the maximum interference value in each grid according to the interference value sorting result;
Dividing the interference value of each clustered cell in each grid by the interference ratio of each clustered cell in the maximum interference value of the grid;
the path loss value of each clustered cell in each grid is sequenced, and the minimum path loss value in each grid is obtained according to the path loss value sequencing result;
dividing the path loss value of each clustered cell in each grid by the minimum path loss value of the grid in turn to obtain the path loss ratio of each clustered cell.
Optionally, the step of obtaining the path loss value of each clustered cell in each grid according to the average level includes:
obtaining the maximum transmitting power of each clustered cell in each grid;
And obtaining the path loss value of each clustered cell in each grid according to the maximum transmitting power and the average level.
Optionally, the step of obtaining the average level of each clustered cell in each grid includes:
Acquiring a measurement report of each grid, wherein the measurement report comprises measurement information of each clustered cell in each grid;
acquiring the level data of each clustered cell in each grid according to the measurement information;
and acquiring the average level of each clustered cell in each grid according to the level data.
Optionally, the step of determining clustered cells from the interference data includes:
acquiring interference wave line characteristics of each cell according to the interference data;
obtaining the similarity of the interference wave line characteristics of each cell and the interference wave line characteristics of the reference cell;
and taking the cells with the similarity larger than a preset value as the clustering cells.
Optionally, the step of obtaining a polygon formed by surrounding a plurality of clustered cells includes:
Taking the clustered cells with interference values larger than a preset interference value as target clustered cells;
and acquiring polygons formed by the surrounding of the plurality of target cluster cells.
In addition, in order to achieve the above object, another aspect of the present application provides a signal interference positioning device, which includes a memory, a processor, and a signal interference positioning program stored on the memory and running on the processor, wherein the processor implements the steps of the signal interference positioning method as described above when executing the signal interference positioning program.
In addition, in order to achieve the above object, another aspect of the present application provides a terminal, where the terminal includes a memory, a processor, and a signal interference location program stored on the memory and running on the processor, and the processor implements the steps of the signal interference location method as described above when executing the signal interference location program.
In addition, in order to achieve the above object, another aspect of the present application provides a computer-readable storage medium having stored thereon a signal interference location program which, when executed by a processor, implements the steps of the signal interference location method as described above.
The application provides a signal interference positioning method, which comprises the steps of obtaining interference data of each cell and determining clustered cells according to the interference data; acquiring a polygon formed by enclosing a plurality of clustered cells, and carrying out grid division on the polygon to obtain a plurality of grids; acquiring the average level of each clustered cell in each grid, and acquiring the path loss value of each clustered cell in each grid according to the average level; determining a target grid where an interference source is located according to the interference value and the path loss value of each clustered cell in each grid; and determining an interference source according to the position information corresponding to the target grid. The grid where the interference source is located is determined according to the interference value and the path loss value of each clustered cell in each grid, so that the accuracy of interference positioning is improved.
Drawings
FIG. 1 is a schematic diagram of a terminal structure of a hardware operating environment according to an embodiment of the present application;
FIG. 2 is a flowchart of a signal interference positioning method according to a first embodiment of the present application;
Fig. 3 is a schematic flow chart of determining clustered cells according to the interference data in the signal interference positioning method of the present application;
fig. 4 is a schematic flow chart of acquiring an average level of each clustered cell in each grid in the signal interference positioning method of the present application;
Fig. 5 is a schematic flow chart of determining a target grid where an interference source is located according to an interference value and the path loss value of each clustered cell in each grid in the signal interference positioning method of the present application;
Fig. 6 is a schematic diagram of grid division of multiple clustered cells in the signal interference positioning method of the present application.
The achievement of the objects, functional features and advantages of the present application will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
The main solutions of the embodiments of the present application are: acquiring interference data of each cell, and determining clustered cells according to the interference data; acquiring a polygon formed by enclosing a plurality of clustered cells, and carrying out grid division on the polygon to obtain a plurality of grids; acquiring the average level of each clustered cell in each grid, and acquiring the path loss value of each clustered cell in each grid according to the average level; determining a target grid where an interference source is located according to the interference value and the path loss value of each clustered cell in each grid; and determining an interference source according to the position information corresponding to the target grid.
The traditional external interference checking means is mainly carried out by a mode of rough finding of a person, a car and a sweep generator, and meanwhile, after an interference cell is selected, an interference source can be found only by road traversal sweep in an area, so that the traditional interference positioning method has the problem of low accuracy. The application determines clustered cells according to interference data by acquiring the interference data of each cell; acquiring a polygon formed by enclosing a plurality of clustered cells, and carrying out grid division on the polygon to obtain a plurality of grids; acquiring the average level of each clustered cell in each grid, and acquiring the path loss value of each clustered cell in each grid according to the average level; determining a target grid where an interference source is located according to the interference value and the path loss value of each clustered cell in each grid; and determining an interference source according to the position information corresponding to the target grid. The grid where the interference source is located is determined according to the interference value and the path loss value of each clustered cell in each grid, so that the accuracy of interference positioning is improved.
As shown in fig. 1, fig. 1 is a schematic diagram of a terminal device structure of a hardware running environment according to an embodiment of the present application.
As shown in fig. 1, the terminal device may include: a processor 1001, such as a CPU, a network interface 1004, a user interface 1003, a memory 1005, a communication bus 1002. Wherein the communication bus 1002 is used to enable connected communication between these components. The user interface 1003 may include a Display, an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may further include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface). The memory 1005 may be a high-speed RAM memory or a stable memory (non-volatile memory), such as a disk memory. The memory 1005 may also optionally be a storage device separate from the processor 1001 described above.
It will be appreciated by those skilled in the art that the terminal device structure shown in fig. 1 does not constitute a limitation of the terminal device, and may include more or fewer components than shown, or may combine certain components, or a different arrangement of components.
As shown in fig. 1, a signal interference localization program may be included in a memory 1005, which is a computer-readable storage medium.
In the terminal device shown in fig. 1, the network interface 1004 is mainly used for data communication with a background server; the user interface 1003 is mainly used for data communication with a client (user side); the processor 1001 may be configured to invoke the signal interference location program in the memory 1005 and perform the following operations:
acquiring interference data of each cell, and determining clustered cells according to the interference data;
acquiring a polygon formed by enclosing a plurality of clustered cells, and carrying out grid division on the polygon to obtain a plurality of grids;
Acquiring the average level of each clustered cell in each grid, and acquiring the path loss value of each clustered cell in each grid according to the average level;
determining a target grid where an interference source is located according to the interference value and the path loss value of each clustered cell in each grid;
and determining an interference source according to the position information corresponding to the target grid.
Referring to fig. 2, fig. 2 is a flowchart of a first embodiment of a signal interference positioning method according to the present application.
The embodiments of the present application provide a signal interference location method, and it should be noted that although a logic sequence is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than that shown or described herein.
The signal interference positioning method of the embodiment operates at the terminal equipment side and comprises the following steps:
step S10, obtaining interference data of each cell, and determining clustered cells according to the interference data;
It should be noted that, the conventional interference checking means mainly includes the following steps: extracting network management data, analyzing interference characteristics and judging the interference type; preparing equipment: vehicle + sweep generator + sweep narrow beam hand-held antenna, etc.; and (5) field investigation: ① start point: selecting a cell position with the strongest interference as a starting point according to the interference cell list; ② rough measurement: according to DT (road test) roads, detouring from the starting point to each direction for investigation, and searching for an interference position; ③ fine measurement: and testing and checking the travelling cells, shops and buildings. Therefore, the traditional interference checking is mainly performed by a mode of searching for the human body, the vehicle, the sweep frequency narrow beam and the handheld antenna in a rough way, and therefore the problems of low interference checking efficiency and low interference positioning accuracy can be caused. Based on the problem, the application provides an interference grid positioning scheme based on the path loss proportion and the interference proportion, and the relative path loss value and the interference value are different for different positions of interference sources, and the path loss value and the interference value are uplink and have high similarity, so that the grid where the interference source is positioned can be determined through the path loss value and the interference value, and the interference checking efficiency and the interference positioning accuracy are improved.
In an embodiment, interference data of each cell is obtained, where the interference data may include, but is not limited to, a name of an interference cell, a location of the interference cell, a data acquisition time of the interference data, an interference data frequency band region attribute, an interference strength, and the like; and acquiring interference waveform characteristics of the interference cells in time domain and frequency domain from the interference data, and matching the interference waveform characteristics with the interference waveform characteristics in an interference characteristic library to acquire the similarity of each cell and a reference cell, wherein the cells with high similarity are classified as clustered cells.
Step S20, a polygon formed by surrounding a plurality of clustered cells is obtained, and the polygon is subjected to grid division to obtain a plurality of grids;
after determining the clustered cells, inputting the industrial parameter information of the clustered cells, calculating the position (including longitude and latitude) of the interference source according to the longitude and latitude, azimuth angle, interference level intensity, coverage grid and other information of the clustered cells of the same interference source, combining with a map, and geographically presenting and outputting the position (including longitude and latitude) of the interference source for on-site investigation.
N (N is more than or equal to 3) cells with the strongest interference value are selected from the clustered cells to be connected to form a polygon, and the polygon is subjected to grid division to obtain a plurality of grids. In an embodiment, referring to fig. 6, fig. 6 is a block diagram of a cluster cell with 4 strongest interference values selected, and black cells in the block diagram are a cell a, a cell B, a cell C and a cell D, where the selected 4 cluster cells are connected to form a quadrangle, and the quadrangle is subjected to 50×50 grid division to obtain 100 grids.
Step S30, obtaining the average level of each clustered cell in each grid, and obtaining the path loss value of each clustered cell in each grid according to the average level;
The average level of each clustered cell in each grid is obtained according to the average coverage condition of the grids, and the path loss value of each clustered cell in each grid is obtained according to the average level, wherein the path loss is called path loss or propagation loss, which refers to the loss generated by the propagation of electric waves in space and is caused by the radiation diffusion of the transmitting power and the propagation characteristics of a channel, and the change of the power average value of received signals in a macroscopic range is reflected.
In one embodiment, referring to fig. 4, the step of obtaining the average level of each clustered cell in each of the grids includes:
step S31, obtaining a measurement report of each grid, wherein the measurement report comprises measurement information of each clustered cell in each grid;
Step S32, obtaining the level data of each clustered cell in each grid according to the measurement information;
and step S33, obtaining the average level of each clustered cell in each grid according to the level data.
A test report (Measurement Report, MR) sent by the terminal to the base station is obtained, where the measurement report includes measurement information of each clustered cell in each grid, and the measurement information may include a cell location area identification code, a cell frequency point, a cell scrambling code, cell location information, a cell reception level value, and the like. After receiving the test report sent by the terminal, the base station sends the test report to the MR analysis platform, and because the test report has the ID of the corresponding cell, the base station can acquire the level data of each clustered cell in each grid based on the analysis of the MR analysis platform, and acquire the average level of each clustered cell in each grid according to the level data. For example, when 4 pieces of level data are acquired from the test report in the grid a, 6 pieces of level data are acquired from the cell a, and 10 pieces of level data are acquired from the cell B, the average level of the cell a is the average value of the 4 pieces of level data, and the average levels corresponding to the cell B and the cell C are also the average value of all the level data in the cell.
In an embodiment, after obtaining the average level of each clustered cell in each grid, obtaining the maximum transmitting power of each clustered cell in each grid, and obtaining the path loss value of each clustered cell in each grid according to the maximum transmitting power and the average level, wherein the maximum transmitting power configured by each clustered cell can be directly obtained on network management parameters. For example, the maximum transmission power of each cell in the grid=maximum transmission power of the cell-average level, and the maximum transmission power of the 5G cell is typically 33.9dbm. If the average level of the cell A is-75 dbm, the average level of the cell B is-90 dbm, the average level of the cell C is-105 dbm, and the average level of the cell D is-86 dbm, the corresponding path loss value of each cell is:
Cell a path loss value = 33.9dbm- (-75 dbm) = 108.9dbm;
cell B path loss value = 33.9dbm- (-90 dbm) = 123.9dbm;
Cell C path loss value = 33.9dbm- (-105 dbm) = 138.9dbm;
cell D path loss value=33.9 dbm- (-86 dbm) =119.9 dbm.
In addition, in an ideal situation, each grid covers all the selected cells, and in a real situation, there are some grids that do not cover all the selected cells.
Step S40, determining a target grid where an interference source is located according to the interference value and the path loss value of each clustered cell in each grid;
after the interference value and the path loss value of each clustered cell in each grid are obtained, determining a target grid where an interference source is located according to the interference value and the path loss value of each clustered cell, wherein the base station measures and counts the interference value of each cell, so that the interference value of each clustered cell can be directly obtained from the base station.
In an embodiment, referring to fig. 5, the step of determining the target grid where the interference source is located according to the interference value and the path loss value of each clustered cell in each grid includes:
Step S41, obtaining the interference proportion of each grid according to the interference value of each clustered cell in each grid, and obtaining the path loss proportion of each grid according to the interference value of each clustered cell in each grid;
Step S42, obtaining a ratio difference value of the interference ratio and the path loss ratio of each clustered cell in each grid;
Step S43, obtaining the number of clustered cells with the ratio difference value within a preset difference value range in each grid, and taking the grid with the largest number as a target grid where the interference source is located.
In this embodiment, the calculated path loss values of the clustered cells are ranked, and the lowest path loss value is respectively and comprehensively calculated in proportion with the highest path loss value, the next highest path loss value and the next lowest path loss value. For example, the path loss value of the cell a is 108.9dbm, the path loss value of the cell B is 123.9dbm, the path loss value of the cell C is 138.9dbm, and the path loss value of the cell D is 119.9dbm, and the corresponding path loss ratio is:
Road loss ratio 1 (ratio of highest road loss value to lowest road loss value): 138.9/108.9=1.27
Road loss ratio 2 (ratio of the next highest road loss value to the lowest road loss value): 123.9/108.9=1.14
Road loss ratio 3 (ratio of the second lowest road loss value to the lowest road loss value): 119.9/108.9=1.10
And sequencing the interference values of the clustered cells, and comprehensively calculating the proportion of the highest interference value to the next highest interference value and the lowest interference value. For example, the interference value of the cell A is-85 dbm, the interference value of the cell B is-95 dbm, the interference value of the cell C is-105 dbm, and the interference value of the cell D is-91 dbm, and the corresponding interference ratio is:
interference ratio 1 (ratio of lowest interference value to highest interference value): -105/-85 = 1.24
Interference ratio 2 (ratio of the second lowest interference value to the highest interference value): -95/-85 = 1.12
Interference ratio 3 (ratio of second highest interference value to highest interference value): -91/-85=1.07
Calculating the ratio difference value of the interference ratio and the path loss ratio of each clustered cell in each grid, acquiring the number of clustered cells with the ratio difference value within a preset difference value range in each grid, and taking the grid with the largest number as a target grid where an interference source is located. Wherein the ratio difference = path loss ratio 1-interference ratio 1 or path loss ratio 2-interference ratio 2 or path loss ratio 3-interference ratio 3, for example:
Ratio difference 1=1.27-1.24=0.03
Ratio difference 2=1.14-1.12=0.02
Ratio difference 3=1.10-1.07=0.03
Determining the priority of the grids according to the proportion difference value, and taking the grid with the highest priority as a target grid where an interference source is located, for example:
The three ratio differences are within plus or minus 0.05, which is defined as a high priority interference grid.
The two ratio differences are within plus or minus 0.05, i.e. defined as medium priority interference grid.
At most one ratio difference is within plus or minus 0.05, then defined as a low priority interference grid.
And S50, determining an interference source according to the position information corresponding to the target grid.
After determining the target grid (high-priority interference grid), the target grid is examined, so that an interference source is determined according to the position information corresponding to the target grid. For example, if the target grid a is found to be the grid where the interference source is located, the position information of the target grid a, such as coordinate information, distance information, angle information, longitude and latitude information, and the like, is obtained, and the interference source can be determined based on the position information of the target grid a.
The advantages of the present embodiment of locating the interference source by using the geographic grid based on the path loss include: and fully combining coverage, calculating a path loss value by using a coverage grid, and obtaining a high-medium-low priority interference check grid through correlation of the path loss proportion and the interference proportion. Therefore, the efficiency of interference investigation and the accuracy of interference positioning are improved, and meanwhile, manpower and material resources can be saved.
Further, referring to fig. 3, a second embodiment of the signal interference positioning method of the present application is provided.
The second embodiment of the signal interference location method is different from the first embodiment of the signal interference location method in that the step of determining clustered cells according to the interference data includes:
Step S11, obtaining interference wave line characteristics of each cell according to the interference data;
step S12, obtaining the similarity between the interference wave line characteristics of each cell and the interference wave line characteristics of the reference cell;
And S13, taking the cell with the similarity larger than a preset value as the clustering cell.
It should be noted that, in the conventional interference positioning method, the interference cells are manually identified in a converging manner, so that the accuracy of identification is low by manually identifying the interference cells. The application identifies the clustered cells based on the time domain and frequency domain interference wave line characteristic algorithm, wherein the clustered cells are the cells affected by the same time period when the interference source appears, so that the identification accuracy of the interference cells is improved.
In an embodiment, cells with the same interference source surrounding the interfering cells are identified and screened based on adjacent cell time domain, frequency domain (RB) interference synchronization and similar rules:
1. Clustering algorithm input and data preprocessing:
wherein the data input includes the following 3 requirements:
A. Input interference (e.g., floor noise greater than-110 dBm) and near (e.g., within 5 km) interference risk cells (e.g., floor noise greater than-112 dBm) are at least over 24 hours of average floor noise and per RB floor noise values. The background noise refers to total noise except useful signals, such as noise generated when certain public facilities in a cell operate, and noise generated when people exchange, and the like, which are the content of the background noise;
B. distinguishing 4/5G interference cells, determining whether the cells are of the same frequency, and carrying out clustering analysis on non-same-frequency cells separately due to the fact that possible interference source signals are different, wherein 0-99 total 100 sampling points need to be input to the 4G cells for similarity identification, and 0-272 total 273 sampling points need to be input to the 5G cells;
C. The clustering algorithm requires that 1 interference source cell to be checked be used as a reference (e.g. TopA cells), so that the next step of similarity and synchronous identification can be performed.
Data preprocessing:
Carrying out validity identification on input data, and removing abnormal data, such as a background noise of 0 value, a null value or an abnormally low value, such as minus 130dBm or below;
And (3) similarity recognition, taking a 4G cell as an example, carrying out data normalization processing (such as normalization processing of about-130 dBm to about-50 dBm to a range of 0-1) on the bottom noise value of 100 sampling points.
2. Similarity recognition:
And using TopA cells as reference standards, searching for interference cells with high waveform similarity and near interference cells (for example, more than-112 dBm), and preferentially selecting cells with more than-110 dBm, and if too few clustered cells are found to be unfavorable for positioning of subsequent interference sources, properly relaxing threshold conditions to perform waveform similarity screening.
The similarity algorithm is introduced, taking a vector space cosine similarity algorithm as an example, and the similarity algorithm mainly considers the relative difference between dimensions (the relative difference of analog waveform trend lines), and can reflect the interference background noise waveform similarity more than the absolute difference (background noise difference) on the comparison value. The calculation formula of the similarity is as follows:
Cosine similarity measures the difference between two individuals by taking the cosine value of the angle between two vectors in the vector space. The closer the cosine value is to 1, the closer the included angle is to 0 degrees, the more similar the two vectors are, which is referred to as "cosine similarity". The similarity of waveforms is calculated by calculating the magnitude of the difference of several or more different vectors.
Wherein Similarity is a Similarity measurement value; a represents Top cell a (i=0 to 99 total 100 sampling points); b is a cell with Similarity compared with A, wherein the larger the Similarity value is, the more similar A and B are, and the cell with high Similarity with Top cell A is classified as an intra-cluster cell, and the Similarity threshold is set to be more than 70%, namely the cells are considered to be highly similar.
3. Synchronous identification:
The synchronization is regarded as that the interference occurrence time of the clustered cells with high similarity needs to be kept consistent in time, namely, the cells influenced by the same time period when the common interference source occurs can be identified as clustered cells, and the waveform similarity of different time periods needs to be considered separately;
And outputting the problem cells with granularity of at least 24 hours and the cells with high waveform similarity based on a similarity algorithm, screening the cells with high waveform similarity for 12 hours and more to output clustered cell results, and finally, sorting the output results based on cell interference bottom noise values, so that the associated cells can be better identified, and the cells with higher interference values can be preferentially analyzed in the subsequent analysis.
4. The clustering result is presented:
And carrying out industrial parameter matching on the clustered cells determined after similarity and synchronous identification, and carrying out geographical presentation as a reference for positioning of subsequent interference sources.
According to the waveform characteristics of the interference cells in the time domain and the frequency domain, the interference cells with the same converging interference waveform characteristics are an interference cluster algorithm, and compared with the traditional manual interference cell converging scheme, the clustering algorithm provided by the application has the advantages that the identification is faster, and the identification accuracy is higher.
In addition, the application also provides a signal interference positioning device, which comprises a memory, a processor and a signal interference positioning program stored on the memory and running on the processor, wherein the device determines clustered cells according to interference data by acquiring the interference data of each cell; acquiring a polygon formed by enclosing a plurality of clustered cells, and carrying out grid division on the polygon to obtain a plurality of grids; acquiring the average level of each clustered cell in each grid, and acquiring the path loss value of each clustered cell in each grid according to the average level; determining a target grid where an interference source is located according to the interference value and the path loss value of each clustered cell in each grid; and determining an interference source according to the position information corresponding to the target grid. The grid where the interference source is located is determined according to the interference value and the path loss value of each clustered cell in each grid, so that the accuracy of interference positioning is improved.
In addition, the application also provides a terminal, which comprises a memory, a processor and a signal interference positioning program stored on the memory and running on the processor, wherein the terminal determines clustered cells according to interference data by acquiring the interference data of each cell; acquiring a polygon formed by enclosing a plurality of clustered cells, and carrying out grid division on the polygon to obtain a plurality of grids; acquiring the average level of each clustered cell in each grid, and acquiring the path loss value of each clustered cell in each grid according to the average level; determining a target grid where an interference source is located according to the interference value and the path loss value of each clustered cell in each grid; and determining an interference source according to the position information corresponding to the target grid. The grid where the interference source is located is determined according to the interference value and the path loss value of each clustered cell in each grid, so that the accuracy of interference positioning is improved.
In addition, the application also provides a computer readable storage medium, the computer readable storage medium stores a signal interference positioning method program, and the signal interference positioning method program realizes the steps of the signal interference positioning method when being executed by a processor.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It should be noted that in the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The application may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The use of the words first, second, third, etc. do not denote any order. These words may be interpreted as names.
While alternative embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following appended claims be interpreted as including alternative embodiments and all such alterations and modifications as fall within the scope of the application.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the spirit or scope of the application. Thus, it is intended that the present application also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (9)
1. A method of signal interference location, the method comprising:
acquiring interference data of each cell, and determining clustered cells according to the interference data;
acquiring a polygon formed by enclosing a plurality of clustered cells, and carrying out grid division on the polygon to obtain a plurality of grids;
Acquiring the average level of each clustered cell in each grid, and acquiring the path loss value of each clustered cell in each grid according to the average level;
Dividing the interference value of each clustered cell in each grid by the interference ratio of each clustered cell in the maximum interference value of the grid, and dividing the path loss value of each clustered cell in each grid by the path loss ratio of each clustered cell in the minimum path loss value of the grid;
acquiring a ratio difference value of the interference ratio and the path loss ratio of each clustered cell in each grid;
Acquiring the number of clustered cells with the proportion difference value within a preset difference value range in each grid, and taking the grid with the largest number as a target grid where an interference source is located;
and determining an interference source according to the position information corresponding to the target grid.
2. The signal interference positioning method as claimed in claim 1, wherein before the step of dividing the interference value of each clustered cell in each grid by the interference ratio of each clustered cell in turn by the maximum interference value of the grid, and dividing the path loss value of each clustered cell in each grid by the minimum path loss value of the grid to obtain the path loss ratio of each clustered cell, the method further comprises:
Sorting the interference values of each clustered cell in each grid, and acquiring the maximum interference value in each grid according to an interference value sorting result; and
And sequencing the path loss values of each clustered cell in each grid, and acquiring the minimum path loss value in each grid according to a path loss value sequencing result.
3. The signal interference location method according to claim 1, wherein the step of obtaining the path loss value of each of the clustered cells in the respective grids according to the average level comprises:
obtaining the maximum transmitting power of each clustered cell in each grid;
And obtaining the path loss value of each clustered cell in each grid according to the maximum transmitting power and the average level.
4. The signal interference location method of claim 1, wherein the step of obtaining an average level for each clustered cell in the respective grid comprises:
Acquiring a measurement report of each grid, wherein the measurement report comprises measurement information of each clustered cell in each grid;
acquiring the level data of each clustered cell in each grid according to the measurement information;
and acquiring the average level of each clustered cell in each grid according to the level data.
5. The signal-to-interference location method of claim 1, wherein the step of determining clustered cells from the interference data comprises:
Acquiring interference waveform characteristics of each cell according to the interference data;
obtaining the similarity between the interference waveform characteristics of each cell and the interference waveform characteristics of the reference cell;
and taking the cells with the similarity larger than a preset value as the clustering cells.
6. The signal interference positioning method as claimed in claim 1, wherein the step of obtaining a polygon formed by a plurality of clustered cells comprises:
Taking the clustered cells with interference values larger than a preset interference value as target clustered cells;
and acquiring polygons formed by the surrounding of the plurality of target cluster cells.
7. A signal interference location device comprising a memory, a processor and a signal interference location program stored on the memory and running on the processor, the processor implementing the steps of the method of any one of claims 1 to 6 when the signal interference location program is executed.
8. A terminal comprising a memory, a processor and a signal interference location program stored on the memory and running on the processor, the processor implementing the steps of the method according to any one of claims 1 to 6 when the signal interference location program is executed.
9. A computer readable storage medium, characterized in that it has stored thereon a signal interference localization program, which when executed by a processor, implements the steps of the method according to any of claims 1 to 6.
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| US6978151B2 (en) * | 2001-05-10 | 2005-12-20 | Koninklijke Philips Electronics N.V. | Updating path loss estimation for power control and link adaptation in IEEE 802.11h WLAN |
| CN102045854B (en) * | 2009-10-22 | 2015-05-20 | 中兴通讯股份有限公司 | Method for interference coordination of LTE (Long Term Evolution) system |
| CN103067966B (en) * | 2011-10-20 | 2018-01-16 | 上海无线通信研究中心 | The recognition methods of interference source in up going route in hybrid network |
| US9326163B2 (en) * | 2013-03-13 | 2016-04-26 | Alcatel Lucent | Methods and systems for reducing interference in networks |
| CN104105110B (en) * | 2013-04-01 | 2017-11-03 | 中国移动通信集团北京有限公司 | A kind of method and apparatus for analyzing interference in small zone |
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| CN110677865B (en) * | 2019-09-25 | 2020-08-04 | 北京邮电大学 | Method for positioning external interference source of mobile communication network |
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