An interference identification method and device

Abstract

The application provides an interference identification method and device. The method comprises the following steps: determining a corresponding first feature matrix according to a bottom noise value of each resource block (RB) of a target subframe; determining occurrence probabilities of each type of interference according to the first feature matrix and a preset interference probability library; and determining a type of target interference as a type of interference with the largest occurrence probability. The interference identification method and device provided by the application can accurately and efficiently identify interference.

Description

Technical Field

[0001] This invention relates to the field of mobile communication technology, and in particular to an interference identification method and apparatus. Background Technology

[0002] In mobile communication systems, interference detection and troubleshooting are crucial tasks, and rapid interference identification is a necessary prerequisite for subsequent optimization and assurance.

[0003] Existing interference identification methods mainly rely on the subjective judgment of optimization personnel. Often, the interference value needs to exceed a threshold to extract the interference waveform, and the interference category needs to be determined by observing the waveform manually. Then, the interference needs to be further determined based on on-site testing.

[0004] Existing interference identification technologies mainly have two problems:

[0005] (1) Slow response to interference: Human identification of interference requires a lot of time to collect a lot of data, resulting in a serious lag in identification.

[0006] (2) Low interference identification efficiency: It is necessary to extract interference data and KPI (Key Performance Indicator) from the local cell and surrounding cells for comprehensive judgment. The data volume is large and multiple indicators need to be analyzed in a comprehensive manner, which results in low efficiency.

[0007] (3) Low accuracy of interference identification: The interference identification method in the current network relies heavily on the optimization experience of engineers, and there is a problem that different people get different results for the same interference. Summary of the Invention

[0008] This invention provides an interference identification method and apparatus to solve the technical problems of low identification lag, low identification efficiency and low accuracy in the prior art.

[0009] This invention provides an interference identification method, comprising:

[0010] The first feature matrix is ​​determined based on the noise floor value of each resource block (RB) of the target subframe.

[0011] Based on the first feature matrix and the preset interference probability library, the occurrence probability of each type of interference is determined;

[0012] The type of interference with the highest probability of occurrence is identified as the type of target interference.

[0013] In one embodiment, the first feature matrix is ​​determined as follows:

[0014] The noise floor value of each RB is discretized to obtain a preset number of intervals;

[0015] Based on the preset number of intervals, a two-dimensional feature matrix is ​​constructed;

[0016] The first feature matrix is ​​determined based on the two-dimensional feature matrix and the noise floor value.

[0017] In one embodiment, the preset interference probability library is constructed in the following manner:

[0018] Based on a preset time period, obtain all known interferences in the target cell;

[0019] The second feature matrix is ​​determined based on the noise floor value of each RB in the subframe corresponding to each type of interference.

[0020] Based on the second feature matrix and the occurrence frequency of each type of interference, determine the first occurrence probability of the elements in the second feature matrix;

[0021] The preset interference probability library is constructed based on the first occurrence probability, the second occurrence probability of each type of interference among all known interferences, and the third occurrence probability of the element under all known interferences.

[0022] In one embodiment, the interference identification method further includes:

[0023] Based on the preset time period, the second occurrence probability is determined according to the occurrence frequency of all known interferences and the occurrence frequency of each type of interference;

[0024] Based on the preset time period, the third occurrence probability is determined according to the number of times the element appears in all known interferences and the number of times each type of interference appears.

[0025] In one embodiment, determining the occurrence probability of each type of interference based on the first feature matrix and a preset interference probability library includes:

[0026] Based on the first feature matrix, the occurrence probability of each type of interference is determined according to the first occurrence rate, the second occurrence probability, and the third occurrence probability.

[0027] In one embodiment, the interference identification method further includes:

[0028] Based on the target interference, update the occurrence count of the target interference and the feature matrix;

[0029] Update the occurrence count of elements in the first feature matrix based on the first feature matrix;

[0030] The preset interference probability library is updated based on the number of times the target interference occurs, the first feature matrix, and the number of times each element in the first feature matrix occurs.

[0031] This invention provides an interference identification device, comprising:

[0032] The first determining module is used to determine the corresponding first feature matrix based on the noise floor value of each resource block (RB) of the target subframe.

[0033] The second determining module is used to determine the occurrence probability of each type of interference based on the first feature matrix and the preset interference probability library.

[0034] The third determination module is used to identify the type of interference with the highest probability of occurrence as the type of target interference.

[0035] In one embodiment, the first feature matrix is ​​determined as follows:

[0036] The noise floor value of each RB is discretized to obtain a preset number of intervals;

[0037] Based on the preset number of intervals, a two-dimensional feature matrix is ​​constructed;

[0038] The first feature matrix is ​​determined based on the two-dimensional feature matrix and the noise floor value.

[0039] The present invention provides an electronic device, including a memory and a memory storing a computer program, wherein the processor executes the program to implement the steps of the interference identification method.

[0040] The present invention provides a processor-readable storage medium storing a computer program for causing the processor to perform the steps of the interference identification method.

[0041] The interference identification method and apparatus provided by this invention determine the corresponding first feature matrix by taking the noise floor value of each resource block (RB) of the target subframe and performing feature transformation on the noise floor value; and by monitoring the noise floor of each subframe in real time according to a preset interference probability library to determine the occurrence probability of each type of interference in the target subframe, thereby determining the final interference type, thus realizing interference identification at the subframe level and greatly improving the accuracy and timeliness of interference identification. Attached Figure Description

[0042] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0043] Figure 1 This is one of the flowcharts illustrating the interference identification method provided by the present invention;

[0044] Figure 2 This is the second flowchart illustrating the interference identification method provided by the present invention;

[0045] Figure 3 This is a schematic diagram of the structure of the interference identification method and apparatus provided by the present invention;

[0046] Figure 4 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0048] Figure 1 This is one of the flowcharts illustrating the interference identification method provided by the present invention. (Refer to...) Figure 1 The interference identification method provided by this invention includes:

[0049] Step 110: Determine the corresponding first feature matrix based on the noise floor values ​​of each resource block (RB) of the target subframe;

[0050] Step 120: Determine the occurrence probability of each type of interference based on the first feature matrix and the preset interference probability library;

[0051] Step 130: Determine the type of interference with the highest probability of occurrence as the type of target interference.

[0052] The interference identification method provided by this invention can be implemented by an electronic device, a component within an electronic device, an integrated circuit, or a chip. The electronic device can be a mobile electronic device or a non-mobile electronic device. For example, a mobile electronic device can be a mobile phone, tablet computer, laptop computer, PDA, ultra-mobile personal computer (UMPC), netbook, or personal digital assistant (PDA), etc., while a non-mobile electronic device can be a server, network attached storage (NAS), personal computer (PC), etc. This invention does not impose specific limitations.

[0053] The technical solution of this invention will be described in detail below using the example of a computer executing the interference identification method provided by this invention.

[0054] Optionally, in step 110, the noise floor values ​​of all RBs under the target subframe are determined, and then the noise floor values ​​of all RBs are used for feature extraction, and the corresponding first feature matrix is ​​determined; wherein, the noise floor values ​​of each RB do not affect each other, that is, each element in the first feature matrix is ​​independent of each other.

[0055] Optionally, in step 120, based on the first feature matrix and a preset interference probability library, the occurrence probability of each type of interference under the first feature matrix corresponding to the target subframe is calculated, and the interference type with the highest occurrence probability is determined. Each type of interference corresponds to an interference type classified according to existing technical specifications, such as: jamming interference, spurious interference, intermodulation interference, co-channel interference, etc. Each type of interference is distinguished by its corresponding interference waveform.

[0056] Optionally, in step 130, the type of interference with the highest probability of occurrence is determined as the type of target interference.

[0057] The interference identification method provided by this invention determines the corresponding first feature matrix by taking the noise floor value of each resource block (RB) of the target subframe and performing feature transformation on the noise floor value; and by monitoring the noise floor of each subframe in real time according to a preset interference probability library to determine the occurrence probability of each type of interference in the target subframe, thereby determining the final interference type, thus realizing interference identification at the subframe level and greatly improving the accuracy and timeliness of interference identification.

[0058] In one embodiment, the first feature matrix is ​​determined as follows:

[0059] The noise floor value of each RB is discretized to obtain a preset number of intervals;

[0060] Based on the preset number of intervals, a two-dimensional feature matrix is ​​constructed;

[0061] The first feature matrix is ​​determined based on the two-dimensional feature matrix and the noise floor value.

[0062] Optionally, the noise floor values ​​of all RBs in the target subframe are discretized to obtain a preset number of intervals with the same duration. It can be assumed that there are n RBs in the target subframe, and the target subframe is the k-th subframe. Then the noise floor value of the i-th RB in the k-th subframe is... Discretizing the noise floor values ​​of all RBs in the k-th subframe into m intervals of length s, the corresponding step size s is calculated using the following formula:

[0063]

[0064] if It fell within the corresponding interval s t,i , then s t,i =1, otherwise s t,i =0; The method for determining the interval number t after discretization is as follows:

[0065]

[0066] Where round_up is the rounding function.

[0067] For example: the target subframe has 10 RBs, the preset quantity m = 10, and the existing... and Then step length Therefore, the noise floor values ​​of all RBs in subframe k are discretized into 10 intervals with a step size of 12, and the intervals are numbered as follows: arrive The corresponding interval numbers t are 1, 0, 3, 5, 4, 8, 7, 2, 6, and 9.

[0068] Optionally, the noise floor value of each RB is discretized into a preset number of intervals, and all the intervals of all discretized RBs are used as elements to construct a two-dimensional feature matrix. For example, if 1 RB is discretized into m intervals, and n RBs are discretized into n×m intervals, then the two-dimensional feature matrix S is obtained. m,n for:

[0069]

[0070] Optionally, the values ​​of the elements in the first feature matrix are determined based on the noise floor and range of each RB. Then, for element s... t，i If the noise floor value of RB falls within the interval t, s t，i =1, otherwise s t，i=0, therefore the first characteristic matrix is ​​a two-dimensional matrix with each element having a value of 1 or 0.

[0071] The interference identification method provided by this invention extracts and discretizes the noise floor value of each RB, converting the noise floor value of each RB into a first feature matrix, making the integrated noise floor value data more stable; and it can customize the step size of the noise floor value, which is independent of bandwidth and standard, and is not limited by network standard. It can be applied to both LTE (Long Term Evolution) networks and NR (New Radio) networks.

[0072] In one embodiment, the preset interference probability library is constructed in the following manner:

[0073] Based on a preset time period, obtain all known interferences in the target cell;

[0074] The second feature matrix is ​​determined based on the noise floor value of each RB in the subframe corresponding to each type of interference.

[0075] Based on the second feature matrix and the occurrence frequency of each type of interference, determine the first occurrence probability of the elements in the second feature matrix;

[0076] The preset interference probability library is constructed based on the first occurrence probability, the second occurrence probability of each type of interference among all known interferences, and the third occurrence probability of the element under all known interferences.

[0077] Optionally, all interference and non-interference data of the target cell within a preset historical time period can be obtained. The preset time period is a pre-set period, which can be a past day, month, or year, etc., and this invention does not impose a specific limitation. Based on the historical data within the preset time period, the occurrence frequency of each type of interference and the total occurrence frequency of all interference can be determined.

[0078] Optionally, a second feature matrix is ​​constructed based on the noise floor values ​​of each RB in the subframes corresponding to each type of interference in the collected data of a preset historical period; and the first occurrence probability of the elements in the second feature matrix is ​​determined based on the second feature matrix and the occurrence frequency of each type of interference.

[0079] For example: for interference e k Based on the RB noise floor distribution in each subframe, a second feature matrix is ​​constructed. This yields the interference etype. k Features s in the second feature matrix t，i The total number of times is 1 And this type of interference e k Total number of occurrences This leads to the result that the interference is e k At that time, feature st，i The first occurrence probability P(s) t,i |e k )for:

[0080]

[0081] Optionally, based on the first occurrence probability, the second occurrence probability of each type of interference among all known interferences, and the third occurrence probability of the element under all known interferences, a preset interference probability library is constructed, that is, the prior and conditional probability library of each type of interference and the background noise feature is obtained.

[0082] The interference identification method provided by this invention constructs an interference probability database by using all interference data of the target cell within a preset time period, thereby improving the accuracy of interference identification.

[0083] In one embodiment, the interference identification method further includes:

[0084] Based on the preset time period, the second occurrence probability is determined according to the occurrence frequency of all known interferences and the occurrence frequency of each type of interference;

[0085] Based on the preset time period, the third occurrence probability is determined according to the number of times the element appears in all known interferences and the number of times each type of interference appears.

[0086] Optionally, the total number of occurrences of all known interferences and the number of occurrences of each type of interference within a preset time period are counted to obtain a second occurrence probability; and a third occurrence probability is determined based on the number of occurrences of the element among all known interferences and the number of occurrences of each type of interference.

[0087] For example: for interference e k Count the total number of times w occurs for all known interferences within a certain period of time. e and this type of interference e k Number of times This leads to the result that the interference is e k At that time, feature s t,i The second occurrence probability among all known interferences

[0088]

[0089] For each feature s in the second feature matrix t,i Count the number of times it appears in all known interferences. Thus, s is obtained t,i The probability of the third occurrence under all disturbances, P(s) t,i ):

[0090]

[0091] The interference identification method provided by this invention constructs an interference probability database by using all interference data of the target cell within a preset time period, thereby improving the accuracy of interference identification.

[0092] In one embodiment, determining the occurrence probability of each type of interference based on the first feature matrix and a preset interference probability library includes:

[0093] Based on the first feature matrix, the occurrence probability of each type of interference is determined according to the first occurrence rate, the second occurrence probability, and the third occurrence probability.

[0094] Optionally, based on a preset time period u, according to the first feature matrix and conditional probability formula Obtain the first characteristic matrix Below, interference e k The probability of its occurrence is:

[0095]

[0096] in, To interfere with e under the characteristics of the target subframe k The probability of its occurrence is called interference e. k The posterior probability.

[0097] Alternatively, according to the conditional probability formula, the second occurrence probability P(e k ), the probability of the third occurrence First probability of occurrence These three components, pre-calculated and stored in the interference probability database, together constitute the feature matrix of all types of interference in the target subframe. The probability of occurrence of the following is determined, and the disturbance with the highest probability is obtained by iterating through the data. j And output as this type of interference e j Final identified interference type:

[0098]

[0099] The interference identification method provided by this invention calculates the occurrence probability of each type of interference and then determines the type of interference with the highest occurrence probability as the type of target interference, which can realize automated interference identification and greatly improve accuracy and timeliness.

[0100] In one embodiment, the interference identification method further includes:

[0101] Based on the target interference, update the occurrence count of the target interference and the feature matrix;

[0102] Update the occurrence count of elements in the first feature matrix based on the first feature matrix;

[0103] The preset interference probability library is updated based on the number of times the target interference occurs, the first feature matrix, and the number of times each element in the first feature matrix occurs.

[0104] Optionally, the noise floor feature library can be updated based on the type of target interference. For example, if the current interference type is j, then the total number of times the features under interference j are updated is: If this time s t,i If it appears, update s. t,i Number of occurrences: If the current disturbance is j, then update the occurrence count of disturbance j:

[0105] Optionally, by updating the preset interference probability library based on the updated background noise feature library, we can obtain: P(e j ),

[0106] The interference identification method provided by this invention can further improve the efficiency and accuracy of interference identification by automatically updating the background noise feature library and the preset interference probability library by acquiring the target interference.

[0107] The interference identification device provided by the present invention is described below. The interference identification device described below and the interference identification method described above can be referred to in correspondence.

[0108] Figure 2 This is a second schematic flowchart illustrating the interference identification method provided by the present invention. (Refer to...) Figure 2 The interference identification method provided by this invention includes:

[0109] Step 210: Information collection, determining the noise floor values ​​of each RB in the target subframe;

[0110] Step 220: Extraction of noise floor time-frequency features. Discretize the noise floor values ​​of each RB to obtain the corresponding first feature matrix.

[0111] Step 230: Construct a preset interference probability library. Based on the interference data and non-interference data of the target cell in a preset time period, construct a preset interference probability library of noise floor characteristics under various types of interference.

[0112] Step 240: Subframe-level interference dynamic identification. Based on the preset interference probability library and the first feature matrix, determine the occurrence probability of each type of interference, and identify the interference type with the highest occurrence probability as the type of target interference.

[0113] Step 250: Feature library reverse update. Based on the type of target interference, update the background noise feature library, and based on the updated background noise feature library, update the preset interference probability library.

[0114] Figure 3 A schematic diagram of the interference device provided by the present invention is shown below. Figure 3 As shown, the device includes:

[0115] The first determining module 310 is used to determine the corresponding first feature matrix based on the noise floor value of each resource block (RB) of the target subframe.

[0116] The second determining module 320 is used to determine the occurrence probability of each type of interference based on the first feature matrix and the preset interference probability library.

[0117] The third determination module 330 is used to determine the type of interference with the highest probability of occurrence as the type of target interference.

[0118] The present invention provides an interference identification device that determines the corresponding first feature matrix by analyzing the noise floor value of each resource block (RB) of the target subframe; and determines the occurrence probability of each type of interference in the target subframe according to a preset interference probability library, thereby determining the final interference type, thus realizing interference identification at the subframe level and greatly improving the accuracy and timeliness of interference identification.

[0119] In one embodiment, the first feature matrix is ​​determined as follows:

[0120] The noise floor value of each RB is discretized to obtain a preset number of intervals;

[0121] Based on the preset number of intervals, a two-dimensional feature matrix is ​​constructed;

[0122] The first feature matrix is ​​determined based on the two-dimensional feature matrix and the noise floor value.

[0123] In one embodiment, the preset interference probability library is constructed in the following manner:

[0124] Based on a preset time period, obtain all known interferences in the target cell;

[0125] The second feature matrix is ​​determined based on the noise floor value of each RB in the subframe corresponding to each type of interference.

[0126] Based on the second feature matrix and the occurrence frequency of each type of interference, determine the first occurrence probability of the elements in the second feature matrix;

[0127] The preset interference probability library is constructed based on the first occurrence probability, the second occurrence probability of each type of interference among all known interferences, and the third occurrence probability of the element under all known interferences.

[0128] In one embodiment, the interference identification device further includes:

[0129] Based on the preset time period, the second occurrence probability is determined according to the occurrence frequency of all known interferences and the occurrence frequency of each type of interference;

[0130] Based on the preset time period, the third occurrence probability is determined according to the number of times the element appears in all known interferences and the number of times each type of interference appears.

[0131] In one embodiment, the second determining module 320 is further configured to:

[0132] Based on the first feature matrix, the occurrence probability of each type of interference is determined according to the first occurrence rate, the second occurrence probability, and the third occurrence probability.

[0133] In one embodiment, the interference identification device further includes:

[0134] Based on the target interference, update the occurrence count of the target interference and the feature matrix;

[0135] Update the occurrence count of elements in the first feature matrix based on the first feature matrix;

[0136] The preset interference probability library is updated based on the number of times the target interference occurs, the first feature matrix, and the number of times each element in the first feature matrix occurs.

[0137] Figure 4 An example is a schematic diagram of the structure of an electronic device, such as... Figure 4 As shown, the electronic device may include: a processor 410, a communication interface 420, a memory 430, and a communication bus 440, wherein the processor 410, the communication interface 420, and the memory 430 communicate with each other via the communication bus 440. The processor 410 can call a computer program in the memory 430 to execute the steps of the interference identification method, such as including:

[0138] The first feature matrix is ​​determined based on the noise floor value of each resource block (RB) of the target subframe.

[0139] Based on the first feature matrix and the preset interference probability library, the occurrence probability of each type of interference is determined;

[0140] The type of interference with the highest probability of occurrence is identified as the type of target interference.

[0141] Furthermore, the logical instructions in the aforementioned memory 430 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, essentially, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0142] On the other hand, the present invention also provides a computer program product, the computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions, wherein when the program instructions are executed by a computer, the computer is able to execute the interference identification method provided by the above methods, the method comprising:

[0143] The first feature matrix is ​​determined based on the noise floor value of each resource block (RB) of the target subframe.

[0144] Based on the first feature matrix and the preset interference probability library, the occurrence probability of each type of interference is determined;

[0145] The type of interference with the highest probability of occurrence is identified as the type of target interference.

[0146] On the other hand, embodiments of this application also provide a processor-readable storage medium storing a computer program for causing the processor to execute the interference identification methods provided in the above embodiments, such as including:

[0147] The first feature matrix is ​​determined based on the noise floor value of each resource block (RB) of the target subframe.

[0148] Based on the first feature matrix and the preset interference probability library, the occurrence probability of each type of interference is determined;

[0149] The type of interference with the highest probability of occurrence is identified as the type of target interference.

[0150] The processor-readable storage medium can be any available medium or data storage device that the processor can access, including but not limited to magnetic memory (e.g., floppy disk, hard disk, magnetic tape, magneto-optical disk (MO)), optical memory (e.g., CD, DVD, BD, HVD), and semiconductor memory (e.g., ROM, EPROM, EEPROM, non-volatile memory (NAND FLASH), solid-state drive (SSD)).

[0151] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0152] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0153] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An interference identification method, characterized in that, include: The first feature matrix is ​​determined based on the noise floor values ​​of each resource block (RB) in the target subframe, including: discretizing the noise floor values ​​of each RB to obtain a preset number of intervals; constructing a two-dimensional feature matrix based on the preset number of intervals; and determining the first feature matrix based on the two-dimensional feature matrix and the noise floor values. Based on the first feature matrix and the preset interference probability library, the occurrence probability of each type of interference is determined; The type of interference with the highest probability of occurrence is identified as the type of target interference.

2. The interference identification method according to claim 1, characterized in that, The preset interference probability library is constructed in the following way: Based on a preset time period, obtain all known interferences in the target cell; The second feature matrix is ​​determined based on the noise floor value of each RB in the subframe corresponding to each type of interference. Based on the second feature matrix and the occurrence frequency of each type of interference, determine the first occurrence probability of the elements in the second feature matrix; The preset interference probability library is constructed based on the first occurrence probability, the second occurrence probability of each type of interference among all known interferences, and the third occurrence probability of the element under all known interferences.

3. The interference identification method according to claim 2, characterized in that, Also includes: Based on the preset time period, the second occurrence probability is determined according to the occurrence frequency of all known interferences and the occurrence frequency of each type of interference; Based on the preset time period, the third occurrence probability is determined according to the number of times the element appears in all known interferences and the number of times each type of interference appears.

4. The interference identification method according to claim 3, characterized in that, The step of determining the occurrence probability of each type of interference based on the first feature matrix and the preset interference probability library includes: Based on the first feature matrix, the occurrence probability of each type of interference is determined according to the first occurrence probability, the second occurrence probability, and the third occurrence probability.

5. The interference identification method according to claim 1, characterized in that, Also includes: Based on the target interference, update the occurrence count of the target interference and the feature matrix; Update the occurrence count of elements in the first feature matrix based on the first feature matrix; The preset interference probability library is updated based on the number of times the target interference occurs, the first feature matrix, and the number of times each element in the first feature matrix occurs.

6. An interference identification device, characterized in that, include: The first determining module is used to determine the corresponding first feature matrix based on the noise floor values ​​of each resource block (RB) of the target subframe, including: discretizing the noise floor values ​​of each RB to obtain a preset number of intervals; constructing a two-dimensional feature matrix based on the preset number of intervals; and determining the first feature matrix based on the two-dimensional feature matrix and the noise floor values. The second determining module is used to determine the occurrence probability of each type of interference based on the first feature matrix and the preset interference probability library. The third determination module is used to identify the type of interference with the highest probability of occurrence as the type of target interference.

7. An electronic device comprising a processor and a memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the interference identification method according to any one of claims 1 to 5.

8. A processor-readable storage medium, characterized in that, The processor-readable storage medium stores a computer program for causing the processor to perform the steps of the interference identification method according to any one of claims 1 to 5.

Images