Method and apparatus for high background noise analysis

Abstract

The embodiments of the present application provide a method and device for high background noise analysis. The method includes: determining high background noise cells based on network management data metrics; determining the high background noise type of a target cell based on the background noise data of the target cell, where the background noise data is obtained through the cell PRB-level uplink average interference level counter of the target cell and / or through the sweep data of the target cell; and the target cell is any cell among the high background noise cells. The method for high background noise analysis provided by the embodiments of the present application can significantly improve work efficiency, and has strong portability and scalability by determining the high background noise type through network management data metrics and / or analyzing the corresponding high background noise type after screening the sweep data of high background noise cells to assist in determining the interference source location.

Description

Technical Field

[0001] This application relates to the field of wireless communication technologies, and in particular, to a method and device for high noise floor analysis. Background Art

[0002] One of the important parameters for evaluating the link coverage quality in a communication network is the noise floor DNF (Digital Noise Floor), which is the background noise. It refers to the total noise in the system except for the useful signal; excessive background noise reduces the signal-to-noise ratio and dynamic range of the sound, and the online call quality is damaged. Usually, the higher the noise floor, some weaker coverage signals will be submerged under the noise floor, resulting in a decrease in the base station's receiving sensitivity and a smaller SINR (Signal to Interference plus Noise Ratio) value, causing poor call quality and even easy to lead to voice call drops or data service disconnections. High noise floor is also called high interference.

[0003] The existing analysis methods mainly use excel to analyze the noise floor data, and there are problems such as being unable to view or analyze the noise floor spectrum curves at the PRB (Physical Resource Block) level in multiple time periods simultaneously, and being unable to correlate multiple data sources to analyze the noise floor spectrum characteristics, and unable to quickly and accurately locate the cause of the noise floor. Summary of the Invention

[0004] In view of the problems existing in the prior art, embodiments of this application provide a method and device for high noise floor analysis.

[0005] In a first aspect, embodiments of this application provide a method for high noise floor analysis, including:

[0006] Determining high noise floor cells based on network management data metrics;

[0007] Determining the high noise floor type of the target cell based on the noise floor data of the target cell;

[0008] Wherein, the noise floor data is obtained through the cell PRB-level uplink average interference level counter of the target cell, and / or through the sweep data of the target cell; the target cell is any cell among the high noise floor cells.

[0009] Optionally, the determining high noise floor cells based on network management data metrics includes:

[0010] Determining the average noise floor value of all cells throughout the day or the average noise floor value of each time period throughout the day based on the cell uplink average interference level counter in the network management data metrics;

[0011] If the average floor noise value of a specific cell throughout the day is greater than the first preset threshold, or the floor noise mean value of the specific cell in a preset number of time periods within the whole day is greater than the first preset threshold, then determine that the specific cell is a high floor noise cell.

[0012] Optionally, when the floor noise data is obtained through the cell PRB-level uplink average interference level counter of the target cell, based on the floor noise data of the target cell, determine the high floor noise type of the target cell, including:

[0013] Based on the cell PRB-level uplink average interference level counter, determine the average floor noise value of the target cell on each type of PRB;

[0014] Based on the interference type feature location classification rule and the number of PRBs with an average floor noise value greater than the second preset threshold on each type of PRB, determine the high floor noise type of the target cell.

[0015] Optionally, the interference type feature location classification rule is determined according to different access network systems and different network bandwidths.

[0016] Optionally, when the high floor noise data is obtained through the sweep frequency data of the target cell, based on the floor noise data of the target cell, determine the high floor noise type of the target cell, including:

[0017] Based on the longitude and latitude of the target cell, determine the position of the center point in units of the target cell;

[0018] Based on the center point, the first search radius, and the reverse screening criterion, conduct a preliminary screening of the sweep frequency data;

[0019] Based on targeted precise positioning, conduct another screening of the preliminarily screened sweep frequency data to determine the sweep frequency data slices;

[0020] Based on the sweep frequency data slices, determine the high floor noise type of the target cell.

[0021] Optionally, the step of conducting a preliminary screening of the sweep frequency data based on the center point, the first search radius, and the reverse screening criterion includes:

[0022] Based on the center point and the first search radius, use trigonometric functions to convert the linear distance represented by the first search radius into a spherical distance, and determine the geographical upper and lower limits of the screening range;

[0023] Judge whether the longitude and latitude in the sweep frequency data are within the range of the geographical upper and lower limits, and conduct a preliminary screening of the sweep frequency data;

[0024] Among them, the geographical superscript includes the maximum value of geographical latitude and the maximum value of longitude, and the geographical subscript includes the minimum value of geographical latitude and the minimum value of longitude.

[0025] Optionally, based on the targeted precise positioning, screening the pre-screened swept-frequency data again to determine the swept-frequency data slice, including:

[0026] Determine the azimuth angle between the center point and the midline of each sector as the first azimuth angle;

[0027] Determine the azimuth angle between the sampling point in the swept-frequency data and the center point as the second azimuth angle;

[0028] If the absolute value of the difference between the first azimuth angle and the second azimuth angle is within the preset half-power angle range, then the sampling point belongs to the swept-frequency data slice.

[0029] Optionally, determining the azimuth angle between the sampling point in the swept-frequency data and the center point as the second azimuth angle includes:

[0030] Based on the calculation formula of the azimuth angle between two points, using the longitude and latitude information of the center point and the longitude and latitude information of the sampling point converted to the Pi system as inputs, determine the second azimuth angle between the center point and the sampling point.

[0031] Optionally, based on the swept-frequency data slice, determining the high and low noise type of the target cell includes:

[0032] Based on the association relationship between the object name of the high and low noise in the network management data index corresponding to the high and low noise cell and the cell name in the base station site engineering parameters information, determine the geographical location information of the target cell;

[0033] Based on the association relationship between the geographical location information of the target cell and the swept-frequency data, determine the swept-frequency data slice;

[0034] Based on the swept-frequency data slice, determine the arithmetic mean of the noise floor of each frequency point within the swept-frequency data slice, and generate a high and low noise spectrum feature map of the high and low noise cell;

[0035] Based on the high and low noise spectrum feature map, count the bandwidth size and frequency band position where the noise floor appears to determine the high and low noise type of the target cell;

[0036] Among them, the geographical location information of the target cell includes: longitude, latitude and direction angle; the base station site includes the existing network planning site and the installed site.

[0037] Optionally, the method further includes:

[0038] Based on the geographical location information corresponding to the swept frequency data and the high and low noise type of the target cell, determine the geographical location of the interference source and the rectification plan for the high and low noise.

[0039] Optionally, when the noise floor data is obtained through the cell PRB-level uplink average interference level counter of the target cell and through the swept frequency data of the target cell, based on the noise floor data of the target cell, determining the high and low noise type of the target cell includes:

[0040] Based on the noise floor data obtained through the cell PRB-level uplink average interference level counter of the target cell, determine the first high and low noise type of the target cell;

[0041] Based on the noise floor data obtained through the swept frequency data of the target cell, determine the second high and low noise type of the target cell;

[0042] If both the first high and low noise type and the second high and low noise type exist, use the second high and low noise type as the high and low noise type of the target cell.

[0043] In a second aspect, an electronic device for high and low noise analysis provided by an embodiment of the present application includes a memory, a transceiver, and a processor, where:

[0044] The memory is used to store computer programs; the transceiver is used to send and receive data under the control of the processor; the processor is used to read the computer programs in the memory and implement the steps of the method for high and low noise analysis described in the first aspect above.

[0045] In a third aspect, an apparatus for high and low noise analysis provided by an embodiment of the present application includes:

[0046] A determination module, configured to determine a high and low noise cell based on network management data metrics;

[0047] An analysis module, configured to determine the high and low noise type of the target cell based on the noise floor data of the target cell;

[0048] Wherein, the noise floor data is obtained through the cell PRB-level uplink average interference level counter of the target cell and / or through the swept frequency data of the target cell; the target cell is any cell in the high and low noise cells.

[0049] In a fourth aspect, an embodiment of the present application further provides a processor-readable storage medium, where the processor-readable storage medium stores a computer program, and the computer program is used to cause the processor to execute the steps of the method for high and low noise analysis described in the first aspect above.

[0050] The method and device for high background noise analysis provided by the embodiments of the present application can determine the high background noise type through network management data metrics and / or perform frequency scanning on high background noise cells, and then analyze the corresponding high background noise type after screening the frequency scanning data to assist in determining the interference source location, which can greatly improve work efficiency and has strong portability and scalability. Description of the Drawings

[0051] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0052] Figure 1 It is a schematic diagram of interference analysis of high background noise cells using excel in the prior art;

[0053] Figure 2 It is a schematic diagram of the subframe configuration format of the SA network in the existing network of China Mobile;

[0054] Figure 3 It is a schematic flow chart of the method for high background noise analysis provided by the embodiments of the present application;

[0055] Figure 4 It is a schematic diagram of the implementation principle of geographical subscript and geographical superscript provided by the embodiments of the present application;

[0056] Figure 5 It is a schematic diagram of the azimuth angle corresponding to the center point provided by the embodiments of the present application;

[0057] Figure 6 It is a schematic diagram of the background noise spectrum characteristics determined from the frequency scanning data of high background noise cells provided by the embodiments of the present application;

[0058] Figure 7 It is a full-band interference characteristic diagram provided by the embodiments of the present application;

[0059] Figure 8 It is a radio and television background noise interference characteristic diagram provided by the embodiments of the present application;

[0060] Figure 9 It is a false base station interference characteristic diagram provided by the embodiments of the present application;

[0061] Figure 10 It is a schematic structural diagram of an electronic device for high background noise analysis provided by the embodiments of the present application;

[0062] Figure 11 It is a schematic structural diagram of a device for high background noise analysis provided by the embodiments of the present application. Detailed Embodiments

[0063] In the embodiments of the present application, the term "and / or" describes the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. The character " / " generally represents an "or" relationship between the associated objects before and after.

[0064] In the embodiments of the present application, the term "plurality" refers to two or more, and other quantifiers are similar thereto.

[0065] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0066] Figure 1 It is a schematic diagram of the interference analysis of high and low noise floor cells using excel in the prior art, as Figure 1 shown. According to the network management counter indicators, use Microsoft excel to generate a graph, and manually screen and evaluate the interference intensity and initially locate the interference type.

[0067] When analyzing the noise floor data using excel data analysis in the prior art, it is impossible to view and analyze the PRB-level noise floor spectrum curves of multiple time periods simultaneously. And the existing methods cannot associate multiple data sources to analyze the noise floor spectrum characteristics and cannot quickly and accurately locate the reasons for the high noise floor.

[0068] Based on the problems existing in the above prior art, the technical solutions of the present application are proposed. The uplink noise floor statistical indicators are obtained through the gateway counter, and the correlation between interference and time period, and the correlation between the wireless signal time domain and frequency domain are combined. By comparing the noise floor indicators of the cell in the time domain and the sweep data, the range of the interference source is accurately located. Here, the cell mainly refers to an NR (New Radio, representing the 5th generation wireless communication technology) cell or an LTE (Long Term Evolution, representing the 4th generation wireless communication technology) cell.

[0069] Figure 2 It is a schematic diagram of the subframe configuration format of the SA (standalone) network in the existing network of China Mobile, as Figure 2 shown. According to the uplink and downlink subframe configuration format of the SA network in the existing network of China Mobile, it is a 2.5ms double cycle, and the uplink signals are included in slots 4, 8, and 9. Then, when analyzing the uplink link noise floor, focus on the trends of the noise floor spectrograms of slots 4, 8, and 9.

[0070] Specifically, SA stand-alone networking means that the NAS (Non-Access Stratum) signaling (such as registration, authentication, etc.) between the 5G radio network and the core network is transmitted through the LTE base station, and 5G can work independently; the 2.5ms double cycle means that there are two different types of cycles within 5ms. The first 2.5ms is DDDSU, and the second 2.5ms is DDSUU. Together, they are: DDDSUDDSUU. Among them, D represents the downlink time slot, U represents the uplink time slot, and S represents the special time slot. The downlink time slot is composed of downlink symbols, the uplink time slot is composed of uplink symbols, and the special time slot is composed of downlink + uplink + special symbols. The default configuration of the S time slot is 10:2:2; it can be configured as 9:3:2 / 8:4:2 / 12:2:0 according to the networking coverage requirements and interference conditions.

[0071] The network management data metrics mainly include various counters, as shown in the following table:

[0072] Counter Name Indicator Description R1022 Average Uplink Interference Level of Cell R1023 Average Uplink Interference Level of Cell at PRB Level

[0073] Among them, for a cell, R1022 statistically calculates the average uplink interference level of the entire cell, and there is only one value of R1022 in a cell;

[0074] R1023 is at the cell PRB level. According to different cell bandwidths, the number of PRBs is different. The current operating wireless network bandwidths are 100MHz, 30MHz, 20MHz in the NR network and 20MHz in the LTE network, and their corresponding PRB numbers are 273, 160, 106, and 100 respectively. The number of values of R1023 is determined by the number of PRBs corresponding to the cell.

[0075] Figure 3 It is a schematic flowchart of the method for high background noise analysis provided by the embodiments of the present application. As Figure 3 shown, the embodiments of the present application provide a method for high background noise analysis, including:

[0076] Step 301: Determine high background noise cells based on network management data metrics;

[0077] Step 302: Determine the high background noise type of the target cell based on the background noise data of the target cell;

[0078] Among them, the background noise data is obtained through the cell PRB-level uplink average interference level counter of the target cell, and / or through the sweep data of the target cell; the target cell is any cell among the high background noise cells.

[0079] Specifically, the network management data metric is the cell uplink noise floor statistical metric, including R1022, R1023, etc. Based on the cell uplink noise floor statistical metric, cells with high noise floors are determined, and multiple cells with high noise floors form a list of cells with high noise floors.

[0080] The noise floor data of each cell in the list of cells with high noise floors is obtained respectively, which can reflect the noise floor of the cell and the specific noise floor value. There are three specific ways:

[0081] Way 1: Obtain the average uplink interference level of the cell through relevant counters in the network management data metric, such as R1022, to determine the noise floor data of the cell.

[0082] Way 2: Perform a frequency sweep operation on each cell in the list of cells with high noise floors to obtain the frequency sweep data of the cell, and further screen and analyze the obtained frequency sweep data to obtain the noise floor data of the cell.

[0083] Way 3: A combination of Way 1 and Way 2.

[0084] After determining the noise floor data of any cell (i.e., the target cell) in the cell with high noise floor through the above method, according to the distribution of the noise floor data of the cell and the noise floor value, determine the high noise floor type of the target cell.

[0085] Among them, when determining the high noise floor type of the target cell, big data analysis is adopted. It can be understood that big data analysis is the characteristic library data obtained by statistically analyzing the characteristics of the noise floor values corresponding to the existing high noise floor types and the distribution of the noise floor values. By comparing the average uplink interference level of the cell captured and statistically obtained in real time with the characteristic library data, the item with matching characteristics is determined, and the high noise floor type of the target cell can be determined.

[0086] The method for high noise floor analysis provided in the embodiment of the present application, after determining the high noise floor type through network management data metrics and / or performing a frequency sweep on the cell with high noise floor, screening the frequency sweep data and analyzing the corresponding high noise floor type to assist in determining the location of the interference source, can greatly improve work efficiency, and has strong portability and scalability.

[0087] Optionally, determining the cell with high noise floor based on the network management data metric includes:

[0088] Based on the cell uplink average interference level counter in the network management data metric, determine the average noise floor value of all cells throughout the day or the average noise floor value of each time period throughout the day.

[0089] If the average noise floor value of a specific cell throughout the day is greater than the first preset threshold, or the average noise floor value of a specific cell in a preset number of time periods throughout the day is greater than the first preset threshold, then determine that the specific cell is a cell with high noise floor.

[0090] Specifically, the cell uplink average interference level counter R1022 in the network management data metrics reflects the average value of the noise floor in each cell, and statistically calculates the average noise floor value of R1022 in each cell throughout the day and the average noise floor value in each time period throughout the day.

[0091] If the following conditions are met:

[0092] 1) The average noise floor value throughout the day is greater than the first preset threshold;

[0093] 2) There are N time periods throughout the day when the average noise floor value is greater than the first preset threshold.

[0094] It can be the case where either one or both of the above two conditions are met, then it is determined that the cell is a high noise floor cell, where the first preset threshold is set by those skilled in the art according to experience or according to the statistical data results.

[0095] Optionally, when the noise floor data is obtained through the cell PRB-level uplink average interference level counter of the target cell, based on the noise floor data of the target cell, determine the high noise floor type of the target cell, including:

[0096] Based on the cell PRB-level uplink average interference level counter, determine the average noise floor value of the target cell on each type of PRB;

[0097] Based on the interference type feature location classification rule and the number of PRBs with an average noise floor value greater than the second preset threshold on each type of PRB, determine the high noise floor type of the target cell.

[0098] Specifically, when the noise floor data is obtained through the cell PRB-level uplink average interference level counter R1022 of the target cell, according to the cell PRB-level uplink average interference level counter R1023, determine the average noise floor value of the target cell on each type of PRB. According to the different network bandwidths, the number of PRBs (i.e., the number of types of PRBs) is different. For example, when the bandwidth is 100 MHz, the number of PRBs is 273, then the types of PRBs include {PRB0, PRB1, PRB2,..., PRB271, PRB272}. When the bandwidth is 30 MHz, the corresponding number of PRBs is 160, then the types of PRBs include {PRB0, PRB1, PRB2,..., PRB158, PRB159}.

[0099] Determine the average noise floor value of each type of PRB according to the value of R1023, and compare it with the second preset threshold to determine the number of PRBs greater than the second preset threshold, that is, determine the number of types of PRBs greater than the second preset threshold.

[0100] Combined with the interference type feature location classification rule, determine the high and low noise type of the target cell. Here, the interference type feature location classification rule is the characteristic data of each high and low noise type determined through statistical analysis of past high and low noise data, that is, the counter determines the high and low noise type feature library. By comparing the number of classes of PRBs greater than the second preset threshold in R1023 and the distribution of the noise floor values of the corresponding PRBs with the counter-determined high and low noise type feature library, in the case of matching corresponding features, determine that the high and low noise type of the matching item is the high and low noise type of the target cell.

[0101] Optionally, the interference type feature location classification rule is determined according to different access network systems and different network bandwidths.

[0102] Specifically, according to different network operators, the access network systems include NR, LTE, 3G, 2G, etc. Currently, NR and LTE are more commonly used. There may also be various situations in the configuration of network bandwidth, such as 10M, 30M, 50M, 100M, 300M, etc.

[0103] Because under different access network systems and different network bandwidths, the corresponding number of PRBs is different, that is, the PRB classification number is different.

[0104] For example, the current operating wireless network bandwidths are 100MHz, 30MHz, 20MHz of NR and 20MHz of LTE network. The corresponding number of PRBs is 273, 160, 106, 100.

[0105] According to the different PRB classification numbers, the interference type feature location classification rule is also different.

[0106] Optionally, when the high and low noise data is obtained through the sweep frequency data of the target cell, based on the noise floor data of the target cell, determining the high and low noise type of the target cell includes:

[0107] Based on the longitude and latitude of the target cell, determine the position of the center point with the target cell as the unit;

[0108] Based on the center point, the first search radius, and the reverse screening criterion, perform a preliminary screening on the sweep frequency data;

[0109] Based on targeted precise positioning, perform another screening on the preliminarily screened sweep frequency data to determine the sweep frequency data slice;

[0110] Based on the sweep frequency data slice, determine the high and low noise type of the target cell.

[0111] Specifically, after determining the high background noise cell, using the longitude and latitude of the high background noise cell as the center point, a sample screening range is determined with a first search radius, that is, a circle is drawn with the center point as the center and the first search radius, and the sweep samples within the circle can be screened out.

[0112] The existing calculation method is to calculate the GPS (Global Positioning System) longitude and latitude information of the center point and all sampling points, and through the spherical distance calculation formula, obtain the distance between two points, and screen out the data within the first search radius.

[0113] In this application, a reverse screening criterion is used to screen the sweep data. The reverse screening criterion specifically includes: according to the above center point and the first search radius, determining the intermediate variable geographical subscript and geographical superscript, and initially screening the sweep data by comparing whether the longitude and latitude information of the sampling point is within the range of the geographical subscript and superscript.

[0114] Then, based on the targeted precise positioning, the initially screened sweep data is further screened. The targeted precise positioning specifically includes: calculating the azimuth angle between the sampling point and the center point, and then comparing the azimuth angle with the preset orientation difference, and determining whether the azimuth angle and the preset orientation difference are within the preset targeted range, and retaining the sampling point data within the preset targeted range. That is, the initially screened sweep data is further screened to determine the sweep data slice.

[0115] Finally, based on the sweep data slice, the high background noise type of the target cell is determined.

[0116] The method for high background noise analysis provided by the embodiments of this application can, after determining the high background noise type through network management data indicators and / or sweeping the high background noise cell, screen the sweep data and then analyze its corresponding high background noise type to assist in determining the location of the interference source, which can greatly improve work efficiency, and has strong portability and scalability.

[0117] Optionally, the initially screening the sweep data based on the center point, the first search radius, and the reverse screening criterion includes:

[0118] Based on the center point and the first search radius, using trigonometric functions to convert the linear distance represented by the first search radius into a spherical distance, and determining the geographical superscript and geographical subscript of the screening range;

[0119] Judging whether the longitude and latitude in the sweep data are within the range of the geographical superscript and geographical subscript, and initially screening the sweep data;

[0120] Wherein, the geographical superscript includes the maximum value of geographical latitude and the maximum value of longitude, and the geographical subscript includes the minimum value of geographical latitude and the minimum value of longitude.

[0121] Specifically, taking the longitude and latitude of the high background noise cell as the center point, and determining the corresponding first search radius, where the first search radius is usually set within the range of 150m to 300m, and can be iteratively adjusted according to the analysis results.

[0122] As shown in Figure 4 which is the schematic diagram of the implementation of the geographical subscript and superscript provided by the embodiment of the present application. As shown in Figure 4 described, N in the figure is the central position, R is the radius of the earth, T1 is the geographical subscript, T2 is the geographical superscript, and r is the search radius.

[0123] Referring to the Haversin haversine formula:

[0124] Hav(θ) = Sin 2 (θ) = (1 - Cos(θ)) / 2

[0125] According to the spherical cosine formula, the spherical haversine formula can be known as:

[0126] Haversin(θ)

[0127] = Haversin(Lat1 - Lat2)

[0128] + Cos(Lat1)Cos(Lat2)Haversin(Long1 - Long2)

[0129] where: θ = r / R;

[0130] Long_m and Lat_m respectively correspond to the longitude and latitude of the central reference point.

[0131] Long1, Long2, Lat1, Lat2 respectively correspond to any two longitude and latitude points.

[0132] Let Long1 = Long2, the latitude difference ΔLat = (Lat1 - Lat2) = r / R between any two points in the pi - based system can be obtained. Converting the latitude difference between any two points from the pi - based system to degrees gives:

[0133]

[0134] Let Lat1 = Lat2 = Lat_m, the difference ΔLong between the longitude of the sampling point and the longitude of the central reference point in the pi - based system can be obtained: ΔLong = r / (R * cos(Lat_m)). Converting the difference between the longitude of the sampling point and the longitude of the central reference point in the pi - based system into the form expressed in degrees gives:

[0135] Then the longitude and latitude of the geographical superscript and subscript can be calculated as follows:

[0136] Geographical minimum longitude coordinate:

[0137] Geographical minimum latitude coordinate:

[0138] Geographical maximum longitude coordinate:

[0139] Geographical maximum latitude coordinate:

[0140] The corresponding geographical subscript is (minLong_T1, minLat_T1);

[0141] The geographical superscript is (maxLong_T2, maxLat_T2).

[0142] Optionally, based on the targeted precise positioning, the pre-screened swept-frequency data is screened again to determine the swept-frequency data slice, including:

[0143] Determine the azimuth angle between the center point and the midline of each sector as the first azimuth angle;

[0144] Determine the azimuth angle between the sampling point in the swept-frequency data and the center point as the second azimuth angle;

[0145] If the absolute value of the difference between the first azimuth angle and the second azimuth angle is within the preset half-power angle range, then the sampling point belongs to the swept-frequency data slice.

[0146] Specifically, to determine the targeted orientation, usually taking the midline of the sector corresponding to the center point as the targeted orientation, determine the azimuth angle between the center point and the midline of the sector. As Figure 5 shown, taking the midline of the third sector as the targeted orientation, with the due north direction of the center point as the starting side, the included angle between the starting side and the targeted orientation in the clockwise direction is used to determine the azimuth angle between the center point and the midline of the sector as the first azimuth angle. That is Figure 5 the included angle represented by the dotted arc of the midpoint.

[0147] Taking the due north direction of the center point as the starting side and the line connecting the sampling point in the swept-frequency data and the center point as the ending side, determine the included angle between the two sides in the clockwise direction as the second azimuth angle. That is Figure 5 the included angle represented by the solid arc in

[0148] Calculate the difference between the first azimuth angle and the second azimuth angle, that is Figure 5 the bold dotted included angle in . If the absolute value of the difference between the two is within the preset half-power angle range, then the sampling point data is screened out, and at the same time, the swept-frequency data slice is determined, that is, all the swept-frequency data within the first search radius centered on the center point and with an included angle within the preset half-power angle range with the targeted orientation.

[0149] Optionally, determining the azimuth angle between the sampling point and the center point in the swept-frequency data as the second azimuth angle includes:

[0150] Based on the calculation formula for the azimuth angle between two points, using the longitude and latitude information of the center point and the longitude and latitude information of the sampling point converted to the Pi (pi) system as input, determine the second azimuth angle between the center point and the sampling point.

[0151] Specifically, taking the center point as the starting point and the sampling point as the ending point, calculate the azimuth angle between two longitudes and latitudes in space with respect to the due north direction. The implementation steps are as follows:

[0152] 1) Convert the longitude and latitude units from degrees to the Pi (pi) system, such as longitude * Pi / 180, Latitude * Pi / 180; where longitude represents the longitude value of any sampling point, and Latitude represents the latitude value of any sampling point.

[0153] 2) Calculate the azimuth angle between two points. The calculation formula is as follows:

[0154] Azimuth angle between two points = 360 - ((Atan(Y, X) * 180 / pi + 360) % 360);

[0155] Where:

[0156] X = cos(Pi_Latitude_1) * sin(Pi_Latitude_2) - sin(Pi_Latitude_1) * cos(Pi_Latitude_2) * cos(Pi_Longitude_1 - Pi_Longitude_2);

[0157] Y = sin(Pi_Longitude_1 - Pi_Longitude_2) * cos(Pi_Latitude_2);

[0158] Pi_Longitude_1 and Pi_Latitude_1 are the values of the longitude and latitude of the center point after being converted to the Pi (pi) system respectively;

[0159] Pi_Longitude_2 and Pi_Latitude_2 are the values of the longitude and latitude of the sampling point after being converted to the Pi (pi) system respectively.

[0160] In addition, as Figure 5 shown, after determining the second azimuth angle between the center point and the sampling point, calculate the azimuth angle of the reference point sector three (Figure 5 The azimuth angle from the reference point to the sampling point of the dotted arc shown) Figure 5 The difference from the solid arc shown) Figure 5 The included angle of the bold dotted line shown), and if the absolute value of the difference is within the half-power angle range, it is determined as a valid targeted sampling slice.

[0161] Optionally, determining the high and low noise type of the target cell based on the swept-frequency data slice includes:

[0162] Based on the association relationship between the object name of the high and low noise in the network management data index corresponding to the high and low noise cell and the cell name in the base station site engineering parameter information, determine the geographical location information of the target cell;

[0163] Based on the association relationship between the geographical location information of the target cell and the swept-frequency data, determine the swept-frequency data slice;

[0164] Based on the swept-frequency data slice, determine the arithmetic mean of the noise floor of each frequency point within the swept-frequency data slice, and generate the high and low noise spectral feature map of the high and low noise cell;

[0165] Based on the high and low noise spectral feature map, count the bandwidth size and frequency band position where the noise floor appears, and determine the high and low noise type of the target cell;

[0166] Wherein, the geographical location information of the target cell includes: longitude, latitude and azimuth angle; the base station sites include the existing network planning sites and the installed sites.

[0167] Specifically, the network management data index corresponding to the high and low noise cell includes object names, longitude and latitude information, etc. Among them, the object names include cell names, base station numbers, cell ID information, etc. Based on the cell name among them and the cell name in the base station site engineering parameter information, establish an association relationship. If the cell names of both are the same, then determine the geographical location information of this cell in the base station site engineering parameter information as the geographical location information of the target cell, that is, determine the geographical location information of the high and low noise cell. The geographical location information here includes: longitude, latitude and azimuth angle.

[0168] After determining the geographical location information of the high and low noise cell, according to this geographical location information and the geographical location information in the engineering parameter information of the base station site, determine a certain sector of the base station corresponding to the high and low noise cell, and then screen the swept-frequency data according to the geographical location, and screen out the part of the swept-frequency data whose geographical location information is within the geographical location range of the above high and low noise cell, and determine the slice of the swept-frequency data. That is, determine which data in the swept-frequency data belong to the sector range corresponding to this high and low noise cell.

[0169] Next, in the corresponding swept-frequency data slice, determine the arithmetic mean of the noise floor for each frequency point, that is, there is a corresponding noise floor value for each frequency point, and generate a high-noise-floor spectral feature map for the high-noise-floor cell. The abscissa of the high-noise-floor spectral feature map is the frequency point, representing all frequency points under the corresponding network; the ordinate is the noise floor value. Each point in the high-noise-floor spectral feature map represents the noise floor value corresponding to each frequency point.

[0170] Based on the high-noise-floor spectral feature map, it can be statistically obtained which frequency points the noise floor appears in, and the corresponding frequency band position and bandwidth size can be determined.

[0171] Based on the high-noise-floor spectral feature library of various high-noise-floor types or the judgment of experienced technicians, determine the high-noise-floor type of the target cell. Among them, the high-noise-floor spectral feature library is obtained by statistically analyzing the spectral features corresponding to each high-noise-floor type in the early stage.

[0172] The method for high-noise-floor analysis provided by the embodiments of the present application, after determining the high-noise-floor type through network management data indicators and / or sweeping the frequency of the high-noise-floor cell, screening the swept-frequency data and analyzing its corresponding high-noise-floor type, can assist in determining the location of the interference source, greatly improving work efficiency, and having strong portability and scalability.

[0173] Optionally, the method further includes:

[0174] Based on the geographical location information corresponding to the swept-frequency data and the high-noise-floor type of the target cell, determine the geographical location of the interference source and the high-noise-floor rectification plan.

[0175] Specifically, based on the swept-frequency data slice, determine the high-noise-floor type of the target cell, that is, the specific steps of analyzing the associated data by establishing an association between the swept-frequency data and the network management data indicators include:

[0176] 1) Obtain the engineering parameter information of the existing network planning sites and the sites that have been put into operation. The engineering parameter information of the sites includes keyword field information such as base station name, cell name, longitude, latitude, azimuth angle, etc.

[0177] 2) Conduct an associated query on the object name with high and low noise in the network management data indicators and the cell name in the site engineering parameter information to obtain the geographical location information of the high-noise-floor cell: longitude, latitude, and direction angle; among them, the object names with high noise floor include: cell name, base station number, cell ID information.

[0178] 3) Associate the geographical location information of the high-noise-floor cell with the swept-frequency data of the high-noise-floor cell. According to the custom search radius and the set value of the half-power angle difference, complete the swept-frequency data slice, calculate the arithmetic mean of the sampled data of the filtered slice, use the frequency point as the X-axis and the swept-frequency noise floor data as the Y-axis, and reflect the noise floor spectral characteristics in a graphical presentation manner, specifically as Figure 6as shown Figure 6-1 is the full-bandwidth interference spectrum feature map, Figure 6-2 is the guard-band interference spectrum feature map, Figure 6-3 is the uplink interference spectrum feature map, Figure 6-4 is the downlink interference spectrum feature map.

[0179] 4) Statistically analyze the bandwidth size and frequency band position where the background noise appears, and determine the type of high and low noise, such as in-system interference (PUCCH (Physical Uplink Control Channel) interference, GPS out-of-step interference, etc.), external interference (pseudo base station interference, jammer interference, etc.).

[0180] 5) Based on the type of high and low noise and the geographical location information of the high and low noise cells, determine the geographical location of the interference source, and implement a rectification plan for high and low noise, such as shutting down external interference sources, adjusting equipment engineering parameters, etc.

[0181] The method for analyzing high and low noise provided in the embodiments of the present application, after determining the type of high and low noise through network management data metrics and / or scanning the high and low noise cells, screening the scanning data and then analyzing the corresponding type of high and low noise, can assist in determining the location of the interference source, greatly improving work efficiency, and having strong portability and scalability.

[0182] Optionally, when the background noise data is obtained through the cell PRB-level uplink average interference level counter of the target cell and through the scanning data of the target cell, based on the background noise data of the target cell, determining the type of high and low noise of the target cell includes:

[0183] Based on the background noise data obtained through the cell PRB-level uplink average interference level counter of the target cell, determine the first type of high and low noise of the target cell;

[0184] Based on the background noise data obtained through the scanning data of the target cell, determine the second type of high and low noise of the target cell;

[0185] If both the first type of high and low noise and the second type of high and low noise exist simultaneously, use the second type of high and low noise as the type of high and low noise of the target cell.

[0186] Specifically, after determining the high and low noise cells, the type of high and low noise of the target cell can be determined based on the counter of the high and low noise cells, or the high and low noise cells can be scanned in parallel to obtain scanning data. According to the reverse screening criterion for the scanning data, determine the geographical subscripts, preliminarily screen the scanning data, and further screen the preliminarily screened scanning data according to the target precise positioning method to obtain scanning data slices, and analyze the scanning data slices to determine the type of high and low noise of the target cell.

[0187] The high and low noise types of the target cell can be determined by both of the above two methods. The final high and low noise type of the target cell is the high and low noise type determined by the sweep data of the high and low noise cell as the final result.

[0188] In addition, if there is only a counter based on the high and low noise cell to determine the high and low noise type of the target cell, the high and low noise type of the target cell is the high and low noise type determined according to the counter of the high and low noise cell; or if there is only sweep data based on the high and low noise cell to determine the high and low noise type of the target cell, the high and low noise type of the target cell is the high and low noise type determined according to the sweep data based on the high and low noise cell.

[0189] The method for high and low noise analysis provided in the embodiments of the present application can determine the high and low noise type through network management data indicators and / or after sweeping the high and low noise cell, screening the sweep data and analyzing the corresponding high and low noise type to assist in determining the interference source location, which can greatly improve work efficiency and has strong portability and scalability.

[0190] To illustrate the interference type feature location and classification rules provided in the present application, the following will be described with common interference feature spectra and relevant configurations of mobile networks, radio and television networks, and telecom networks.

[0191] Extract and summarize the interference spectrum characteristics into specific identification criteria to accurately locate the low noise interference type. By correlating the interference type data results and adding an error correction and verification mechanism, the interference type features are refined to improve the location accuracy.

[0192] Common interference feature spectra include:

[0193] 1) When the interference frequency domain is greater than 70% of the range, it is classified as an external full-band interference feature, such as Figure 7 shown;

[0194] 2) When there are multiple consecutive fixed 22 PRB square wave interferences within the first 120 PRBs in the frequency domain, it is classified as an MMDS (Multichannel Multipoint Distribution Services) radio and television low noise interference feature, such as Figure 8 shown;

[0195] 3) When the frequency domain position is not fixed but the PRB is in the range of 3 to 28 PRBs and the spectral waveform layer is in a normal distribution shape, it is classified as a pseudo base station interference feature, such as Figure 9 shown.

[0196] The relevant configurations of mobile networks, radio and television networks, and telecom networks are described as follows:

[0197] 1) When the mobile Band41 is configured with a 100 MHz bandwidth, the corresponding number of PRBs is 273:

[0198] Step 1.1: Determine the number of frequency-domain PRBs with a noise floor value greater than a set decision threshold, such as -110. If the number of PRBs greater than the decision threshold accounts for more than 70% of the total number of PRBs, determine that the high noise floor type is full-band interference. If the number of PRBs greater than the decision threshold is 0, that is, the proportion of the total number of PRBs is 0, determine that the high noise floor type is normal. Other proportions enter Step 1.2 for decision-making.

[0199] Step 1.2: Count the number of PRBs with high noise floor in the first 136 and the last 137 PRBs. If there are more than 40 PRBs with high interference in the last 137 PRBs, enter Step 1.3 for decision-making; if the number of PRBs with high noise floor is between 22 and 120 and the number of PRBs with high noise floor in the last 137 PRBs is zero, enter Step 1.4 for decision-making; if the number of PRBs with high noise floor is between 3 and 22, enter Step 1.5 for decision-making; in other cases, locate it as the 'other interference' type.

[0200] Step 1.3: Determine the total number of PRBs with high noise floor at positions 163 to 217 and / or positions 218 to 272. If the total number of PRBs with high noise floor in both exceeds 40 PRBs, that is, respectively determine the total number of PRBs with high noise floor at positions 163 to 217 and the total number of PRBs with high noise floor at positions 218 to 272. If the total number of PRBs with high noise floor in any one of the two segments exceeds 40 PRBs, or the total number of PRBs with high noise floor in both exceeds 40 PRBs, locate it as 'D1D2 interference', and other cases are located as 'D1D2 mixed interference', then the decision-making exits.

[0201] Step 1.4: Determine if there are more than 48 consecutive PRBs with high noise floor in the first 136 PRBs and it only occurs once. If the condition is met, locate it as 'bridge interference'; determine if there are multiple square waveforms of 22 PRBs in the first 120 PRBs and the number of square waves is one more than the number of wave troughs. If the condition is met, locate it as 'radio and television MMDS interference', then the decision-making exits.

[0202] Step 1.5: Determine if there is a wave trough in the waveform of consecutive PRBs with high noise floor. If there is no wave trough, it is considered to conform to the normal distribution and located as 'pseudo base station interference'; in 273 PRBs, determine if the number of PRBs with high noise floor in the first 16 and the last 16 PRBs is greater than 1. If the condition is met, locate it as 'control channel interference'; then the decision-making exits.

[0203] 2) The radio and television network configures a 30 MHz bandwidth for Band28, and the corresponding number of PRBs is 160:

[0204] Step 2.1: Determine the number of frequency-domain PRBs with a noise floor value greater than a set decision threshold, such as -110. If the number of PRBs greater than the decision threshold accounts for more than 70% of the total number of PRBs, determine that the high noise floor type is 'full-band interference'. If the number of PRBs greater than the decision threshold is 0, that is, the proportion of the total number of PRBs is 0, determine that the high noise floor type is normal. Other proportions enter Step 2.2 for decision-making.

[0205] Step 2.2: Determine that the total number of PRBs with a noise floor is greater than 48 and is continuously distributed, and determine it as radio and television MMDS interference. The number of other high-interference PRBs enters Step 2.3 for decision-making.

[0206] Step 2.3: Determine that the total number of PRBs with a noise floor is greater than 16 and is discontinuously distributed. Determine that there is high noise floor at the first and last 16 PRB positions, and determine it as 'control channel mixed interference'. Other cases are determined as 'other interference'.

[0207] Step 2.4: Determine that the total number of PRBs with a noise floor is less than or equal to 16, and the positions of the PRBs with high noise floor are all distributed within the first and last 16 PRB intervals. If the conditions are met, it is located as 'control channel interference'; other types are located as 'other interference' type, and then the decision is exited.

[0208] 3) The configuration of the 100MHz bandwidth for the Telecom and China Unicom network Band78

[0209] Step 3.1: Determine the number of frequency-domain PRBs with a noise floor value greater than a set decision threshold, such as -110. If the number of PRBs greater than the decision threshold accounts for more than 70% of the total number of PRBs, determine that the high noise floor type is 'full-band interference'. If the number of PRBs greater than the decision threshold is 0, that is, the proportion of the total number of PRBs is 0, determine that the high noise floor type is normal. Other proportions enter Step 3.2 for decision-making.

[0210] Step 3.2: Determine that the total number of PRBs with a noise floor is less than or equal to 16, and the positions of the PRBs with high noise floor are all distributed within the first and last 16 PRB intervals. If the conditions are met, it is located as 'control channel interference'; other cases perform Step 3.3 for judgment.

[0211] Step 3.3: If there is no interference at the sites within 10 kilometers near the site with high noise floor to be analyzed, but a co-system Telecom and China Unicom site with high noise floor is found more than 20 kilometers away, and the spectral characteristics are the same as those of the site to be analyzed, determine it as 'atmospheric duct interference', and then the decision is exited.

[0212] Figure 10 It is a schematic structural diagram of an electronic device for high noise floor analysis provided by an embodiment of the present application. As Figure 10 shown, the electronic device for high noise floor analysis includes a memory 1020, a transceiver 1010, and a processor 1000; among them, the processor 1000 and the memory 1020 can also be physically separated.

[0213] A memory 1020 for storing computer programs; a transceiver 1010 for transmitting and receiving data under the control of a processor 1000.

[0214] Specifically, the transceiver 1010 is used to receive and transmit data under the control of the processor 1000.

[0215] Among them, in Figure 10 The bus architecture may include any number of interconnected buses and bridges, specifically, various circuits represented by one or more processors represented by the processor 1000 and a memory represented by the memory 1020 are linked together. The bus architecture can also link together various other circuits such as peripheral devices, voltage regulators, and power management circuits, etc., which are well known in the art. Therefore, the present application will not further describe them. The bus interface provides an interface. The transceiver 1010 may be multiple components, that is, including a transmitter and a receiver, providing a unit for communicating with various other devices on a transmission medium, and these transmission media include transmission media such as wireless channels, wired channels, and optical cables.

[0216] The processor 1000 is responsible for managing the bus architecture and general processing, and the memory 1020 can store data used by the processor 1000 when executing operations.

[0217] The processor 1000 may be a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or a Complex Programmable Logic Device (CPLD), and the processor may also adopt a multi-core architecture.

[0218] The processor 1000 is used to execute any of the methods provided in the embodiments of the present application by calling the computer programs stored in the memory 1020 according to the obtained executable instructions. For example:

[0219] Determine a high background noise cell based on network management data metrics;

[0220] Determine the high background noise type of a target cell based on the background noise data of the target cell;

[0221] Among them, the background noise data is obtained through the cell PRB-level uplink average interference level counter of the target cell, and / or obtained through the sweep data of the target cell; the target cell is any cell in the high background noise cells.

[0222] Optionally, determining high background noise cells based on network management data metrics includes:

[0223] Based on the cell uplink average interference level counter in the network management data metrics, determine the average background noise value of all cells throughout the day or the average background noise value of each time period throughout the day;

[0224] If the average background noise value of a specific cell throughout the day is greater than a first preset threshold, or the average background noise value of a specific cell in a preset number of time periods throughout the day is greater than the first preset threshold, then determine that the specific cell is a high background noise cell.

[0225] Optionally, when the background noise data is obtained through the cell PRB-level uplink average interference level counter of the target cell, based on the background noise data of the target cell, determining the high background noise type of the target cell includes:

[0226] Based on the cell PRB-level uplink average interference level counter, determine the average background noise value of the target cell on each type of PRB;

[0227] Based on the interference type feature positioning classification rule and the number of PRBs with an average background noise value greater than a second preset threshold on each type of PRB, determine the high background noise type of the target cell.

[0228] Optionally, the interference type feature positioning classification rule is determined according to different access network systems and different network bandwidths.

[0229] Optionally, when the high background noise data is obtained through the sweep data of the target cell, based on the background noise data of the target cell, determining the high background noise type of the target cell includes:

[0230] Based on the longitude and latitude of the target cell, determine the position of the center point in the unit of the target cell;

[0231] Based on the center point, the first search radius, and the reverse screening criterion, perform a preliminary screening on the sweep data;

[0232] Based on targeted precise positioning, perform another screening on the preliminarily screened sweep data to determine the sweep data slices;

[0233] Based on the sweep data slices, determine the high background noise type of the target cell.

[0234] Optionally, the performing a preliminary screening on the sweep data based on the center point, the first search radius, and the reverse screening criterion includes:

[0235] Based on the center point and the first search radius, use trigonometric functions to convert the linear distance represented by the first search radius into a spherical distance, and determine the geographical upper and lower bounds of the screening range;

[0236] Judge whether the longitude and latitude in the sweep data are within the range of the geographical upper and lower bounds, and perform preliminary screening on the sweep data;

[0237] Among them, the geographical upper bound includes the maximum value of geographical latitude and the maximum value of longitude, and the geographical lower bound includes the minimum value of geographical latitude and the minimum value of longitude.

[0238] Optionally, based on the targeted precise positioning, screening the pre-screened sweep data again to determine the sweep data slice, including:

[0239] Determine the azimuth angle between the center point and the midline of each sector as the first azimuth angle;

[0240] Determine the azimuth angle between the sampling point in the sweep data and the center point as the second azimuth angle;

[0241] If the absolute value of the difference between the first azimuth angle and the second azimuth angle is within the preset half-power angle range, then the sampling point belongs to the sweep data slice.

[0242] Optionally, the determining the azimuth angle between the sampling point in the sweep data and the center point as the second azimuth angle includes:

[0243] Based on the calculation formula of the azimuth angle between two points, using the longitude and latitude information of the center point and the longitude and latitude information of the sampling point converted to the pi-based system as the input, determine the second azimuth angle between the center point and the sampling point.

[0244] Optionally, the determining the high and low noise type of the target cell based on the sweep data slice includes:

[0245] Based on the association relationship between the object name of the high and low noise in the network management data index corresponding to the high and low noise cell and the cell name in the base station site engineering parameters information, determine the geographical location information of the target cell;

[0246] Based on the association relationship between the geographical location information of the target cell and the sweep data, determine the sweep data slice;

[0247] Based on the sweep data slice, determine the arithmetic mean of the noise floor of each frequency point in the sweep data slice, and generate a high and low noise spectrum feature map of the high and low noise cell;

[0248] Based on the high and low noise spectrum feature map, count the bandwidth size and frequency band position where the noise floor appears, and determine the high and low noise type of the target cell;

[0249] Among them, the geographical location information of the target cell includes: longitude, latitude and azimuth angle; the base station sites include the existing network planned sites and the sites that have been put into operation.

[0250] Optionally, the step further includes:

[0251] Based on the geographical location information corresponding to the sweep data and the high and low noise type of the target cell, determine the geographical location of the interference source and the rectification plan for the high and low noise.

[0252] Optionally, when the noise floor data is obtained through the cell PRB-level uplink average interference level counter of the target cell and through the sweep data of the target cell, based on the noise floor data of the target cell, determining the high and low noise type of the target cell includes:

[0253] Based on the noise floor data obtained through the cell PRB-level uplink average interference level counter of the target cell, determine the first high and low noise type of the target cell;

[0254] Based on the noise floor data obtained through the sweep data of the target cell, determine the second high and low noise type of the target cell;

[0255] If both the first high and low noise type and the second high and low noise type exist at the same time, use the second high and low noise type as the high and low noise type of the target cell.

[0256] It should be noted here that the above-mentioned electronic device for high and low noise analysis provided by the embodiments of the present application can implement all the method steps implemented by the above-mentioned method embodiments, and can achieve the same technical effects. The same parts and beneficial effects as those in the method embodiments in this embodiment will not be specifically described here.

[0257] Figure 11 It is a schematic structural diagram of the device for high and low noise analysis provided by the embodiments of the present application, as Figure 11 shown, the device includes:

[0258] A determination module 1101, configured to determine a high and low noise cell based on network management data metrics;

[0259] An analysis module 1102, configured to determine the high and low noise type of the target cell based on the noise floor data of the target cell;

[0260] Among them, the noise floor data is obtained through the cell PRB-level uplink average interference level counter of the target cell, and / or through the sweep data of the target cell; the target cell is any cell in the high and low noise cells.

[0261] Optionally, the determination module 1101 is further configured to:

[0262] Based on the cell uplink average interference level counter in the network management data metrics, determine the average noise floor value of all cells throughout the day or the average noise floor value of each time period throughout the day for each cell;

[0263] If the average noise floor value of a specific cell throughout the day is greater than the first preset threshold, or the average noise floor value of the specific cell in a preset number of time periods throughout the day is greater than the first preset threshold, then determine that the specific cell is a high noise floor cell.

[0264] Optionally, the analysis module 1102 is further configured to:

[0265] Based on the cell PRB-level uplink average interference level counter, determine the average noise floor value of the target cell on each type of PRB;

[0266] Based on the interference type feature location classification rule and the number of PRBs with an average noise floor value greater than the second preset threshold on each type of PRB, determine the high noise floor type of the target cell.

[0267] Optionally, the interference type feature location classification rule is determined according to different access network systems and different network bandwidths.

[0268] Optionally, the analysis module 1102 is further configured to:

[0269] Based on the longitude and latitude of the target cell, determine the position of the center point in units of the target cell;

[0270] Based on the center point, the first search radius, and the reverse screening criterion, perform preliminary screening on the sweep data;

[0271] Based on targeted precise positioning, perform screening on the preliminarily screened sweep data again to determine the sweep data slices;

[0272] Based on the sweep data slices, determine the high noise floor type of the target cell.

[0273] Optionally, the analysis module 1102 is further configured to:

[0274] Based on the center point and the first search radius, use trigonometric functions to convert the linear distance represented by the first search radius into a spherical distance, and determine the geographical upper limit and geographical lower limit of the screening range;

[0275] Judge whether the longitude and latitude in the sweep data are within the range of the geographical upper limit and geographical lower limit, and perform preliminary screening on the sweep data;

[0276] Wherein, the geographical upper limit includes the maximum value of geographical latitude and the maximum value of longitude, and the geographical lower limit includes the minimum value of geographical latitude and the minimum value of longitude.

[0277] Optionally, the analysis module 1102 is further configured to:

[0278] Determine the azimuth angle between the center point and the midline of each sector as the first azimuth angle;

[0279] Determine the azimuth angle between the sampling point in the sweep data and the center point as the second azimuth angle;

[0280] If the absolute value of the difference between the first azimuth angle and the second azimuth angle is within the preset half-power angle range, the sampling point belongs to the sweep data slice.

[0281] Optionally, the analysis module 1102 is further configured to:

[0282] Based on the calculation formula of the azimuth angle between two points, using the longitude and latitude information of the center point and the longitude and latitude information of the sampling point converted to the Pi number system as inputs, determine the second azimuth angle between the center point and the sampling point.

[0283] Optionally, the analysis module 1102 is further configured to:

[0284] Based on the association relationship between the object name of the high base noise in the network management data metrics corresponding to the high base noise cell and the cell name in the base station site engineering parameters information, determine the geographical location information of the target cell;

[0285] Based on the association relationship between the geographical location information of the target cell and the sweep data, determine the sweep data slice;

[0286] Based on the sweep data slice, determine the arithmetic mean of the base noise of each frequency point within the sweep data slice, and generate a high base noise spectrum feature map of the high base noise cell;

[0287] Based on the high base noise spectrum feature map, count the bandwidth size and frequency band position where the base noise appears, and determine the high base noise type of the target cell;

[0288] Wherein, the geographical location information of the target cell includes: longitude, latitude and direction angle; the base station sites include the existing network planning sites and the installed sites.

[0289] Optionally, the apparatus further includes a positioning module 1103, and the positioning module 1103 is configured to:

[0290] Based on the geographical location information corresponding to the sweep data and the high base noise type of the target cell, determine the geographical location of the interference source and the high base noise rectification plan.

[0291] Optionally, the analysis module 1102 is further configured to:

[0292] Determine the first highest noise floor type of the target cell based on the noise floor data obtained by the cell PRB-level uplink average interference level counter of the target cell;

[0293] Determine the second highest noise floor type of the target cell based on the noise floor data obtained from the sweep data of the target cell;

[0294] If both the first highest noise floor type and the second highest noise floor type exist simultaneously, use the second highest noise floor type as the high noise floor type of the target cell.

[0295] It should be noted that the division of units in the embodiments of this application is illustrative, merely a logical function division, and there may be other division methods in actual implementation. Additionally, in each embodiment of this application, each functional unit may be integrated in a processing unit, or each unit may exist physically alone, or two or more units may be integrated in one unit. The above integrated units may be implemented in the form of hardware or in the form of software functional units.

[0296] If the above integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a processor-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of this 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 for causing a computer device (which may be a personal computer, a server, or a network device, etc.) or a processor to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes: USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs, etc., which can store program codes.

[0297] It should be noted here that the above device provided in the embodiments of this application can implement all the method steps implemented in the above method embodiments and can achieve the same technical effects. Therefore, the same parts and beneficial effects as those in the method embodiments will not be specifically described in this embodiment.

[0298] On the other hand, the embodiments of this application also provide a processor-readable storage medium, and the processor-readable storage medium stores a computer program, and the computer program is used to cause the processor to execute the method for high noise floor analysis provided in the above embodiments.

[0299] The processor-readable storage medium can be any available medium or data storage device accessible by the processor, including but not limited to magnetic memories (such as floppy disks, hard disks, magnetic tapes, magneto-optical discs (MO), etc.), optical memories (such as CDs, DVDs, BDs, HVDs, etc.), and semiconductor memories (such as ROM, EPROM, EEPROM, non-volatile memories (NAND FLASH), solid state drives (SSD)), etc.

[0300] The technical solutions provided by the embodiments of this application can be applicable to multiple systems, especially 5G systems. For example, the applicable systems can be global system of mobile communication (GSM) systems, code division multiple access (CDMA) systems, Wideband Code Division Multiple Access (WCDMA) general packet radio service (GPRS) systems, long term evolution (LTE) systems, LTE frequency division duplex (FDD) systems, LTE time division duplex (TDD) systems, long term evolution advanced (LTE-A) systems, universal mobile telecommunication system (UMTS), worldwide interoperability for microwave access (WiMAX) systems, 5G New Radio (NR) systems, etc. Both terminal devices and network devices are included in these multiple systems. The core network part can also be included in the system, such as the Evolved Packet System (EPS), 5G System (5GS), etc.

[0301] The network device involved in the embodiments of this application can be a base station, which can include multiple cells that provide services to terminals. Depending on the specific application scenarios, the base station can also be referred to as an access point, or it can be a device in the access network that communicates with wireless terminal devices through one or more sectors over the air interface, or other names. The network device can be used to mutually replace the received air frames and Internet Protocol (IP) packets, and act as a router between the wireless terminal device and the rest of the access network, where the rest of the access network can include an Internet Protocol (IP) communication network. The network device can also coordinate the management of the attributes of the air interface. For example, the network device involved in the embodiments of this application can be a network device (Base Transceiver Station, BTS) in the Global System for Mobile communications (GSM) or Code Division Multiple Access (CDMA), or it can be a network device (NodeB) in Wide-band Code Division Multiple Access (WCDMA), or it can also be an evolved network device (evolutional Node B, eNB or e-NodeB) in the Long Term Evolution (LTE) system, a 5G base station (gNB) in the 5G network architecture (next generation system), or it can be a Home evolved Node B (HeNB), a relay node, a femto, a pico, etc. This application does not limit this in the embodiments. In some network architectures, the network device can include a centralized unit (centralized unit, CU) node and a distributed unit (distributed unit, DU) node, and the centralized unit and the distributed unit can also be geographically separated.

[0302] The terminal involved in the embodiments of the present application can be a device that provides voice and / or data connectivity to users, such as a handheld device with wireless connection capabilities, or other processing devices connected to a wireless modem, etc. In different systems, the name of the terminal may also be different. For example, in a 5G system, the terminal can be called a user terminal or a user equipment (UE). The wireless terminal device can communicate with one or more core networks (CN) via a radio access network (RAN). The wireless terminal device can be a mobile terminal device, such as a mobile phone (or a "cellular" phone) and a computer with a mobile terminal device. For example, it can be a portable, pocket-sized, handheld, computer-integrated, or vehicle-mounted mobile device that exchanges voice and / or data with the radio access network. For example, devices such as personal communication service (PCS) phones, cordless phones, session initiated protocol (SIP) phones, wireless local loop (WLL) stations, and personal digital assistants (PDAs). The wireless terminal device can also be called a system, a subscriber unit, a subscriber station, a mobile station, a mobile, a remote station, an access point, a remote terminal device, an access terminal device, a user terminal device, a user agent, a user device, which is not limited in the embodiments of the present application.

[0303] The network device and the terminal can each use one or more antennas for multi-input multi-output (MIMO) transmission. The MIMO transmission can be single-user MIMO (SU-MIMO) or multi-user MIMO (MU-MIMO). According to the form and number of the combined antennas, the MIMO transmission can be 2D-MIMO, 3D-MIMO, FD-MIMO, or massive-MIMO, or it can also be diversity transmission, precoding transmission, beamforming transmission, etc.

[0304] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) that contain computer-usable program code.

[0305] The present application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer-executable instructions. These computer-executable instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one or more of the flows Figure 1 or blocks or the combination of blocks.

[0306] These processor-executable instructions can also be stored in a processor-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the processor-readable memory generate a manufactured article including instruction means that implement the functions specified in Figure 1 one or more of the flows Figure 1 or blocks or the combination of blocks.

[0307] These processor-executable instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one or more of the flows Figure 1 or blocks or the combination of blocks.

[0308] Obviously, those skilled in the art can make various modifications and variations to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalent technologies, the present application is also intended to include these modifications and variations.

Claims

1. A method for high background noise analysis, characterized in that, Including: Determine high-noise-floor cells based on network management data metrics; Determine the high-noise-floor type of the target cell based on the noise-floor data of the target cell; Wherein, the noise-floor data is obtained through the cell PRB-level uplink average interference level counter of the target cell, and / or obtained through the sweep data of the target cell; the target cell is any cell among the high-noise-floor cells; When the high-noise-floor data is obtained through the sweep data of the target cell, the determining the high-noise-floor type of the target cell based on the noise-floor data of the target cell includes: Determine the position of the center point in units of the target cell based on the longitude and latitude of the target cell; Perform primary screening on the sweep data based on the center point, the first search radius, and the reverse screening criterion; Perform secondary screening on the pre-screened sweep data based on targeted precise positioning to determine sweep data slices; Determine the high-noise-floor type of the target cell based on the sweep data slices; When the noise-floor data is obtained through the cell PRB-level uplink average interference level counter of the target cell, the determining the high-noise-floor type of the target cell based on the noise-floor data of the target cell includes: Determine the average noise-floor value of the target cell on each type of PRB based on the cell PRB-level uplink average interference level counter; Determine the high-noise-floor type of the target cell based on the interference type feature positioning classification rule and the number of PRBs with an average noise-floor value greater than a second preset threshold on each type of PRB.

2. The method for high background noise analysis according to claim 1, characterized in that The determining the high-noise-floor cells based on network management data metrics includes: Determine the average noise-floor value of all cells throughout the day or the average noise-floor value of each time period throughout the day based on the cell uplink average interference level counter in the network management data metrics; If the average noise-floor value of a specific cell throughout the day is greater than a first preset threshold, or the average noise-floor value of a specific cell in a preset number of time periods throughout the day is greater than the first preset threshold, then determine the specific cell as a high-noise-floor cell.

3. The method for high background noise analysis according to claim 1, wherein The interference type feature positioning classification rule is determined according to different access network systems and different network bandwidths.

4. The method for high background noise analysis according to claim 1, characterized in that The performing primary screening on the sweep data based on the center point, the first search radius, and the reverse screening criterion includes: Based on the center point and the first search radius, use trigonometric functions to convert the linear distance represented by the first search radius into a spherical distance, and determine the geographical upper bound and geographical lower bound of the screening range; Judge whether the longitude and latitude in the sweep data are within the range of the geographical upper bound and geographical lower bound, and perform primary screening on the sweep data; Wherein, the geographical upper bound includes the maximum value of geographical latitude and the maximum value of longitude, and the geographical lower bound includes the minimum value of geographical latitude and the minimum value of longitude.

5. The method for high background noise analysis according to claim 1, wherein The performing secondary screening on the pre-screened sweep data based on targeted precise positioning to determine sweep data slices includes: Determine the azimuth angle between the center point and the midline of each sector as the first azimuth angle; Determine the azimuth angle between the sampling point in the sweep data and the center point as the second azimuth angle; If the absolute value of the difference between the first azimuth angle and the second azimuth angle is within the preset half-power angle range, then the sampling point belongs to the swept-frequency data slice.

6. The method for high background noise analysis according to claim 5, characterized in that Determining the azimuth angle between the sampling point and the center point in the swept-frequency data as the second azimuth angle includes: Based on the calculation formula for the azimuth angle between two points, using the longitude and latitude information of the center point and the longitude and latitude information of the sampling point converted to the pi-based system as inputs, determining the second azimuth angle between the center point and the sampling point.

7. The method for high background noise analysis according to claim 1, characterized in that, Based on the swept-frequency data slice, determining the high and low noise type of the target cell includes: Based on the association relationship between the object name of the high and low noise in the network management data metrics corresponding to the high and low noise cell and the cell name in the base station site engineering parameters information, determining the geographical location information of the target cell; Based on the association relationship between the geographical location information of the target cell and the swept-frequency data, determining the swept-frequency data slice; Based on the swept-frequency data slice, determining the arithmetic mean of the noise floor of each frequency point within the swept-frequency data slice, generating a high and low noise spectrum feature map of the high and low noise cell; Based on the high and low noise spectrum feature map, statistically analyzing the bandwidth size and frequency band position where the noise floor appears, determining the high and low noise type of the target cell; Wherein, the geographical location information of the target cell includes: longitude, latitude, and direction angle; the base station sites include the existing network planning sites and the sites that have been put into operation.

8. The method for high background noise analysis according to claim 1, wherein The method further includes: Based on the geographical location information corresponding to the swept-frequency data and the high and low noise type of the target cell, determining the geographical location of the interference source and the high and low noise rectification plan.

9. The method for high background noise analysis according to claim 1, characterized in that, When the noise floor data is obtained through the cell PRB-level uplink average interference level counter of the target cell and through the swept-frequency data of the target cell, based on the noise floor data of the target cell, determining the high and low noise type of the target cell includes: Based on the noise floor data obtained through the cell PRB-level uplink average interference level counter of the target cell, determining the first high and low noise type of the target cell; Based on the noise floor data obtained through the swept-frequency data of the target cell, determining the second high and low noise type of the target cell; If both the first high and low noise type and the second high and low noise type exist simultaneously, using the second high and low noise type as the high and low noise type of the target cell.

10. An electronic device for high background noise analysis, characterized in that, Including a memory, a transceiver, and a processor; The memory is used to store computer programs; the transceiver is used to send and receive data under the control of the processor; the processor is used to execute the computer programs in the memory and implement the following steps: Based on the network management data metrics, determining the high and low noise cells; Based on the noise floor data of the target cell, determining the high and low noise type of the target cell; Wherein, the noise floor data is obtained through the cell PRB-level uplink average interference level counter of the target cell and / or through the swept-frequency data of the target cell; the target cell is any cell among the high and low noise cells; When the high and low noise data is obtained through the swept-frequency data of the target cell, based on the noise floor data of the target cell, determining the high and low noise type of the target cell includes: Based on the longitude and latitude of the target cell, determining the position of the center point in units of the target cell; Perform preliminary screening on the swept frequency data based on the center point, the first search radius, and the reverse screening criterion; Perform screening on the preliminarily screened swept frequency data again based on targeted precise positioning to determine swept frequency data slices; Determine the high and low noise types of the target cell based on the swept frequency data slices; When the background noise data is obtained through the cell PRB-level uplink average interference level counter of the target cell, determining the high and low noise types of the target cell based on the background noise data of the target cell includes: Determine the average background noise value of the target cell on each type of PRB based on the cell PRB-level uplink average interference level counter; Determine the high and low noise types of the target cell based on the interference type feature positioning classification rule and the number of PRBs with an average background noise value greater than the second preset threshold on each type of PRB.

11. The electronic device for high background noise analysis according to claim 10, characterized in that, Determining the high background noise cells based on the network management data metrics includes: Determine the average background noise value of all cells throughout the day or the average background noise value of each time period throughout the day based on the cell uplink average interference level counter in the network management data metrics; If the average background noise value of a specific cell throughout the day is greater than the first preset threshold, or the average background noise value of a specific cell in a preset number of time periods throughout the day is greater than the first preset threshold, then determine that the specific cell is a high background noise cell.

12. The electronic device for high background noise analysis according to claim 10, wherein The interference type feature positioning classification rule is determined according to different access network systems and different network bandwidths.

13. The electronic device for high background noise analysis according to claim 10, characterized in that, The performing preliminary screening on the swept frequency data based on the center point, the first search radius, and the reverse screening criterion includes: Based on the center point and the first search radius, use trigonometric functions to convert the linear distance represented by the first search radius into a spherical distance, and determine the geographical upper and lower bounds of the screening range; Judge whether the longitude and latitude in the swept frequency data are within the range of the geographical upper and lower bounds, and perform preliminary screening on the swept frequency data; Wherein, the geographical upper bound includes the maximum value of geographical latitude and the maximum value of longitude, and the geographical lower bound includes the minimum value of geographical latitude and the minimum value of longitude.

14. The electronic device for high background noise analysis according to claim 10, wherein The performing screening on the preliminarily screened swept frequency data again based on targeted precise positioning to determine swept frequency data slices includes: Determine the azimuth angle between the center point and the midline of each sector as the first azimuth angle; Determine the azimuth angle between the sampling point in the swept frequency data and the center point as the second azimuth angle; If the absolute value of the difference between the first azimuth angle and the second azimuth angle is within the preset half-power angle range, then the sampling point belongs to the swept frequency data slice.

15. The electronic device for high background noise analysis according to claim 14, wherein The determining the azimuth angle between the sampling point in the swept frequency data and the center point as the second azimuth angle includes: Based on the calculation formula of the azimuth angle between two points, use the longitude and latitude information of the center point and the longitude and latitude information of the sampling point converted to the pi radian system as inputs to determine the second azimuth angle between the center point and the sampling point.

16. The electronic device for high background noise analysis according to claim 10, wherein The determining the high and low noise types of the target cell based on the swept frequency data slices includes: Determine the geographical location information of the target cell based on the association relationship between the object name of the high background noise in the network management data metrics corresponding to the high background noise cell and the cell name in the base station site engineering parameters information; Determine the sweep data slice based on the correlation between the geographical location information of the target cell and the sweep data; Based on the sweep data slice, determine the arithmetic mean of the noise floor of each frequency point within the sweep data slice, and generate a high noise floor spectrum feature map of the high noise floor cell; Based on the high noise floor spectrum feature map, count the bandwidth size and frequency band position where the noise floor appears, and determine the high noise floor type of the target cell; Among them, the geographical location information of the target cell includes: longitude, latitude, and azimuth angle; the base station sites include the existing network planned sites and the installed sites.

17. The electronic device for high background noise analysis according to claim 10, characterized in that, The step further includes: Based on the geographical location information corresponding to the sweep data and the high noise floor type of the target cell, determine the geographical location of the interference source and the high noise floor rectification plan.

18. The electronic device for high background noise analysis according to claim 10, characterized in that, When the noise floor data is obtained through the cell PRB-level uplink average interference level counter of the target cell and through the sweep data of the target cell, based on the noise floor data of the target cell, determining the high noise floor type of the target cell includes: Based on the noise floor data obtained through the cell PRB-level uplink average interference level counter of the target cell, determine the first high noise floor type of the target cell; Based on the noise floor data obtained through the sweep data of the target cell, determine the second high noise floor type of the target cell; If both the first high noise floor type and the second high noise floor type exist, use the second high noise floor type as the high noise floor type of the target cell.

19. A device for high background noise analysis, characterized in that, The device includes: A determination module, configured to determine high noise floor cells based on network management data metrics; An analysis module, configured to determine the high noise floor type of the target cell based on the noise floor data of the target cell; Among them, the noise floor data is obtained through the cell PRB-level uplink average interference level counter of the target cell, and / or through the sweep data of the target cell; the target cell is any cell in the high noise floor cells; When the high noise floor data is obtained through the sweep data of the target cell, the determining the high noise floor type of the target cell based on the noise floor data of the target cell includes: Based on the longitude and latitude of the target cell, determine the position of the center point in units of the target cell; Based on the center point, the first search radius, and the reverse screening criterion, perform preliminary screening on the sweep data; Based on targeted precise positioning, perform screening on the preliminarily screened sweep data again to determine the sweep data slice; Based on the sweep data slice, determine the high noise floor type of the target cell; When the noise floor data is obtained through the cell PRB-level uplink average interference level counter of the target cell, the determining the high noise floor type of the target cell based on the noise floor data of the target cell includes: Based on the cell PRB-level uplink average interference level counter, determine the average noise floor value of the target cell on each type of PRB; Based on the interference type feature positioning classification rule and the number of PRBs where the average noise floor value on each type of PRB is greater than the second preset threshold, determine the high noise floor type of the target cell.

20. A processor-readable storage medium, characterized in that, The processor-readable storage medium stores a computer program, and the computer program is used to cause the processor to execute the method for high background noise analysis according to any one of claims 1 to 9.

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