Signal fast fading identification method, device, equipment, medium and program product

Abstract

The invention relates to the technical field of communication, and provides a signal fast fading identification method, device and equipment, a medium and a program product, and the method comprises the steps: obtaining MRO data collected by a base station, and extracting wireless signal data of each terminal user from the MRO data; based on the first window size, performing sliding window processing on the wireless signal data, performing coarse identification on a first signal segment in a current first window, and determining whether fast fading exists in the first signal segment; if fast fading exists in the first signal segment, sliding window processing is carried out on the first signal segment based on the second window size, mutation detection is carried out on the second signal segment in each second window by adopting a Mangkendall algorithm, and a fast fading time period in the first signal segment is determined; the second window size is smaller than the first window size. And through double sliding window processing and sudden change detection of the Mangkendall algorithm, misjudgment of fast fading caused by instantaneous change of the signal is avoided, and the accuracy of fast fading identification is improved.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and in particular to a method, device, equipment, medium and program product for identifying fast fading of a signal. Background Art

[0002] Fast fading refers to the phenomenon that the received signal strength of a radio signal changes rapidly in a short period of time. It is mainly caused by multipath propagation and the Doppler effect. This phenomenon usually occurs in mobile communication systems. When scatterers, reflectors, and refractors near the mobile station cause multipath transmission of the signal, these multipath signals overlap at the receiving point, causing the amplitude of the received signal to fluctuate rapidly. Fast fading is an important characteristic of wireless signal propagation. However, in daily network optimization work, it is difficult to quickly and accurately locate fast fading problem points through basic data intuitively. It usually requires user feedback or active network testing to find out, which often causes a lag in network optimization work. In order to deal with the fast fading phenomenon of wireless signals, a lot of manpower and time are required for on-site testing. However, daily network testing cannot traverse all network coverage areas, and the comprehensiveness of fast fading identification is insufficient. At present, a more comprehensive way to identify fast fading areas is to compare the reference signal power of adjacent data points in the MRO (Measurement Report Original) data reported by the connected UE (User Equipment). When the reference signal power fluctuates to a certain extent, the UE is judged as a fast fading UE. The UE reports the instantaneous signal strength. The instantaneous fluctuation of the radio signal does not necessarily mean fast fading, so there is a great possibility of misjudgment. Summary of the invention

[0003] The present application provides a signal fast fading identification method, device, equipment, medium and program product to solve the defect that the existing fast fading identification method is prone to misjudgment of fast fading.

[0004] The present application provides a method for identifying fast fading of a signal, comprising: Acquire MRO data collected by the base station, and extract wireless signal data of each terminal user from the MRO data; Based on the first window size, perform sliding window processing on the wireless signal data, and roughly identify the first signal segment in the current first window to determine whether the first signal segment has fast fading; If fast fading exists in the first signal segment, sliding window processing is performed on the first signal segment based on the second window size, and the Mann-Kendall algorithm is used to perform mutation detection on the second signal segments in each second window to determine the fast fading time period in the first signal segment; the second window size is smaller than the first window size.

[0005] In one embodiment, the step of using the Mann-Kendall algorithm to perform mutation detection on the second signal segment in the current second window to determine the fast fading time period in the first signal segment includes: A Mann-Kendall algorithm is used to calculate a first order sequence based on a sequential time series, and a first statistical sequence of the first order sequence is calculated according to a preset first statistical algorithm; the sequential time series is composed of signal strengths of each sampling point in the second signal segment; Calculating a second order sequence based on the reverse time series of the sequential time series, and calculating a second statistical sequence of the second order sequence according to a preset second statistical algorithm; determining a signal change trend of the second signal segment according to the first statistic sequence, and determining whether there is fast signal fading in the second signal segment based on the signal change trend; If there is fast signal fading in the second signal segment, determining the fading start time according to the intersection point of the first statistic sequence and the second statistic sequence; A fast fading time period in the first signal segment is determined according to the fading start time.

[0006] In one embodiment, the step of calculating the first order sequence based on the sequential time series and calculating the first statistical sequence of the first order sequence according to a preset first statistical algorithm includes: Compare the first sequence value and each second sequence value in the sequential time series, and construct a first feature sequence corresponding to the first sequence value according to the comparison result; wherein, if the comparison result indicates that the first sequence value is greater than the second sequence value, the sequence value corresponding to the second sequence value in the first feature sequence is a first preset value; if the comparison result indicates that the first sequence value is less than or equal to the second sequence value, the sequence value corresponding to the second sequence value in the first feature sequence is a second preset value; the first sequence value is any one in the sequential time series, and the second sequence value is any one before the first sequence value; Based on the first characteristic sequence, a first target number of second sequence values ​​less than the first sequence value is counted, and a first order sequence is constructed according to the first target number corresponding to each first sequence value; Calculate the eigenvalue corresponding to the first order sequence; the eigenvalue includes a mean and a variance; According to a preset first statistical algorithm, each sequence value and the characteristic value in the first order sequence are calculated respectively to obtain a first statistical sequence.

[0007] In one embodiment, the calculating of the second order sequence based on the reverse time sequence of the sequential time sequence, and the calculating of the second statistical sequence of the second order sequence according to a preset second statistical algorithm, comprises: The third sequence value and each fourth sequence value in the reverse time series of the sequential time series are compared, and a second characteristic sequence is constructed according to the comparison result; wherein, if the comparison result indicates that the third sequence value is greater than the fourth sequence value, the sequence value corresponding to the fourth sequence value in the second characteristic sequence is a first preset value; if the comparison result indicates that the third sequence value is less than or equal to the fourth sequence value, the sequence value corresponding to the fourth sequence value in the first characteristic sequence is a second preset value; the third sequence value is any one in the reverse time series, and the fourth sequence value is any one before the first sequence value; Based on the second characteristic sequence, a second target number of fourth sequence values ​​less than the third sequence value is counted, and a second order sequence is constructed according to the second target numbers corresponding to each third sequence value; Calculate the eigenvalue corresponding to the second order sequence; the eigenvalue includes a mean and a variance; According to a preset second statistical algorithm, each sequence value and the characteristic value in the second order sequence are calculated respectively to obtain a first statistical sequence.

[0008] In one embodiment, the roughly identifying the first signal segment in the current first window to determine whether the first signal segment has fast fading includes: Roughly identifying the first signal segment in the current first window, and determining a target sampling point in the first signal segment where the signal strength is less than a preset signal threshold; Based on the target time corresponding to the target sampling point, obtaining a forward sampling point of a first preset time length before the target sampling point and a backward sampling point of a second preset time length after the target sampling point; Based on the signal strengths corresponding to the forward sampling points, the average value of the signal strengths of the forward sampling points is calculated to obtain a forward average level of the target sampling point; based on the signal strengths corresponding to the backward sampling points and the target sampling points, the average value of the signal strengths of the backward sampling points and the target sampling points is calculated to obtain a backward average level of the target sampling point; Determine whether the first signal segment has fast fading according to the backward average level and the forward average level; if the backward average level is less than the preset signal threshold and the difference between the backward average level and the forward average level is greater than the preset level value, then the first signal segment has fast fading.

[0009] In one embodiment, extracting the wireless signal data of each terminal user from the MRO data includes: Extracting the IMSI identifier of each terminal user from the MRO data; Reverse filling the characteristic data in the MRO data based on the IMSI identifier to obtain the filled characteristic data; the characteristic data includes at least one of a base station number, a cell number, an AMF area number, an AMF set number, an AMF pointing number, a collection time identifier, a measurement object, a measurement signal strength, a physical cell identifier, and a frequency number; According to the IMSI identifier, the wireless signal data of each terminal user is extracted from the filled feature data.

[0010] The present application also provides a signal fast fading identification device, comprising the following modules: A data acquisition module, used to obtain the MRO data collected by the base station and extract the wireless signal data of each terminal user from the MRO data; A first identification module, configured to perform sliding window processing on the wireless signal data based on a first window size, and perform coarse identification on a first signal segment within a current first window to determine whether the first signal segment has fast fading; The second identification module is used to perform sliding window processing on the first signal segment based on a second window size if fast fading exists in the first signal segment, and use the Mann-Kendall algorithm to perform mutation detection on the second signal segments in each second window to determine the fast fading time period in the first signal segment; the second window size is smaller than the first window size.

[0011] The present application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the signal fast fading identification method as described in any one of the above is implemented.

[0012] The present application also provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the signal fast fading identification method as described in any one of the above is implemented.

[0013] The present application also provides a computer program product, including a computer program, and when the computer program is executed by a processor, the signal fast fading identification method as described in any one of the above is implemented.

[0014] The signal fast fading identification method, device, equipment, medium and program product provided by the present application roughly identify the fast fading of wireless signals from the user granularity by means of sliding window processing, and further use the Mann-Kendall algorithm to perform mutation detection on the signal segment with fast fading obtained by rough identification, and determine the fast fading time period by means of sliding window processing. Through sliding window processing, on the basis of rough identification, the Mann-Kendall algorithm is used to perform further mutation detection on the rough identified fast fading, thereby avoiding the misjudgment of fast fading caused by instantaneous changes in the signal, and improving the accuracy of fast fading identification. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the present application or the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0016] Figure 1 It is a flowchart of a signal fast fading identification method provided in an embodiment of the present application.

[0017] Figure 2 It is a structural schematic diagram of the signal fast fading identification device provided by the present invention.

[0018] Figure 3 It is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solutions and advantages of this application clearer, the technical solutions in this application will be clearly and completely described below in conjunction with the drawings in this application. Obviously, the described embodiments are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0020] Figure 1 is a flow chart of a method for identifying fast fading of a signal provided in an embodiment of the present application, such as Figure 1 As shown, the method comprises the following steps: Step 100, acquiring MRO data collected by the base station, and extracting wireless signal data of each terminal user from the MRO data; Step 200, based on the first window size, performing sliding window processing on the wireless signal data, and roughly identifying the first signal segment in the current first window to determine whether the first signal segment has fast fading; Step 300: If fast fading exists in the first signal segment, sliding window processing is performed on the first signal segment based on the second window size, and the Mann-Kendall algorithm is used to perform mutation detection on the second signal segment in the current second window to determine the fast fading time period in the second signal segment; the second window size is smaller than the first window size.

[0021] First, the MRO data collected by the base station is obtained. The MRO data may be reported by the user terminal UE. The MRO data includes information such as the signal level or signal quality of the cell where the user terminal is located. The wireless signal data of each terminal user is extracted from the MRO data. The wireless signal data includes the signal level and / or signal quality. The MRO data may be extracted according to the device identification of the user terminal, etc. The MRO data of the same terminal user may be extracted and aggregated to obtain the MRO data of each terminal user, and the wireless signal data may be extracted therefrom.

[0022] Furthermore, based on the first window size, sliding window processing is performed on the wireless signal data of any terminal user, and the first signal segment in the current first window is roughly identified to determine whether the first signal segment has fast fading. The identification method for roughly identifying the first signal segment is not specifically limited, and the change of the signal strength in the first signal segment can be detected. If the signal strength fluctuates greatly in a short period of time, it is considered that fast fading exists in the first signal segment.

[0023] Optionally, the first window size can be determined based on the sampling rate of the MRO data. Optionally, the first window size is inversely proportional to the sampling rate of the MRO data, the larger the sampling rate of the MRO data, the smaller the first window size, and the smaller the sampling rate of the MRO data, the larger the first window size. Optionally, the first window size can also be a preset first fixed size.

[0024] Optionally, in the process of performing sliding window processing based on the first window size, the sliding step size of the first window can be determined according to the first window size, or can be a preset first sliding step size. Exemplarily, if non-overlapping sliding window processing is performed on the wireless signal data, the first sliding step size is the same as the window width in the first window size, and if overlapping sliding window processing is performed on the wireless signal data, the first sliding step size is smaller than the window width in the first window size.

[0025] In one embodiment, the first window size and the first sliding step of the first window can be determined according to the detection period of fast fading, that is, the signal segment in the first window is composed of data points of wireless signal data collected for a duration corresponding to a detection period.

[0026] Based on the rough recognition of the first signal segment, the first signal segment is subjected to sliding window processing based on the second window size, and the Mann-Kendall (MK) algorithm is used to perform mutation detection on the second signal segment in the current second window to determine the fast fading time period in the second signal segment. The second window size is smaller than the first window size, and optionally, the second sliding step size of the second window is smaller than the first sliding step size.

[0027] Among them, the MK algorithm is a non-parametric statistical test method. Its sample data points do not need to follow a specific distribution and are not affected by a small number of outliers. It is more suitable for type variables and ordinal variables.

[0028] Optionally, the second window size may be a preset second fixed size, or may be determined based on the first window size, or may be determined based on the sampling rate of the MRO data. In one embodiment, the first window size is an integer multiple of the second window size, and the signal length of the first signal segment is an integer multiple of the length of the second signal segment.

[0029] In one embodiment, the MK algorithm is used to perform mutation detection on the second signal segment in each second window to determine whether there is fast fading in the second signal segment, so as to further accurately identify the fast fading roughly identified in the first signal segment to avoid misjudgment. Based on the mutation detection of the second signal segment in the second window, when a second signal segment with fast fading having a signal mutation is detected, the fast fading time period of the first signal segment is determined according to the second signal segment with fast fading.

[0030] In this way, the fast fading of the wireless signal of any terminal user can be identified from the user granularity. On this basis, the number of users with fast fading in any cell in any time period can be determined according to the cell where the user terminal is located. According to the number of users with fast fading in the cell, it can be determined whether the fast fading in the cell is an individual case caused by a few users or a common situation caused by the cell, so that a better network optimization strategy can be formulated for network optimization.

[0031] In this embodiment, fast fading is roughly identified for wireless signals from the user granularity by means of sliding window processing, and further by means of sliding window processing, the Mann-Kendall algorithm is used to perform mutation detection on the signal segment with fast fading obtained by rough identification, and the fast fading time period is determined. Through sliding window processing, on the basis of rough identification, the Mann-Kendall algorithm is used to perform further mutation detection on the fast fading obtained by rough identification, thereby avoiding misjudgment of fast fading due to instantaneous changes in the signal and improving the accuracy of fast fading identification.

[0032] In one embodiment, the extraction of the wireless signal data of each terminal user is achieved based on reverse filling of the user's unique identifier IMSI (International Mobile Subscriber Identity). In step 100, the wireless signal data of each terminal user is extracted from the MRO data, which may also include: Step 101, extracting the IMSI identifier of each terminal user from the MRO data; Step 102, reverse filling the characteristic data in the MRO data based on the IMSI identifier to obtain the filled characteristic data; the characteristic data includes at least one of a base station number, a cell number, an AMF area number, an AMF set number, an AMF pointing number, a collection time identifier, a measurement object, a measurement signal strength, a physical cell identifier, and a frequency number; Step 103: extracting the wireless signal data of each terminal user from the filled feature data according to the IMSI identifier.

[0033] First, extract the IMSI identifier of each terminal user from the MRO data, and reverse fill the characteristic data in the MRO data based on the IMSI identifier, so as to associate the AMF (Access and Mobility Management Function) identifier, time identifier, and AMFUE NGAPID in the terminal user's measurement report MR (Measurement Report) to achieve the purpose of accurately matching wireless data with core network data. Among them, NGAP ID is Non-Access Stratum Protocol Identification, and AMF UE NGAPID is the NGAP identifier between AMF and UE. In the 5G network, AMF UE NGAPID is used to uniquely identify a communication session between a specific UE and AMF.

[0034] Based on the IMSI identifier, the characteristic data in the MRO data is reversely filled to obtain the characteristic data filled with the IMSI identifier, and the characteristic data includes at least one of the base station number, cell number, AMF area number, AMF set number, AMF pointing number, collection time identifier, measurement object, measurement signal strength, physical cell identifier and frequency number.

[0035] Based on the filled IMSI identifier, the wireless signal data of each terminal user is extracted from the filled feature data. Exemplarily, for the wireless signal data of different terminal users, the user identifier is used as the data set label of the wireless signal data, so as to distinguish the data sets of different terminal users. Optionally, the wireless signal data of the terminal user includes at least the base station identifier of the base station where the user terminal is located, the longitude and latitude of the location where the user data occurs, and the reference signal received power of the user terminal based on the time series, that is, the time series of the RSRP of the user terminal.

[0036] Further, the sliding window processing of the wireless signal data of any terminal user, that is, the sliding window processing of the time series of the RSRP of the terminal user, in step 200, the first signal segment in the current first window is roughly identified to determine whether the first signal segment has fast fading, and may also include: Step 201, roughly identifying the first signal segment in the current first window, and determining a target sampling point in the first signal segment where the signal strength is less than a preset signal threshold; Step 202, based on the target time corresponding to the target sampling point, obtain a forward sampling point of a first preset time length before the target sampling point and a backward sampling point of a second preset time length after the target sampling point; Step 203, based on the signal strength corresponding to the forward sampling point, the average value of the signal strength of the forward sampling point is calculated to obtain the forward average level of the target sampling point; based on the signal strength corresponding to the backward sampling point and the target sampling point, the average value of the signal strength of the backward sampling point and the target sampling point is calculated to obtain the backward average level of the target sampling point; Step 204, determining whether the first signal segment has fast fading based on the backward average level and the forward average level; if the backward average level is less than the preset signal threshold, and the difference between the backward average level and the forward average level is greater than the preset level value, then the first signal segment has fast fading.

[0037] The first signal segment in the current first window is roughly identified, a target sampling point in the first signal segment whose signal strength is less than a preset signal threshold is determined, and based on the target time corresponding to the target sampling point, a forward sampling point of a first preset time length before the target sampling point and a backward sampling point of a second preset time length after the target sampling point are obtained. Optionally, the first preset time lengths may be the same or different, and there is no specific limitation on this.

[0038] Furthermore, when a sampling point with a signal strength less than a preset signal threshold is obtained, a sampling point of a first preset time length before the sampling point and a sampling point of a second preset time length after the sampling point are obtained, and based on the obtained forward sampling point and backward sampling point, it is determined whether the first signal segment has fast fading at the sampling point.

[0039] In one embodiment, the first preset duration is the same as the second preset duration, and the first preset duration and the second preset duration correspond to a cycle respectively. When a target sampling point whose signal strength is less than a preset signal threshold is obtained, a forward sampling point of a cycle before the target sampling point and a backward sampling point of a cycle after the target sampling point are obtained, and whether the target sampling point is a fast fading point is determined based on the obtained forward sampling point and backward sampling point. If the target sampling point is a fast fading point, fast fading exists in the first signal segment, and if the target sampling point is not a fast fading point, fast fading does not exist in the first signal segment.

[0040] Optionally, the target sampling point may include one or more, and when there is at least one target sampling point that is a fast fading point, fast fading exists in the first signal segment.

[0041] Further, the forward sampling point includes one or more sampling points, and similarly, the backward sampling point also includes one or more sampling points. Based on the signal strength of each forward sampling point, the average value of the signal strength of the forward sampling point is calculated to obtain the forward average level of the target sampling point; based on the signal strength of each backward sampling point and the signal strength of the target sampling point, the average value of the signal strength of the backward sampling point and the target sampling point is calculated to obtain the backward average level of the target sampling point.

[0042] For any target sampling point, its forward sampling point and backward sampling point are obtained, and its forward average level and backward average level are calculated, and whether the target sampling point is a fast fading point is determined according to the calculated forward average level and backward average level. Specifically, if the backward average level is less than a preset signal threshold, and the difference between the backward average level and the forward average level is equal to the preset level value, then the target sampling point is a fast fading point, and fast fading exists in the first signal segment.

[0043] For example, two sampling points correspond to one cycle, -110dB is the preset signal threshold, and in the first signal segment, if the sampling point with a signal strength less than -110dB corresponds to time t=m+2, the corresponding signal strength is RSRP m+2 , forward calculation (RSRP m +RSRP m+1 ) / 2, obtain its forward sampling point and calculate the forward average level; calculate backward (RSRP m+2 +RSRP m+3) / 2, obtain its backward sampling point and calculate the backward average level. If the backward average level is less than -110dB, and the difference between the backward average level and the forward average level is greater than the preset level value of 10dB, then it is considered that time t=m+2 is a weak coverage point caused by fast fading, that is, a fast fading point.

[0044] Furthermore, the mutation test of the MK algorithm is implemented based on constructing an order sequence of the time series. The wireless signal data of the user terminal includes the signal strength of the user terminal, and the signal strength can be represented by the RSRP (Reference Signal Receiving Power) level value of the user terminal in the wireless signal data. The wireless signal data of the user terminal is a time series composed of sampling points at different times. In step 300, the Mann-Kendall algorithm is used to perform mutation detection on the second signal segment in each second window to determine the fast fading time period in the first signal segment, which can also include: Step 301, using the Mann-Kendall algorithm to calculate a first order sequence based on the sequential time series, and calculating a first statistical sequence of the first order sequence according to a preset first statistical algorithm; the sequential time series is composed of signal strengths of each sampling point in the second signal segment; Step 302, calculating a second order sequence based on the reverse time sequence of the sequential time sequence, and calculating a second statistical sequence of the second order sequence according to a preset second statistical algorithm; Step 303: determining a signal change trend of the second signal segment according to the first statistic sequence, and determining whether there is fast signal fading in the second signal segment based on the signal change trend; Step 304: if there is fast signal fading in the second signal segment, determine the fading start time according to the intersection point of the first statistic sequence and the second statistic sequence; Step 305: determine a fast fading time period in the first signal segment according to the fading start time.

[0045] The MK algorithm is used to calculate the first order series based on the sequential time series, and the first statistical sequence of the first order series is calculated according to the preset first statistical algorithm, wherein the sequential time series is a time series composed of the signal strengths of each sampling point in the second signal segment.

[0046] Further, based on the reverse time series of the sequential time series, a second order series is calculated, and according to a preset second statistical algorithm, a second statistical series of the second order series is calculated. The signal change trend of the second signal segment is determined according to the first statistical series, and based on the signal change trend, it is determined whether there is a fast signal fading in the second signal segment.

[0047] In the case where there is fast signal fading in the second signal segment, the fading start time is determined according to the intersection of the first statistic sequence and the second statistic sequence. Optionally, the moment corresponding to the intersection of the first statistic sequence and the second statistic sequence is the fading start time, and the fast fading time period in the first signal segment is determined according to the fading start time.

[0048] The mutation test of the MK algorithm is a non-parametric hypothesis testing method used to test the trend change of data in a time series. The algorithm detects monotonic trends (increasing, decreasing, or no trend) in a time series by comparing the size of each data point with the previous data point. Specifically, the MK algorithm compares each data point in the time series with all the previous data points, calculates the number of data points that are smaller than the current data point, and the number of data points that are larger than the current data point, and then compares the size relationship of the two data point numbers to determine whether there is a monotonic trend.

[0049] Based on this, in step 301, calculating the first order sequence based on the sequential time series, and calculating the first statistical sequence of the first order sequence according to a preset first statistical algorithm may also include: Step 311, compare the first sequence value and each second sequence value in the sequential time series, and construct a first feature sequence corresponding to the first sequence value according to the comparison result; wherein, if the comparison result indicates that the first sequence value is greater than the second sequence value, the sequence value corresponding to the second sequence value in the first feature sequence is a first preset value; if the comparison result indicates that the first sequence value is less than or equal to the second sequence value, the sequence value corresponding to the second sequence value in the first feature sequence is a second preset value; the first sequence value is any one in the sequential time series, and the second sequence value is any one before the first sequence value; Step 321, counting a first target number of second sequence values ​​that are smaller than the first sequence value based on the first characteristic sequence, and constructing a first order sequence according to the first target number corresponding to each first sequence value; Step 331, calculating the eigenvalue corresponding to the first order sequence; the eigenvalue includes a mean and a variance; Step 341 : Calculate each sequence value and the characteristic value in the first order sequence according to a preset first statistical algorithm to obtain a first statistical sequence.

[0050] If the time series of the second signal segment is extracted from the MRO data of a single terminal user, it is: , n is the number of sampling points, construct the order sequence The expression The cumulative number of sampling points is defined as for: ; (1)

[0051] When constructing the first order sequence, first compare the first sequence value in the sequential time series with each second sequence value, and construct a first feature sequence based on the comparison result, wherein if the comparison result indicates that the first sequence value is greater than the second sequence value, then the sequence value corresponding to the first sequence value in the first feature sequence is a first preset value, and if the comparison result indicates that the first sequence value is less than or equal to the second sequence value, then the sequence value corresponding to the first sequence value in the first feature sequence is a second preset value, the first sequence value is any sequence value in the sequential time series, and the second sequence value is any sequence value in the sequential time series before the first sequence value.

[0052] The first characteristic sequence constructed is the order sequence Based on the first characteristic sequence, the first target number of the second sequence value less than the first sequence value is counted in the manner shown in Formula 1 , the counted first target quantity is used as the sequence value corresponding to the first sequence value in the first order sequence, so that each sequence value in the sequential time series can be used as the first sequence value, and the sequence value corresponding to each sequence value in the sequential time series in the first order sequence is obtained by statistics in the manner shown in Formula 1, so as to construct the first order sequence.

[0053] Furthermore, the eigenvalue corresponding to the first order sequence is calculated, and the eigenvalue includes a mean and a variance. According to a preset first statistical algorithm, each sequence value and the eigenvalue in the second order sequence are calculated respectively to obtain a first statistical sequence.

[0054] Optionally, the first statistical algorithm is defined under the assumption that the time series is randomly independent, as shown in Formula 2: ; (2)

[0055] Among them, the initial value of the first statistic sequence is , and are the sequence values ​​in the first order column The mean and variance of When they are independent and have the same continuous distribution, then: ; is a standard normal distribution, which is a time series order The calculated statistical sequence, at a given significance level In the case of , it indicates that there is an obvious trend change in the statistical sequence.

[0056] Based on the reverse time series of the sequential time series, a second order series and a second statistic series are calculated in the same manner as the sequential time series. Step 302 may further include: Step 312, compare the third sequence value and each fourth sequence value in the reverse time series of the sequential time series, and construct a second characteristic sequence according to the comparison result; wherein, if the comparison result indicates that the third sequence value is greater than the fourth sequence value, the sequence value corresponding to the fourth sequence value in the second characteristic sequence is a first preset value; if the comparison result indicates that the third sequence value is less than or equal to the fourth sequence value, the sequence value corresponding to the fourth sequence value in the first characteristic sequence is a second preset value; the third sequence value is any one in the reverse time series, and the fourth sequence value is any one before the first sequence value; Step 322, counting the second target quantity of the fourth sequence value that is smaller than the third sequence value based on the second characteristic sequence, and constructing a second order sequence according to the second target quantity corresponding to each of the third sequence values; Step 332, calculating the eigenvalue corresponding to the second order sequence; the eigenvalue includes a mean and a variance; Step 342: Calculate each sequence value and the characteristic value in the second order sequence according to a preset second statistical algorithm to obtain a first statistical sequence.

[0057] The third sequence value and each fourth sequence value in the reverse time series of the sequential time series are compared, and a second characteristic sequence is constructed according to the comparison result, wherein if the comparison result indicates that the third sequence value is greater than the fourth sequence value, then the sequence value corresponding to the fourth sequence value in the second characteristic sequence is a first preset value, and if the comparison result indicates that the third sequence value is less than or equal to the fourth sequence value, then the sequence value corresponding to the fourth sequence value in the second characteristic sequence is a second preset value. The third sequence value is any one in the reverse time series, and the fourth sequence value is any one of the sequence values ​​before the third sequence value in the reverse time series.

[0058] Furthermore, based on the constructed second feature sequence, the second target quantity of the fourth sequence value that is smaller than the third sequence value is counted, and the second target quantity is used as the sequence value corresponding to the third sequence value in the second order sequence, and the second order sequence is constructed according to the second target quantity corresponding to each third sequence value.

[0059] Then, the eigenvalue corresponding to the second order sequence is calculated, and the eigenvalue includes the mean and the variance. According to the preset second statistical algorithm, each sequence value and the eigenvalue in the second order sequence are calculated respectively, and the calculation result is used as the sequence value corresponding to the sequence value in the second order sequence in the second statistical sequence, thereby constructing the second statistical sequence.

[0060] That is, in reverse order of the sequential time series , calculate the second statistical series in the same way as the sequential time series, while making , Initial value of , and obtain the second statistical sequence.

[0061] By analyzing the first statistical series and the second statistic series , we can further analyze the sequential time series The time node of the mutation is determined to determine the mutation area, and the time period corresponding to the mutation area is the fast fading time period.

[0062] Specifically, if , it indicates that the time series is on an upward trend. On the contrary, if , it indicates that the time series is in a downward trend. When it exceeds any critical straight line, it indicates that the upward or downward trend is significant. and If there is an intersection point, and the intersection point is between the two critical lines, then the time corresponding to the intersection point is the start time of the mutation. Indicates that there is a fast fading point, and the mutation area is , if there is no fast fading, then mark , indicating that there is no fast fading point.

[0063] In this embodiment, based on the mutation detection of the MK algorithm, a sliding window processing method is adopted, and the window size can be adaptively adjusted according to the sampling rate, etc., which can better adapt to the characteristics of different signals. For some fast fading problems that only manifest in a local range, a sliding window processing method can be used on the basis of coarse recognition to perform more detailed and accurate detection, thereby improving the accuracy of fast fading recognition.

[0064] It should be noted that the MK algorithm is pre-trained, and the algorithm parameters that need to be pre-trained include the significance level , in different environments, The value of may be different. In practical applications, we can obtain The corresponding reference value and confidence interval serve as the basis for selecting the dynamic threshold when identifying fast fading.

[0065] The dynamic threshold is a dynamically selected threshold used by the MK algorithm to determine fast fading. By adjusting the threshold, the false alarm rate and missed alarm rate can be reduced while ensuring that the fast fading signal is accurately detected, thereby improving the robustness and applicability of the algorithm.

[0066] Furthermore, based on the identified fast fading, after confirming whether the identification result is correct, the MK algorithm can be corrected to improve the recognition accuracy of the MK algorithm for fast fading.

[0067] In this embodiment, by reverse filling the IMSI identifier in the MRO data, the user's wireless data and the core network signaling data can be accurately matched, thereby extracting the wireless signal data of each terminal user, realizing user-granular fast fading identification, and improving the accuracy of fast fading identification.

[0068] Furthermore, on the basis of double sliding window processing, the Mann-Kendall algorithm is used to detect mutations in the user's wireless signal segment, which improves the accuracy of fast fading recognition. In addition, dynamic thresholds and adaptive window sizes can be used to make the MK algorithm suitable for dynamic recognition of 5G fast fading scenarios, thereby improving the applicability and generalization ability of signal fast fading recognition.

[0069] The following describes a signal fast fading identification device provided in an embodiment of the present application. The signal fast fading identification device described below and the signal fast fading identification method described above can refer to each other.

[0070] Reference Figure 2 , the signal fast fading identification device provided in the embodiment of the present application includes: The data collection module 10 is used to obtain the MRO data collected by the base station and extract the wireless signal data of each terminal user from the MRO data; A first identification module 20 is used to perform sliding window processing on the wireless signal data based on a first window size, and to roughly identify a first signal segment within a current first window to determine whether the first signal segment has fast fading; The second identification module 30 is used to perform sliding window processing on the first signal segment based on a second window size if fast fading exists in the first signal segment, and use the Mann-Kendall algorithm to perform mutation detection on the second signal segment in each second window to determine the fast fading time period in the first signal segment; the second window size is smaller than the first window size.

[0071] In one embodiment, the second identification module 30 is further configured to: A Mann-Kendall algorithm is used to calculate a first order sequence based on a sequential time series, and a first statistical sequence of the first order sequence is calculated according to a preset first statistical algorithm; the sequential time series is composed of signal strengths of each sampling point in the second signal segment; Calculating a second order sequence based on the reverse time series of the sequential time series, and calculating a second statistical sequence of the second order sequence according to a preset second statistical algorithm; determining a signal change trend of the second signal segment according to the first statistic sequence, and determining whether there is fast signal fading in the second signal segment based on the signal change trend; If there is fast signal fading in the second signal segment, determining the fading start time according to the intersection point of the first statistic sequence and the second statistic sequence; A fast fading time period in the first signal segment is determined according to the fading start time.

[0072] In one embodiment, the second identification module 30 is further configured to: Compare the first sequence value and each second sequence value in the sequential time series, and construct a first feature sequence corresponding to the first sequence value according to the comparison result; wherein, if the comparison result indicates that the first sequence value is greater than the second sequence value, the sequence value corresponding to the second sequence value in the first feature sequence is a first preset value; if the comparison result indicates that the first sequence value is less than or equal to the second sequence value, the sequence value corresponding to the second sequence value in the first feature sequence is a second preset value; the first sequence value is any one in the sequential time series, and the second sequence value is any one before the first sequence value; Based on the first characteristic sequence, a first target number of second sequence values ​​less than the first sequence value is counted, and a first order sequence is constructed according to the first target number corresponding to each first sequence value; Calculate the eigenvalue corresponding to the first order sequence; the eigenvalue includes a mean and a variance; According to a preset first statistical algorithm, each sequence value and the characteristic value in the first order sequence are calculated respectively to obtain a first statistical sequence.

[0073] In one embodiment, the second identification module 30 is further configured to: The third sequence value and each fourth sequence value in the reverse time series of the sequential time series are compared, and a second characteristic sequence is constructed according to the comparison result; wherein, if the comparison result indicates that the third sequence value is greater than the fourth sequence value, the sequence value corresponding to the fourth sequence value in the second characteristic sequence is a first preset value; if the comparison result indicates that the third sequence value is less than or equal to the fourth sequence value, the sequence value corresponding to the fourth sequence value in the first characteristic sequence is a second preset value; the third sequence value is any one in the reverse time series, and the fourth sequence value is any one before the first sequence value; Based on the second characteristic sequence, a second target number of fourth sequence values ​​less than the third sequence value is counted, and a second order sequence is constructed according to the second target numbers corresponding to each third sequence value; Calculate the eigenvalue corresponding to the second order sequence; the eigenvalue includes a mean and a variance; According to a preset second statistical algorithm, each sequence value and the characteristic value in the second order sequence are calculated respectively to obtain a first statistical sequence.

[0074] In one embodiment, the first identification module 20 is further configured to: Roughly identifying the first signal segment in the current first window, and determining a target sampling point in the first signal segment where the signal strength is less than a preset signal threshold; Based on the target time corresponding to the target sampling point, obtaining a forward sampling point of a first preset time length before the target sampling point and a backward sampling point of a second preset time length after the target sampling point; Based on the signal strengths corresponding to the forward sampling points, the average value of the signal strengths of the forward sampling points is calculated to obtain a forward average level of the target sampling point; based on the signal strengths corresponding to the backward sampling points and the target sampling points, the average value of the signal strengths of the backward sampling points and the target sampling points is calculated to obtain a backward average level of the target sampling point; Determine whether the first signal segment has fast fading according to the backward average level and the forward average level; if the backward average level is less than the preset signal threshold and the difference between the backward average level and the forward average level is greater than the preset level value, then the first signal segment has fast fading.

[0075] In one embodiment, the data acquisition module 10 is further used for: Extracting the IMSI identifier of each terminal user from the MRO data; Reverse filling the characteristic data in the MRO data based on the IMSI identifier to obtain the filled characteristic data; the characteristic data includes at least one of a base station number, a cell number, an AMF area number, an AMF set number, an AMF pointing number, a collection time identifier, a measurement object, a measurement signal strength, a physical cell identifier, and a frequency number; According to the IMSI identifier, the wireless signal data of each terminal user is extracted from the filled feature data.

[0076] Figure 3 An example of a physical structure diagram of an electronic device is shown in FIG. Figure 3As shown, the electronic device may include: a processor 310, a communication interface 320, a memory 330 and a communication bus 340, wherein the processor 310, the communication interface 320 and the memory 330 communicate with each other through the communication bus 340. The processor 310 may call the logic instructions in the memory 330 to execute the signal fast fading identification method, which includes: Acquire MRO data collected by the base station, and extract wireless signal data of each terminal user from the MRO data; Based on the first window size, perform sliding window processing on the wireless signal data, and roughly identify the first signal segment in the current first window to determine whether the first signal segment has fast fading; If fast fading exists in the first signal segment, sliding window processing is performed on the first signal segment based on the second window size, and the Mann-Kendall algorithm is used to perform mutation detection on the second signal segments in each second window to determine the fast fading time period in the first signal segment; the second window size is smaller than the first window size.

[0077] In addition, the logic instructions in the above-mentioned memory 330 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0078] On the other hand, an embodiment of the present application further provides a computer program product, the computer program product including a computer program, the computer program can be stored on a non-transitory computer-readable storage medium, and when the computer program is executed by a processor, the computer can execute the signal fast fading identification method provided by the above methods, the method including: Acquire MRO data collected by the base station, and extract wireless signal data of each terminal user from the MRO data; Based on the first window size, performing sliding window processing on the wireless signal data, and roughly identifying the first signal segment in the current first window to determine whether the first signal segment has fast fading; If fast fading exists in the first signal segment, sliding window processing is performed on the first signal segment based on the second window size, and the Mann-Kendall algorithm is used to perform mutation detection on the second signal segments in each second window to determine the fast fading time period in the first signal segment; the second window size is smaller than the first window size.

[0079] On the other hand, the embodiment of the present application further provides a non-transitory computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the method for identifying fast fading of a signal provided by the above methods is implemented, and the method includes: Acquire MRO data collected by the base station, and extract wireless signal data of each terminal user from the MRO data; Based on the first window size, perform sliding window processing on the wireless signal data, and roughly identify the first signal segment in the current first window to determine whether the first signal segment has fast fading; If fast fading exists in the first signal segment, sliding window processing is performed on the first signal segment based on the second window size, and the Mann-Kendall algorithm is used to perform mutation detection on the second signal segments in each second window to determine the fast fading time period in the first signal segment; the second window size is smaller than the first window size.

[0080] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0081] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0082] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for identifying fast fading of a signal, characterized in that: include: Acquire MRO data collected by the base station, and extract wireless signal data of each terminal user from the MRO data; Based on the first window size, perform sliding window processing on the wireless signal data, and roughly identify the first signal segment in the current first window to determine whether the first signal segment has fast fading; If fast fading exists in the first signal segment, sliding window processing is performed on the first signal segment based on the second window size, and the Mann-Kendall algorithm is used to perform mutation detection on the second signal segments in each second window to determine the fast fading time period in the first signal segment; the second window size is smaller than the first window size.

2. The signal fast fading identification method according to claim 1, characterized in that: The step of using the Mann-Kendall algorithm to perform mutation detection on the second signal segment in the current second window to determine the fast fading time period in the first signal segment includes: A Mann-Kendall algorithm is used to calculate a first order sequence based on a sequential time series, and a first statistical sequence of the first order sequence is calculated according to a preset first statistical algorithm; the sequential time series is composed of signal strengths of each sampling point in the second signal segment; Calculating a second order sequence based on the reverse time series of the sequential time series, and calculating a second statistical sequence of the second order sequence according to a preset second statistical algorithm; determining a signal change trend of the second signal segment according to the first statistic sequence, and determining whether there is fast signal fading in the second signal segment based on the signal change trend; If there is fast signal fading in the second signal segment, determining the fading start time according to the intersection point of the first statistic sequence and the second statistic sequence; A fast fading time period in the first signal segment is determined according to the fading start time.

3. The signal fast fading identification method according to claim 2, characterized in that: The step of calculating a first order sequence based on a sequential time series and calculating a first statistical sequence of the first order sequence according to a preset first statistical algorithm includes: Compare the first sequence value and each second sequence value in the sequential time series, and construct a first feature sequence corresponding to the first sequence value according to the comparison result; wherein, if the comparison result indicates that the first sequence value is greater than the second sequence value, the sequence value corresponding to the second sequence value in the first feature sequence is a first preset value; if the comparison result indicates that the first sequence value is less than or equal to the second sequence value, the sequence value corresponding to the second sequence value in the first feature sequence is a second preset value; the first sequence value is any one in the sequential time series, and the second sequence value is any one before the first sequence value; Based on the first characteristic sequence, a first target number of second sequence values ​​less than the first sequence value is counted, and a first order sequence is constructed according to the first target number corresponding to each first sequence value; Calculate the eigenvalue corresponding to the first order sequence; the eigenvalue includes a mean and a variance; According to a preset first statistical algorithm, each sequence value and the characteristic value in the first order sequence are calculated respectively to obtain a first statistical sequence.

4. The method for identifying fast fading of a signal according to claim 2, characterized in that: The step of calculating a second order sequence based on the reverse time sequence of the sequential time sequence, and calculating a second statistical sequence of the second order sequence according to a preset second statistical algorithm, includes: The third sequence value and each fourth sequence value in the reverse time series of the sequential time series are compared, and a second characteristic sequence is constructed according to the comparison result; wherein, if the comparison result indicates that the third sequence value is greater than the fourth sequence value, the sequence value corresponding to the fourth sequence value in the second characteristic sequence is a first preset value; if the comparison result indicates that the third sequence value is less than or equal to the fourth sequence value, the sequence value corresponding to the fourth sequence value in the first characteristic sequence is a second preset value; the third sequence value is any one in the reverse time series, and the fourth sequence value is any one before the first sequence value; Based on the second characteristic sequence, a second target number of fourth sequence values ​​less than the third sequence value is counted, and a second order sequence is constructed according to the second target numbers corresponding to each third sequence value; Calculate the eigenvalue corresponding to the second order sequence; the eigenvalue includes a mean and a variance; According to a preset second statistical algorithm, each sequence value and the characteristic value in the second order sequence are calculated respectively to obtain a first statistical sequence.

5. The method for identifying fast fading of a signal according to claim 1, characterized in that: The roughly identifying the first signal segment in the current first window to determine whether the first signal segment has fast fading includes: Roughly identifying the first signal segment in the current first window, and determining a target sampling point in the first signal segment where the signal strength is less than a preset signal threshold; Based on the target time corresponding to the target sampling point, obtaining a forward sampling point of a first preset time length before the target sampling point and a backward sampling point of a second preset time length after the target sampling point; Based on the signal strengths corresponding to the forward sampling points, the average value of the signal strengths of the forward sampling points is calculated to obtain a forward average level of the target sampling point; based on the signal strengths corresponding to the backward sampling points and the target sampling points, the average value of the signal strengths of the backward sampling points and the target sampling points is calculated to obtain a backward average level of the target sampling point; Determine whether the first signal segment has fast fading according to the backward average level and the forward average level; if the backward average level is less than the preset signal threshold and the difference between the backward average level and the forward average level is greater than the preset level value, then the first signal segment has fast fading.

6. The method for identifying fast fading of a signal according to claim 1, characterized in that: The extracting the wireless signal data of each terminal user from the MRO data includes: Extracting the IMSI identifier of each terminal user from the MRO data; Reverse filling the characteristic data in the MRO data based on the IMSI identifier to obtain the filled characteristic data; the characteristic data includes at least one of a base station number, a cell number, an AMF area number, an AMF set number, an AMF pointing number, a collection time identifier, a measurement object, a measurement signal strength, a physical cell identifier, and a frequency number; According to the IMSI identifier, the wireless signal data of each terminal user is extracted from the filled feature data.

7. A signal fast fading identification device, characterized in that: include: A data acquisition module, used to obtain the MRO data collected by the base station and extract the wireless signal data of each terminal user from the MRO data; A first identification module, configured to perform sliding window processing on the wireless signal data based on a first window size, and perform coarse identification on a first signal segment within a current first window to determine whether the first signal segment has fast fading; The second identification module is used to perform sliding window processing on the first signal segment based on a second window size if fast fading exists in the first signal segment, and use the Mann-Kendall algorithm to perform mutation detection on the second signal segments in each second window to determine the fast fading time period in the first signal segment; the second window size is smaller than the first window size.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the signal fast fading identification method according to any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the signal fast fading identification method according to any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the signal fast fading identification method according to any one of claims 1 to 6 is implemented.

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