CN118677817B - A jitter component separation method, device, equipment and medium - Google Patents
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Abstract
本申请公开了一种抖动成份分离方法、装置、设备及介质，涉及抖动测量技术领域，用于解决现有技术不能有效地对传输数据中的抖动成份进行分离的问题。所述方法包括：采用传输数据的时间间隔误差进行混合高斯模型建模，获得初始混合高斯模型；采用最大期望算法对所述初始混合高斯模型进行收敛，获得目标混合高斯模型的模型数据；其中，所述模型数据包括所述目标混合高斯模型中每个子高斯的均值、方差和生成概率；根据所述模型数据、预设的随机抖动定义式和预设的确定性抖动定义式，对所述传输数据进行抖动分离，获得随机抖动成份及确定性抖动成份，以对传输数据中的抖动成份进行有效分离。
The present application discloses a jitter component separation method, device, equipment and medium, which relates to the field of jitter measurement technology and is used to solve the problem that the prior art cannot effectively separate jitter components in transmission data. The method comprises: using the time interval error of the transmission data to model a mixed Gaussian model to obtain an initial mixed Gaussian model; using the maximum expectation algorithm to converge the initial mixed Gaussian model to obtain model data of the target mixed Gaussian model; wherein the model data includes the mean, variance and generation probability of each sub-Gaussian in the target mixed Gaussian model; according to the model data, a preset random jitter definition formula and a preset deterministic jitter definition formula, the transmission data is jitter-separated to obtain random jitter components and deterministic jitter components, so as to effectively separate the jitter components in the transmission data.
Description
Technical Field
The application relates to the technical field of jitter measurement and provides a jitter component separation method, device, equipment and medium.
Background
As is well known, in serial bus communication, jitter is one of the main causes affecting high-speed serial link data transmission as the data communication rate is continuously increased, and jitter can be classified into random jitter and deterministic jitter according to the characteristics of jitter and its formation cause, wherein deterministic jitter can be classified into data-dependent jitter, periodic jitter, and bounded uncorrelated jitter.
In the prior art, the separation method of jitter includes a frequency domain method, a time domain method, and the like, according to the characteristics of the jitter component. For example, the frequency points greater than the set threshold may be determined as deterministic jitter components by a frequency domain fast fourier transform (Fast Fourier Transform, FFT) first, then, after the deterministic jitter components are removed in the frequency domain, discrete fourier transform (Discrete Fourier Transformation, DFT) is performed to convert into time domain data, and a mixture gaussian model is used to obtain random jitter components. However, the method for combining the time domain and the frequency domain needs to set a threshold value, which is determined by the characteristics of an analysis system and has a certain error, and in addition, in the process of converting the time domain into the frequency domain and converting the frequency domain into the time domain, the calculation steps are more, and the method has limitations in systems with big data and strict requirements on the measurement time.
Therefore, how to effectively separate the jitter components in the transmission data is a problem to be solved.
Disclosure of Invention
The application provides a jitter component separation method, a device, equipment and a medium, which are used for solving the problem that the prior art can not effectively separate jitter components in transmission data.
In one aspect, there is provided a dither component separating method, the method comprising:
modeling a mixed Gaussian model by adopting a time interval error of transmission data to obtain an initial mixed Gaussian model;
Converging the initial mixed Gaussian model by adopting a maximum expected algorithm to obtain model data of a target mixed Gaussian model, wherein the model data comprises the mean value, variance and generation probability of each sub-Gaussian in the target mixed Gaussian model;
And carrying out jitter separation on the transmission data according to the model data, the preset random jitter definition type and the preset deterministic jitter definition type to obtain random jitter components and deterministic jitter components.
Optionally, the step of modeling the hybrid gaussian model by using the time interval error of the transmission data to obtain an initial hybrid gaussian model includes:
determining the time interval error and the number of sub-gaussians in the initial mixture gaussians model according to the transmission data;
And modeling a mixed Gaussian model by adopting the time interval error and the sub-Gaussian quantity to obtain the initial mixed Gaussian model.
Optionally, the step of performing jitter separation on the transmission data according to the model data, a preset random jitter definition type and a preset deterministic jitter definition type to obtain a random jitter component and a deterministic jitter component includes:
determining the random jitter component according to the variance, the generation probability and the preset random jitter definition formula of each sub-Gaussian, wherein the preset random jitter definition formula comprises the following formulas:
Wherein, N represents the number of sub-gaussians in the initial mixture gaussians model; representing the variance of the ith sub-Gaussian in the target-Gaussian mixture model; representing the generation probability of the ith sub-Gaussian in the target-Gaussian mixture model.
Optionally, the step of performing jitter separation on the transmission data according to the model data, a preset random jitter definition type and a preset deterministic jitter definition type to obtain a random jitter component and a deterministic jitter component includes:
Determining the deterministic jitter component according to the mean value of all the sub-gauss and the preset deterministic jitter definition formula, wherein the preset deterministic jitter definition formula comprises the following formula:
Wherein, The representation takes absolute value; Representing the mean value of the ith sub-Gaussian in the target-Gaussian mixture model, Representation ofIs set at the maximum value of (c),Representation ofIs a minimum of (2).
Optionally, after performing jitter separation on the transmission data according to the model data, a preset random jitter definition formula and a preset deterministic jitter definition formula to obtain a random jitter component and a deterministic jitter component, the method further includes:
Obtaining a curve-fitted abscissa sequence according to the discrete histogram obtained by the time interval error;
for any one sub-Gaussian curve, according to the abscissa sequence, obtaining a theoretical ordinate of the any one sub-Gaussian curve;
Obtaining a first integration result of any one of the sub-Gaussian curves according to an improved trapezoidal integration formula, a counted time point sequence and a discrete histogram obtained by time interval errors;
Obtaining a second integration result of any one of the sub-Gaussian curves according to the improved trapezoidal integration formula, the abscissa sequence and the theoretical ordinate of any one of the sub-Gaussian curves;
determining an ordinate sequence of any one of the sub-Gaussian curves according to the theoretical ordinate of the any one of the sub-Gaussian curves, the first integration result and the second integration result;
And obtaining a curve-fitting mixed Gaussian curve ordinate sequence according to the sum of the ordinate values of the sub-Gaussian curves corresponding to the time of each sampling point.
Optionally, the step of obtaining the first integration result according to the improved trapezoidal integration formula, the counted time point sequence and the discrete histogram obtained by the time interval error includes:
Determining whether the input length of the histogram discrete points is equal to the input length of the time point sequence after statistics, and whether the input length is more than 2;
if the input length of the histogram discrete points is determined to be unequal to the input length of the time point sequence after statistics, or the input length is not more than 2, prompting that the input data is wrong;
If the input length of the histogram discrete points is equal to the input length of the counted time point sequence and is larger than 2, sequencing the counted time point sequence from small to large to obtain a sequenced time point sequence;
obtaining corresponding ordered histogram discrete points according to the ordered time point sequence;
and carrying the ordered time point sequence and the ordered histogram discrete points into an improved trapezoidal integration formula for integral calculation to obtain the first integral result.
Optionally, after obtaining the ordinate sequence of the curve-fitted mixed gaussian curve according to the sum of the ordinate values of the respective sub-gaussian curves corresponding to the respective sampling point times, the method further includes:
Performing curve fitting according to the abscissa sequence and the ordinate sequence of each sub-Gaussian curve to obtain a plurality of sub-Gaussian curves representing random jitter;
And performing curve fitting according to the abscissa sequence and the ordinate sequence of the Gaussian mixture curve to obtain the Gaussian mixture curve representing random jitter.
In one aspect, there is provided a shaking component separation apparatus comprising:
The model obtaining unit is used for carrying out mixed Gaussian model modeling by adopting a time interval error of transmission data to obtain an initial mixed Gaussian model;
The model data acquisition unit is used for converging the initial mixed Gaussian model by adopting a maximum expected algorithm to acquire model data of a target mixed Gaussian model, wherein the model data comprises a mean value, a variance and a generation probability of each sub-Gaussian in the target mixed Gaussian model;
And the jitter separation unit is used for performing jitter separation on the transmission data according to the model data, the preset random jitter definition type and the preset deterministic jitter definition type to obtain random jitter components and deterministic jitter components.
In one aspect, an electronic device is provided that includes a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing any of the methods described above when executing the computer program.
In one aspect, a computer storage medium having stored thereon computer program instructions which, when executed by a processor, implement any of the methods described above.
Compared with the prior art, the application has the beneficial effects that:
In the method, when jitter component separation is carried out, firstly, a mixed Gaussian model can be modeled by adopting a time interval error of transmission data to obtain an initial mixed Gaussian model, then, a maximum expected algorithm can be adopted to converge the initial mixed Gaussian model to obtain model data of a target mixed Gaussian model, wherein the model data comprises a mean value, a variance and a generation probability of each sub-Gaussian in the target mixed Gaussian model, and finally, the jitter separation can be carried out on the transmission data according to the model data, a preset random jitter definition formula and a preset deterministic jitter definition formula to obtain random jitter components and deterministic jitter components. Therefore, in the application, since modeling analysis of the arbitrary order Gaussian mixture model (Gaussian Mixture Model, GMM) is directly performed by the time interval error and the maximum expectation algorithm, deterministic jitter and random jitter calculation are performed based on the modeling analysis to obtain different jitter components. Therefore, compared with the prior art that the time domain is converted into the frequency domain, the frequency points larger than the threshold value are removed and then converted into the time domain to determine deterministic jitter, the method can directly perform mixed Gaussian model modeling on the time domain through time interval errors and process the mixed Gaussian model to obtain random jitter and deterministic jitter, so that the method not only avoids various conversions of the time domain and the frequency domain, reduces calculation steps, but also improves test rate, and is applied to a system with strict requirements on time.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the related art, the drawings that are required to be used in the embodiments or the related technical descriptions will be briefly described, and it is apparent that the drawings in the following description are only embodiments of the present application, and other drawings may be obtained according to the provided drawings without inventive effort for those skilled in the art.
Fig. 1 is a schematic view of an application scenario provided in an embodiment of the present application;
FIG. 2 is a flow chart of a method for separating jitter components according to an embodiment of the present application;
FIG. 3 is a schematic diagram of the actual curve fitting results provided by the embodiment of the present application;
fig. 4 is a schematic diagram of a jitter component separation apparatus according to an embodiment of the present application.
The figure shows 10-jitter component separating apparatus, 101-processor, 102-memory, 103-I/O interface, 104-database, 40-jitter component separating device, 401-model obtaining unit, 402-model data obtaining unit, 403-jitter separating unit, 404-curve fitting unit.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present application more apparent, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application. Embodiments of the application and features of the embodiments may be combined with one another arbitrarily without conflict. Also, while a logical order is depicted in the flowchart, in some cases, the steps depicted or described may be performed in a different order than presented herein.
As is well known, in serial bus communication, jitter is one of the main causes affecting high-speed serial link data transmission as the data communication rate is continuously increased, and jitter can be classified into random jitter and deterministic jitter according to the characteristics of jitter and its formation cause, wherein deterministic jitter can be classified into data-dependent jitter, periodic jitter, and bounded uncorrelated jitter.
In the prior art, the separation method of jitter includes a frequency domain method, a time domain method, and the like, according to the characteristics of the jitter component. For example, the frequency points greater than the set threshold may be determined as deterministic jitter components by a frequency domain fast fourier transform (Fast Fourier Transform, FFT) first, then, after the deterministic jitter components are removed in the frequency domain, discrete fourier transform (Discrete Fourier Transformation, DFT) is performed to convert into time domain data, and a mixture gaussian model is used to obtain random jitter components. However, the method for combining the time domain and the frequency domain needs to set a threshold value, which is determined by the characteristics of an analysis system and has a certain error, and in addition, in the process of converting the time domain into the frequency domain and converting the frequency domain into the time domain, the calculation steps are more, and the method has limitations in systems with big data and strict requirements on the measurement time.
Based on the above, the embodiment of the application provides a jitter component separation method, in the method, firstly, a mixed Gaussian model can be modeled by adopting a time interval error of transmitted data to obtain an initial mixed Gaussian model, then, the initial mixed Gaussian model can be converged by adopting a maximum expected algorithm to obtain model data of a target mixed Gaussian model, wherein the model data comprises a mean value, a variance and a generation probability of each sub-Gaussian in the target mixed Gaussian model, and finally, jitter separation can be carried out on the transmitted data according to the model data, a preset random jitter definition formula and a preset deterministic jitter definition formula to obtain random jitter components and deterministic jitter components. Therefore, in the embodiment of the application, because the modeling analysis of the GMM of any order is directly performed through the time interval error and the maximum expected algorithm, the deterministic jitter and the random jitter are calculated based on the modeling analysis, so as to obtain different jitter components. Therefore, compared with the prior art that the time domain is converted into the frequency domain, the frequency points larger than the threshold value are removed and then converted into the time domain to determine deterministic jitter, the method can directly perform mixed Gaussian model modeling on the time domain through time interval errors and process the mixed Gaussian model to obtain random jitter and deterministic jitter, so that the method not only avoids various conversions of the time domain and the frequency domain, reduces calculation steps, but also improves test rate, and is applied to a system with strict requirements on time.
After the design idea of the embodiment of the present application is introduced, some simple descriptions are made below for application scenarios applicable to the technical solution of the embodiment of the present application, and it should be noted that the application scenarios described below are only used for illustrating the embodiment of the present application and are not limiting. In the specific implementation process, the technical scheme provided by the embodiment of the application can be flexibly applied according to actual needs.
Fig. 1 is a schematic diagram of an application scenario provided in an embodiment of the present application. The jitter component separation apparatus 10 may be included in the application scenario.
The jitter component separation apparatus 10 may be used to separate jitter components in transmission data, for example, a personal computer (Personal Computer, PC), a server, a portable computer, and the like. The jitter component separation apparatus 10 may include one or more processors 101, memory 102, I/O interfaces 103, and a database 104. In particular, the processor 101 may be a central processing unit (Central Processing Unit, CPU), or a digital processing unit, or the like. The memory 102 may be a volatile memory (RAM), such as a random-access memory (RAM), or the memory 102 may be a non-volatile memory (non-volatile memory), such as a read-only memory (rom), a flash memory (flash memory), a hard disk (HARD DISK DRIVE, HDD) or a Solid State Disk (SSD), or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer, but is not limited thereto. The memory 102 may be a combination of the above. The memory 102 may store therein some program instructions of the jitter component separation method provided in the embodiment of the present application, which when executed by the processor 101, can be used to implement the steps of the jitter component separation method provided in the embodiment of the present application, so as to solve the problem that the prior art cannot effectively separate the jitter component in the transmission data. The database 104 may be used to store data related to transmission data, time interval errors, initial mixture gaussian models, target mixture gaussian models, model data, random jitter components, deterministic jitter components, and the like in the scheme provided by the embodiments of the present application.
In the embodiment of the present application, the jitter component separation apparatus 10 may obtain the time interval error of the transmission data through the I/O interface 103, and then the processor 101 of the jitter component separation apparatus 10 may effectively separate the jitter component in the transmission data according to the program instruction of the jitter component separation method provided in the embodiment of the present application in the memory 102. In addition, data such as transmission data, time interval errors, initial mixture gaussian models, target mixture gaussian models, model data, random jitter components, deterministic jitter components, etc. may also be stored in the database 104.
Of course, the method provided by the embodiment of the present application is not limited to the application scenario shown in fig. 1, but may be used in other possible application scenarios, and the embodiment of the present application is not limited. The functions that can be implemented by each device in the application scenario shown in fig. 1 will be described together in the following method embodiments, which are not described in detail herein. The method according to the embodiment of the present application will be described below with reference to the accompanying drawings.
As shown in fig. 2, a flow chart of a jitter component separation method according to an embodiment of the present application may be implemented by the jitter component separation apparatus 10 in fig. 1, and in particular, the flow chart of the method is described below.
And 201, modeling a mixed Gaussian model by adopting a time interval error of transmitted data to obtain an initial mixed Gaussian model.
Specifically, first, the time interval error can be determined based on the transmission data(Wherein, the method comprises the steps of, wherein,Represent the firstTime interval error of the time of each sampling point) and the number of sub-gaussians in the initial mixture-gaussians model,Is a positive integer, then, the time interval error can be adoptedNumber of sub-gaussiansAnd carrying out mixed Gaussian model modeling to obtain an initial mixed Gaussian model.
And 202, converging the initial mixed Gaussian model by adopting a maximum expected algorithm to obtain model data of the target mixed Gaussian model.
In an embodiment of the present application, the model data may include a mean, variance, and generation probability of each sub-gaussian in the target hybrid gaussian model.
Specifically, a target hybrid gaussian model (convergence result) obtained through convergence of the maximum expectation algorithm EM is shown in the following formula (1):
(1)
Wherein, Representing the mean of the ith sub-Gaussian curve in the target hybrid Gaussian model, wherein;Representing the variance of an ith sub-Gaussian curve in the target mixed Gaussian model; and representing the generation probability of the ith sub-Gaussian curve in the target mixed Gaussian model.
Step 203, performing jitter separation on the transmission data according to the model data, the preset random jitter definition type and the preset deterministic jitter definition type to obtain a random jitter component and a deterministic jitter component.
In order to improve the accuracy of jitter separation, in the embodiment of the application, random jitter components and deterministic jitter components are redefined.
Specifically, first, the random jitter component can be determined according to the variance, the probability of generation and the preset random jitter definition of each sub-Gaussian in the target mixed Gaussian modelThe preset random jitter definition formula (2) is as follows:
(2)
Wherein, N represents the number of sub-gaussians in the initial mixture gaussians model; representing the variance of the ith sub-Gaussian in the target-Gaussian mixture model; Representing the generation probability of the ith sub-Gaussian in the target-Gaussian mixture model. Further, since redefined equation (2) is employed to calculate the random dither component, i.e., using the target hybrid Gaussian model And (3) withThe parameters are used to calculate the random jitter component and therefore the application has less error than the prior art.
Then, determining deterministic jitter components according to the mean value of each sub-Gaussian in the target mixed Gaussian model and the preset deterministic jitter definition typeThe preset deterministic jitter definition formula (3) is as follows:
(3)
Wherein, The representation takes absolute value; Representing the mean value of the ith sub-Gaussian in the target-Gaussian mixture model, Representation ofIs set at the maximum value of (c),Representation ofIs a minimum of (2).
Further, since the modeling analysis of the GMM of any order is directly performed by the "time interval error" and the "maximum expectation algorithm", the deterministic jitter and the random jitter are calculated based on this, so as to obtain different jitter components. Therefore, compared with the prior art that the time domain is converted into the frequency domain, the frequency points larger than the threshold value are removed and then converted into the time domain to determine deterministic jitter, the method can directly perform mixed Gaussian model modeling on the time domain through time interval errors and process the mixed Gaussian model to obtain random jitter and deterministic jitter, so that the method not only avoids various conversions of the time domain and the frequency domain, reduces calculation steps, but also improves test rate, and is applied to a system with strict requirements on time.
In one possible implementation, in order to intuitively analyze the random jitter component, in an embodiment of the present application, a mixture gaussian curve and each sub-gaussian curve representing the random jitter curve may be fitted.
Specifically, after the transmitted data is subjected to jitter separation according to the model data, a preset random jitter definition formula and a preset deterministic jitter definition formula to obtain a random jitter component and a deterministic jitter component, the abscissa and the ordinate of the fitted curve can be determined.
Wherein, the abscissa of the mixed Gaussian curve and the curves of the sub-gaussians is time. However, since the Gaussian curve is normally distributed, the abscissa is theoretically as if the curve was fittedTherefore, the abscissa needs to be processed to obtain a suitable abscissa range.
Furthermore, in embodiments of the present application, the "abscissa sequence" of the fitted curve may be determined first, i.e. the abscissa sequence of the curve fitting may be obtained from the discrete histogram obtained from the time interval error. The specific process is as follows:
first step, time interval error The data stream is subjected to discrete histogram statistics, and the statistical interval is set to 1000 to obtain a statistical time point sequence (time range) which is recorded as. And the statistical process can be represented by the following formula (4):
(4)
Wherein, ;Representing an input sequenceTime interval error) Is used to determine the histogram of the particles,Representing a histogram function.
Second step, for time point sequenceAnd (3) resampling the ramp signals of 200 sample points to obtain a curve-fitted abscissa sequenceThe following formula (5) shows:
(5)
In this formula (5) of the present invention, ;Representing a resampling function.
Then, a determination may be made of the "ordinate sequence of the respective sub-Gaussian curves", i.e. for the firstA sub-Gaussian curve can be obtained according to the abscissa sequenceTo obtain the firstTheoretical ordinate of individual sub-Gaussian curvesThe following formula (6) shows:
(6)
Next, according to the improved trapezoidal integral formula, the time point sequence after statistics is carried out Discrete histograms obtained from time interval errorsTo obtain the firstFirst integral result of sub-Gaussian curve。
Then, according to the improved trapezoidal integral formula and the abscissa sequenceFirst of allTheoretical ordinate of individual sub-Gaussian curvesTo obtain the firstSecond integral result of sub-Gaussian curve。
Next, according to the firstTheoretical ordinate of individual sub-Gaussian curvesFirst integral resultAnd a second integration resultTo determine the firstOrdinate sequence of individual sub-Gaussian curvesThe following formula (7) shows:
(7)
Finally, the ordinate sequence of the Gaussian mixture curve can be determined, namely, the ordinate values of the sub-Gaussian mixture curves corresponding to the time of each sampling point can be determined The sum is used for obtaining a curve fitting mixed Gaussian curve ordinate sequenceThe following formula (8) shows:
(8)
in this formula (8) of the present invention, 。
In the embodiment of the application, the original trapezoidal integration method can be represented by the following formula (9):
(9)
Wherein, Representing the integration result; Representing the total number of integrated data points; Represent the first Data to be integrated; Represent the first Sampling point times.
Based on this, in the embodiment of the present application, in order to improve the curve fitting effect, before the integration of the data values, the data values may be further integratedReorder in ascending order and cause orderedAnd (3) withOne-to-one correspondence of the order of (c).
Furthermore, the application can improve the original trapezoidal integral method as follows:
First step, checking the integral input data And (3) withAnd if the input length is equal and the input length is more than 2, performing a second step if the input length is satisfied, otherwise prompting that the input data is wrong.
The second step is to make the pairs from small to largeOrdering and, at the same timeWith input ofMake a corresponding one-to-one correspondence, i.e. eachCorresponding toAt the position ofThe ordering is unchanged before and after. The data after the sorting is recorded as,、。
Third step, will、Substituting into the formula (1) to perform trapezoidal integral operation to obtain an integral result。
In one possible embodiment, based on the above formula (9) and the improvement of the original trapezoidal integration method, "according to the improved trapezoidal integration formula, the statistical time point sequenceDiscrete histograms obtained from time interval errorsTo obtain the firstFirst integral result of sub-Gaussian curve"Step, deformable as follows:
first, histogram discrete points can be determined With a counted time point sequenceWhether the input lengths of the input signals are equal to each other, and whether the input lengths are more than 2;
Next, if a histogram discrete point is determined With a counted time point sequenceIf the input lengths of the data are not equal or the input length is not more than 2, prompting that the input data are wrong;
If the discrete points of the histogram and the time point sequence after statistics are determined The input length of (2) is equal to or greater than 2, and the counted time point sequence is counted from small to largeSequencing to obtain a sequenced time point sequence;
Then, according to the ordered time point sequenceObtaining corresponding ordered histogram discrete points;
Finally, the ordered time point sequence can be usedAnd ordered histogram discrete pointsCarrying out integral calculation in the improved trapezoidal integral formula shown in the formula (9) to obtain a first integral resultThe following formula (10) shows:
(10)
similarly, based on the formula (9) and the improvement on the original trapezoidal integral method, the method is based on the improved trapezoidal integral formula and the abscissa sequence First of allTheoretical ordinate of individual sub-Gaussian curvesTo obtain the firstSecond integral result of sub-Gaussian curve"Step, deformable as follows:
first, the first can be determined Theoretical ordinate of individual sub-Gaussian curvesAnd abscissa sequenceWhether the input lengths of the input signals are equal to each other, and whether the input lengths are more than 2;
Next, if the first is determined Theoretical ordinate of individual sub-Gaussian curvesAnd abscissa sequenceIf the input lengths of the data are not equal or the input length is not more than 2, prompting that the input data are wrong;
If it is determined that Theoretical ordinate of individual sub-Gaussian curvesAnd abscissa sequenceThe input lengths of the two are equal, and the input length is larger than 2, the abscissa sequence is from small to largeSequencing to obtain a sequenced abscissa sequence;
Then, according to the ordered abscissa sequenceObtaining the corresponding ordered theoretical ordinate;
Finally, the ordered abscissa sequence can be used forAnd ordered theoretical ordinateCarrying out integral calculation in the modified trapezoidal integral formula shown in the formula (9) to obtain the firstSecond integral result of sub-Gaussian curveThe following formula (11) shows:
(11)
In one possible embodiment, the ordinate values of the respective sub-Gaussian curves are corresponding in terms of the respective sample point times Summing, obtaining a curve-fitted mixed Gaussian curve ordinate sequenceThen, the method can also directly depend on the determined abscissa sequenceOrdinate sequence of the respective sub-Gaussian curvesMixed Gaussian Curve ordinate sequenceFitting to obtain a mixed Gaussian curve representing the random jitter curve and each sub-Gaussian curve so as to intuitively analyze the random jitter components.
In particular, it can be based on the abscissa sequenceAnd the ordinate sequence of the respective sub-Gaussian curvesPerforming curve fitting to obtain curves of a plurality of sub-gaussians representing random jitter;
According to the abscissa sequence And mixed Gaussian curve ordinate sequenceAnd (5) performing curve fitting to obtain a mixed Gaussian curve representing random jitter.
In the embodiment of the present application, in order to further describe the technical effects of the present application, the technical effects of the present application are described in detail below in conjunction with actual engineering tests:
The experimental device comprises an arbitrary wave generator and a bandwidth oscilloscope.
Specifically, firstly, a square wave clock signal with the frequency of 100MHz and the amplitude of 1V can be generated by adopting an arbitrary wave generator, wherein the frequency of a periodic jitter signal in the square wave clock signal is 10MHz, the amplitude of the periodic jitter signal is 200ps, the mean value of an embedded random jitter signal is 0, the standard deviation is 25ps, and the random seed is 3.
A bandwidth oscilloscope may then be used to collect the signals generated by the arbitrary wave generator and set the gaussian mixture model sub-gaussian to 10.
Then, the technical scheme of the application can be utilized to analyze the data collected by the bandwidth oscilloscope so as to analyze and obtain random jitterDeterministic jitter。
It can be seen that the random jitter obtained by the analysisApproximately similar to the random jitter 25ps of the input signal, the deterministic jitter obtained by analysisThe method is similar to the deterministic jitter of the input signal by 200ps, so the deterministic jitter component can be correctly obtained through the technical scheme of the application.
Fig. 3 is a schematic diagram of the actual curve fitting result provided in the embodiment of the present application. As can be seen from this fig. 3, the TIE histogram statistics exhibit tail tailing, which is a significant gaussian statistic with significant random jitter components. The fit mixture gaussian curve has a general trend with the statistics of TIE, and it is apparent that the random jitter curve fits correctly.
In addition, it can be seen that the curves obtained by adding the 2nd Gaussian curve, the 3rd sub-Gaussian curve, the 4th sub-Gaussian curve, the 11th sub-Gaussian curve are consistent with the mixed Gaussian curve, and obviously, the mixed Gaussian curve and each sub-Gaussian curve representing the random jitter curve can be well fitted, and the random jitter components can be intuitively analyzed.
In summary, since the modeling analysis of the GMM of any order is directly performed by the "time interval error" and the "maximum expectation algorithm", the deterministic jitter and the random jitter are calculated based on the modeling analysis, so as to obtain different jitter components. Therefore, compared with the prior art that the time domain is converted into the frequency domain, the frequency points larger than the threshold value are removed and then converted into the time domain to determine deterministic jitter, the method can directly perform mixed Gaussian model modeling on the time domain through time interval errors and process the mixed Gaussian model to obtain random jitter and deterministic jitter, so that the method not only avoids various conversions of the time domain and the frequency domain, reduces calculation steps, but also improves test rate, and is applied to a system with strict requirements on time.
Based on the same inventive concept, an embodiment of the present application provides a jitter component separation apparatus 40, as shown in fig. 4, the jitter component separation apparatus 40 includes:
a model obtaining unit 401, configured to perform mixed gaussian model modeling by using a time interval error of transmission data, so as to obtain an initial mixed gaussian model;
A model data obtaining unit 402, configured to converge the initial mixture gaussian model by using a maximum expectation algorithm, so as to obtain model data of a target mixture gaussian model, where the model data includes a mean value, a variance, and a generation probability of each sub-gaussian in the target mixture gaussian model;
the jitter separation unit 403 is configured to perform jitter separation on the transmission data according to the model data, the preset random jitter definition formula and the preset deterministic jitter definition formula, so as to obtain a random jitter component and a deterministic jitter component.
Optionally, the model obtaining unit 401 is further configured to:
Determining a time interval error and the number of sub-gaussians in an initial Gaussian mixture model according to the transmission data;
And modeling the mixed Gaussian model by adopting the time interval error and the number of sub-Gaussian to obtain an initial mixed Gaussian model.
Optionally, the jitter separation unit 403 is further configured to:
Determining random jitter components according to variances of all sub-gaussians, generation probabilities and preset random jitter definition formulas, wherein the preset random jitter definition formulas comprise the following formulas:
Wherein, N represents the number of sub-gaussians in the initial mixture gaussians model; representing the variance of the ith sub-Gaussian in the target-Gaussian mixture model; representing the generation probability of the ith sub-Gaussian in the target-Gaussian mixture model.
Optionally, the jitter separation unit 403 is further configured to:
Determining deterministic jitter components according to the mean value of all the sub-gauss and a preset deterministic jitter definition formula, wherein the preset deterministic jitter definition formula comprises the following formula:
Wherein, The representation takes absolute value; Representing the mean value of the ith sub-Gaussian in the target-Gaussian mixture model, Representation ofIs set at the maximum value of (c),Representation ofIs a minimum of (2).
Optionally, the jitter component separation apparatus 40 further comprises a curve fitting unit 404 for:
Obtaining a curve-fitted abscissa sequence according to the discrete histogram obtained by the time interval error;
For any one of the sub-Gaussian curves, according to the abscissa sequence, obtaining the theoretical ordinate of any one of the sub-Gaussian curves;
obtaining a first integration result of any one sub-Gaussian curve according to an improved trapezoidal integration formula, a counted time point sequence and a discrete histogram obtained by time interval errors;
obtaining a second integration result of any one of the sub-Gaussian curves according to an improved trapezoidal integration formula, an abscissa sequence and a theoretical ordinate of any one of the sub-Gaussian curves;
determining an ordinate sequence of any one of the sub-Gaussian curves according to the theoretical ordinate of any one of the sub-Gaussian curves, the first integration result and the second integration result;
And obtaining a curve-fitting mixed Gaussian curve ordinate sequence according to the sum of the ordinate values of the sub-Gaussian curves corresponding to the time of each sampling point.
Optionally, the curve fitting unit 404 is further configured to:
Determining whether the input length of the histogram discrete points is equal to the input length of the time point sequence after statistics, and whether the input length is more than 2;
If the input length of the histogram discrete points is determined to be unequal to the input length of the time point sequence after statistics, or the input length is not more than 2, prompting that the input data is wrong;
if the input length of the discrete points of the histogram is equal to the input length of the counted time point sequence and the input length is more than 2, sequencing the counted time point sequence from small to large to obtain a sequenced time point sequence;
Obtaining corresponding ordered histogram discrete points according to the ordered time point sequence;
and carrying the ordered time point sequence and the ordered histogram discrete points into an improved trapezoidal integral formula for integral calculation to obtain a first integral result.
Optionally, the curve fitting unit 404 is further configured to:
Performing curve fitting according to the abscissa sequence and the ordinate sequence of each sub-Gaussian curve to obtain a plurality of sub-Gaussian curves representing random jitter;
And performing curve fitting according to the abscissa sequence and the ordinate sequence of the mixed Gaussian curve to obtain the mixed Gaussian curve representing random jitter.
The jitter component separation apparatus 40 may be used to perform the method performed in the embodiment shown in fig. 2, and thus, the description of the functions and the like that can be implemented by the functional modules of the jitter component separation apparatus 40 may be referred to in the embodiment shown in fig. 2, and will not be repeated.
In some possible implementations, aspects of the method provided by the present application may also be implemented in the form of a program element comprising program code for causing a computer device to carry out the steps of the method according to the various exemplary embodiments of the application described herein above, when the program element is run on the computer device, e.g. the computer device may carry out the method as carried out in the example shown in fig. 2.
It will be appreciated by those of ordinary skill in the art that implementing all or part of the steps of the above method embodiments may be implemented by hardware associated with program instructions, where the above program may be stored in a computer readable storage medium, where the program when executed performs the steps comprising the above method embodiments, where the above storage medium includes various media that may store program code, such as a removable storage device, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic or optical disk, etc. Or the above-described integrated units of the invention may be stored in a computer-readable storage medium if implemented in the form of software functional modules and sold or used as separate parts. Based on such understanding, the technical solution of the embodiments of the present invention may be embodied in essence or a part contributing to the prior art in the form of a software component stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the embodiments of the present invention. The storage medium includes various media capable of storing program codes such as a removable storage device, a ROM, a RAM, a magnetic disk or an optical disk.
While preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the application.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the spirit or scope of the application. Thus, it is intended that the present application also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (7)
1. A method of separating dither components, the method comprising:
Modeling a mixed Gaussian model by adopting a time interval error of transmission data to obtain an initial mixed Gaussian model; the step of adopting the time interval error of transmission data to carry out mixed Gaussian model modeling to obtain an initial mixed Gaussian model comprises the steps of determining the time interval error and the number of sub-gauss in the initial mixed Gaussian model according to the transmission data;
Converging the initial mixed Gaussian model by adopting a maximum expected algorithm to obtain model data of a target mixed Gaussian model, wherein the model data comprises the mean value, variance and generation probability of each sub-Gaussian in the target mixed Gaussian model;
The step of obtaining the random jitter component and the deterministic jitter component by performing jitter separation on the transmission data according to the model data, a preset random jitter definition formula and a preset deterministic jitter definition formula comprises the steps of determining the random jitter component according to the variance of each sub-Gaussian, the generation probability and the preset random jitter definition formula, wherein the preset random jitter definition formula comprises the following formulas:
Wherein, N represents the number of sub-gaussians in the initial mixture gaussians model; representing the variance of the ith sub-Gaussian in the target-Gaussian mixture model; Representing the generation probability of the ith sub-Gaussian in the target-Gaussian mixture model;
The step of performing jitter separation on the transmission data according to the model data, a preset random jitter definition type and a preset deterministic jitter definition type to obtain a random jitter component and a deterministic jitter component comprises the following steps:
Determining the deterministic jitter component according to the mean value of all the sub-gauss and the preset deterministic jitter definition formula, wherein the preset deterministic jitter definition formula comprises the following formula:
Wherein, The representation takes absolute value; Representing the mean value of the ith sub-Gaussian in the target-Gaussian mixture model, Representation ofIs set at the maximum value of (c),Representation ofIs a minimum of (2).
2. The method of claim 1, wherein after performing jitter separation on the transmission data according to the model data, a predetermined random jitter definition and a predetermined deterministic jitter definition to obtain a random jitter component and a deterministic jitter component, the method further comprises:
Obtaining a curve-fitted abscissa sequence according to the discrete histogram obtained by the time interval error;
for any one sub-Gaussian curve, according to the abscissa sequence, obtaining a theoretical ordinate of the any one sub-Gaussian curve;
Obtaining a first integration result of any one of the sub-Gaussian curves according to an improved trapezoidal integration formula, a counted time point sequence and a discrete histogram obtained by time interval errors;
Obtaining a second integration result of any one of the sub-Gaussian curves according to the improved trapezoidal integration formula, the abscissa sequence and the theoretical ordinate of any one of the sub-Gaussian curves;
determining an ordinate sequence of any one of the sub-Gaussian curves according to the theoretical ordinate of the any one of the sub-Gaussian curves, the first integration result and the second integration result;
And obtaining a curve-fitting mixed Gaussian curve ordinate sequence according to the sum of the ordinate values of the sub-Gaussian curves corresponding to the time of each sampling point.
3. The method of claim 2, wherein the step of obtaining a first integration result from the modified trapezoidal integration formula, the counted time point sequence, and the discrete histogram obtained from the time interval error comprises:
Determining whether the input length of the histogram discrete points is equal to the input length of the time point sequence after statistics, and whether the input length is more than 2;
if the input length of the histogram discrete points is determined to be unequal to the input length of the time point sequence after statistics, or the input length is not more than 2, prompting that the input data is wrong;
If the input length of the histogram discrete points is equal to the input length of the counted time point sequence and is larger than 2, sequencing the counted time point sequence from small to large to obtain a sequenced time point sequence;
obtaining corresponding ordered histogram discrete points according to the ordered time point sequence;
and carrying the ordered time point sequence and the ordered histogram discrete points into an improved trapezoidal integration formula for integral calculation to obtain the first integral result.
4. The method of claim 2, wherein after obtaining the curve-fitted mixture gaussian ordinate sequence from the sum of the ordinate values of the respective sub-gaussian curves corresponding to the respective sample point times, the method further comprises:
Performing curve fitting according to the abscissa sequence and the ordinate sequence of each sub-Gaussian curve to obtain a plurality of sub-Gaussian curves representing random jitter;
And performing curve fitting according to the abscissa sequence and the ordinate sequence of the Gaussian mixture curve to obtain the Gaussian mixture curve representing random jitter.
5. A jitter component separation apparatus, said apparatus comprising:
The model obtaining unit is used for carrying out mixed Gaussian model modeling by adopting a time interval error of transmission data to obtain an initial mixed Gaussian model; the step of adopting the time interval error of transmission data to carry out mixed Gaussian model modeling to obtain an initial mixed Gaussian model comprises the steps of determining the time interval error and the number of sub-gauss in the initial mixed Gaussian model according to the transmission data;
The model data acquisition unit is used for converging the initial mixed Gaussian model by adopting a maximum expected algorithm to acquire model data of a target mixed Gaussian model, wherein the model data comprises a mean value, a variance and a generation probability of each sub-Gaussian in the target mixed Gaussian model;
The jitter separation unit is used for performing jitter separation on the transmission data according to the model data, a preset random jitter definition formula and a preset deterministic jitter definition formula to obtain random jitter components and deterministic jitter components, and performing jitter separation on the transmission data according to the model data, the preset random jitter definition formula and the preset deterministic jitter definition formula to obtain the random jitter components and the deterministic jitter components, wherein the step of determining the random jitter components according to variances of all sub-gauss, generation probabilities and the preset random jitter definition formula comprises the following formulas:
Wherein, N represents the number of sub-gaussians in the initial mixture gaussians model; representing the variance of the ith sub-Gaussian in the target-Gaussian mixture model; Representing the generation probability of the ith sub-Gaussian in the target-Gaussian mixture model;
The step of performing jitter separation on the transmission data according to the model data, a preset random jitter definition type and a preset deterministic jitter definition type to obtain a random jitter component and a deterministic jitter component comprises the following steps:
Determining the deterministic jitter component according to the mean value of all the sub-gauss and the preset deterministic jitter definition formula, wherein the preset deterministic jitter definition formula comprises the following formula:
Wherein, The representation takes absolute value; Representing the mean value of the ith sub-Gaussian in the target-Gaussian mixture model, Representation ofIs set at the maximum value of (c),Representation ofIs a minimum of (2).
6. An electronic device, the device comprising:
A memory for storing program instructions;
A processor for invoking program instructions stored in said memory and for executing the method of any of claims 1-4 in accordance with the obtained program instructions.
7. A storage medium having stored thereon computer executable instructions for causing a computer to perform the method of any one of claims 1-4.
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