CN109245954B - Network service flow modeling method for EPON and LTE wireless dual-mode converged communication - Google Patents
Network service flow modeling method for EPON and LTE wireless dual-mode converged communication Download PDFInfo
- Publication number
- CN109245954B CN109245954B CN201811402438.7A CN201811402438A CN109245954B CN 109245954 B CN109245954 B CN 109245954B CN 201811402438 A CN201811402438 A CN 201811402438A CN 109245954 B CN109245954 B CN 109245954B
- Authority
- CN
- China
- Prior art keywords
- network
- epon
- traffic
- network traffic
- walsh
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000004891 communication Methods 0.000 title claims abstract description 42
- 238000000034 method Methods 0.000 title claims abstract description 36
- 238000005259 measurement Methods 0.000 claims description 21
- 238000005316 response function Methods 0.000 claims description 11
- 238000012886 linear function Methods 0.000 claims description 4
- 230000008569 process Effects 0.000 claims description 4
- 238000005070 sampling Methods 0.000 claims description 4
- 238000001914 filtration Methods 0.000 claims description 2
- 239000000126 substance Substances 0.000 claims 1
- 238000004422 calculation algorithm Methods 0.000 abstract description 16
- 238000004088 simulation Methods 0.000 abstract description 10
- 238000007619 statistical method Methods 0.000 abstract description 2
- 230000009977 dual effect Effects 0.000 abstract 1
- 238000005309 stochastic process Methods 0.000 abstract 1
- GNOIPBMMFNIUFM-UHFFFAOYSA-N hexamethylphosphoric triamide Chemical compound CN(C)P(=O)(N(C)C)N(C)C GNOIPBMMFNIUFM-UHFFFAOYSA-N 0.000 description 18
- 230000006872 improvement Effects 0.000 description 12
- 230000007613 environmental effect Effects 0.000 description 8
- 230000005540 biological transmission Effects 0.000 description 5
- ZEFNOZRLAWVAQF-UHFFFAOYSA-N Dinitolmide Chemical group CC1=C(C(N)=O)C=C([N+]([O-])=O)C=C1[N+]([O-])=O ZEFNOZRLAWVAQF-UHFFFAOYSA-N 0.000 description 3
- 238000011161 development Methods 0.000 description 3
- 102100029469 WD repeat and HMG-box DNA-binding protein 1 Human genes 0.000 description 2
- 101710097421 WD repeat and HMG-box DNA-binding protein 1 Proteins 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 239000003550 marker Substances 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 238000013528 artificial neural network Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 230000005484 gravity Effects 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 230000001788 irregular Effects 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 238000010295 mobile communication Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 230000006855 networking Effects 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/14—Network analysis or design
- H04L41/145—Network analysis or design involving simulating, designing, planning or modelling of a network
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Mobile Radio Communication Systems (AREA)
Abstract
The invention provides a network service flow modeling method for EPON and LTE wireless dual-mode converged communication. Walsh transform is used for constructing a model of network traffic end-to-end traffic oriented to the converged communication of the EPON and the LTE wireless dual modes. First, the end-to-end network traffic is represented as an independent and equally distributed random time-varying sequence. In the stochastic process, there are several parameters that need to be estimated accurately. This is very difficult for limited traffic information. Second, walsh transforms are used to characterize end-to-end network traffic. The parameters are calculated by a statistical method, and the model is correctly determined. Furthermore, the model for end-to-end network traffic has been correctly constructed. Finally, a new algorithm is proposed to build the model. Simulation results show that the method is feasible and effective.
Description
Technical Field
The invention belongs to the field of network service flow modeling facing EPON and LTE wireless dual-mode converged communication in communication network service flow modeling.
Background
Ethernet Passive Optical Networks (EPONs) and LTE have been widely used in communication networks, which can provide data transmission capabilities with greater bandwidth and shorter latency than conventional transmission networks. In a power distribution communication system of a power communication network, two communication modes, namely an EPON system of an optical fiber networking and a GPRS of a leased operator, are still mainly adopted at present, and a single communication system has inherent defects and use limitations, so that great challenges are brought to the security of the power distribution communication network and the expansion of new functional services, such as: during construction, the optical cable is dug to be broken, so that a large number of ONU is disconnected, and the collected data of the power terminal monitoring equipment cannot be transmitted and collected; the transmission bandwidth of GPRS itself is very small, and it is easily subjected to external electromagnetic interference, etc., which all limit the development of power distribution communication system networks and the deployment of new functions, and cannot meet the demand of diversified service functions of power distribution communication systems. Therefore, the integration of EPON and LTE to construct a wireless dual-mode integrated communication system is a necessary trend for the development of power distribution communication systems. In a power distribution communication system, compared with a traditional communication mode combining an EPON and a GPRS, the flow characteristics and the management mode of an EPON and LTE wireless dual-mode converged communication system are greatly changed, and the flow characteristics and the management mode are also greatly different from the service flow of a mobile communication service network. However, the nature and characteristics of network traffic flow of EPON and LTE wireless dual-mode converged communication in a power distribution communication system have not been studied in depth, and the gap between theoretical analysis and practical application is still large. The method aims to solve the problem of flow modeling of an EPON and LTE wireless dual-mode converged communication network in a power distribution communication system, and provides a modeling method based on Walsh transformation to describe end-to-end flow of the EPON and LTE wireless dual-mode converged communication network. First, the end-to-end traffic is represented as an independent and identically distributed random time-varying sequence. Walsh transforms are then used to characterize the end-to-end traffic of the network traffic. The correctness of establishing the model is proved by calculating corresponding parameters. Simulation results show that the method has feasibility and effectiveness.
With the continuous development of data transmission requirements and new applications in a power distribution communication system, an EPON and LTE wireless dual-mode converged communication network has been widely deployed in the current power distribution communication network. Traffic in the EPON and LTE wireless dual-mode converged communication network has new functions, which brings new challenges to transmission network performance and traffic engineering of a power distribution communication system. Accurately describing and simulating the flow of the EPON and LTE wireless dual-mode converged communication network has important significance for improving the performance of the EPON and LTE wireless dual-mode converged communication network. Moreover, network traffic in the power distribution communication system has the characteristics of self-similarity, self-correlation, heavy tail distribution and the like, which has important influence on network optimization and routing. The flow of the EPON and LTE wireless dual-mode converged communication network characterizes the network behavior of power distribution communication. Therefore, simulating end-to-end traffic in an EPON and LTE wireless dual-mode converged communication network has received extensive attention from researchers, operators, and developers.
End-to-end traffic behavior in a communication network embodies path-level and network-level features in the network, which can be used to describe network state and properties such as path load, throughput, network utilization, etc. Using statistical methods to represent and describe a network traffic model from a source node to a destination node; gravity models, general evolution, hybrid methods, compressive sensing, etc. can be used to capture the attributes of end-to-end network traffic. In performing the modeling, these methods may better predict and estimate end-to-end flow. However, these methods require information from the link load or prior information about the end-to-end network traffic, which necessarily increases the computational complexity and overhead of obtaining the model parameters. And time-frequency domain analysis can be used to capture multi-scale features and dynamic characteristics; these methods can build models representing end-to-end network traffic using neural networks as they are used to model network traffic, but it is very difficult to accurately capture their functionality and build accurate and appropriate network traffic models for traffic engineering in communication networks. Therefore, the invention provides a new modeling method for estimating the end-to-end flow in the EPON and LTE wireless dual-mode converged communication network.
Disclosure of Invention
Aiming at the defects of the existing method, the invention provides a simpler, more convenient, high-accuracy and high-efficiency traffic modeling method of an EPON and LTE wireless dual-mode converged communication network, which mainly comprises the following steps:
the method comprises the following steps: obtaining measured value in network according to network flow measuring instrument and writing it into expression
The nonlinear function shown represents the true value of the network traffic; wherein [ tau ] is1,τ2]Is the length of the filter window; tau is1And τ2Respectively the starting time and the ending time of the filtering window; f (t) is an arbitrary continuous time function; t represents time; x represents the true value of the measurement; h is the instrument response function.
Step two: according to the equation
Formula of nonlinear function
Written as discrete form y (k); k is an independent variable; h (i) is the discrete response function of the instrument. [ i ]1,i2]Is the discrete filter window length; wherein i1And i2Respectively, a start point and an end point of the filter window; n is the number of sample points.
Step three: according to Walsh transform theory
For a sequence with N samples, the Walsh transform pair can be expressed as
And obtaining a measurement curve through inversion, and removing the influence of instruments and environments. F (k) and f (t) are a Walsh transform pair; x is the number ofiIs a sequence of actual values of the measurement, XkIs xiThe walsh transform of (3).
Step four: using least squares, the sum of squares of errors Q (X) is obtained0,X1,...,XN-1) Then by the equation
To obtain XnN is the number of sample points. Wherein
Q (x) is the sum of the squared errors; j is the total number of sample points.
Step five: xnEvaluating by inverse transform of Walsh to obtain estimated value of true value x of network flow
Step six: the model is corrected using the estimation error. If the process is finished, please exit and save the result to a file, or return to step 1.
The walsh function is a complete, standardized orthogonal system defined as [0,1] and is denoted as wal (n, k), where n is the order and k is an argument with values of only +1 and-1. The walsh function is a non-sinusoidal function, and any one of the time functions f (t) is 1 in the [0,1) period. Time-varying network traffic can be decomposed as a weighted sum of a series of walsh functions. Thus, for any continuous time function we can derive the following equation:
wherein
Therefore, the Walsh transform f (t) of the continuous function can be expressed as
This is a walsh transform pair.
For a discrete sequence of N samples, the Walsh transform pair is
And obtaining a measurement curve through inversion, and removing the influence of instruments and environments. F (k) and f (t) are a Walsh transform pair; x is the number ofiIs a sequence of actual values of the measurement, XkIs xiThe walsh transform of (3).
The methods referred to herein are based on the following assumptions: in the measurement interpretation model, the network traffic is uniform and stable in a short time, and thus the measurement curve can be approximated to a rectangular wave in a short time.
The network traffic data obtained by the meter is actually a non-linear function affected by the response function of the meter, environmental conditions, etc., and is usually the true value of the network traffic. Accordingly, the following equation can be obtained:
wherein x represents the actual value of the measurement; h is the instrument response function; [ tau ] to1,τ2]Indicating a filter window length; t represents time. Thus, we can obtain the following discrete forms:
wherein [ i1,i2]Represents the filter window length; k represents a time sample marker; n is the number of sample points in the window. The purpose of inversion of the measurement profile is to remove the influence of the instrument response function h, environmental conditions, etc. from the measured value y to recover an estimated value of the true value x of the flow
Thus measured value
Is the same as the true value x. The walsh transform reflects the fundamental characteristics of time varying traffic more accurately than the fourier transform. Therefore, we describe network traffic using a walsh transform based measurement curve inversion technique. Least squares method for obtaining an estimate of the true value of a measured value
Based on the previous assumptions, inverse walsh transform is performed on x (k-i) and the equation is completed:
j is the total number of sample points;
X＝(X0,X1,...,XN-1)Tis a walsh transform of x (k-i).
XnThe solution of (c) can be estimated using a least squares method. Based on the inverse walsh transform, we can obtain an estimate of the network traffic x. The formula is as follows
It has been proved that the least square solution model under the walsh transform has the highest resolution, and an accurate solution x of the network traffic can be obtained without considering the influence of the environmental noise, and the interpolation result is close to the obtained result data value. The equation (7) can make the error equal
Obtaining XnIs measured.
The invention has the advantages that: demonstrating a walsh transform-based traffic modeling method (WTMA) through several tests ultimately verifies the accuracy of the traffic model. We need to use real network data. We validated WTMA using real data of the american true Abilene backbone network. Detailed simulation experiments were performed using Matlab 2010. And comparing the WTMA with PCA, WABR and HMPA network flow model algorithms. We also evaluated the performance improvement of WTMA on PCA, WABR and HMPA.
Traffic modeling method based on Walsh transform
Experimental results show that the WTMA can effectively capture the dynamic change of the end-to-end network flow, the real end-to-end network flow shows obvious time-varying property for different time slots, and the WTMA can capture the trend of the end-to-end network flow. Although the WTMA has a large estimation error for the end-to-end network traffic, it can still capture the variation trend. This further demonstrates that WTMAs can effectively estimate the end-to-end network traffic over time.
While we compare the relative estimation errors of all algorithms. The average relative estimation error of end-to-end network traffic is defined as:
n is the number of simulation processes in 1, 2. I | · | purple wind2Is L2Norm of (d).
Refers to an end-to-end estimate of the ith time t.
The conclusion is reached: compared to PCA, WABR and HMPA, WTMA has better estimation capability for end-to-end network flows, while WTMA does have the best estimation capability. More importantly, WABR, HMPA and WTMA fluctuate over time below PCA in terms of relative error. This shows that WTMA can build an end-to-end network flow with dynamic and time-varying characteristics more efficiently than the other three algorithms.
Analyzing the improvement of WTMA to other three algorithms of end-to-end network traffic. The results were obtained: WTMA may achieve the greatest performance improvement over PCA. For WABR, only minor improvements can be achieved for WTMA. However, the improvement of WTMA on HMPA was minimal, less than 5%.
Drawings
Fig. 1 is a general flowchart of an embodiment of a flow modeling method for an EPON and LTE wireless dual-mode converged communication network according to the present invention;
fig. 2 depicts the true values and WTMA prediction results for the end-to-end traffic flows 67 and 107. Where (a) is the relative error of the end-to-end OD stream 67; (b) is the relative error of the end-to-end OD stream 107;
fig. 3 shows the average relative error of the four algorithms PCA, WABR, HMPA and WTMA for the end-to-end traffic flows 67 and 107. Where (a) is the relative error of the end-to-end OD stream 67; (b) is the relative error of the end-to-end OD stream 107;
fig. 4 shows the improved ratio of WTMA to PCA, WABR and HMPA end-to-end traffic flows 67 and 107, respectively.
Detailed Description
Embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.
Fig. 1 is a general flowchart of an embodiment of a method for modeling a wireless dual-mode converged communication network stream for EPON and LTE. The flow starts at step S101. In step S102, a measurement value in the network is obtained according to the network traffic meter, and written into the equation
The actual value of the network traffic is shown as a non-linear function. The equation for this step is derived as follows: the walsh function is a complete, standardized orthogonal system defined as [0,1] and is denoted as wal (n, k), where n is the order and k is an argument with values of only +1 and-1. The walsh function is a non-sinusoidal function, and any one of the time functions f (t) is 1 in the [0,1) period. Time-varying network traffic can be decomposed as a weighted sum of a series of walsh functions. Thus, for any continuous time function we can get the following equation:
wherein
Therefore, the Walsh transform f (t) of the continuous function can be expressed as
This is a walsh transform pair. For the discrete case with N samples, the walsh transform pair can be represented as
Obtaining a measurement curve by inversion, and removing the instrument and the ringEnvironmental impact. F (k) and f (t) are a Walsh transform pair; xkIs xiThe walsh transform of (3).
The method to which this patent relates is based on the following assumptions: in the measurement interpretation model, the network traffic is uniform and stable in a short time, and thus the measurement curve can be approximated to a rectangular wave in a short time.
The network traffic data obtained by a network traffic meter is actually a non-linear function affected by the response function of the meter, environmental conditions, etc., and is usually the true value of the network traffic. Accordingly, the equation can be obtained:
in step S103, the equation in step S102 is transformed into a discrete form. In this equation x represents the true value of the measurement; h is the instrument response function; [ tau ] to1,τ2]Indicating a filter window length; t represents time. Thus, we can obtain the following discrete forms:
wherein [ i1,i2]Represents the filter window length; k represents a time sample marker; n is the objective of the quantity measurement curve reflection of the sampling points in the window to remove the influence of the instrument response function h, environmental conditions, etc. from the measured value y to recover the estimated value of the true value x of the flow
Thus measured value
Is the same as the true value x. The walsh transform is more accurate than the fourier transform, which reflects the basic characteristics of time-varying traffic.
Step S104, according to Walsh transform theory, a measurement curve can be obtained through the formulas (2) and (3), and the influence of instrument response, environmental conditions and the like can be removed from the measurement value y.
The purpose of inversion of the measurement profile is to remove the influence of the instrument response function h, environmental conditions, etc. from the measured value y to recover an estimated value of the true value x of the flow
Thereby making the estimated value
Is the same as x. The walsh transform is more accurate than the fourier transform to reflect the basic characteristics of time-varying traffic. Therefore, we describe network traffic using a walsh transform based measurement curve inversion technique. Least squares method for obtaining an estimate of the true value of a measured value
Step S105, obtaining Q (X) by using least square method0,X1,...,XN-1) The error of the sum of squares of (1), and then X is obtained by the formula (7)nIs measured. Based on the previous assumptions, inverse walsh transform is performed on x (k-i) and the equation is completed:
j is the total number of sample points;
X＝(X0,X1,...,XN-1)Tis a walsh transform of x (k-i).
XnThe solution of (c) can be estimated using a least squares method. Based on the inverse walsh transform, we can obtain an estimated value of the network traffic x, which is expressed as follows
It has been proved that the least square solution model under the walsh transform has the highest resolution, and an accurate solution x of the network traffic can be obtained without considering the influence of the environmental noise, and the interpolation result is close to the obtained result data value. The equation (7) can make the error equal
Obtaining XnMinimum value of (2)
Step S106, processing X by Walsh transformnTo obtain the true value of the network flow x
Step S107, the mathematical model is adjusted by the estimated error.
Actual data required by the simulation experiment are collected by the network node; we use the real data of the us real Abilene backbone network to validate WTMAs. Detailed simulation experiments were performed using Matlab 2010. The PCA, WABR and HMPA network flow model algorithms have better performance. Therefore, we compared WTMAs to them. In the following, the prediction results of the end-to-end network traffic will be analyzed to illustrate the WTMA algorithm. The average relative error of the end-to-end network traffic for the four algorithms will be shown. Finally, we also evaluated the performance improvement of WTMA over PCA, WABR and HMPA. In our simulations, the data of the first 500 slots were used to train the model for the four methods, while the other data were used to verify its performance.
Fig. 2 shows the estimates of network flows 67 and 107, where network flows 67 and 107 are randomly selected from 144 end-to-end network flow pairs in an Abilene backbone network. Other end-to-end network traffic pairs showed similar results as our simulation experiments showed. Without loss of generality, we only discuss network flows 67 and 107 herein. In addition, here the end-to-end network traffic is equal to the Original Destination (OD) pair. Fig. 2 (a) shows that WTMA can efficiently catch up with dynamic changes 67 in end-to-end network traffic. True end-to-end network traffic exhibits significant time-varying properties for different timeslots. As can be seen from the graph (a) in fig. 2, the WTMA can capture the trend of the end-to-end network traffic. Also, as shown in fig. 2 (b), the end-to-end network flow 107 exhibits irregular and dynamic variations in time. As is clear from the graph (a) in fig. 2, although the WTMA has a large estimation error for the end-to-end network traffic 67, it can still capture the variation trend. This further demonstrates that BTMA can effectively estimate the end-to-end network traffic change over time.
Next, the estimation errors of the four algorithms are analyzed. In general, the time-varying nature of end-to-end network traffic is difficult to capture by model alone. To further validate the algorithm, we compared the relative estimation errors of all algorithms. To avoid randomness in the simulation, we performed 500 runs to calculate the average relative estimation error.
The average relative estimation error of end-to-end network traffic is defined as:
n is the number of simulation processes in 1, 2. I | · | purple wind2Is L2Norm of (d).
Refers to an end-to-end estimate of the ith time t.
Fig. 3 shows the average relative estimation error over time for the four algorithms of the end-to-end network flows 67 and 107. It is very interesting that for the end-to-end network flows 67 and 107, the WABR, HMPA and WTMA show low relative errors, while the PCA keeps large estimation bias. Also, fig. 2 shows that WTMA has the lowest relative error. This tells us that WTMA has better estimation capability for end-to-end network flows than PCA, WABR and HMPA, while WTMA does have the best estimation capability. More importantly, WABR, HMPA and WTMA fluctuate over time below PCA in terms of relative error. This shows that WTMA can establish end-to-end network flows with dynamic and time-varying characteristics more efficiently than the other three algorithms.
We now analyze the improvement of WTMA to the other three algorithms for end-to-end network traffic.
Fig. 4 plots the improvement ratio of end-to-end flows 67 and 107. For the end-to-end flow 67, the WTMA achieved performance improvements of about 4.95%, 2.11%, and 3.39% over PCA, WABR, and HMPA. Similarly, for the end-to-end traffic flow 107, WTMAs achieve performance improvements of approximately 19.5%, 12.9%, and 5.0% over PCA, WABR, and HMPA, respectively. Experiments show that our WTMA algorithm can indeed model end-to-end network traffic more efficiently than PCA, WABR and HMPA. It can also be seen from fig. 4 that WTMA can achieve the greatest performance improvement over PCA. For WABR, only minor improvements can be achieved for WTMA. However, WTMA improves HMPA to the least extent, i.e., less than 5%. As shown in fig. 3, this further demonstrates that the WABR, HMPA and WTMA have better modeling capabilities for end-to-end network traffic. In addition, WTMA and HMPA also behave similarly. Therefore, the WTMA can properly model end-to-end traffic.
Although specific embodiments of the present invention have been described above, it will be appreciated by those skilled in the art that these are merely illustrative and that various changes or modifications may be made to these embodiments without departing from the principles and spirit of the invention. The scope of the invention is only limited by the appended claims.
Claims (1)
1. The network service flow modeling method facing the EPON and LTE wireless dual-mode converged communication is characterized by comprising the following steps: the method comprises the following steps:
the method comprises the following steps: obtaining measured value in network according to network flow measuring instrument and writing it into expression
The non-linear function y (t) shown represents the network flow obtained by the meter; wherein [ tau ] is1,τ2]Is a filter windowThe length of the mouth; tau is1And τ2Respectively the starting time and the ending time of the filtering window; f (t) is an arbitrary continuous time function; t represents time; x represents the true value of the flow; h is the instrument response function;
step two: according to the equation
Formula of nonlinear function
Written as discrete form y (k); k is an independent variable; h (i) is the discrete response function of the instrument; [ i ]1,i2]Is the discrete filter window length; wherein i1And i2Respectively, a start point and an end point of the filter window; n is the number of sampling points;
step three: according to Walsh transform theory
For a sequence with N samples, the Walsh transform pair can be expressed as
Obtaining a measurement curve through inversion, and removing the influence of instruments and environments; f (l) and f(t) is a walsh transform pair; x is the number ofmIs a sequence of true values of the flow, XlIs xmThe walsh transform of (a);
step four: using least squares, the sum of squares of errors Q (X) is obtained0,X1,...,XN-1) Then by the equation
To obtain XnN is the number of sampling points, wherein
Q (x) is the sum of the squared errors; j is the total number of sampling points;
wherein the content of the first and second substances,
Gk＝(G0(k),G1(k),...,GN-1(k))T，X＝(X0,X1,...,XN-1)Twalsh transform of x (k-i);
step five: xnEvaluating by inverse transform of Walsh to obtain estimated value of true value x of network flow
Step six: the estimated error is used to correct the model and if the process is finished, either exit and save the result to a file or return to step 1.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811402438.7A CN109245954B (en) | 2018-11-23 | 2018-11-23 | Network service flow modeling method for EPON and LTE wireless dual-mode converged communication |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811402438.7A CN109245954B (en) | 2018-11-23 | 2018-11-23 | Network service flow modeling method for EPON and LTE wireless dual-mode converged communication |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109245954A CN109245954A (en) | 2019-01-18 |
| CN109245954B true CN109245954B (en) | 2021-09-14 |
Family
ID=65076398
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811402438.7A Active CN109245954B (en) | 2018-11-23 | 2018-11-23 | Network service flow modeling method for EPON and LTE wireless dual-mode converged communication |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109245954B (en) |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5729124A (en) * | 1994-03-14 | 1998-03-17 | Industrial Technology Research Institute | Estimation of signal frequency using fast walsh transform |
| CN108388741A (en) * | 2018-03-02 | 2018-08-10 | 西安费斯达自动化工程有限公司 | Aircraft flutter analysis grid model Walsh modeling method |
-
2018
- 2018-11-23 CN CN201811402438.7A patent/CN109245954B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5729124A (en) * | 1994-03-14 | 1998-03-17 | Industrial Technology Research Institute | Estimation of signal frequency using fast walsh transform |
| CN108388741A (en) * | 2018-03-02 | 2018-08-10 | 西安费斯达自动化工程有限公司 | Aircraft flutter analysis grid model Walsh modeling method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109245954A (en) | 2019-01-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| JP5204295B2 (en) | Estimating available end-to-end bandwidth in packet-switched communication networks | |
| Llatser et al. | Diffusion-based channel characterization in molecular nanonetworks | |
| CN110110529B (en) | Software network key node mining method based on complex network | |
| Toral-Cruz et al. | Accurate modeling of VoIP traffic QoS parameters in current and future networks with multifractal and Markov models | |
| Nguyen et al. | Effects of wireless sensor network uncertainties on output-only modal analysis employing merged data of multiple tests | |
| CN113537788B (en) | Urban traffic jam recognition method based on virus propagation theory | |
| CN103139804B (en) | Energy-saving transmission self-adaption recursive least squares (RLS) distributed-type detection method of wireless sensor network | |
| CN104168131A (en) | Flow generation method of power dispatching exchange network based on multicast communication | |
| CN105227689A (en) | Based on the Target IP location algorithm of local time delay distribution similarity tolerance | |
| Varet et al. | Realistic network traffic profile generation: Theory and practice | |
| CN105577473A (en) | Multi-business flow generation system based on network flow model | |
| Dey et al. | Estimating network link characteristics using packet-pair dispersion: A discrete-time queueing theoretic analysis | |
| CN109245954B (en) | Network service flow modeling method for EPON and LTE wireless dual-mode converged communication | |
| US10771348B2 (en) | Inferring component parameters for components in a network | |
| Hága et al. | Understanding Packet Pair Separation Beyond the Fluid Model: The Key Role of Traffic Granularity. | |
| Masugi | Recurrence plot-based approach to the analysis of IP-network traffic in terms of assessing nonstationary transitions over time | |
| CN108599834B (en) | Method and system for analyzing utilization rate of satellite communication network link | |
| CN107563103A (en) | Uniformity filter design method based on local condition | |
| Karimov et al. | Models of network processes for describing operation of network protection tools | |
| Torres et al. | Implications of a dynamical-network's graph on the estimability of its modes | |
| Csóka et al. | Binary error models for wireless sensor networks | |
| Barbe et al. | Finite record effects of the errors-in-variables estimator for linear dynamic systems | |
| Hága et al. | Granular model of packet pair separation in Poissonian traffic | |
| Ost et al. | Modeling and Evaluation of pseudo selfsimilar traffic with Infinite-State Stochastic Petri Nets | |
| Cantillo et al. | Modeling of communication delays aiming at the design of networked supervisory and control systems. A first approach |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |