CN109245954A - Network traffic modeling method towards EPON Yu LTE wireless double mode converged communication - Google Patents
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Abstract
The Network traffic modeling method towards EPON Yu LTE wireless double mode converged communication that the invention proposes a kind of.It is constructed using Walsh transformation about the model towards EPON Yu the network flow end-to-end flux of LTE wireless double mode converged communication.Firstly, end to end network flow is expressed as an independent identically distributed random time-dependent sequence.In random process, there are several parameters to need accurate estimation.This is extremely difficult for limited flow information.Secondly, characterizing end to end network flow using Walsh transformation.Using statistical method calculating parameter, model is correctly determined.In addition, the model in relation to end to end network flow correctly constructs.Finally, proposing the new algorithm of one kind to establish model.Simulation result shows that method of the invention is feasible and effective.
Description
Technical field
The present invention is the network towards EPON Yu LTE wireless double mode converged communication belonged in the modeling of communication network service stream
Business Stream models field.
Background technique
Ethernet passive optical network (EPON) and LTE have been widely used in communication network, with traditional transmission network
It compares, they can provide the data transmission capabilities of bigger bandwidth, more short time-delay.And in the power distribution communication of powerline network
In system, at present still mainly using the EPON system of optical fiber networking and rental two kinds of communication patterns of operator GPRS, single communication
There are inherent shortcoming and the limitations used for standard, the expansion of safety and new function business to power distribution communication network
Great challenge is brought, such as: optical cable is dug to break to will lead to a large amount of ONU and lose and be connected in work progress, and electric power terminal is caused to monitor
Devices collect data can not be transmitted and collect；The transmission bandwidth of GPRS itself is just very small, and is easy by extraneous electromagnetism
Interference etc., these all limit the development of power distribution communication grid and the deployment of new function, and it is logical to can no longer meet distribution
Believe the diversified demand of system service functions.Therefore, merging EPON and LTE building wireless double mode converged communication system is distribution
The inexorable trend of communication system development.In power distribution communication system, the flow of EPON and LTE wireless double mode converged communication system
Huge variation has occurred compared to traditional EPON communication pattern combined with GPRS in characteristic and management mode, simultaneously
Also also there is huge difference with the service traffics of mobile communication service network.However, in power distribution communication system about EPON with
The property and feature of the network service traffic of LTE wireless double mode converged communication are not yet furtherd investigate, theory analysis and practical application
Between gap it is still larger.This patent is intended to solve EPON and LTE wireless double mode converged communication network in power distribution communication system
Flowmeter factor problem, and propose a kind of EPON that describes based on the modeling method of Walsh transformation and merged with LTE wireless double mode
The end-to-end flux of communication network traffic.Firstly, teleservice is expressed as independent same distribution random time-dependent sequence.Then
The end-to-end flux of network flow is characterized with Walsh transformation.Pass through and calculate corresponding parameter, it was demonstrated that establishing model is just
True property.Simulation result shows that this patent method has feasibility and validity.
With the continuous development of data transfer demands and new opplication in power distribution communication system, EPON melts with LTE wireless double mode
Hop communication network has widely been deployed in current power distribution communication network.EPON and LTE wireless double mode converged communication network
In flow have new function, this brings new challenge to the transmission network performance and traffic engineering of power distribution communication system.It is quasi-
It really describes and simulates EPON and raising EPON is merged with LTE wireless double mode with the flow of LTE wireless double mode converged communication network
The performance of communication network is of great significance.Moreover, the network flow in power distribution communication system has self-similarity, auto-correlation
The features such as property, heavytailed distribution, this has a major impact the network optimization and routing.EPON and LTE wireless double mode converged communication network
Flow characterize the network behavior of power distribution communication.Therefore, the end in simulation EPON and LTE wireless double mode converged communication network
To the end flow extensive concern by researcher, operator and developer.
End-to-end flux behavior in communication network embodies path-level and network level feature in network, this can be used for
Network state and property, such as path load, handling capacity, network utilization are described.It is used to represent and is retouched using statistical method
State the Model of network traffic from source node to destination node；Gravity Models, general evolution, mixed method and compressed sensing etc. can
With the attribute for capturing end to end network flow.During executing modeling, these methods can be better anticipated and estimate
Count end-to-end flux.But these methods need the information from link load or the previous letter about end to end network flow
Breath, this thereby necessarily increases the computational complexities and expense that obtain model parameter.And use Time-Frequency Analysis more for capturing
Scale feature and dynamic characteristic；It is used to analog network flow using neural network, these methods, which can establish, indicates end-to-end
The model of network flow, but accurately to capture their function and be established for the traffic engineering in communication network accurately and suitable
When Model of network traffic it is extremely difficult.For this purpose, the present invention proposes that a kind of new modeling method is wireless to estimate EPON and LTE
End-to-end flux in bimodulus converged communication network.
Summary of the invention
For deficiency existing for existing method, the present invention propose a kind of easier, high accuracy, efficient EPON with
The flowmeter factor method of LTE wireless double mode converged communication network, key step are as follows:
Step 1: the measured value in network is obtained according to network flow programming method instrument, and is write as expression formula
Shown in nonlinear function characterization network flow true value；Wherein [τ1,τ2] be filtering window length；τ1
And τ2It is the initial time and end time of filter window respectively；F (t) is an arbitrary continuation function of time；T represents the time；
X indicates the true value of measurement；H is Instrument response function.
Step 2: according to equation
Nonlinear function formulaIt is written as discrete form y (k)；K is to become certainly
Amount；H (i) is the discrete receptance function of instrument.[i1,i2] it is discrete filtering window length；Wherein, i1And i2It is filter respectively
The starting point and end point of wave device window；N is sampling number.
Step 3: according to Walsh transformation theory
To the sequence with N number of sampled point, Walsh transformation is to can be expressed as
Experiment curv is obtained by inverting, and removes the influence of instrument and environment.F (k) and f (t) is Walsh transformation
It is right；xiIt is the true value sequence of measurement, XkIt is xiWalsh transformation.
Step 4: least square method is used, error sum of squares Q (X is obtained0,X1,...,XN-1), then pass through equation
To obtain XnMinimum value, N is sampling number.WhereinQ (x) is
Error sum of squares；J is total sampling number.
Step 5: XnIt is assessed with the inverse transformation of Walsh, to obtain the estimated value of network flow true value x
Step 6: carry out correction model using estimation error.If process terminates, it please exit and result is saved in file
In, or return to step 1.
Walsh function is that a complete, standardized orthogonal system is defined as [0,1], is expressed as wal (n, k),
Middle n is order, and k is independent variable, and value is only+1 and -1.Walsh function is a non-sine function, any one time letter
Number f (t) is [0,1) period is all 1.Time-varying network flow can be decomposed into a series of weighted sum of walsh functions.Cause
This, for any Continuous time functions, we can obtain following equation:
Wherein
Therefore, the Walsh transformation f (t) of continuous function can be expressed as
This is a Walsh transformation pair.
For the discrete series with N number of sampled point, Walsh transformation is to being
Experiment curv is obtained by inverting, and removes the influence of instrument and environment.F (k) and f (t) is Walsh transformation
It is right；xiIt is the true value sequence of measurement, XkIt is xiWalsh transformation.
For method involved in this paper based on the assumption that in measurement interpretation model, network flow is uniform in a short time
And stablize, therefore experiment curv can be approximately rectangular wave in a short time.
The network flow data that measuring instrument obtains is actually by Instrument response function, and the factors such as environmental condition influence non-
Linear function, it is usually the true value of network flow.Correspondingly, following equation can be obtained:
Wherein x indicates the true value of measurement；H is Instrument response function；[τ1,τ2] instruction filtering window length；T is represented
Time.Therefore, our available following discrete forms:
Wherein [i1,i2] indicate filtering window length；K indicates time sampling label；N is the quantity of sampled point in window.
The purpose of experiment curv inverting is removal Instrument response function h, the influence of environmental condition etc., to restore flow from measured value y
True value x estimated valueTherefore measured valueMean square error with true value x be as.Walsh transformation compares Fourier
The essential characteristic of variable-flow is more acurrate when leaf transformation reflects.Therefore, we use the experiment curv inverting based on Walsh transformation
Technology describes network flow.Least square method is used to obtain the estimated value of the true value of measured value
According to before it is assumed that carrying out inverse Walsh transform to x (k-i) and completing equation:
J is the sum of sampled point； X
=(X0,X1,...,XN-1)TIt is the Walsh transformation of x (k-i).
XnSolution Least Square Method can be used.According to inverse Walsh transform, we can obtain network flow x's
Estimated value.Formula is as follows
It has been proved that the least square solution model under Walsh transformation has highest resolution ratio, ring can be not being considered
The accurate solution x of network flow is obtained in the case where the influence of noise of border, interpolation result and acquired results data value are close.Formula (7)
Can make error mean square andObtain XnMinimum value.
Advantages of the present invention: it is final that the flowmeter factor method (WTMA) based on Walsh transformation is demonstrated by some tests
Demonstrate the accuracy of service traffics model.We need using true network data.We are real using the U.S.
The truthful data of Abilene backbone network verifies WTMA.Detailed simulated experiment is carried out using Matlab2010.And it will
WTMA is compared with PCA, WABR and HMPA network flow model algorithm.We also assess WTMA to PCA, WABR and HMPA
Performance improvement.
Based on Walsh transformation flowmeter factor method
The experimental results showed that WTMA can effectively grab the dynamic change of end-to-end network flow for it is different when
Gap, true end to end network flow show significant time-varying property, and WTMA can capture becoming for end to end network flow
Gesture.Although WTMA is for end to end network flow, there are biggish evaluated errors, it still can capture its variation tendency.
This further demonstrates that WTMA can be effectively estimated end to end network flow and change with time.
We compare the opposite evaluated error of all algorithms simultaneously.The average opposite evaluated error of end to end network flow
Is defined as:
In i=1,2 ..., N, N is the number of simulation process.||·||2It is L2Norm.When referring to i-th
Between t end-to-end estimated value.
It is concluded that compared with PCA, WABR and HMPA, WTMA has preferably estimation energy for end to end network stream
Power, and WTMA has best estimated capacity really.Importantly, WABR, HMPA and WTMA exist in terms of relative error
Temporal fluctuation is lower than PCA.This shows compared with other three kinds of algorithms, WTMA can more effectively establish have dynamic and
The end to end network stream of time varying characteristic.
Analyze improvement of the WTMA to other three kinds of algorithms of end-to-end network flow.Obtain result: relative to PCA, WTMA
It can achieve maximum performance boost.For WABR, WTMA can only realize lesser improvement.But improvement of the WTMA to HMPA
Degree is minimum, less than 5%.
Detailed description of the invention
Fig. 1 is to provide the embodiment of the invention towards EPON Yu LTE wireless double mode converged communication network stream modeling method
General flow chart；
Fig. 2 depicts the true value and WTMA prediction result of teleservice stream 67 and 107.Wherein (a) is end-to-end OD
The relative error of stream 67；It (b) is the relative error of end-to-end OD stream 107；
Fig. 3 is four kinds of algorithms PCA, WABR of teleservice stream 67 and 107, the average relative error of HMPA and WTMA.
Wherein (a) is the relative error of end-to-end OD stream 67；It (b) is the relative error of end-to-end OD stream 107；
Fig. 4 is WTMA respectively to the improvement ratio of PCA, WABR and HMPA teleservice amount flow 67 and 107.
Specific embodiment
Embodiments of the present invention are described in further detail with reference to the accompanying drawing.
Fig. 1 is the embodiment general flow chart towards EPON Yu LTE wireless double mode converged communication network stream modeling method.It should
Process starts from step S101.In step S102, according to network flow programming method instrument, obtain the measured value in network, and be written etc.
Formula
Shown in nonlinear function network flow true value.The equation source of this step is as follows: walsh function is one
A complete, standardized orthogonal system is defined as [0,1], is expressed as wal (n, k), and wherein n is order, and k is independent variable,
Value is only+1 and -1.Walsh function is a non-sine function, any one function of time f (t) [0,1) period is all
1.Time-varying network flow can be decomposed into a series of weighted sum of walsh functions.Therefore, for any Continuous time functions
We can obtain following equation:
Wherein
Therefore, the Walsh transformation f (t) of continuous function can be expressed as
This is a Walsh transformation pair.For the discrete case with N number of sampled point, Walsh transformation is to can indicate
For
Experiment curv is obtained by inverting, and removes the influence of instrument and environment.F (k) and f (t) is Walsh transformation
It is right；XkIt is xiWalsh transformation.
For method involved in this patent based on the assumption that in measurement interpretation model, network flow is equal in a short time
It is even and stable, therefore experiment curv can be approximately rectangular wave in a short time.
The network flow data that network flow programming method instrument obtains is actually by Instrument response function, the factors such as environmental condition
The nonlinear function of influence, it is usually the true value of network flow.Correspondingly, equation can be obtained:
In step S103, the equation in step S102 is transformed to discrete form.X indicates the true of measurement in the equation
Value；H is Instrument response function；[τ1,τ2] instruction filtering window length；T represents the time.Therefore, we are available following
Discrete form:
Wherein [i1,i2] indicate filtering window length；K indicates time sampling label；N is the quantity of sampled point in window
The purpose of experiment curv inverting is removal Instrument response function h, the influence of environmental condition etc., to restore flow from measured value y
True value x estimated valueTherefore measured valueMean square error with true value x be as.Walsh transformation compares Fourier
The essential characteristic of variable-flow is more acurrate when leaf transformation reflects.
Step S104, according to Walsh transformation theory, by formula (2) (3) it can be concluded that experiment curv, and can be from measurement
Instrument response, the influence of environmental condition etc. are removed in value y.
The purpose of experiment curv inverting is that Instrument response function h is removed from measured value y, the influence of environmental condition etc., with
Restore the estimated value of the true value x of flowTo make estimated valueMean square error with x be as.Walsh transformation ratio
Fourier transform is more acurrate, the essential characteristic of variable-flow when reflecting.Therefore, we use the measurement based on Walsh transformation
Curve inversion technology describes network flow.Least square method is used to obtain the estimated value of the true value of measured value
Step S105 obtains Q (X using least square method0,X1,...,XN-1) quadratic sum error, then pass through formula
(7) X is obtainednMinimum value.According to before it is assumed that carrying out inverse Walsh transform to x (k-i) and completing equation:
J is the sum of sampled point； X
=(X0,X1,...,XN-1)TIt is the Walsh transformation of x (k-i).
XnSolution Least Square Method can be used.According to inverse Walsh transform, we can obtain network flow x's
Estimated value, formula are as follows
It has been proved that the least square solution model under Walsh transformation has highest resolution ratio, ring can be not being considered
The accurate solution x of network flow is obtained in the case where the influence of noise of border, interpolation result and acquired results data value are close.Formula (7)
Can make error mean square andObtain XnMinimum value
Step S106 handles X by Walsh transformationnTo obtain the true value of network flow x
Step S107 adjusts mathematical model by the error of estimation.
Real data needed for simulated experiment is collected by network node；We use the real Abilene backbone network in the U.S.
The truthful data of network verifies WTMA.Detailed simulated experiment is carried out using Matlab2010.PCA, WABR and HMPA net
The performance of network flow model algorithm is more preferable.Therefore, we compare WTMA with them.Below, end to end network business
Prediction result will be analyzed to illustrate WTMA algorithm.The average relative error of the end to end network flow of four kinds of algorithms will be by
It shows.Finally, we also assess performance improvement of the WTMA relative to PCA, WABR and HMPA.It is preceding in our emulation
The data of 500 time slots are used to train the model of four kinds of methods, and other data are then used to verify its performance.
Fig. 2 shows the estimated values of network flow 67 and 107, and wherein network flow 67 and 107 is from Abilene backbone network
144 end to end network stream centerings it is randomly selected.As shown in our simulated experiment, other end-to-end network flows are to table
Show similar result.Without loss of generality, we only discuss network flow 67 and 107 herein.In addition, end-to-end net here
It is right that network flow is equal to original destination (OD).(a) chart is bright in Fig. 2, and WTMA can effectively catch end to end network flow
Dynamic change 67.For different time slots, true end to end network flow shows significant time-varying property.From Fig. 2
(a) figure, which can be seen that WTMA, can capture the trend of end to end network flow.Equally, as shown in (b) figure in Fig. 2, end is arrived
End network flow 107 shows irregular and dynamic variation in time.Though from Fig. 2 in (a) figure it can be clearly seen that
There are biggish evaluated errors for end to end network flow 67 by right WTMA, but it still can capture its variation tendency.This into
One step shows that BTMA can be effectively estimated end to end network flow and change with time.
Next, the evaluated error of four kinds of algorithms of analysis.In general, the property that end to end network flow changes over time is difficult
Only captured by model.In order to further verify the algorithm, we compare the opposite evaluated error of all algorithms.In order to keep away
Exempt from the randomness in simulation, we execute 500 operations to calculate averagely opposite evaluated error.
The average opposite evaluated error of end to end network flow is defined as:
In i=1,2 ..., N, N is the number of simulation process.||·||2It is L2Norm.When referring to i-th
Between t end-to-end estimated value.
Fig. 3 shows the average opposite evaluated error of four kinds of algorithms of end to end network stream 67 and 107 in time.Very
It is interesting that HMPA and WTMA show lower relative error, and PCA is protected for end to end network stream 67 and 107, WABR
Hold biggish estimated bias.Meanwhile Fig. 2 also indicates that WTMA has minimum relative error.This teaches that, with PCA, WABR
It is compared with HMPA, WTMA has better estimated capacity for end to end network stream, and WTMA has best estimation energy really
Power.Importantly, the fluctuation of WABR, HMPA and WTMA in time is lower than PCA in terms of relative error.This shows and it
He compares three kinds of algorithms, and WTMA can more effectively establish the end to end network stream with dynamic and time varying characteristic.
Now, we analyze improvement of the WTMA to other three kinds of algorithms of end-to-end network flow.
Fig. 4 depicts the improvement ratio of stream 67 and 107 end to end.For flow end to end 67, WTMA relative to PCA,
WABR has reached the performance improvement and HMPA of about 4.95%, 2.11% and 3.39%.Similarly, for teleservice stream
107, WTMA obtain the performance improvement relative to PCA, WABR and HMPA about 19.5%, 12.9% and 5.0% respectively.Test table
Bright, compared with PCA, WABR and HMPA, our WTMA algorithm can more effectively model end to end network flow really.From
We are it can also be seen that WTMA can achieve maximum performance boost relative to PCA in Fig. 4.For WABR, WTMA can only be real
Existing lesser improvement.But WTMA is minimum to the improvement degree of HMPA, i.e., less than 5%.As shown in figure 3, this is further demonstrated that,
WABR, HMPA and WTMA have better modeling ability for end to end network flow.In addition, WTMA also have with HMPA it is similar
Performance.Therefore, WTMA can correctly simulate end-to-end flux.
Although specific embodiments of the present invention have been described above, one skilled in the art should be managed
Solution, these are merely examples, and many changes and modifications may be made, without departing from original of the invention
Reason and essence.The scope of the present invention is only limited by the claims that follow.
Claims (3)
1. the Network traffic modeling method towards EPON Yu LTE wireless double mode converged communication, it is characterised in that: including following step
It is rapid:
Step 1: the measured value in network is obtained according to network flow programming method instrument, and is write as expression formula
Shown in nonlinear function network flow true value；Wherein [τ1,τ2] be filtering window length；τ1And τ2It is respectively
The initial time and end time of filter window；F (t) is an arbitrary continuation function of time；T represents the time；X indicates measurement
True value；H is Instrument response function；
Step 2: according to equation
Nonlinear function formulaIt is written as discrete form y (k)；K is independent variable；h
(i) be instrument discrete receptance function；[i1,i2] it is discrete filtering window length；Wherein, i1And i2It is filter respectively
The starting point and end point of window；N is sampling number；
Step 3: according to Walsh transformation theory
To the sequence with N number of sampled point, Walsh transformation is to can be expressed as
Experiment curv is obtained by inverting, and removes the influence of instrument and environment；F (k) and f (t) is Walsh transformation pair；xi
It is the true value sequence of measurement, XkIt is xiWalsh transformation；
Step 4: least square method is used, error sum of squares Q (X is obtained0,X1,...,XN-1), then pass through equation
To obtain XnMinimum value, N is sampling number, whereinQ (x) is that error is flat
Fang He；J is total sampling number；
Step 5: XnIt is assessed with the inverse transformation of Walsh, to obtain the estimated value of network flow true value x
Step 6: carrying out correction model using estimation error, if process terminates, please exit and result is saved in file, or
Back to step 1.
2. the Network traffic modeling method according to claim 1 towards EPON Yu LTE wireless double mode converged communication,
It is characterized in that: according to Walsh transformation theory described in step 3:
To the sequence with N number of sampled point, Walsh transformation is to can indicate are as follows:
Experiment curv is obtained by inverting, and removes the influence of instrument and environment, F (k) and f (t) are Walsh transformations pair；xi
It is the true value sequence of measurement, XkIt is xiWalsh transformation, N is sampling number.
3. the Network traffic modeling method according to claim 2 towards EPON Yu LTE wireless double mode converged communication,
It is characterized in that: obtaining Q (X using least variance method0,X1,...,XN-1) error of sum square, pass through formula
To obtain XnMinimum value, whereinQ (x) is error sum of squares；J is sampling
The number of point.
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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 |
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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 |
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