CN112532314A - Method and device for predicting transmission performance of optical network - Google Patents
Method and device for predicting transmission performance of optical network Download PDFInfo
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Abstract
The invention discloses a method and a device for predicting the transmission performance of an optical network, wherein the method comprises the following steps: acquiring a back-to-back OSNR-BER curve of the optical module through experimental tests; for any specific line configuration, calculating the nonlinear noise-signal ratio NLN of the optical module under the line configuration; wherein the line configuration comprises one or more parameters; and calculating to obtain the OSNR-BER curve of the optical module under the line configuration through the back-to-back OSNR-BER curve of the optical module and the NLN under the line configuration. The scheme comprehensively considers the influence of inherent non-ideal factors of the optical module, the OSNR at the tail end of the circuit and the nonlinear effect on the transmission performance, and adopts an efficient mode to acquire data, so that the transmission performance of the optical network can be accurately and quickly estimated.
Description
Technical Field
The present invention belongs to the technical field of optical communication, and more particularly, to a method and an apparatus for predicting optical network transmission performance.
Background
With the increasing of the optical network speed, the used modulation code types are more and more diversified, and it becomes more and more important to accurately and quickly predict the transmission performance of the optical network (i.e. the performance of the optical module when transmitting in the optical network), and especially to have an important meaning for the route switching. The performance of the optical module in transmission is mainly represented by a Bit Error Rate (BER) or a Q value; the Q value is actually an equivalent description of BER in the communication field, and it and BER can be converted by formula. In optical network transmission, the BER or Q value is related to many factors, mainly including the following three aspects: 1) inherent non-ideality of optical modules; 2) OSNR (Optical Signal Noise Ratio) at the end of the Optical fiber line; 3) nonlinear effects of fiber optic lines.
The traditional technical scheme can not accurately predict the transmission performance of the optical network and mainly has the following problems:
1) or the performance of the optical module after passing through the optical transmission link is evaluated assuming that the optical module is in an ideal state. Although the method is high in speed, the method is ideal and does not consider inherent non-ideal factors of the optical module, so that the actual situation cannot be accurately reflected; moreover, modeling of nonlinear effects is simplified, and only for typical simple configurations, for example, only Erbium-Doped Fiber Amplifier EDFA (i.e., Erbium taped Fiber Amplifier) amplification is required to exist in an optical network, and all channel widths, channel spacings, and all spans are identical.
2) Or the non-ideal factors of the optical modules are considered, specific simulation model parameters are established for different optical modules, and the three main influence factors are considered to be unified for simulation. This method is theoretically accurate, but the realization efficiency is low, and the non-ideal factors of the optical module are too many to be obtained.
Therefore, the conventional technical scheme is difficult to consider various factors and operation efficiency which affect the transmission performance of the optical network, and the transmission performance of the optical module in the optical network cannot be accurately and quickly estimated.
Disclosure of Invention
Aiming at the defects or the improvement requirements in the prior art, the invention provides a method and a device for predicting the transmission performance of an optical network, aiming at obtaining an OSNR-BER curve under actual line configuration by utilizing a back-to-back OSNR-BER curve of an optical module and a nonlinear noise-to-signal ratio NLN under actual line configuration, and predicting the BER of the optical module under any transmission link, thereby solving the technical problem that the transmission performance of the optical network cannot be accurately and quickly predicted by the traditional technical scheme.
To achieve the above object, according to an aspect of the present invention, there is provided a method for predicting transmission performance of an optical network, including:
acquiring a back-to-back OSNR-BER curve of the optical module through experimental tests;
for any specific line configuration, calculating the nonlinear noise-signal ratio NLN of the optical module under the line configuration; wherein the line configuration comprises one or more parameters;
and calculating to obtain the OSNR-BER curve of the optical module under the line configuration through the back-to-back OSNR-BER curve of the optical module and the NLN under the line configuration.
Preferably, for any particular line configuration, the calculating a nonlinear noise-to-signal ratio NLN of the optical module in the line configuration specifically includes:
respectively simulating to obtain error vector magnitude EVM under back-to-back configuration and the line configuration through a step-by-step Fourier algorithm, and further calculating the noise-signal ratio NSR under the back-to-back configuration and the line configuration;
and (4) calculating the difference between the noise-signal ratio NSR under the back-to-back configuration and the line configuration to obtain the nonlinear noise-signal ratio NLN under the line configuration.
Preferably, the method further comprises:
establishing a nonlinear database, and respectively storing nonlinear noise-signal ratios (NLNs) obtained by simulation under each line configuration in the nonlinear database in a coordinate form of (line configuration, NLN);
and after the data volume in the nonlinear database reaches a preset value, performing interpolation fitting on the new line configuration based on the existing data in the nonlinear database to obtain the nonlinear noise-signal ratio (NLN) of the optical module under the new line configuration.
Preferably, when the data amount in the nonlinear database is n, the interpolation fitting is performed on the new line configuration based on the existing data in the nonlinear database to obtain a nonlinear noise-signal ratio NLN of the optical module under the new line configuration, and specifically:
respectively constructing an n-dimensional line configuration matrix and an n-dimensional NLN matrix based on n groups of existing (line configuration, NLN) data in the nonlinear database;
normalizing the n-dimensional line configuration matrix by using any ith line configuration in the n-dimensional line configuration matrix as a reference; meanwhile, normalizing the n-dimensional NLN matrix by taking the NLN of the ith row in the n-dimensional NLN matrix as a reference;
for any new line configuration, calculating a normalized line configuration corresponding to the new line configuration according to the line configuration of the ith row in the n-dimensional line configuration matrix as a reference;
based on the two normalization matrixes and the normalization line configuration corresponding to the new line configuration, the normalization nonlinear noise-signal ratio corresponding to the new line configuration is calculated by using a reverse distance weighting method, and then the actual nonlinear noise-signal ratio NLN under the new line configuration is obtained.
Preferably, for any new line configuration, the corresponding nonlinear noise-to-signal ratio NLN0The calculation formula (2) includes:
rk＝|C0-Ck|；
NLN0＝nln0·NLNi；
wherein, C0Configuring correspondence for new circuitNormalized line configuration, CkConfiguring corresponding normalized line configuration, r, for the k-th line in the n-dimensional line configuration matrixkRepresents a vector C0And vector CkEuropean distance therebetween, nln0Configuring C for new circuit0Normalized nonlinear noise-to-signal ratio of nlnkFor the normalization NLN, NLN corresponding to the k-th line NLN in the NLN matrixiAnd the NLN of the ith row in the n-dimensional NLN matrix is defined, and p is an adjusting factor.
Preferably, the adjusting factor p is a value within the range of 0.5-3.
Preferably, the method further comprises:
for any line configuration comprising signal power parameters, changing the signal power parameters in the line configuration, and respectively calculating to obtain OSNR-BER curves of the optical module under different signal powers;
calculating the OSNR at the tail end of the line under each signal power according to the parameters in the line configuration, further finding the BER corresponding to the OSNR on an OSNR-BER curve corresponding to the signal power, and marking corresponding data points;
and connecting the data points marked on each OSNR-BER curve to obtain a BER variation trend curve along with the signal power, and further evaluating the optimal signal power according to the BER lowest point on the curve.
Preferably, when the OSNR at the end of the line changes, the method further comprises:
according to the OSNR change value, each marked data point is moved to the left or right on a respective OSNR-BER curve to obtain a new data point;
and connecting new data points on each OSNR-BER curve to obtain a new BER change trend curve along with the signal power, and further judging the BER change degree and the BER optimization mode according to the curve.
Preferably, the line configuration comprises one or more of modulation format, signal power, number of wavelengths, channel spacing, fiber length, fiber attenuation coefficient, fiber dispersion coefficient, fiber nonlinearity coefficient, and baud rate.
According to another aspect of the present invention, there is provided an apparatus for predicting transmission performance of an optical network, including at least one processor and a memory, where the at least one processor and the memory are connected through a data bus, and the memory stores instructions executable by the at least one processor, and the instructions are configured to, after being executed by the processor, perform the method for predicting transmission performance of an optical network according to the first aspect.
Generally, compared with the prior art, the technical scheme of the invention has the following beneficial effects: according to the method for predicting the optical network transmission performance, firstly, a back-to-back OSNR-BER curve of an optical module is obtained through experimental tests, then, a nonlinear noise-signal ratio NLN of the optical module under the line configuration is obtained through calculation for any line configuration, and finally, the OSNR-BER curve of the optical module on an optical network to be predicted can be obtained through the back-to-back OSNR-BER curve and the NLN under the line configuration. The method comprehensively considers the influence of inherent non-ideal factors of the optical module, the OSNR at the tail end of the line and the nonlinear effect on the transmission performance, and adopts an efficient mode to acquire data, so that the transmission performance of the optical network can be accurately and quickly estimated.
Drawings
Fig. 1 is a flowchart of a method for predicting transmission performance of an optical network according to an embodiment of the present invention;
fig. 2 is an overall framework diagram of the prediction of the transmission performance of the optical network according to the embodiment of the present invention;
fig. 3 is a schematic diagram of a distribution of actual constellation points on a constellation diagram according to an embodiment of the present invention;
fig. 4 is a schematic diagram of a distribution of ideal constellation points on a constellation diagram according to an embodiment of the present invention;
fig. 5 is a schematic diagram of a deviation between an ideal constellation point and an actual constellation point on a constellation diagram according to an embodiment of the present invention;
fig. 6 is a schematic diagram of OSNR-BER curves for different line configurations (different signal powers) according to an embodiment of the present invention;
FIG. 7 is a flowchart of a method for determining an optimal signal power according to an embodiment of the present invention;
FIG. 8 is a diagram illustrating a curve for determining an optimal signal power according to an embodiment of the present invention;
fig. 9 is a flowchart of a method for fast acquiring NLNs according to an embodiment of the present invention;
FIG. 10 is a schematic illustration of a non-linear database provided by an embodiment of the present invention;
fig. 11 is a flowchart of a method for calculating NLN by interpolation fitting according to an embodiment of the present invention;
fig. 12 is a diagram of a device architecture for predicting transmission performance of an optical network according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
Example 1
In order to accurately and quickly estimate the performance of an optical module during transmission in an optical network, an embodiment of the present invention provides a method for predicting the transmission performance of an optical network, as shown in fig. 1, which mainly includes the following steps:
In optical network transmission, "back-to-back" specifically means that only a short optical fiber connection (typically 10m or less, such as 1m) is used between the transmitting end and the receiving end, and no long-distance optical fiber transmission (such as several tens of km) is performed. In a back-to-back configuration, the nonlinear effects of the fiber optic line are negligible because the fibers are very short. A back-to-back OSNR-BER curve (which may also be abbreviated as B2B OSNR-BER curve) of an optical module on an optical network is obtained through experimental tests, specifically, an OSNR value of a system is continuously changed in an experiment, a corresponding BER value is recorded, and then an OSNR-BER curve is obtained through drawing.
The line configuration includes one or more parameters, specifically, one or more parameters of modulation format, signal power, wavelength number, channel interval, fiber length, fiber attenuation coefficient, fiber dispersion coefficient, fiber nonlinearity coefficient, baud rate, and the like. In an optical transmission network, for a specific line configuration, a nonlinear Noise-signal ratio NLN (Non-Linear Noise to signal ratio) is determined and does not significantly change with the OSNR of the system, and can be obtained by means of numerical simulation. For any line configuration, the process of acquiring the nonlinear noise-signal ratio NLN is as follows:
firstly, a line configuration is led into a simulation system, as shown in fig. 2, a back-to-back configuration and an Error Vector Magnitude EVM (Error Vector Magnitude) under the line configuration are respectively obtained through simulation by a step-by-step fourier algorithm, and then a Noise-to-Signal Ratio NSR (Noise to Signal Ratio) under the back-to-back configuration and the line configuration is calculated; and then, carrying out difference calculation on the noise-signal ratio NSR under the back-to-back configuration and the line configuration to obtain the nonlinear noise-signal ratio NLN under the line configuration. The computational principle of NLN is: in a back-to-back scene, because the optical fiber is very short, the nonlinear effect of the optical fiber line can be ignored, and therefore the noise term in the signal-to-noise ratio of the signal only contains ASE noise; in the actual transmission line scene, the noise term in the signal-to-noise ratio of the signal contains ASE noise and nonlinear noise, so that the nonlinear signal-to-noise ratio NLN can be obtained by subtracting the signal-to-noise ratio NSR in the two configurations.
Because the signal is polluted by noise in the process of being transmitted from the transmitting end to the receiving end, a certain error exists between a constellation point (shown in fig. 3) and an ideal constellation point (shown in fig. 4) of the signal actually received by the receiving end, and the error (or distance) between the actual constellation point and the ideal constellation point is the EVM; for any r-th constellation point on the constellation diagram, its corresponding EVM can be denoted as EVMr. While the overall EVM of the constellation is a statistic, the EVM over each constellation pointrIs calculated asThe following:
EVMr＝|Sideal，r-Vr|；
in the above formula, SrRepresenting the coordinates of the actual constellation points on the constellation diagram, Sideal,rRepresents the coordinates of the corresponding ideal constellation point on the constellation diagram, as shown in fig. 5, | Sideal,r-SrAnd | represents the distance between the ideal constellation point and the actual constellation point.
The EVM under back-to-back configuration and under actual line configuration can be respectively obtained according to the formula; further, by NSR ═ EVM2The noise-signal ratio NSR under back-to-back configuration can be obtained respectivelyBack to backAnd the noise-to-signal ratio NSR under the actual line configurationPhysical linkThen the nonlinear noise-to-signal ratio NLN ═ NSR under the actual line configurationPhysical link-NSRBack to back。
And 103, calculating to obtain an OSNR-BER curve of the optical module under the line configuration through a back-to-back OSNR-BER curve of the optical module and the NLN under the line configuration.
If the optical module transmits in the optical network with a certain line configuration, the back-to-back OSNR-BER curve obtained in step 101 is corrected through the nonlinear noise-to-signal ratio NLN obtained in step 102, so as to obtain the OSNR-BER curve of the optical module in the line configuration, thereby realizing the transmission performance prediction of the optical module in the line configuration, and further performing parameter adjustment, switching judgment and the like according to the prediction result. The calculation results of the OSNR-BER curves under different line configurations are shown in fig. 6, where for convenience of understanding, only the signal power that changes in different line configurations is taken as an example, specifically, the other line configurations have the same parameters, and the signal powers are respectively 10dBm, 8dBm, 6dBm, 4dBm, 2dBm, 0dBm, -2dBm, and-4 dBm; of course, in practical applications, the calculation may be performed by arbitrarily changing each parameter in the line configuration, and is not specifically limited herein. Based on the NLN under the actual line configuration, the BER calculation formula under the actual line configuration is specifically as follows:
BER＝f(SNR)/(log2M)；
wherein
In the above formula, M represents the number of signal constellation points (fig. 5 is taken as an example, where M is 16), erfc is a complementary error function, and BER isb2bThe back-to-back OSNR-BER curve represents the BER value corresponding to a certain OSNR, and the SNR represents the signal-to-noise ratio corresponding to the OSNR under the actual line configuration. Since the back-to-back OSNR-BER curve is known, the BER corresponding to a certain OSNRb2bThe value is known, and the NLN under the actual line configuration is also known, so the BER corresponding to the OSNR under the actual line configuration can be obtained through the formula; BER for each OSNR on a back-to-back OSNR-BER curveb2bAnd correcting according to the method to obtain the BER corresponding to each OSNR under the actual line configuration, and obtaining an OSNR-BER curve under the actual line configuration.
The embodiment of the invention mainly uses BER to represent the transmission performance, if Q value is used to represent the transmission performance, the Q value under actual line configuration can be further calculated by the following formula based on the calculated BER:
where dB represents a unit. When the Q value is used to characterize the transmission performance, the whole prediction process can still refer to the above embodiment, except that the Q value is finally calculated by BER.
In summary, the embodiment of the present invention obtains a back-to-back OSNR-BER curve of an optical module through an experimental test, obtains a nonlinear noise-signal ratio NLN under any actual line configuration through simulation, and then corrects the actually measured back-to-back OSNR-BER curve by using the NLN under the actual line configuration obtained through simulation to obtain an OSNR-BER curve of the optical module on an optical network to be predicted; the transmission performance of the optical network under any line configuration can be accurately and quickly estimated through the method.
Inherent non-ideal factors of the optical module can be taken into account through back-to-back measured data, and the OSNR and the nonlinear effect at the tail end of the line can be taken into account through an OSNR-BER curve and a nonlinear noise-signal ratio NLN; in addition, a small amount of measured data reflects non-ideal factors, a large amount of nonlinear factors generated by line configuration are obtained by a simulation means, and the most efficient mode is selected to obtain two types of data, so that the transmission performance of the optical network can be accurately and quickly estimated.
Example 2
On the basis of the above embodiment 1, when the line configuration includes the parameter of signal power, the optimal signal power can be further determined by means of the corresponding OSNR-BER curve by converting the signal power parameter in the line configuration. Fig. 7 shows a process for determining the optimal signal power, which mainly includes the following steps:
Here, taking the example of setting 8 different signal powers, the actual corresponding 8 different line configurations are, that is, the parameters except the signal power in the different line configurations are the same. The signal power is different, the corresponding nonlinear noise-signal ratio NLN is different, and 8 different OSNR-BER curves can be obtained by combining the back-to-back OSNR-BER curves, as shown in fig. 8, the signal power is 10dBm, 8dBm, 6dBm, 4dBm, 2dBm, 0dBm, -2dBm, -4dBm in sequence from top to bottom.
For each signal power, the end of line OSNR can be calculated, combining the signal power and other line configuration parameters. Therefore, the BER corresponding to a certain signal power can be obtained by plotting the corresponding OSNR value on the corresponding OSNR-BER curve, identifying the corresponding (OSNR, BER) coordinates, and then performing data point labeling on the corresponding OSNR-BER curve based on the coordinates, and finally labeling the corresponding data points on all 8 OSNR-BER curves, as shown by open circles on the curves in fig. 8.
And step 203, connecting the data points marked on each OSNR-BER curve to obtain a change trend curve of the BER along with the signal power, and further estimating the optimal signal power according to the BER lowest point on the curve.
As shown in fig. 8, the open circles on each OSNR-BER curve are connected in sequence to obtain a trend curve of BER with signal power, i.e., a real curve on the right side in the figure, it can be seen that BER first decreases and then increases with increasing signal power, and therefore there is an optimal signal power, i.e., the lowest BER point, marked with ≧ and located on the OSNR-BER curve corresponding to 4dBm, and thus 4dBm is the optimal signal power.
Further, when there is a change in the line, the OSNR changes accordingly, and the signal power does not change, so the previously labeled data points will slide to the left or right on the respective OSNR-BER curves. At this time, the BER variation degree and the BER optimization mode can be determined as follows:
first, each data point marked previously is shifted to the left or right on the respective OSNR-BER curve according to the change value of the OSNR, resulting in a new data point. For example, OSNR degradation of 2dB, the open circles in fig. 8 are left-slid by 2dB on the respective OSNR-BER curves, respectively, to obtain new data points.
And then, connecting new data points on each OSNR-BER curve to obtain a new BER change trend curve along with the signal power, and further judging the BER change degree and the BER optimization mode according to the curve. As shown in fig. 8, when the OSNR deteriorates by 2dB, a new BER minimum point is obtained as a new curve, i.e., a left-hand dashed curve in the graph, and is marked by it; at this time, it is on the OSNR-BER curve corresponding to 6dBm, so 6dBm is the optimal signal power, and at this time, it is advantageous to increase the signal power from 4dBm to 6dBm, so that BER can be reduced, and signal quality can be optimized.
By the method, the optimal signal power of the optical module during transmission in the optical network can be accurately and quickly predicted, and reference is provided for actual transmission; meanwhile, when the line changes, the change degree of the BER can be reflected in real time, and whether the BER can be optimized by increasing the signal power can be quickly judged.
Example 3
For an optical network, the change of the line configuration parameters is relatively limited, the nonlinear noise-to-signal ratio (NLN) is a continuous function (non-abrupt change) of the line configuration, when the predicted line configuration reaches a certain number, for a new line configuration, in order to acquire the corresponding NLN more quickly, the NLN of the new line configuration can be fitted through the existing line configuration and the corresponding NLN, so that the link performance on a target path is evaluated quickly to judge whether the switching condition is met, and the like.
Therefore, on the basis of embodiment 1, referring to fig. 9, the method may further include:
With reference to fig. 2, after calculating the nonlinear noise-to-signal ratio NLN under a certain line configuration through simulation each time, storing the line configuration and the corresponding NLN in the nonlinear database in a (line configuration, NLN) coordinate form, as shown in fig. 10; after multiple times of simulation calculation, a plurality of groups of coordinate data can be stored in the nonlinear database.
Along with the increase and completeness of coordinate data (line configuration, NLN) in the nonlinear database, the method is equivalent to that existing sample data is more and more, when the sample data reaches a certain preset value, interpolation fitting can be directly carried out on new line configuration according to the existing sample data to obtain a corresponding NLN, simulation calculation is not needed, and therefore the obtaining speed of nonlinear factors is accelerated. The more sample data, the more accurate the fitting result, so the preset value can be reasonably selected according to actual requirements, and is not specifically limited herein. When the sample data size in the nonlinear database is n, referring to fig. 11, the interpolation fitting process for the NLN under the new line configuration specifically includes the following steps:
For example, assume that the (line configuration, NLN) data structure is as follows: (L, A, CD, Ps, Γ, N, G, Rs, …, NLN), wherein L is the fiber length, A is the fiber attenuation coefficient, CD is the fiber dispersion coefficient, Ps is the signal power, Γ is the fiber nonlinear coefficient, N is the number of wavelengths, G is the channel spacing, Rs is the baud rate, "…" represents other relevant line configuration parameters, and NLN is the nonlinear noise-to-signal ratio. When n groups of (line configuration, NLN) data exist in the nonlinear database, the constructed n-dimensional line configuration matrix and n-dimensional NLN matrix are respectively as follows:
For example, if i is 1, the first row line is configured with [ L ═ L {1 A1 CD1 Ps1 Γ1 N1 G1 Rs1…]Normalizing the n-dimensional line configuration matrix for reference, and performing NLN1And normalizing the n-dimensional NLN matrix for a reference. It should be noted that the normalization of the line configuration means that each parameter is referenced to the corresponding parameter in the first row. Using C to represent a certain line after normalizationAnd (3) path configuration, wherein the normalization results of the n-dimensional path configuration matrix and the n-dimensional NLN matrix are respectively as follows:
If a new line configuration L is to be derived0 A0 CD0 Ps0 Γ0 N0 G0 Rs0…]Corresponding nonlinear noise-to-signal ratio NLN0To ensure the consistency of the calculation, the first row line is also needed to be configured with [ L ]1 A1 CD1 Ps1 Γ1 N1 G1Rs1…]Normalizing the line configuration for reference to obtain normalized line configuration C corresponding to the new line configuration0＝[l0 a0 cd0 ps0γ0 n0 g0 rs1…]Then based on C0And performing subsequent fitting.
And step 3024, based on the two normalization matrices and the normalization line configuration corresponding to the new line configuration, calculating a normalization nonlinear noise-signal ratio corresponding to the new line configuration by using a reverse distance weighting method, and further obtaining an actual nonlinear noise-signal ratio NLN under the new line configuration.
For the new line configuration, the corresponding normalized nonlinear noise-to-signal ratio is first calculated using the inverse distance weighting method nln0Then according to nln0Calculating nonlinear noise-to-signal ratio (NLN) of new line configuration0The specific calculation formula is as follows:
rk＝|C0-Ck|；
NLM0＝nln0·NLNi；
wherein, C0Normalized line configuration for new line configuration, CkConfiguring corresponding normalized line configuration, r, for the k-th line in the n-dimensional line configuration matrixkRepresents a vector C0And vector CkEuropean distance therebetween, nln0Normalized nonlinear noise-to-signal ratio for new line configuration, nlnkFor the normalization NLN, NLN corresponding to the k-th line NLN in the NLN matrixiAnd the NLN of the ith row in the n-dimensional NLN matrix is defined, and p is an adjusting factor. The adjusting factor p can be valued in the range of 0.5-3, the larger p is, the larger the influence of a closer point on an interpolation result is, and the smaller a farther point on the interpolation result is, and the better the interpolation result is generally about 2.0. In the above embodiment, the fitting is performed based on the first line, i.e., i is 1, so that NLN exists0＝nln0·NLN1。
Compared with simulation calculation, the nonlinear noise-signal ratio NLN under the new line configuration can be acquired more quickly through the interpolation fitting method, and the efficiency of performance prediction is further improved. In addition, after each interpolation fitting, the new line configuration and the corresponding NLN are also stored in the nonlinear database in the form of (line configuration, NLN) coordinates, so that sample data in the nonlinear database is continuously increased and completed, and the subsequent interpolation fitting is more accurate.
Example 4
On the basis of the prediction methods of the optical network transmission performance provided in embodiments 1 to 3, the present invention further provides a prediction apparatus of the optical network transmission performance, which can be used to implement the above methods, as shown in fig. 12, which is a schematic diagram of an apparatus architecture in an embodiment of the present invention. The prediction apparatus for optical network transmission performance of the present embodiment includes one or more processors 21 and a memory 22. In fig. 12, one processor 21 is taken as an example.
The processor 21 and the memory 22 may be connected by a bus or other means, and fig. 12 illustrates the connection by a bus as an example.
The memory 22, as a non-volatile computer-readable storage medium for predicting the transmission performance of the optical network, may be used to store non-volatile software programs, non-volatile computer-executable programs, and modules, such as the prediction method of the transmission performance of the optical network in embodiment 1. The processor 21 executes various functional applications and data processing of the prediction apparatus of optical network transmission performance by running the nonvolatile software program, instructions and modules stored in the memory 22, that is, implements the prediction method of optical network transmission performance of embodiments 1 to 3.
The memory 22 may include high speed random access memory and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some embodiments, the memory 22 may optionally include memory located remotely from the processor 21, and these remote memories may be connected to the processor 21 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The program instructions/modules are stored in the memory 22, and when executed by the one or more processors 21, perform the method for predicting the transmission performance of the optical network according to the embodiment 1, for example, perform the steps shown in fig. 1, fig. 7, fig. 9 and fig. 11 described above.
It will be understood by those skilled in the art that the foregoing is only a preferred embodiment of the present invention, and is not intended to limit the invention, and that any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. A method for predicting transmission performance of an optical network, comprising:
acquiring a back-to-back OSNR-BER curve of the optical module through experimental tests;
for any specific line configuration, calculating the nonlinear noise-signal ratio NLN of the optical module under the line configuration; wherein the line configuration comprises one or more parameters;
and calculating to obtain the OSNR-BER curve of the optical module under the line configuration through the back-to-back OSNR-BER curve of the optical module and the NLN under the line configuration.
2. The method according to claim 1, wherein for any particular line configuration, the calculating the nonlinear noise-to-signal ratio NLN of the optical module in the line configuration specifically includes:
respectively simulating to obtain error vector magnitude EVM under back-to-back configuration and the line configuration through a step-by-step Fourier algorithm, and further calculating the noise-signal ratio NSR under the back-to-back configuration and the line configuration;
and (4) calculating the difference between the noise-signal ratio NSR under the back-to-back configuration and the line configuration to obtain the nonlinear noise-signal ratio NLN under the line configuration.
3. The method for predicting optical network transmission performance as claimed in claim 2, wherein the method further comprises:
establishing a nonlinear database, and respectively storing nonlinear noise-signal ratios (NLNs) obtained by simulation under each line configuration in the nonlinear database in a coordinate form of (line configuration, NLN);
and after the data volume in the nonlinear database reaches a preset value, performing interpolation fitting on the new line configuration based on the existing data in the nonlinear database to obtain the nonlinear noise-signal ratio (NLN) of the optical module under the new line configuration.
4. The method according to claim 3, wherein when the data amount in the nonlinear database is n, the new line configuration is interpolated and fitted based on the existing data in the nonlinear database to obtain a nonlinear noise-to-signal ratio NLN of the optical module under the new line configuration, specifically:
respectively constructing an n-dimensional line configuration matrix and an n-dimensional NLN matrix based on n groups of existing (line configuration, NLN) data in the nonlinear database;
normalizing the n-dimensional line configuration matrix by using any ith line configuration in the n-dimensional line configuration matrix as a reference; meanwhile, normalizing the n-dimensional NLN matrix by taking the NLN of the ith row in the n-dimensional NLN matrix as a reference;
for any new line configuration, calculating a normalized line configuration corresponding to the new line configuration according to the line configuration of the ith row in the n-dimensional line configuration matrix as a reference;
based on the two normalization matrixes and the normalization line configuration corresponding to the new line configuration, the normalization nonlinear noise-signal ratio corresponding to the new line configuration is calculated by using a reverse distance weighting method, and then the actual nonlinear noise-signal ratio NLN under the new line configuration is obtained.
5. The method of claim 4, wherein for any new line configuration, the corresponding NLN is a nonlinear noise-to-signal ratio0The calculation formula (2) includes:
rk＝|C0-Ck|；
NLN0＝nln0·NLNi；
wherein, C0Configuring the new line with the corresponding normalized line configuration, CkConfiguring corresponding normalized line configuration, r, for the k-th line in the n-dimensional line configuration matrixkRepresents a vector C0And vector CkEuropean distance therebetween, nln0Configuring C for new circuit0Normalized nonlinear noise-to-signal ratio of nlnkFor the normalization NLN, NLN corresponding to the k-th line NLN in the NLN matrixiAnd the NLN of the ith row in the n-dimensional NLN matrix is defined, and p is an adjusting factor.
6. The method for predicting optical network transmission performance according to claim 5, wherein the adjustment factor p is a value within a range of 0.5 to 3.
7. The method for predicting optical network transmission performance as claimed in claim 1, wherein the method further comprises:
for any line configuration comprising signal power parameters, changing the signal power parameters in the line configuration, and respectively calculating to obtain OSNR-BER curves of the optical module under different signal powers;
calculating the OSNR at the tail end of the line under each signal power according to the parameters in the line configuration, further finding the BER corresponding to the OSNR on an OSNR-BER curve corresponding to the signal power, and marking corresponding data points;
and connecting the data points marked on each OSNR-BER curve to obtain a BER variation trend curve along with the signal power, and further evaluating the optimal signal power according to the BER lowest point on the curve.
8. The method for predicting transmission performance of an optical network according to claim 7, wherein when the end of line OSNR changes, the method further comprises:
according to the OSNR change value, each marked data point is moved to the left or right on a respective OSNR-BER curve to obtain a new data point;
and connecting new data points on each OSNR-BER curve to obtain a new BER change trend curve along with the signal power, and further judging the BER change degree and the BER optimization mode according to the curve.
9. The method of any of claims 1-8, wherein the line configuration comprises one or more of modulation format, signal power, number of wavelengths, channel spacing, fiber length, fiber attenuation coefficient, fiber dispersion coefficient, fiber nonlinearity coefficient, and baud rate.
10. An apparatus for predicting transmission performance of an optical network, comprising at least one processor and a memory, wherein the at least one processor and the memory are connected via a data bus, and the memory stores instructions executable by the at least one processor, and the instructions are configured to perform the method for predicting transmission performance of an optical network according to any one of claims 1 to 9 after being executed by the processor.
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