CN114329941A - Optical transport network transmission planning optimization method and system - Google Patents
Optical transport network transmission planning optimization method and system Download PDFInfo
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Abstract
The invention provides a method and a system for optimizing optical transport network transmission planning, wherein the method comprises the following steps: on the basis of performance analysis and simulation under different modulation modes, modeling and solving an optical transmission link; establishing an optical transmission network planning optimization model by combining transmission distance, transmission capacity and network topology factors; solving by utilizing a genetic algorithm and Gray codes to obtain an optical transmission network with maximized network value; and forming a new QAM modulation scheme for signal transmission by improving the existing QAM modulation scheme. The method and the system for optimizing the transmission planning of the optical transmission network improve the noise resistance of the signal in the transmission process by applying an improved signal modulation mode.
Description
Technical Field
The present invention relates to the technical field of optical transport network planning, and in particular, to a method and a system for optimizing optical transport network transmission planning.
Background
At present, regarding the planning problem of an optical transmission network, the relation between the error rate and the signal to noise ratio in different modulation methods is explored by researching three modulation modes of QPSK, 8QAM and 16 QAM; in the aspect of modeling, factors such as transmission distance, transmission capacity, network topology and the like are comprehensively considered, and the optimal optical propagation network plan is solved by taking the network value maximization as a target.
In the existing research, only population density of cities and distances among the cities are generally considered when network values are defined, third industries such as network communication and the like are rapidly developed at present, the potential of future development is high, and the third industries are closely related to optical transmission network planning, so that the calculated network values are more comprehensive and representative by taking the factor into consideration when the network values are researched. In addition, in the transmission process of the signals, the anti-noise performance of the existing three signal modulation modes has a space for improvement.
Disclosure of Invention
In view of the defects in the prior art, an object of the present invention is to provide a method and a system for optimizing an optical transport network transmission plan, which improve the noise immunity of a signal in a transmission process by using an improved signal modulation method.
In order to solve the problems, the technical scheme of the invention is as follows:
a method for optimizing an optical transport network transmission plan, the method comprising the steps of:
on the basis of performance analysis and simulation under different modulation modes, modeling and solving an optical transmission link;
establishing an optical transmission network planning optimization model by combining transmission distance, transmission capacity and network topology factors;
solving by utilizing a genetic algorithm and Gray codes to obtain an optical transmission network with maximized network value; and
a new QAM modulation scheme is formed by improving the existing QAM modulation scheme for signal transmission. Optionally, a relationship between the bit error rate and the signal-to-noise ratio in the QPSK modulation mode is:
in the formula PeIs the bit error rate; the relation between the bit error rate and the signal-to-noise ratio under the QAM modulation mode is as follows:
in which L is a system number, rbIs the ratio of energy per bit to the single-sided power density per noise unit.
Optionally, the step of establishing an optical transport network planning optimization model by combining the transmission distance, the transmission capacity, and the network topology factor specifically includes: the network value maximization is taken as an objective function, and the definition of the network value is as follows: network value ∑ weight ∑ capacity · population.
Optionally, the step of obtaining the optical transmission network with maximized network value by using a genetic algorithm and gray code solution specifically includes:
defining a fitness function;
producing a random population of particles;
updating the speed and position of the particles;
judging whether a specified condition is reached;
outputting a local optimal solution;
carrying out selection, crossing and mutation operations;
judging whether a termination condition is met;
and outputting the global optimal solution.
Optionally, the fitness function is defined as:
in the formula, wiThe weight for each link is 1; numiIs the population of each city; dis (disease)maxIs the maximum transmission distance; c is the total capacity at maximum transmission distance.
Optionally, the step of producing a random population of particles specifically comprises: setting the variable as a continuous variable, the length of the chromosome is the same as the dimension of the design variable, and the design variable is: x ═ X1,x2,L,xn]The chromosomes are: vk＝[vk1,vk2,L,vkn]In the formula (I), wherein,
are respectively a design variable xiThe lower limit and the upper limit of (c),
m is the total number of chromosomes, called population size.
Optionally, the step of performing selection, intersection and mutation operations specifically includes:
selecting an operator: on the basis of evaluating the fitness of the individuals, directly inheriting the optimized individuals to the next generation through selection operation, or generating new individuals through pairing and crossing and then inheriting the new individuals to the next generation;
and (3) a crossover operator: defining the probability Pc of cross operation, and performing exchange recombination on partial structures of two parent individuals according to the probability Pc to generate a new individual;
mutation operator: defining a parameter PmAs the probability of mutation operation, non-uniform mutation is adopted: individual X ═ X1,x2L xkL xiIf xkIs a variation point and has a value range of [ Umin，Umax]When the individual X is mutated at this point, a new individual X ═ X can be obtained1,x2L xkL xiWherein the new gene values of the variation points are:
in the formula, Random (0,1) represents one randomly taken from 0,1 with a certain probability; r is a Random number which is uniformly distributed in the range of [0, 1], namely Random (0, 1); g is the current algebra; t is a termination algebra; b is a parameter for adjusting the variation step length, and dynamically changes with the current generation number G.
Optionally, the step of forming a new QAM modulation scheme for signal transmission by improving an existing QAM modulation scheme specifically includes: the position, the number or the probability of each constellation point of the existing 16QAM modulation mode is changed, so that a new 8QAM modulation scheme is formed.
Further, the present invention also provides an optimization system for optical transport network transmission planning, which comprises:
the optical transmission link modeling module is used for modeling and solving the optical transmission link from the bottom layer physical angle on the basis of performance analysis and simulation under different modulation modes;
the optical transmission network planning module is used for establishing an optical transmission network planning optimization model according to the transmission distance, the transmission capacity and the network topology factor;
and the signal modulation improvement module is used for forming a new QAM modulation scheme for signal transmission by improving the existing QAM modulation scheme.
Compared with the prior art, the invention has the advantages that:
1. by considering the total value of third-party industries such as urban network communication and the like in a planning model and adopting a genetic algorithm to search an optimal target, the solved optical transmission network value is more comprehensive and representative;
2. the position and the number of the constellation points or the probability of each constellation point are changed for the existing 16QAM modulation scheme to obtain a new 8QAM modulation scheme, and compared with the original 8QAM modulation scheme, the scheme has the advantages of better noise immunity, better transmission performance and lower SNR (signal to noise ratio) tolerance point.
Drawings
Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments with reference to the following drawings:
fig. 1 is a block diagram of an optical transport network transmission planning optimization system according to an embodiment of the present invention;
FIG. 2 is a diagram of an optical fiber signal transmission model according to an embodiment of the present invention;
fig. 3 is a flowchart of an optimization method for an optical transport network transmission plan according to an embodiment of the present invention;
FIG. 4 is a diagram comparing BER-SNR for three signal modulation methods provided by the embodiment of the present invention;
FIG. 5 is a block diagram of a genetic algorithm provided by an embodiment of the present invention;
FIG. 6 is a 16-link profile with a maximum number of connections of 3;
FIG. 7 is a diagram of a 33-link profile with a maximum number of connections of 3;
fig. 8a and 8b are comparison diagrams of the emission constellations of the original 8QAM and the new 8 QAM;
fig. 9 is a graph comparing BER-SNR curves of original 8QAM and new 8 QAM.
Detailed Description
The present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the invention, but are not intended to limit the invention in any way. It should be noted that it would be obvious to those skilled in the art that various changes and modifications can be made without departing from the spirit of the invention. All falling within the scope of the present invention.
Specifically, fig. 1 is a structural block diagram of an optical transport network transmission planning optimization system according to an embodiment of the present invention, and as shown in fig. 1, the optical transport network transmission planning optimization system includes an optical transport link modeling module 1, an optical transport network planning module 2, and a signal modulation improvement module 3.
The optical transmission link modeling module 1 is used for modeling and solving an optical transmission link from the perspective of underlying physics on the basis of performance analysis and simulation under different modulation modes. In order to study the relationship between the signal-to-noise ratio and the bit error rate of the optical fiber signal under the influence of different factors, an optical fiber signal transmission model is established in this embodiment as shown in fig. 2.
The optical transport network planning module 2 is configured to establish an optical transport network planning optimization model according to a transmission distance, a transmission capacity, and a network topology factor. The optical transport network planning aims to obtain higher link value, and the method not only considers urban population density and distance between cities, but also considers total values of third industries such as urban network communication and the like when calculating the link value, so that the evaluation result is more comprehensive and representative.
The signal modulation improvement module 3 forms a new QAM modulation scheme by changing the position, number or probability of each constellation point of the existing 16QAM modulation scheme. The improved modulation scheme has more concentrated and uniform constellation point distribution and better transmission performance than the original modulation scheme.
Fig. 3 is a flowchart of a method for optimizing an optical transport network transmission plan according to an embodiment of the present invention, and as shown in fig. 3, the method includes the following steps:
s1: on the basis of performance analysis and simulation under different modulation modes, modeling and solving an optical transmission link;
specifically, in this embodiment, in order to study the relationship between the signal-to-noise ratio and the error rate of the optical fiber signal under the influence of different factors, a multilevel keying system is adopted to analyze the error rate and the signal-to-noise ratio under the influence of different factors, and first, the definition formula of the signal-to-noise ratio is as follows:
where SNR is the average power P of the signalsSum noise mean power PnThe ratio of. It can also be rewritten as the ratio of the symbol energy E and the noise single-sided power density n 0:
therein utilizes the relationship
And
where B is the receiver bandwidth and T is the symbol duration. Now, let the carry number of the multi-system symbol be M, and one symbol contains k bits of information, then: k is log2M, if E is divided equally into each bit, the energy per bit
Therefore, the method comprises the following steps:
in the formula rbIs the ratio of energy per bit to the single-sided power density per noise unit. Since the number k of bits contained in each symbol is related to the number M of bits, r is suitable for use in studying the error rates at different values of MbThe performance of the same system is compared by taking the unit as the unit.
The relationship between the bit error rate and the signal-to-noise ratio in the QPSK modulation mode is as follows:
The relation between the bit error rate and the signal-to-noise ratio under the QAM modulation mode is as follows:
In this embodiment, an experimental method is selected to verify the relationship between the two modulation schemes. Firstly, after binary system is converted into Gray code, selecting a signal modulation mode, then adding noise, modulating again, converting and reducing the Gray code into the binary system, counting error rate, and drawing a relation graph of signal-to-noise ratio and error rate.
In order to obtain the relationship between the BER and the SNR, according to the relationship between the BER and the SNR in the QPSK modulation mode and the relationship between the BER and the SNR in the QAM modulation mode, MATLAB is used as a simulation tool to make the QPSK, 8QAM, and 16QAM relationships with the SNR, and when the BER is 0.02, the corresponding SNR values in the three modulation modes are made, and the obtained relationship is shown in fig. 4, and it can be seen from fig. 4 that the BER values in the various modulation modes decrease with the increase of the SNR. When the BER is 0.02, the SNR is 6.31, 10.33, and 13.19 in QPSK, 8QAM, and 16QAM modulation modes, respectively. From these results, it can be seen that the SNR tolerance point is larger in the 16QAM modulation mode and is approximately 2 times larger than in the QPSK modulation mode for the same bit error rate.
S2: establishing an optical transmission network planning optimization model by combining transmission distance, transmission capacity and network topology factors;
specifically, in this embodiment, the network value of the optical transport network plan is maximized as an objective function, and the definition of the network value is a weighted sum of all connection values: the invention not only considers two factors of city population density and distance between cities, but also considers the factor influence of the total value of third-party industries such as city network communication and the like, so that the value evaluation result has higher comprehensiveness. And optimizing the objective function by adopting a genetic algorithm, taking 12 cities in the country as examples, wherein the geographical positions of the selected 12 cities are shown in the following table 1, and the population number of the cities is as follows
Table 2 shows the total city third industry values as shown in table 3 below.
| City | Longitude (G) | Latitude | Integral longitude | Normalization of latitude |
| Harbin | 126°32'5.88" |
45°48'13.59"N | 126.5349667 | 45.803775 |
| Wuluqiqi (black-root and Chinese woodruff) | 87°37'0.65"E | 43°49'32.13"N | 87.61684722 | 43.82559167 |
| Beijing&Tianjin | 116°24'26.63"E | 39°54'15.12"N | 116.4073972 | 39.9042 |
| Xi ' an | 108°56'23.17"E | 34°20'29.67"N | 108.9397694 | 34.341575 |
| Zhengzhou | 113°37'31.18"E | 34°44'47.80"N | 113.6253278 | 34.74661111 |
| Lasa | 91°10'19.60"E | 29°39'8.97"N | 91.17211111 | 29.65249167 |
| |
104°4'0.45" |
30°34'22.13"N | 104.0667917 | 30.57281389 |
| Chongqing | 106°54'44.10"E | 29°25'53.71"N | 106.91225 | 29.43158611 |
| (Wuhan) | 114°18'19.94" |
30°35'34.26"N | 114.3055389 | 30.59285 |
| Shanghai province | 121°28'25.33"E | 31°13'49.41"N | 121.4737028 | 31.23039167 |
| Kunming (a Chinese herbal medicine) | 102°49'58.41"E | 24°52'48.34"N | 102.8328917 | 24.88009444 |
| Guangzhou province&Shenzhen (Shenzhen medicine) | 114°3'28.31"E | 22°32'35.15"N | 114.0578639 | 22.54309722 |
TABLE 1
| City | Population (m million people) |
| Harbin | 9.6205 |
| Wuluqiqi (black-root and Chinese woodruff) | 2.6787 |
| Beijing&Tianjin | 37.35(21.73+15.62) |
| Xi ' an | 8.2493 |
| Zhengzhou | 8.2706 |
| Lasa | 3.31 |
| Chengdu | 13.989 |
| Chongqing | 30.48 |
| (Wuhan) | 8.3385 |
| Shanghai province | 24.2 |
| Kunming (a Chinese herbal medicine) | 5.5979 |
| Guangzhou province&Shenzhen (Shenzhen medicine) | 125.501(87.049+38.452) |
TABLE 2
| City | Total production of billions of millions of third-generation |
| Harbin | 18.3182 |
| Wuluqiqi (black-root and Chinese woodruff) | 17.6459 |
| Beijing&Tianjin | 368.6812 |
| Xi ' an | 29.1702 |
| Zhengzhou | 62.0138 |
| Lasa | 2.2271 |
| Chengdu | 40.7564 |
| Chongqing | 35.9017 |
| (Wuhan) | 57.6338 |
| Shanghai province | 35.7385 |
| Kunming (a Chinese herbal medicine) | 32.3837 |
| Guangzhou province&Shenzhen (Shenzhen medicine) | 104.3763 |
TABLE 3
S3: solving by utilizing a genetic algorithm and Gray codes to obtain an optical transmission network with maximized network value;
specifically, gray code is a cyclic code based on binary coding, which needs to perform exclusive-or operation on each bit and the bit on the left side of the bit in sequence from the last bit of the ordinary binary coding, and the bit on the left side is used as the value of the corresponding gray code on the bit, and the bit on the left side is kept unchanged.
The detailed steps of the setting of the parameters related to the genetic algorithm are shown in fig. 5, and comprise the following steps:
s31: defining a fitness function;
specifically, the fitness function is:
in the formula, wiThe weight for each link is 1; numiThe unit is million persons of the population of each city; dis (disease)maxIs the maximum transmission distance; c is the total capacity at maximum transmission distance.
S32: producing a random population of particles;
the variables are assumed to be continuous variables, and the chromosome length is the same as the dimension of the design variable.
The design variables are:
X＝[x1,x2,L,xn]
the chromosomes are:
Vk＝[vk1,vk2,L,vkn]
in the formula (I), the compound is shown in the specification,
are respectively a design variable xiThe lower limit and the upper limit of (c),
m is the total number of chromosomes, called population size, and the initial population is generated by a random method.
S33: updating the speed and position of the particles;
s34: judging whether a specified condition is reached;
s35: outputting a local optimal solution;
s36: carrying out selection, crossing and mutation operations;
1. selection operator
On the basis of evaluating the fitness of the individuals, the optimized individuals are directly inherited to the next generation through selection operation, or new individuals are generated through pairing and crossing and then inherited to the next generation. If the group scale is m and the fitness of the individual i is Fi, the probability P that the individual i is selected isisIs composed of
2. Crossover operator
Defining outlines of interleaving operationsThe ratio Pc is generally suggested to be in the range of 0.4-0.99, and then the partial structures of the two parent individuals are exchanged and recombined according to the probability Pc to generate a new individual. The floating point number encoding method is generally used for representing the individuals to perform arithmetic intersection when performing intersection. Where it can be optimized in connection with gray coding. Suppose that there are two such entities
Are subjected to arithmetic intersection, two new individuals generated after the intersection operation are
In the formula, a is a cross parameter, and a belongs to (0, 1). It may be a constant, in this case called uniform arithmetic crossover; it may also be a variable determined by evolutionary algebra, in this case called non-uniform arithmetic crossing.
3. Mutation operator
Defining a parameter PmAs the probability of variation operation, the suggested value range is 0.0001-0.1, and non-uniform variation is adopted: x is1,x2L xkL xiIf xkIs a variation point and has a value range of [ Umin，Umax]When the individual X is mutated at this point, a new individual X ═ X can be obtained1,x2L xkL xiWherein the new gene values of the variation points are:
in the formula, Random (0,1) represents one randomly taken from 0,1 with a certain probability; r is a Random number which is uniformly distributed in the range of [0, 1], namely Random (0, 1); g is the current algebra; t is a termination algebra; b is a parameter for adjusting the variation step length, and dynamically changes along with the evolution algebra G.
When the maximum connection number is limited to be 3, the population size is set to be 100, the inheritance number is set to be 100, the matlab simulation is used for realizing the maximum connection number, the network topology and the network value under the condition of 16 connection numbers are shown in fig. 6, and the network value under the condition is 596502.4511; the network topology and network value under 33 link conditions are shown in fig. 7, and the network value under these conditions is 1074753.0129.
S37: judging whether a termination condition is met;
s38: and outputting the global optimal solution.
S4: a new QAM modulation scheme is formed by improving the existing QAM modulation scheme for signal transmission.
The new 8QAM modulation scheme is formed by changing the position, the number or the probability of each constellation point of the existing 16QAM modulation scheme. Under the same BER condition, the new 8QAM modulation scheme has a lower SNR tolerance point than the original 8QAM modulation scheme, and meanwhile, the information entropy is kept to be 3 bits in the 8QAM modulation mode.
Therefore, it is considered to reduce the number of points in the constellation of 16QAM to 8 and then to shift the positions of the constellation points. Common constellations are mainly circular, rectangular, cross-shaped, triangular, etc. After the number of constellation points and various parameter indexes of the constellation diagram are fully considered, a new 8QAM constellation diagram arrangement is provided, as shown in FIGS. 8a and 8b, comparing the original 8QAM and the new 8QAM constellation diagram, it can be seen that the original arrangement is that an inner square and an outer square form 45 degrees, the new 8QAM arrangement is a circle, 7 constellation points are uniformly arranged on the circle, and one constellation point is arranged at the center of the circle. The improved modulation scheme has more concentrated and uniform constellation point distribution, namely the noise under the new 8QAM modulation mode is lower than that of the original 8QAM mode, and the transmission performance is better than that of the original 8QAM modulation scheme.
In order to verify the performance of the new 8QAM constellation diagram arrangement changed by 16QAM, MATLAB simulation environment is utilized to draw a BER-SNR relation curve of the new 8QAM constellation diagram and the new 8QAM constellation diagram under 100000 symbol quantity for comparison analysis, as shown in FIG. 9, after comparison, the SNR of the new 8QAM is lower than that of the original 8QAM under the condition of the same BER, so that the new 8QAM has a lower SNR tolerance point than that of the original 8 QAM.
Compared with the prior art, the invention has the advantages that:
1. by considering the total value of third-party industries such as urban network communication and the like in a planning model and adopting a genetic algorithm to search an optimal target, the solved optical transmission network value is more comprehensive and representative;
2. the position and the number of the constellation points or the probability of each constellation point are changed for the existing 16QAM modulation scheme to obtain a new 8QAM modulation scheme, and compared with the original 8QAM modulation scheme, the scheme has the advantages of better noise immunity, better transmission performance and lower SNR (signal to noise ratio) tolerance point.
The foregoing description of specific embodiments of the present invention has been presented. It is to be understood that the present invention is not limited to the specific embodiments described above, and that various changes or modifications may be made by one skilled in the art within the scope of the appended claims without departing from the spirit of the invention. The embodiments and features of the embodiments of the present application may be combined with each other arbitrarily without conflict.
Claims (9)
1. A method for optimizing an optical transport network transmission plan, the method comprising:
on the basis of performance analysis and simulation under different modulation modes, modeling and solving an optical transmission link;
establishing an optical transmission network planning optimization model by combining transmission distance, transmission capacity and network topology factors;
solving by utilizing a genetic algorithm and Gray codes to obtain an optical transmission network with maximized network value; and
a new QAM modulation scheme is formed by improving the existing QAM modulation scheme for signal transmission.
2. The optical transport network transmission plan optimization method of claim 1, wherein the optical transport network transmission plan optimization method is based on the optical transport network transmission plan
The relation between the bit error rate and the signal-to-noise ratio in the QPSK modulation mode is as follows:
in the formula PeIs the bit error rate(ii) a The relation between the bit error rate and the signal-to-noise ratio under the QAM modulation mode is as follows:
in which L is a system number, rbIs the ratio of energy per bit to the single-sided power density per noise unit.
3. The method according to claim 1, wherein the step of establishing the optical transport network planning optimization model by combining the transmission distance, the transmission capacity, and the network topology factors specifically includes: the network value maximization is taken as an objective function, and the definition of the network value is as follows: network value ∑ weight ∑ capacity · population.
4. The method according to claim 1, wherein the step of solving the optical transport network with maximized network value by using a genetic algorithm and gray coding specifically comprises:
defining a fitness function;
producing a random population of particles;
updating the speed and position of the particles;
judging whether a specified condition is reached;
outputting a local optimal solution;
carrying out selection, crossing and mutation operations;
judging whether a termination condition is met;
and outputting the global optimal solution.
5. The optical transport network transmission plan optimization method of claim 4, wherein the fitness function is defined as:
in the formula, wiThe weight for each link is 1; numiIs the population of each city; dis (disease)maxIs the maximum transmission distance; c is the total capacity at maximum transmission distance.
6. The method according to claim 4, wherein the step of generating a random population of particles comprises: setting the variable as a continuous variable, the length of the chromosome is the same as the dimension of the design variable, and the design variable is: x ═ X1,x2,L,xn]The chromosomes are: vk＝[vk1,vk2,L,vkn]In the formula (I), wherein,
are respectively a design variable xiThe lower limit and the upper limit of (c),
m is the total number of chromosomes, called population size.
7. The method according to claim 4, wherein the step of performing selection, intersection and mutation operations specifically comprises:
selecting an operator: on the basis of evaluating the fitness of the individuals, directly inheriting the optimized individuals to the next generation through selection operation, or generating new individuals through pairing and crossing and then inheriting the new individuals to the next generation;
and (3) a crossover operator: defining the probability Pc of cross operation, and performing exchange recombination on partial structures of two parent individuals according to the probability Pc to generate a new individual;
mutation operator: defining a parameter PmAs the probability of mutation operation, non-uniform mutation is adopted: x is1,x2L xkL xiIf xkIs a variation point and has a value range of[Umin，Umax]When the individual X is mutated at this point, a new individual X ═ X can be obtained1,x2L xkL xiWherein the new gene values of the variation points are:
in the formula, Random (0,1) represents one randomly taken from 0,1 with a certain probability; r is a Random number which is uniformly distributed in the range of [0, 1], namely Random (0, 1); g is the current algebra; t is a termination algebra; b is a parameter for adjusting the variation step length, and dynamically changes with the current generation number G.
8. The method according to claim 1, wherein the step of performing signal transmission by improving an existing QAM modulation scheme to form a new QAM modulation scheme specifically comprises: the position, the number or the probability of each constellation point of the existing 16QAM modulation mode is changed, so that a new 8QAM modulation scheme is formed.
9. An optical transport network transmission plan optimization system, the system comprising:
the optical transmission link modeling module is used for modeling and solving the optical transmission link from the bottom layer physical angle on the basis of performance analysis and simulation under different modulation modes;
the optical transmission network planning module is used for establishing an optical transmission network planning optimization model according to the transmission distance, the transmission capacity and the network topology factor;
and the signal modulation improvement module is used for forming a new QAM modulation scheme for signal transmission by improving the existing QAM modulation scheme.
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