Transmitting power optimization method and system for improving simulated annealing algorithm

Abstract

The invention discloses a transmitting power optimization method and system for improving a simulated annealing algorithm, and the method comprises the steps: carrying out the modeling of the spontaneous radiation noise of an amplifier of an ultra-wideband wavelength division multiplexing system, obtaining the linear noise of the ultra-wideband wavelength division multiplexing system, and obtaining the nonlinear noise of the ultra-wideband wavelength division multiplexing system according to an ISRS GN model; determining the relation between the throughput of the ultra-wideband wavelength division multiplexing system and the channel transmitting power through a Shannon formula, the linear noise and the nonlinear noise; and optimizing the transmitting power of the ultra-wideband wavelength division multiplexing system through an improved simulated annealing algorithm based on the relation between the throughput of the ultra-wideband wavelength division multiplexing system and the channel transmitting power. According to the method, the convergence speed of the algorithm can be improved while the optimal transmitting power distribution is searched.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and in particular to a transmission power optimization method and system for an improved simulated annealing algorithm. Background Art

[0002] With the development of communication technology, many applications have emerged in recent years, such as the Internet of Things, ultra-high-definition video streaming, and cloud computing, which require higher transmission capacity. In order to meet this challenge, in addition to using space division multiplexing technology to increase capacity in optical fiber communication systems, another key research direction is to use ultra-wideband wavelength division multiplexing technology. The main factor limiting the capacity of ultra-wideband wavelength division multiplexing communication systems is the nonlinear effects occurring in the optical fiber, which are mainly manifested as four-wave mixing between frequency components and stimulated Raman scattering between channels. Stimulated Raman scattering between channels consumes the energy of high-frequency photons and amplifies the energy of low-frequency photons, resulting in significant differences in the performance of each wavelength division multiplexing channel. These differences become more significant as the total input power or modulation bandwidth increases. The classic uniform transmission power distribution (assuming that the total input power is evenly distributed among all channels) cannot achieve the best system performance, so finding an appropriate transmission power distribution is crucial to improving system throughput.

[0003] Currently, the commonly used transmit power optimization schemes are divided into heuristic algorithms and convex approximation algorithms. Heuristic algorithms provide better transmit power distribution but have a slower convergence speed, while convex approximation algorithms provide faster convergence speed but have a worse transmit power distribution. Summary of the invention

[0004] In order to solve the technical problems existing in the above-mentioned prior art, the present invention proposes a transmission power optimization method and system of an improved simulated annealing algorithm, which can improve the convergence speed of the algorithm while finding the optimal transmission power distribution.

[0005] On the one hand, to achieve the above object, the present invention provides a transmission power optimization method of an improved simulated annealing algorithm, comprising:

[0006] The amplifier spontaneous emission noise of the UWB WDM system is modeled to obtain the linear noise of the UWB WDM system, and the nonlinear noise of the UWB WDM system is obtained according to the ISRS GN model.

[0007] Determine the relationship between the throughput of an ultra-wideband wavelength division multiplexing system and the channel transmission power through the Shannon formula and the linear noise and the nonlinear noise;

[0008] Based on the relationship between the throughput of the ultra-wideband wavelength division multiplexing system and the channel transmission power, the transmission power of the ultra-wideband wavelength division multiplexing system is optimized by an improved simulated annealing algorithm, wherein the improved simulated annealing algorithm reconstructs the perturbation function of the traditional simulated annealing algorithm and introduces an iteration number variable so that the variance of the perturbation function decreases as the iteration number increases.

[0009] Preferably, modeling the amplifier spontaneous radiation noise of the ultra-wideband wavelength division multiplexing system includes:

[0010]

[0011] Where Ns is the number of fiber spans, G EDFA is the gain of the fiber amplifier EDFA, k is the channel number, NF is the noise factor of the fiber amplifier EDFA, h is the Planck constant, and f k is the center frequency of the kth channel, Δf is the channel spacing, is the amplifier's spontaneous radiation noise.

[0012] Preferably, the ISRS GN model is obtained by extending the nonlinear noise GN model to the ultra-wideband wavelength division multiplexing system and considering stimulated Raman scattering between channels, wherein the expression of the nonlinear coefficient of the ISRS GN model is:

[0013]

[0014] Among them, P k,n is the incident power of the kth channel and the nth span, P k is the incident power of the kth channel and the first span, η SPM,n is the effect of self-phase modulation of the nth span, η XPM,n is the effect of cross-phase modulation of the nth span, k is the channel number, Ns is the number of fiber spans, ε is the coherence factor, η k is the nonlinear coefficient.

[0015] Preferably, the relationship between the throughput and the channel transmission power of the ultra-wideband wavelength division multiplexing system is determined as follows:

[0016]

[0017] Where P k is the optical power of the kth channel, N is the total number of channels, η k is the nonlinear coefficient, is the amplifier spontaneous radiation noise, and C is the system throughput.

[0018] Preferably, the transmission power of the ultra-wideband wavelength division multiplexing system is optimized by using an improved simulated annealing algorithm, including:

[0019] S1. Number the channels in the ultra-wideband wavelength division multiplexing system, and use the transmission power of each channel as an independent variable x;

[0020] S2, by generating a set of random independent variables x' within the preset upper and lower limits as the initial solution;

[0021] S3, obtaining a system throughput function Fitness(x) uniquely related to the transmit power of each channel through a linear noise and nonlinear noise calculation model, and solving the system throughput function Fitness(x) as an object to be optimized, replacing the perturbation function with a fixed variance in the traditional simulated annealing algorithm with a piecewise Gaussian function, updating the independent variable x through the piecewise Gaussian function to obtain x1, and obtaining a new fitness function value Fitness(x1);

[0022] S4. According to the Metropolis criterion, x1 is accepted as the current solution with a preset probability. If the new fitness function value Fitness(x1) is greater than the previous fitness function value, the optimal solution is updated to x1 and the optimal value is Fitness(x1);

[0023] S5. After the loop iterates for a specified number of times, the optimal solution for the transmission power distribution and the optimal value of the channel capacity are output.

[0024] On the other hand, this embodiment also provides a transmission power optimization system of an improved simulated annealing algorithm, comprising:

[0025] A linear noise generation module is used to model the amplifier spontaneous radiation noise of the ultra-wideband wavelength division multiplexing system and obtain the linear noise of the ultra-wideband wavelength division multiplexing system;

[0026] A nonlinear noise generation module is used to obtain the nonlinear noise of the ultra-wideband wavelength division multiplexing system according to the ISRS GN model;

[0027] A system capacity determination module, used to determine the relationship between the throughput of the ultra-wideband wavelength division multiplexing system and the channel transmission power through the Shannon formula and the linear noise and the nonlinear noise;

[0028] The power optimization module is used to optimize the transmission power of the ultra-wideband wavelength division multiplexing system through an improved simulated annealing algorithm based on the relationship between the throughput of the ultra-wideband wavelength division multiplexing system and the channel transmission power.

[0029] Preferably, the nonlinear noise generation module comprises an ISRS GN model construction unit, and the ISRS GN model construction unit is used to extend the nonlinear noise GN model to the ultra-wideband wavelength division multiplexing system, and consider stimulated Raman scattering between channels to construct the ISRS GN model.

[0030] Preferably, the power optimization module comprises a disturbance function reconstruction unit, which is used to reconstruct the disturbance function of the simulated annealing algorithm and introduce an iteration number variable so that the variance of the disturbance function decreases as the iteration number increases.

[0031] A computer device comprises a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the optimization method.

[0032] A computer-readable storage medium stores a computer program / instruction, which implements the steps of the optimization method when executed by a processor.

[0033] Compared with the prior art, the present invention has the following advantages and technical effects:

[0034] In ultra-wideband wavelength division multiplexing systems, the nonlinear noise generated during signal transmission is accurately modeled by considering stimulated Raman scattering between channels. On this basis, an improved simulated annealing algorithm is introduced to optimize the transmitter power. Compared with the currently commonly used heuristic algorithm, the convergence speed is improved without losing accuracy. Therefore, the method of the present invention has great advantages in evaluating and optimizing the performance of ultra-wideband wavelength division multiplexing systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0036] Figure 1 A flow chart of a method for optimizing transmission power using an improved simulated annealing algorithm according to an embodiment of the present invention;

[0037] Figure 2 The improved simulated annealing algorithm flow chart of the embodiment of the present invention;

[0038] Figure 3 A comparison diagram of the optimization results of a transmission power optimization method of an improved simulated annealing algorithm according to an embodiment of the present invention and a genetic algorithm and a particle swarm algorithm;

[0039] Figure 4This is a comparison chart of the optimization speeds of a transmission power optimization method of an improved simulated annealing algorithm according to an embodiment of the present invention and a genetic algorithm, a particle swarm algorithm, and a traditional simulated annealing algorithm. DETAILED DESCRIPTION

[0040] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0041] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0042] The following is an explanation of the technical terms in this embodiment:

[0043] 1. The traditional simulated annealing algorithm (SAA) is a probability-based optimization algorithm, which is inspired by the principle of solid annealing. It is a random optimization algorithm based on the Monte-Carlo iterative solution strategy, and its starting point is based on the similarity between the annealing process of solid materials in physics and general combinatorial optimization problems. Starting from a relatively high initial temperature, the simulated annealing algorithm combines the probability jump characteristics to randomly search for the global optimal solution of the objective function in the solution space as the temperature parameter continues to decrease, that is, the local optimal solution can be probabilistically jumped out and eventually tend to the global optimal solution.

[0044] The traditional simulated annealing algorithm process includes:

[0045] Initial state: The algorithm starts from a random solution or an initial solution;

[0046] Temperature parameters: set an initial temperature and gradually reduce it during the iteration process;

[0047] Neighborhood search: In each iteration, the algorithm randomly selects a new solution in the neighborhood of the current solution;

[0048] Acceptance criteria: If the new solution is better than the current solution, it is always accepted; if the new solution is worse than the current solution, it may still be accepted according to a probability function related to the temperature parameter. This probability function is usually related to the cost difference between the new and old solutions and the current temperature. As the temperature decreases, the probability of accepting a worse solution gradually decreases;

[0049] Cooling scheme: defines a cooling rate, i.e. how to reduce the temperature parameter over time;

[0050] Termination condition: The algorithm terminates when a certain number of iterations is reached, the temperature drops below a certain threshold, or no better solution is found within a certain time.

[0051] 2. The principle of ultra-wideband wavelength division multiplexing transmission system (WDM) is based on optical wavelength multiplexing technology, which allows multiple optical signals of different wavelengths to be transmitted simultaneously in a single optical fiber, thereby significantly increasing the transmission capacity of the optical fiber.

[0052] The working principle of ultra-wideband wavelength division multiplexing transmission system includes:

[0053] Muxing: On the transmitting end, a multiplexer (Mux) combines multiple signals from different light sources into one signal. The light signal emitted by each light source has a unique wavelength, and these wavelengths are spaced a certain distance apart to avoid cross-interference. The multiplexer precisely aligns and combines these light signals of different wavelengths through a series of optical components such as prisms, filters or waveguides. The combined signal is then sent into a single optical fiber for transmission.

[0054] Transmission: The composite signal is transmitted over long distances through optical fiber. Optical fiber is used as a transmission medium to carry the composite optical signal for transmission.

[0055] Demultiplexing (DeMuxing): At the receiving end, the demultiplexer (DeMux) separates the composite optical signal back into the original multiple signals. The demultiplexer contains similar optical components as the multiplexer, but works in reverse. It is able to identify and separate each individual wavelength in the combined signal and then direct them to their respective photodetectors. In this way, the signal of each wavelength can be received and processed independently.

[0056] The system consists of:

[0057] Light source: generates optical signals of specific wavelengths. In a WDM system, multiple light sources with different wavelengths are required to generate the required optical signals.

[0058] Modulator: Converts electrical signals into optical signals and modulates the optical signals so that they carry the desired information.

[0059] Wavelength division multiplexer (combiner): At the transmitting end, multiple optical signals of different wavelengths are combined into one composite optical signal and sent into the optical fiber for transmission.

[0060] Optical fiber: As a transmission medium, it carries composite optical signals for long-distance transmission.

[0061] Wavelength division multiplexer (WDM): At the receiving end, it separates the composite optical signal into the original optical signals of different wavelengths.

[0062] Demodulator: Converts optical signals back into electrical signals for subsequent processing.

[0063] Optical amplifier: used to compensate for the attenuation of optical signals during transmission and ensure that the signals can be stably transmitted to the receiving end.

[0064] Monitoring and management system: responsible for real-time monitoring and management of the operating status of the WDM system to ensure stable operation and efficient transmission of the system.

[0065] Embodiment 1

[0066] The present invention proposes a transmission power optimization method of an improved simulated annealing algorithm, such as Figure 1 ,include:

[0067] The amplifier spontaneous emission noise of the UWB WDM system is modeled to obtain the linear noise of the UWB WDM system, and the nonlinear noise of the UWB WDM system is obtained according to the ISRS GN model.

[0068] Determine the relationship between the throughput of an ultra-wideband wavelength division multiplexing system and the channel transmission power through the Shannon formula and the linear noise and the nonlinear noise;

[0069] Based on the relationship between the throughput of the ultra-wideband wavelength division multiplexing system and the channel transmission power, the transmission power of the ultra-wideband wavelength division multiplexing system is optimized by using an improved simulated annealing algorithm.

[0070] In the ultra-wideband wavelength division multiplexing system, the nonlinear noise generated during the signal transmission process is accurately modeled by considering the stimulated Raman scattering between channels. On this basis, the improved simulated annealing algorithm is introduced to optimize the transmitter power. Compared with the currently commonly used heuristic algorithm, the convergence speed is improved without losing accuracy. The method of this embodiment has great advantages in evaluating and optimizing the performance of ultra-wideband wavelength division multiplexing systems.

[0071] Furthermore, the amplifier spontaneous emission noise of the ultra-wideband wavelength division multiplexing system is modeled, including:

[0072]

[0073] Where Ns is the number of fiber spans, G EDFA is the gain of the fiber amplifier EDFA, k is the channel number, NF is the noise factor of the fiber amplifier EDFA, h is the Planck constant, and f k is the center frequency of the kth channel, Δf is the channel spacing, is the amplifier's spontaneous radiation noise.

[0074] Furthermore, the ISRS GN model is obtained by extending the nonlinear noise GN model to the ultra-wideband wavelength division multiplexing system and considering stimulated Raman scattering between channels, wherein the expression of the nonlinear coefficient of the ISRS GN model is:

[0075]

[0076] Among them, P k,n is the incident power of the kth channel and the nth span, P k is the incident power of the kth channel and the first span, η SPM,n is the effect of self-phase modulation of the nth span, η XPM,n is the effect of cross-phase modulation of the nth span, k is the channel number, Ns is the number of fiber spans, ε is the coherence factor, η k is the nonlinear coefficient.

[0077] Specifically, the ISRS GN model is an exact closed-form expression used to evaluate the nonlinear interference power in the presence of inter-channel stimulated Raman scattering in coherent optical transmission systems. In addition, the model also takes into account the dispersion slope and arbitrary transmit power distribution.

[0078] Furthermore, the relationship between the throughput of the ultra-wideband wavelength division multiplexing system and the channel transmission power is determined as:

[0079]

[0080] Where P k is the optical power of the kth channel, N is the total number of channels, η k is the nonlinear coefficient, is the amplifier spontaneous radiation noise, and C is the system throughput.

[0081] Furthermore, the transmission power of the ultra-wideband wavelength division multiplexing system is optimized by using the improved simulated annealing algorithm. The flow chart is as follows: Figure 2 As shown, including:

[0082] S1. Number the channels in the ultra-wideband wavelength division multiplexing system, and use the transmission power of each channel as an independent variable x;

[0083] S2, by generating a set of random independent variables x' within the preset upper and lower limits as the initial solution;

[0084] S3, obtaining a system throughput function Fitness(x) uniquely related to the transmit power of each channel through a linear noise and nonlinear noise calculation model, and solving the system throughput function Fitness(x) as an object to be optimized, replacing the perturbation function with a fixed variance in the traditional simulated annealing algorithm with a piecewise Gaussian function, updating the independent variable x through the piecewise Gaussian function to obtain x1, and obtaining a new fitness function value Fitness(x1);

[0085] S4. According to the Metropolis criterion, x1 is accepted as the current solution with a preset probability. If the new fitness function value Fitness(x1) is greater than the previous fitness function value, the optimal solution is updated to x1 and the optimal value is Fitness(x1);

[0086] S5. After the loop iterates for a specified number of times, the optimal solution for the transmission power distribution and the optimal value of the channel capacity are output.

[0087] The optimization results are as follows Figure 3 As shown, the horizontal axis is the central wavelength of each channel, and the vertical axis is the transmission power of each channel. At the same time, the commonly used genetic algorithm and particle swarm algorithm are compared. The ASA algorithm is an improved simulated annealing algorithm, the GA algorithm is a genetic algorithm, and the PSO algorithm is a particle swarm algorithm. It can be seen that the improved simulated annealing algorithm is consistent with the current mainstream algorithm in terms of optimization results.

[0088] Optimize speed as Figure 4 As shown in the figure, the horizontal axis is the running time of each algorithm after 3000 iterations, and the vertical axis is the optimal channel capacity. At the same time, the commonly used genetic algorithm and particle swarm algorithm, as well as the traditional simulated annealing algorithm, are compared. The ASA algorithm is an improved simulated annealing algorithm, the GA algorithm is a genetic algorithm, the PSO algorithm is a particle swarm algorithm, and the SA is a traditional simulated annealing algorithm. It can be seen that the improved simulated annealing algorithm can converge to the global optimum at a very fast speed, greatly reducing the algorithm running time.

[0089] Embodiment 2

[0090] This embodiment provides a transmission power optimization system using an improved simulated annealing algorithm, including:

[0091] A linear noise generation module is used to model the amplifier spontaneous radiation noise of the ultra-wideband wavelength division multiplexing system and obtain the linear noise of the ultra-wideband wavelength division multiplexing system;

[0092] A nonlinear noise generation module is used to obtain the nonlinear noise of the ultra-wideband wavelength division multiplexing system according to the ISRS GN model;

[0093] A system capacity determination module, used to determine the relationship between the throughput of the ultra-wideband wavelength division multiplexing system and the channel transmission power through the Shannon formula and the linear noise and the nonlinear noise;

[0094] The power optimization module is used to optimize the transmission power of the ultra-wideband wavelength division multiplexing system through an improved simulated annealing algorithm based on the relationship between the throughput of the ultra-wideband wavelength division multiplexing system and the channel transmission power.

[0095] Furthermore, the nonlinear noise generation module comprises an ISRS GN model construction unit, and the ISRS GN model construction unit is used to extend the nonlinear noise GN model to the ultra-wideband wavelength division multiplexing system, and consider stimulated Raman scattering between channels to construct the ISRS GN model.

[0096] The power optimization module includes a disturbance function reconstruction unit, which is used to reconstruct the disturbance function of the simulated annealing algorithm and introduce an iteration number variable so that the variance of the disturbance function decreases as the iteration number increases.

[0097] This embodiment also provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the transmission power optimization method of the improved simulated annealing algorithm.

[0098] This embodiment also provides a computer-readable storage medium on which a computer program / instruction is stored. When the computer program / instruction is executed by a processor, the steps of the transmission power optimization method of the improved simulated annealing algorithm are implemented.

[0099] The above are only preferred specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A transmission power optimization method using an improved simulated annealing algorithm, characterized in that: include: The amplifier spontaneous emission noise of the UWB WDM system is modeled to obtain the linear noise of the UWB WDM system, and the nonlinear noise of the UWB WDM system is obtained according to the ISRS GN model. Determine the relationship between the throughput of an ultra-wideband wavelength division multiplexing system and the channel transmission power through the Shannon formula and the linear noise and the nonlinear noise; Based on the relationship between the throughput of the ultra-wideband wavelength division multiplexing system and the channel transmission power, the transmission power of the ultra-wideband wavelength division multiplexing system is optimized by an improved simulated annealing algorithm, wherein the improved simulated annealing algorithm reconstructs the perturbation function of the traditional simulated annealing algorithm and introduces an iteration number variable so that the variance of the perturbation function decreases as the iteration number increases.

2. The transmission power optimization method of the improved simulated annealing algorithm according to claim 1, characterized in that: Modeling the amplifier spontaneous emission noise of the ultra-wideband wavelength division multiplexing system includes: Where Ns is the number of fiber spans, G EDFA is the gain of the fiber amplifier EDFA, k is the channel number, NF is the noise factor of the fiber amplifier EDFA, h is the Planck constant, and f k is the center frequency of the kth channel, Δf is the channel spacing, is the amplifier's spontaneous radiation noise.

3. The transmission power optimization method of the improved simulated annealing algorithm according to claim 1, characterized in that: The ISRS GN model is obtained by extending the nonlinear noise GN model to the ultra-wideband wavelength division multiplexing system and considering stimulated Raman scattering between channels, wherein the expression of the nonlinear coefficient of the ISRS GN model is: Among them, P k,n is the incident power of the kth channel and the nth span, P k is the incident power of the first span of the kth channel, η SPM,n is the effect of self-phase modulation of the nth span, η XPM,n is the effect of cross-phase modulation of the nth span, k is the channel number, Ns is the number of fiber spans, ε is the coherence factor, η k is the nonlinear coefficient.

4. The transmission power optimization method of the improved simulated annealing algorithm according to claim 1, characterized in that: The relationship between the throughput and the channel transmission power of the ultra-wideband wavelength division multiplexing system is determined as follows: Where P k is the optical power of the kth channel, N is the total number of channels, η k is the nonlinear coefficient, is the amplifier spontaneous radiation noise, and C is the system throughput.

5. The transmission power optimization method of the improved simulated annealing algorithm according to claim 1, characterized in that: The transmission power of the ultra-wideband wavelength division multiplexing system is optimized by using an improved simulated annealing algorithm, including: S1. Number the channels in the ultra-wideband wavelength division multiplexing system, and use the transmission power of each channel as an independent variable x; S2, by generating a set of random independent variables x' within the preset upper and lower limits as the initial solution; S3, obtaining a system throughput function Fitness(x) uniquely related to the transmit power of each channel through a linear noise and nonlinear noise calculation model, and solving the system throughput function Fitness(x) as an object to be optimized, replacing the perturbation function with a fixed variance in the traditional simulated annealing algorithm with a piecewise Gaussian function, updating the independent variable x through the piecewise Gaussian function to obtain x1, and obtaining a new fitness function value Fitness(x1); S4. According to the Metropolis criterion, x1 is accepted as the current solution with a preset probability. If the new fitness function value Fitness(x1) is greater than the previous fitness function value, the optimal solution is updated to x1 and the optimal value is Fitness(x1); S5. After the loop iterates for a specified number of times, the optimal solution for the transmission power distribution and the optimal value of the channel capacity are output.

6. A transmission power optimization system based on an improved simulated annealing algorithm, characterized in that: include: A linear noise generation module is used to model the amplifier spontaneous radiation noise of the ultra-wideband wavelength division multiplexing system and obtain the linear noise of the ultra-wideband wavelength division multiplexing system; A nonlinear noise generation module is used to obtain the nonlinear noise of the ultra-wideband wavelength division multiplexing system according to the ISRS GN model; A system capacity determination module, used to determine the relationship between the throughput of the ultra-wideband wavelength division multiplexing system and the channel transmission power through the Shannon formula and the linear noise and the nonlinear noise; The power optimization module is used to optimize the transmission power of the ultra-wideband wavelength division multiplexing system through an improved simulated annealing algorithm based on the relationship between the throughput of the ultra-wideband wavelength division multiplexing system and the channel transmission power.

7. The system according to claim 6, characterized in that The nonlinear noise generation module comprises an ISRS GN model construction unit, and the ISRS GN model construction unit is used to extend the nonlinear noise GN model to the ultra-wideband wavelength division multiplexing system, and consider stimulated Raman scattering between channels to construct the ISRS GN model.

8. The system according to claim 6, characterized in that The power optimization module includes a disturbance function reconstruction unit, which is used to reconstruct the disturbance function of the simulated annealing algorithm and introduce an iteration number variable so that the variance of the disturbance function decreases as the iteration number increases.

9. A computer device comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the optimization method according to any one of claims 1 to 5.

10. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that: When the computer program / instruction is executed by a processor, the steps of the optimization method according to any one of claims 1 to 5 are implemented.

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