Coherent optical communication auxiliary fiber channel modeling method and system and electronic equipment

Abstract

The invention relates to a coherent optical communication auxiliary fiber channel modeling method and system and electronic equipment, and the modeling method comprises the steps: obtaining a multi-span transmission signal sequence, and constructing an initial neural network model; training the initial neural network model by using the multi-span transmission signal sequence to obtain an optimized neural network model; importing the optimized neural network model, and cascading the linear physical model and an amplifier to obtain a single-span optical fiber link channel model; and iteratively transmitting a signal based on the single-span optical fiber link channel model according to a preset transmission scene to obtain a multi-span transmission result. According to the invention, optical communication transmission simulation modeling with low complexity and high prediction precision can be realized at the same time.

Description

Technical Field

[0001] The present invention relates to the field of optical communication technology, and in particular to a method, system and electronic equipment for modeling coherent optical communication auxiliary optical fiber channels. Background Art

[0002] As the main artery of information and communication networks, optical communication is the solid foundation of the contemporary digital economy, deeply empowering emerging industries such as 5G networks, cloud computing, and large-scale computing networks. In the development of optical fiber communication technology, in order to cope with the possible "capacity shortage" problem, it is very important to increase system capacity and optimize system design. Establishing optical fiber channel models for high speed, ultra-wideband, and long distance plays an important role in signal transmission evolution analysis, communication quality evaluation, and overall system optimization design.

[0003] The goal of fiber channel modeling is to determine a method that can quickly transform input signals into output signals, and can characterize channel transmission characteristics with high accuracy and low computational complexity. The transmission response of signals in optical fibers is characterized by the nonlinear Schrödinger equation. For solving this complex two-dimensional partial differential equation, there is no mathematically analytic solution, and it can only be approximated by numerical calculations. Although the traditional distributed Fourier method can provide reliable simulation results, its modeling accuracy is affected by the degree of discreteness of the optical fiber link grid. With the increase of sampling rate and the introduction of complex nonlinear characteristics, the corresponding modeling complexity and simulation time will increase dramatically, which will inevitably lead to large storage requirements and long time consumption, and its application is limited in multi-span wavelength division multiplexing long-distance transmission simulation.

[0004] Based on this, there is an urgent need for a simulation modeling method with high modeling accuracy and low complexity to solve the problems existing in the above-mentioned prior art. Summary of the invention

[0005] In order to solve the problems existing in the above-mentioned prior art, the purpose of the present invention is to provide a coherent optical communication assisted optical fiber channel modeling method, system and electronic equipment, which can simultaneously have low complexity and high prediction accuracy optical communication transmission simulation modeling.

[0006] To achieve the above object, the present invention provides the following solutions:

[0007] A coherent optical communication auxiliary optical fiber channel modeling method, comprising:

[0008] Obtain multi-segment transmission signal sequences and construct an initial neural network model;

[0009] Using a multi-segment transmission signal sequence to train the initial neural network model to obtain an optimized neural network model;

[0010] Importing the tuned neural network model, and cascading the linear physical model and the amplifier to obtain a single-span optical fiber link channel model;

[0011] A transmission scenario is preset, and according to the transmission scenario, a signal is iteratively transmitted based on the single-span optical fiber link channel model to obtain a multi-span transmission result.

[0012] Optionally, a polarization multiplexing-wavelength division multiplexing coherent optical communication system is used to obtain the multi-span transmission signal sequence.

[0013] Optionally, the polarization multiplexing-wavelength division multiplexing coherent optical communication system includes: a laser, which generates an optical pulse and passes through a polarization beam splitter for polarization control and beam splitting, and then outputs an X-polarized and Y-polarized optical carrier, and the X-polarized and Y-polarized optical carriers are respectively acted on an IQ modulator simultaneously with an electrical signal based on a preset modulation format for electrical-optical conversion to complete the modulation of the electrical signal and output an optical signal, and then after being combined and input fiber power controlled by a polarization combiner, a wavelength division multiplexer and an amplifier, the input signal is obtained, and the input signal is input into a channel model to obtain an output signal, which is then received by a wavelength division multiplexing and coherent receiver, and then subjected to subsequent digital signal processing, and demodulated and judged.

[0014] Optionally, the channel model includes: a plurality of optical fiber channels and amplifiers;

[0015] The transmission evolution process of the input signal in the optical fiber channel is:

[0016]

[0017] Among them, z is the transmission distance, t is the time, α, β 2 , γ are the attenuation coefficient, group velocity dispersion coefficient, and nonlinear coefficient, respectively, E x (z, t) is the X-polarized optical carrier, E y (z, t) is the Y-polarized optical carrier, is the partial derivative operation, and j is the imaginary unit.

[0018] Optionally, obtaining the multi-span transmission signal sequence includes:

[0019] Processing the multi-span transmission signal sequence:

[0020] The linear effect of the output signal in the multi-span transmission signal sequence is extracted and compensated once, the input signal at different spans and the compensated output signal are power normalized respectively, the real and imaginary part information of the signal are extracted respectively, and one-dimensional splicing and cutting are performed.

[0021] Optionally, extracting and compensating for linear effects of output signals in the multi-span transmission signal sequence once includes:

[0022] Set the received signal transmission length to have the same linear characteristics as the actual link, and change α, β 2 Set the opposite of the original value:

[0023]

[0024] Where C is the compensation operator, is the frequency domain representation of the electric field at z+L, is the frequency domain representation of the electric field at z, α is the attenuation, β 2 is the group velocity dispersion, ω is the angular frequency, j is the imaginary unit, and L is the transmission distance.

[0025] Optionally, the initial neural network model adopts an MLP-Mixer model, wherein the MLP-Mixer model mainly extracts complex features based on a channel feature fusion module and a spatial feature fusion module, and introduces layer normalization operations and residual connections in the module to further improve the feature extraction and characterization capabilities of the model.

[0026] Optionally, obtaining the tuned neural network model includes:

[0027] Using the stochastic gradient descent method, the processed multi-span transmission signal is input into the MLP-Mixer model to perform model parameter training to obtain a tuned neural network model;

[0028] During the training process, the mean square error loss function is used to calculate the loss value and the loss value is reduced to the minimum in iterative training until it stabilizes, so as to end the training and save the tuned model parameters; the mean square error is the mean square error between the neural network prediction value and the distribution Fourier method calculation result.

[0029] To achieve the above object, the present invention also provides a coherent optical communication auxiliary optical fiber channel modeling system, comprising:

[0030] A sequence acquisition module, used to acquire a multi-span transmission signal sequence;

[0031] Model building module, used to build the initial neural network model;

[0032] A model training module, used to train the initial neural network model using a multi-span transmission signal sequence to obtain an optimized neural network model;

[0033] The multi-span transmission module is used to import the tuned neural network model and cascade it with the linear physical model and the amplifier to obtain a single-span optical fiber link channel model, preset a transmission scenario, and according to the transmission scenario, iteratively transmit the signal based on the single-span optical fiber link channel model to obtain a multi-span transmission result.

[0034] To achieve the above-mentioned purpose, the present invention also provides an electronic device for coherent optical communication-assisted optical fiber channel modeling, comprising a memory, a processor, a communication interface and a communication device, wherein the processor, the communication interface and the memory communicate with each other via a communication bus, and a program is stored on the memory and applied to a channel modeling system, and the described method is implemented when the program is executed by the processor.

[0035] The beneficial effects of the present invention are:

[0036] (1) The present invention selects the MLP-Mixer model as the neural network channel model. Its modeling accuracy is better than the existing deep learning modeling method. Compared with the traditional distributed Fourier method, the simulation time is shortened by dozens of times, and it has the advantages of high accuracy and low complexity.

[0037] (2) The present invention can integrate data-driven neural network models and physical models. The neural network can effectively extract the nonlinear characteristics of the optical fiber transmission channel and cascade the linear physical model and the amplifier to effectively model the real optical fiber transmission link, avoiding the traditional modeling method that uses complex numerical calculations and discrete iterative calculations, and significantly improving the simulation operation speed.

[0038] (3) The technical solution provided by the present invention, including the optical communication-assisted optical fiber channel modeling method, system, and electronic device, can have good generalization performance while ensuring accuracy, and does not require repeated training for different transmission scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0040] Figure 1 A flow chart of a method for modeling a coherent optical communication-assisted optical fiber channel according to an embodiment of the present invention;

[0041] Figure 2 is a block diagram of the coherent optical communication simulation system architecture used in an embodiment of the present invention; wherein, Figure 2 (a) is the block diagram of the coherent optical communication simulation system used. Figure 2(b) is the digital signal processing flow chart of the transmitting end of the coherent optical communication simulation system. Figure 2 (c) is a flow chart of digital signal processing at the receiving end of the coherent optical communication simulation system;

[0042] Figure 3 A schematic diagram of a data set preparation process used in an embodiment of the present invention;

[0043] Figure 4 A schematic diagram of the structure of a neural network model used in an embodiment of the present invention;

[0044] Figure 5 Schematic diagram of the comparison results between the modeling method and the distributed Fourier method under the same conditions in an embodiment of the present invention; wherein, Figure 5 (a) is the time domain waveform comparison result of the modeling method and the distributed Fourier method under the same conditions. Figure 5 (b) is the spectrum distribution comparison result of the modeling method and the distributed Fourier method under the same setting conditions, Figure 5 (c) is the comparison result of the constellation diagram distribution of the modeling method and the distributed Fourier method under the same conditions;

[0045] Figure 6 It is a schematic diagram of error accumulation results and time consumption in link transmission of the modeling method and the distributed Fourier method under the same conditions in an embodiment of the present invention;

[0046] Figure 7 A schematic diagram of a coherent optical communication auxiliary optical fiber channel modeling system according to an embodiment of the present invention;

[0047] Figure 8 A schematic diagram of an electronic device for modeling a coherent optical communication auxiliary optical fiber channel according to an embodiment of the present invention. DETAILED DESCRIPTION

[0048] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0049] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0050] like Figure 1As shown, this embodiment discloses a coherent optical communication assisted optical fiber channel modeling method, including: obtaining a multi-span transmission signal sequence to construct an initial neural network model; using the multi-span transmission signal sequence to train the initial neural network model to obtain a tuned neural network model; importing the tuned neural network model, and cascading the linear physical model and the amplifier to obtain a single-span optical fiber link channel model; presetting a transmission scenario, and according to the transmission scenario, iteratively transmitting the signal based on the single-span optical fiber link channel model to obtain a multi-span transmission result.

[0051] Specifically: This embodiment discloses a coherent optical communication auxiliary optical fiber channel modeling method, including:

[0052] Step 1: Acquire a multi-span transmission signal sequence based on a preset polarization multiplexing-wavelength division multiplexing coherent optical communication system.

[0053] Step 2: Preprocess the input and output signals and create data sets.

[0054] Step 3: Build a neural network model and initialize model parameters.

[0055] Step 4: Complete the training and testing of the neural network based on the data set.

[0056] Step 5: Store the tuned neural network model parameters as a single-span channel model.

[0057] Step 6: For the set long-distance transmission scenario, the neural network model is imported and multi-segment transmission modeling is completed through loop iteration.

[0058] The long-distance transmission scenario here is that the optical signal is transmitted in the optical fiber for a distance of 1200 km. During this process, the optical signal will pass through 15 spans, each span is 80 km of optical fiber transmission, and is relayed and amplified by an erbium-doped fiber amplifier (EDFA) between adjacent spans to compensate for the impact of channel attenuation on the optical signal power.

[0059] Furthermore, a polarization multiplexing-wavelength division multiplexing coherent optical communication system is used to obtain a multi-span transmission signal sequence.

[0060] Furthermore, the polarization multiplexing-wavelength division multiplexing coherent optical communication system includes: a laser, the laser generates an optical pulse, after polarization control and beam splitting by a polarization beam splitter, outputs an X-polarized and a Y-polarized optical carrier, the X-polarized and the Y-polarized optical carriers are respectively acted on an IQ modulator simultaneously with an electrical signal based on a preset modulation format to perform electro-optical conversion to complete the modulation of the electrical signal and output an optical signal, and then after the polarization combiner, the wavelength division multiplexer and the amplifier perform wave combining and fiber input power control, the fiber input optical signal is obtained, the optical signal is input into the channel model, the output signal is obtained, and then after the wavelength division multiplexing and the coherent receiver receive it, it undergoes subsequent digital signal processing, and demodulation judgment, such as Figure 2 (a) shown.

[0061] Specifically, in this embodiment, a polarization multiplexing-wavelength division multiplexing coherent optical communication system is constructed to obtain a multi-span transmission signal sequence. The system framework includes: a transmitting end, a channel and a receiving end, such as Figure 2 (a) is shown. The transmitter parameter settings include the signal modulation format, transmission rate, sampling rate, number of symbols, number of channels, channel spacing, modulator bias voltage, and roll-off coefficient and filter required for DSP processing; channel parameters include attenuation coefficient, group velocity dispersion coefficient, and self-phase modulation coefficient; the receiver parameters include the corresponding parameter settings required for DSP processing. The communication system settings are completed according to the preset parameters, and the quadrature amplitude modulation (QAM) electrical signal is generated based on the transmitter digital signal processing (DSP) process.

[0062] Exemplarily, the number of transmission channels is 5, the frequency interval is 50 GHz, and a single-channel signal is generated by 5 independent transmitters, where 5 lasers are centered at a wavelength of 1550 nm and the frequency interval between adjacent ones is 50 GHz. In each channel, the signal is modulated in QAM format, the modulation order is 16, the upsampling rate is 16, and the pulse shaping roll-off factor is 0.1, so that a 16QAM random bit stream can be obtained. The laser generates and outputs optical pulses. After polarization control and beam splitting by the polarization beam splitter, the X-polarized / Y-polarized optical carrier acts on the IQ modulator simultaneously with the electrical signal to perform electrical-to-optical conversion on the electrical signal, and then is combined by the polarization combiner. After that, optical signals of different wavelengths are combined by the wavelength division multiplexer, and then input into the optical fiber channel model after the fiber input power is controlled by the amplifier. The transmission evolution process of the optical signal in the optical fiber is characterized by the Manakov equation, as shown in the following formula:

[0063]

[0064] Among them, signal E=[E x ,E y] T The signal containing X and Y polarization can be expressed as: z represents the transmission distance, t represents the time, C represents the number of channels, Δf k is the frequency interval, α, β 2 , γ are the attenuation coefficient, group velocity dispersion coefficient, and nonlinear coefficient respectively. The distributed Fourier method is used to solve the input and output signal sequences of each span.

[0065] The distributed Fourier method discretizes the optical fiber link into sub-spans in a symmetrical way, and calculates the nonlinear effect in the time domain or the dispersion effect in the frequency domain based on the following formula, and finally realizes the construction of the optical fiber channel model through iterative calculation. Here, the distributed Fourier solution result is used as a reference benchmark, and the channel output data is the data after passing through 80km of optical fiber. Both the input and output data have all the characteristics of simulated optical signals.

[0066]

[0067] Where L and N represent linear operators and nonlinear operators respectively, E represents the electric field distribution, z represents the transmission distance, ω represents the angular frequency, T represents time, and j is an imaginary unit.

[0068] like Figure 2 As shown in (b), the DSP process at the transmitter end of the coherent optical communication simulation system includes: generating a random bit stream, mapping based on a preset QAM modulation format to generate an electrical signal, up-sampling the electrical signal, and pulse shaping the electrical signal based on a root cosine filter and a preset roll-off factor.

[0069] At the receiving end, the optical signals of each channel are extracted after passing through the wavelength division multiplexer, and the electrical signals received by the coherent receiver are based on Figure 2 (c) Perform DSP processing. The DSP process includes: low-pass filtering, resampling to 2 times the sampling rate, dispersion compensation / digital back propagation, matched filtering based on root cosine filter, carrier phase recovery algorithm to estimate the phase of the signal, demapping based on the preset QAM modulation format to restore the random bit stream, and bit error rate calculation.

[0070] Further, obtaining the multi-span transmission signal sequence includes:

[0071] Processing of multi-span transmission signal sequences: extraction and compensation of linear effects of output signals in multi-span transmission signal sequences, power normalization of input signals at different spans and compensated output signals, extraction of real and imaginary information of signals, and one-dimensional splicing and cutting.

[0072] Specifically, in step 2, the acquired multi-span optical fiber transmission signal is preprocessed, mainly by extracting and compensating the linear effect of the single-span output signal at one time. The specific implementation can be based on the digital back propagation algorithm to convert the optical fiber characteristic parameters α, β 2 The setting is opposite to the original value to simulate the received signal transmission with the same length having the opposite linear characteristic of the actual link. The expression can be simplified to:

[0073]

[0074] like Figure 3 As shown, the power normalization processing is performed on the output signal and the original input signal of each span preprocessing. Considering that the neural network processes real numbers, while the QAM signal is a complex value, the real part and the imaginary part of the signal are extracted here for stacking and splicing, so that different parts of the same signal are represented at the same position (at the same time), and a sliding window is set to segment and arrange the sequence.

[0075] For instance, the window size is selected as 16 symbols. Taking into account the inter-symbol interference introduced by dispersion on the symbols, in order to better extract features, there is an overlap of 8 symbols between the segmented signals. The dimension where the signal length is located is defined as the spatial dimension, and the dimension where the single real-valued part of the corresponding signal at the same time is located is defined as the channel dimension, completing the construction of the data set.

[0076] Furthermore, in step 3, the initial neural network model adopts the MLP-Mixer model, including: input layer, linear layer, MLP-Mixer module layer, linear layer and output layer;

[0077] Input layer, used to receive input sequence information;

[0078] Linear layer, used to extract feature information of input sequence and map it to high-dimensional space;

[0079] The MLP-Mixer module layer is constructed by stacking several feature fusion modules, and each feature fusion module is used to perform channel feature fusion operations and spatial feature fusion operations in succession: feature fusion operations are performed through two linear layers and a nonlinear activation function, layer normalization operations are performed before fusion operations, and residual connections are introduced in the fusion process. Corresponding feature fusion is performed according to the channel dimension and spatial dimension, respectively, so that the neural network can capture and extract key features and has a strong representation ability;

[0080] The linear layer is used to map the features extracted by the MLP-Mixer module layer to the original space where the signal is located;

[0081] The output layer is used to obtain the output prediction sequence results based on the signal mapped in the original space.

[0082] Specifically, the MLP-Mixer structure is as follows Figure 4 As shown, its notable feature is that the entire model is completely built on the Multi-Layer Perception (MLP) layer. Its advantage is that data processing and feature extraction can be performed only through basic matrix multiplication. The nonlinear activation function selects GELU, and introduces advanced technologies such as layer normalization and residual link to help the model learn better to extract features. The core of the structure is stacked by feature fusion modules. The feature fusion module contains two steps. First, the channel dimension feature fusion is performed, and then the position dimension feature fusion is performed. Each feature fusion is mainly completed by two layers of linear layers combined with a nonlinear activation function operation. Before fusion, the data needs to be layer normalized. After the model is built, the neural network is initialized; the initialization adopts the Xavier initialization method. For example, the feature fusion module here is 4, and the MLP-Mixer module layer is expressed by the following formula:

[0083] Z′ l =W 2 σ(W 1 LN(Zl- 1 ))+Z l-1

[0084] Z l =W 4 σ(W 3 LN(Z′ l ))+Z′ l ;

[0085] Among them, W i (=1,2,3,4) is the weight parameter, is the output of the l-1th hidden layer, d c is the channel dimension, d s is the position dimension, LN represents the layer normalization operation, and σ is the nonlinear activation function.

[0086] Furthermore, obtaining the tuned neural network model includes: using the stochastic gradient descent method, inputting the processed multi-span transmission signal into the MLP-Mixer model to train the model parameters, and obtaining the tuned neural network model; during the training process, using the mean square error loss function to calculate the loss value and using the minimum value in iterative training until it stabilizes as a judgment standard to end the training and save the tuned model parameters; the mean square error is the mean square error between the neural network prediction value and the distribution Fourier method calculation result.

[0087] Specifically, in step 4, the training of the neural network is to calculate the error based on the set loss function and update the weights and biases of the neural network through the back-propagation mechanism. In multiple iterative learning, the parameters are adjusted with an appropriate learning rate to minimize the loss function. Exemplarily, the loss function is defined as the mean square difference between the output signal predicted by the neural network and the simulation result based on the distributed Fourier method. The optimizer is Adam, and the learning rate is adjusted by warm up cosine annealing. During the training process, the training mode is full-batch, that is, a batch size of data is randomly captured from the data set for training in one iteration epoch, rather than traversing the entire data set. The iteration cycle is 50,000 times.

[0088] After training is completed, the model parameters are saved and the accuracy and generalization of the model are tested based on the test set data.

[0089] Specifically, step 5 stores the tuned neural network model parameters, mainly saving the model structure, the tuned weights and biases carried by each neuron.

[0090] Specifically, step 6 imports the neural network model for the set transmission scenario and completes the multi-span transmission modeling through cyclic iteration. For example, for a long-distance transmission scenario with 15 spans and 80km of optical fiber per span, the input optical signal will undergo 15 iterative transmissions, and in each round, the output after passing through the neural network channel model, the linear characteristic module and the amplifier module will be used as the output of the single-span optical fiber channel. Figure 5 (a)-5(c) respectively give the distribution of the signal in the time domain, frequency domain and the constellation diagram restored by DSP after reception. It can be seen that the proposed channel model has good consistency with the calculation results of the traditional distributed Fourier method.

[0091] exist Figure 6 The normalized mean square error and the cumulative calculation time required for each span of 131072 symbols in iterative transmission are given in the figure. Under the same conditions, the neural network channel model (11 seconds) is much faster than the distributed Fourier method modeling calculation (259 seconds). The cumulative modeling error after 1200 km transmission is 5.5×10 -3 , which verifies the effectiveness of the proposed channel modeling scheme.

[0092] like Figure 7 As shown, this embodiment also provides a coherent optical communication auxiliary optical fiber channel modeling system, including:

[0093] A sequence acquisition module, used to acquire a multi-span transmission signal sequence;

[0094] Model building module, used to build the initial neural network model;

[0095] A model training module is used to train an initial neural network model using a multi-segment transmission signal sequence to obtain an optimized neural network model;

[0096] The multi-span transmission module is used to import the tuned neural network model and cascade it with the linear physical model and the amplifier to obtain the single-span optical fiber link channel model, preset the transmission scenario, feed the input optical signal of the starting span into the single-span optical fiber link channel model to iteratively transmit the signal according to the transmission scenario, and obtain the multi-span transmission result based on the preset integer multiple transmission distance;

[0097] Among them, the linear physical model includes attenuation and group velocity dispersion effects.

[0098] This embodiment also discloses an electronic device for coherent optical communication-assisted optical fiber channel modeling. Figure 8 The specific structure of the device is shown, including a memory, a processor, a communication interface, a communication device and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus, and a computer program of the optical fiber channel model is stored in the memory, and the coherent optical communication-assisted optical fiber channel modeling method can be implemented when the computer program is executed on the processor.

[0099] The memory is used to store computer programs, that is, all scripts and codes that implement the fiber channel model. The memory can be a storage device such as a USB flash drive, a mobile hard disk, and a random access memory (RAM);

[0100] The processor is used to execute the program stored in the memory to implement the steps of the fiber channel model provided in the above embodiment. The processor can be a general-purpose processor, or a digital signal processor, a field programmable gate array or other device;

[0101] The communication interface is used for communication between the above electronic devices and other devices, including input / output (I / O) interfaces, physical interfaces, and logical interfaces to achieve interconnection between internal devices of the device and between the device and external devices;

[0102] The communication bus is represented by a double-arrow line, but it does not mean that there is only one bus or one type of bus. The communication bus can be a peripheral component interconnection standard bus or an extended industrial standard structure bus, etc., and can be specifically divided into a data bus, a control bus, an address bus, etc.

[0103] Those skilled in the art will understand that Figure 8 The hardware structure shown in the figure does not constitute a limitation on the present application, and may include more or less components than shown in the figure, or combine certain components, or arrange the components differently.

[0104] The embodiments described above are only descriptions of the preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. A coherent optical communication-assisted fiber channel modeling method, characterized in that: include: Obtain multi-segment transmission signal sequences and construct an initial neural network model; Training the initial neural network model using a multi-segment transmission signal sequence to obtain an optimized neural network model; Importing the tuned neural network model, and cascading the linear physical model and the amplifier to obtain a single-span optical fiber link channel model; A transmission scenario is preset, and according to the transmission scenario, a signal is iteratively transmitted based on the single-span optical fiber link channel model to obtain a multi-span transmission result.

2. The coherent optical communication assisted fiber channel modeling method according to claim 1, characterized in that: A polarization multiplexing-wavelength division multiplexing coherent optical communication system is used to obtain the multi-span transmission signal sequence.

3. The coherent optical communication assisted fiber channel modeling method according to claim 2, characterized in that: The polarization multiplexing-wavelength division multiplexing coherent optical communication system includes: a laser, which generates an optical pulse, and after polarization control and beam splitting are performed by a polarization beam splitter, the laser outputs an X-polarized and a Y-polarized optical carrier, and the X-polarized and the Y-polarized optical carriers are respectively acted on an IQ modulator simultaneously with an electrical signal based on a preset modulation format to complete electrical-optical conversion to complete electrical signal modulation and output an optical signal, and then after wavelength division multiplexing and fiber input power control are performed by a polarization combiner, a wavelength division multiplexer and an amplifier, the input signal is obtained, and the input signal is input into a channel model to obtain an output signal, which is then received by wavelength division multiplexing and a coherent receiver, undergoes subsequent digital signal processing, and is demodulated and judged.

4. The coherent optical communication-assisted optical fiber channel modeling method according to claim 3, characterized in that: The channel model includes: a plurality of optical fiber channels and amplifiers; The transmission evolution process of the input signal in the optical fiber channel is expressed as follows: Where z is the transmission distance, t is the time, α, β2, and γ are the attenuation coefficient, group velocity dispersion coefficient, and nonlinear coefficient, respectively. x (z, t) is the X-polarized optical carrier, E y (z, t) is the Y-polarized optical carrier, is the partial derivative operation, and j is the imaginary unit.

5. The coherent optical communication assisted fiber channel modeling method according to claim 1, characterized in that: After obtaining the multi-span transmission signal sequence, the method includes: Processing the multi-span transmission signal sequence: The linear effect of the output signal in the multi-span transmission signal sequence is extracted and compensated once, the input signal at different spans and the compensated output signal are power normalized respectively, the real and imaginary part information of the signal are extracted respectively, and one-dimensional splicing and cutting are performed.

6. The coherent optical communication-assisted optical fiber channel modeling method according to claim 5, characterized in that: Extracting and compensating for linear effects of output signals in the multi-span transmission signal sequence includes: Set the received signal transmission length to have the same linear characteristics as the actual link, and set α and β2 to the opposite of their original values: Where C is the compensation operator, is the frequency domain representation of the electric field at z+L, is the frequency domain representation of the electric field at z, α is the attenuation, β2 is the group velocity dispersion, ω is the angular frequency, j is the imaginary unit, and L is the transmission distance.

7. The coherent optical communication assisted fiber channel modeling method according to claim 1, characterized in that: The initial neural network model adopts the MLP-Mixer model, wherein the MLP-Mixer model mainly extracts complex features based on the channel feature fusion module and the spatial feature fusion module, and introduces layer normalization operations and residual connections in the module to further improve the feature extraction and characterization capabilities of the model.

8. The coherent optical communication-assisted optical fiber channel modeling method according to claim 7, characterized in that: Obtaining the tuned neural network model includes: Using the stochastic gradient descent method, the processed multi-span transmission signal is input into the MLP-Mixer model to perform model parameter training to obtain a tuned neural network model; During the training process, the mean square error loss function is used to calculate the loss value and the loss value is used as the judgment standard when it drops to the minimum during iterative training until it stabilizes, so as to end the training and save the tuned model parameters; the mean square error is the mean square error between the neural network prediction value and the distribution Fourier method calculation result.

9. A coherent optical communication assisted fiber channel modeling system, characterized in that: include: A sequence acquisition module is used to acquire a multi-segment transmission signal sequence; Model building module, used to build the initial neural network model; A model training module is used to train the initial neural network model using a multi-segment transmission signal sequence to obtain an optimized neural network model; The multi-span transmission module is used to import the tuned neural network model and cascade it with the linear physical model and amplifier to obtain a single-span optical fiber link channel model, preset a transmission scenario, and according to the transmission scenario, iteratively transmit the signal based on the single-span optical fiber link channel model to obtain a multi-span transmission result.

10. An electronic device for coherent optical communication-assisted optical fiber channel modeling, comprising a memory, a processor, a communication interface, and a communication device, wherein the processor, the communication interface, and the memory communicate with each other via a communication bus, and a program is stored on the memory and applied to a channel modeling system. When the program is executed by the processor, the method described in any one of claims 1 to 8 is implemented.

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