An optical communication method based on multi-stage nonlinear pre-compensation
By employing a multi-level nonlinear pre-compensation and machine learning-based parameter optimization method, the problems of high bit error rate and high algorithm complexity in optical communication systems have been solved, achieving more efficient communication.
Patent Information
- Application Number
- CN202310135689.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2023-02-17
- Publication Date
- 2026-08-25
- Estimated Expiration
- 2043-02-17
AI Technical Summary
In existing optical communication systems, nonlinear effects lead to high bit error rates and high algorithm complexity, which affects communication efficiency.
An optical communication method based on multi-level nonlinear pre-compensation is adopted. Machine learning is used to optimize the nonlinear pre-compensation parameters at each level, and the amplitude and phase factors are updated through gradient descent algorithm. Pre-compensation is performed in stages to reduce the computational complexity of the receiver.
It significantly reduced the bit error rate, improved communication efficiency, reduced algorithm complexity, and optimized the performance of optical communication systems.
Smart Images
Figure CN116527155B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of nonlinear compensation, and in particular to an optical communication method based on multi-level nonlinear pre-compensation. Background Technology
[0002] With the increase in data transmission capacity and distance in optical fiber communication, nonlinear effects have become a major influencing factor in coherent optical communication systems. Due to the existence of nonlinear Shannon capacity, optical communication systems have an optimal transmit power; excessively high transmit power will exacerbate nonlinear effects. Therefore, it is necessary to employ various algorithms to compensate for nonlinear effects or reduce their impact on communication.
[0003] There are many methods for compensating for dispersion and nonlinear effects in optical fibers. All-optical processing methods include optical phase conjugation and conjugate two-wavelet transmission methods, while digital signal processing methods include digital backpropagation (DBP), Volterra series nonlinear compensation (VSNE), and perturbation nonlinear compensation (PNC). However, these algorithms have high computational complexity. To improve compensation performance and achieve the required bit error rate (BER), the complexity increases with the number of algorithm taps. Many optimized algorithms have been developed to further reduce complexity.
[0004] For example, the paper “Low-Complexity Triplet-Correlative Perturbative FiberNonlinearity Compensation for Long-Haul Optical Transmission” (Fu M, Wu Y, Yang Z, et al. Journal of Lightwave Technology, 2022, 40(16): 5416-5425.) proposes a low-complexity nonlinear compensation algorithm based on perturbation theory, which utilizes the correlation of triplet terms in perturbation theory to construct a low-complexity scheme.
[0005] Meanwhile, pre-compensation schemes for coherent optical communication systems are currently a research hotspot. Pre-compensation transfers the relevant compensation algorithms from the receiver's digital signal processing to the transmitter, reducing the data processing burden at the receiver. For pre-compensation algorithms for nonlinear effects, single-stage perturbation nonlinear compensation is often used. Compared with the traditional DBP algorithm, PNC performs single-stage processing at the transmitter symbol level, greatly reducing the algorithm's complexity. However, single-stage perturbation nonlinear compensation requires longer nonlinear perturbation coefficients, which still leads to excessive complexity.
[0006] For example, the paper "Perturbative Nonlinear Pre-Compensation in Presence of Optical Filtering" (Ghazisaeidi A, Renaudier J, Tran P, et al. 2015 Optical Fiber Communications Conference and Exhibition (OFC), 2015.) proposes to add amplitude and phase factors to the nonlinear perturbation term to optimize the nonlinear compensation efficiency, thereby reducing the impact of intersymbol interference caused by optical filtering on the nonlinear compensation performance.
[0007] Considering the complexity of algorithms and the redundancy of digital signal processing at the receiver, pre-compensation algorithms based on perturbation theory are a very promising application. Extending them to multi-stage pre-compensation cascades can further reduce the number of nonlinear coefficients, thereby reducing algorithm complexity.
[0008] Although the aforementioned nonlinear compensation algorithm can improve the performance of optical communication systems, the transmission parameters can still change unpredictably due to noise caused by signal filtering and channel transmission in the communication system. Machine learning methods can be used to optimize the algorithm parameters. The ideal signal at the transmitting end can be used as a training label, and the parameters obtained by supervised learning can further improve the compensation capability of the algorithm.
[0009] For example, the paper “Learning-based digital back propagation to compensate for fiber nonlinearity considering self-phase and cross-phase modulation for wavelength-division multiplexed systems” (Inoue T, Matsumoto R, Namiki S. Optics Express, 2022, 30(9): 14851-14872.) proposes a learning-based digital back propagation technique. In wavelength division multiplexing (WDM) systems, gradient descent is used to optimize parameters such as dispersion coefficient and nonlinear coefficient in DBP, effectively suppressing self-phase modulation (SPM) and cross-phase modulation (XPM) of the signal.
[0010] Among current nonlinear algorithms based on machine learning, algorithms such as support vector machines and clustering can only process signals separately at the receiving end. Only neural network algorithms can be combined with current nonlinear compensation algorithms to form an interpretable optical communication transmission network structure. The network layer parameters can be optimized through gradient descent and error backpropagation. However, such an interpretable optical communication network still has a large degree of complexity and requires further optimization. Summary of the Invention
[0011] The purpose of this invention is to overcome the shortcomings of the existing technology by providing an optical communication method based on multi-level nonlinear pre-compensation, so as to solve the problem that the high complexity and bit error rate of the existing methods lead to unsatisfactory communication efficiency.
[0012] The objective of this invention can be achieved through the following technical solutions:
[0013] This invention provides an optical communication method based on multi-level nonlinear pre-compensation, applied at the transmitting end. The method includes the following steps:
[0014] The system acquires and modulates the input signal, performs multi-level nonlinear pre-compensation on the modulated signal, acquires the compensated signal, and obtains the transmission signal based on the compensated signal through pre-processing before transmission. The signal is then transmitted to the receiving end via optical fiber. The parameters of each level of nonlinear pre-compensation have been pre-trained. After receiving the transmission signal, the receiving end performs post-processing and demodulation to obtain the target signal, thus realizing communication between the sending end and the receiving end.
[0015] As a preferred technical solution, the parameters of the various levels of nonlinear pre-compensation include amplitude factor and phase factor.
[0016] As a preferred technical solution, the process of obtaining the pre-trained parameters of each level of nonlinear pre-compensation includes the following steps:
[0017] Obtain a training sample set, and train the parameters of each level of nonlinear pre-compensation based on the training sample set. When the cost function value reaches the preset convergence condition, obtain the pre-trained parameters of each level of nonlinear pre-compensation.
[0018] As a preferred technical solution, the process of training the parameters of each level of nonlinear pre-compensation based on the training sample set includes the following steps:
[0019] Calculate the partial derivatives of the cost function with respect to the parameters of each nonlinear pre-compensation stage, and update the parameters using the gradient descent algorithm.
[0020] As a preferred technical solution, the cost function value is obtained based on the compensation term obtained from nonlinear pre-compensation and the nonlinear damage in the training samples.
[0021] As a preferred technical solution, the acquisition of the training sample set includes the following steps:
[0022] A symbol sequence is randomly generated at the transmitting end and transmitted to the receiving end through the nonlinear channel of the optical fiber. After compensation, the receiving end obtains the first bit error rate. The symbol sequence is then transmitted to the receiving end through the dispersion channel of the optical fiber. The receiving end uses a digital backpropagation algorithm to compensate and obtain the second bit error rate.
[0023] Based on the first bit error rate and the second bit error rate, the parameters of the preset signal compensation algorithm at the receiving end are adjusted so that the first bit error rate and the second bit error rate are the same. Based on the nonlinear impairment of the output of the digital backpropagation algorithm at this time, training samples are established.
[0024] The training sample set is obtained based on multiple training samples.
[0025] As a preferred technical solution, the process of obtaining the first bit error rate includes the following steps:
[0026] Based on the symbol sequence, it is transmitted to the receiving end through the nonlinear channel of the optical fiber. The receiving end performs dispersion compensation and polarization mode dispersion compensation to obtain the first bit error rate.
[0027] As a preferred technical solution, the multi-level nonlinear pre-compensation is implemented using the following formula:
[0028]
[0029] In the formula, M is the number of pre-compensation levels, ρ k and These are the amplitude and phase factor of the k-th level, respectively. The initial signal after modulation. This represents the corresponding perturbation term.
[0030] As a preferred technical solution, the modulation is DQPSK modulation.
[0031] As a preferred technical solution, the preprocessing before transmission includes the following steps:
[0032] The pre-compensated signal is then processed through pulse shaping, transmitter filtering, and IQ modulation to obtain the transmitted signal.
[0033] Compared with the prior art, the present invention has the following advantages:
[0034] (1) By extending the perturbation nonlinear pre-compensation algorithm to multi-level operations, machine learning methods are used to optimize the parameters of nonlinearity and compensation at each level. Compared with the single-level multi-parameter scheme, the present invention has fewer pre-compensation optimization parameters at each level, which greatly reduces the complexity compared with the traditional neural network-based pre-compensation algorithm. This solves or partially solves the problem of high complexity and bit error rate in existing methods, which leads to unsatisfactory communication efficiency.
[0035] (2) Verification in coherent optical systems shows that better bit error rate and lower complexity can be obtained, effectively reducing algorithm complexity in nonlinear compensation processing, while also achieving optimal compensation performance. Attached Figure Description
[0036] Figure 1 This is a flowchart of the optical communication method based on multi-level nonlinear pre-compensation in Example 1;
[0037] Figure 2 This is a flowchart of constructing the training sequence in Example 1;
[0038] Figure 3 This is a schematic diagram comparing the performance of pre-compensation levels 1-3 with the change in transmit power in Example 1. Detailed Implementation
[0039] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.
[0040] Example 1
[0041] like Figure 1 This embodiment provides an optical communication method based on multi-level nonlinear pre-compensation, where pre-compensation training is a step in the training process, and nonlinear pre-compensation is a step in both training and communication. This method involves a training data module for constructing amplitude and phase factors, an optimization module for amplitude and phase factors, a signal pre-compensation and transmission module, a signal transmission module, and a signal reception and digital signal processing module. The detailed functions of each module are described below.
[0042] Training data module for amplitude and phase factors: Nonlinear impairment at the transmitter is obtained through digital backpropagation algorithm, and training data is constructed based on this.
[0043] First, a modulated signal is transmitted in a nonlinear fiber channel, and at the receiving end, digital signal processing algorithms such as dispersion compensation are used to calculate the bit error rate. At the same time, the same signal is transmitted in a dispersive channel, and at the receiving end, a digital backpropagation algorithm is used for compensation. By adjusting the algorithm coefficients, the same bit error rate is obtained. At this point, the nonlinear impairment at the transmitting end is obtained and constructed as training data.
[0044] like Figure 2 As shown, for a 56 Gbit / s DQPSK system, a random symbol sequence of length 4096 is generated using two polarization signals (x and y). This sequence is then passed through a 4800 km nonlinear channel, and the bit error rate (BER) is obtained after dispersion compensation, polarization mode dispersion (PMD) compensation, and carrier phase recovery. Using another identical signal through the dispersion channel and a DBP with a step size of 30 km for compensation, the same BER is obtained. In this case, the DBP output is a signal with a nonlinear term at the transmitter, which is the ideal training sequence.
[0045] The magnitude and phase factor optimization module constructs a cost function using the trained nonlinear impairment and updates the magnitude and phase factors using the gradient descent algorithm for multi-level pre-compensation.
[0046] A cost function is constructed using the difference between the pre-compensation term and the nonlinear damage. The partial derivative of the cost function with respect to each level of the pre-compensation coefficient is calculated step by step using the backpropagation algorithm. The magnitude and phase coefficients are then updated using the gradient descent algorithm until convergence.
[0047] Multi-level pre-compensation of a signal can be represented as:
[0048]
[0049] Where M is the pre-compensation level, ρ k and Let the amplitude and phase factors be those of the k-th level. The initial signal after modulation. The corresponding perturbation term is calculated in accordance with the literature "Perturbative Nonlinear Pre-Compensation in Presence of Optical Filtering" (Ghazisaeidi A, Renaudier J, Tran P, et al. 2015 Optical Fiber Communications Conference and Exhibition (OFC), 2015). In this example, the length of the nonlinear perturbation coefficient is 20. After obtaining the training sequence, the cost function is constructed using the above equation, and the partial derivative of the cost function with respect to each level of coefficients is calculated. The coefficients are then updated using the gradient descent algorithm.
[0050] The update process is described in Table 1.
[0051] Table 1. Algorithm for updating amplitude and phase factors
[0052]
[0053] Signal pre-compensation and transmission module: Performs multi-level pre-compensation on the modulated signal based on perturbation theory, and loads the pre-compensated signal onto the carrier wave through an analog laser.
[0054] The multi-stage pre-compensation at the transmitting end is cascaded to process the modulated signal. That is, the output signal of the previous stage of pre-compensation is used as the input to perform the next stage of pre-compensation based on perturbation theory. The pre-compensated signal is pulse-shaped, filtered at the transmitting end, and IQ-modulated before being loaded onto two polarizations and sent to the signal transmission module.
[0055] After performing one to three levels of pre-compensation on the signal, the signal is pulse-shaped, filtered at the transmitter, and modulated by IQ before being loaded onto two polarizations.
[0056] Signal transmission module: Signals are transmitted in the nonlinear optical fiber channel.
[0057] A polarization-multiplexed coherent optical communication system is adopted, and the signal transmission in single-mode optical fiber is simulated using the split-step Fourier method. The effects of fiber loss, chromatic dispersion, polarization mode dispersion and nonlinear effects are considered during the transmission process.
[0058] The total length of the transmission link is 4800km, consisting of multi-span optical fibers with a length of 80km, and an erbium-doped fiber amplifier (EDFA) inserted in each span. The dispersion coefficient of the optical fiber is 16 ps / nm / km, the nonlinear coefficient of the optical fiber is 1.3 / W / km, and the noise figure of the EDFA is 5.5dB.
[0059] Signal reception and digital signal processing module: Receives signals using a coherent receiver and performs digital signal processing and error analysis at the receiving end.
[0060] The signal is received and filtered using a coherent receiver. At the receiving end, dispersion compensation, polarization mode dispersion compensation, and carrier phase recovery are performed on the signal, and then the bit error rate is calculated.
[0061] The received signal undergoes coherent detection, receiver filtering, and sampling before being fed into a digital signal processing module for dispersion compensation, PMD compensation, and carrier phase recovery. Finally, it is demodulated to obtain the received signal sequence. The obtained signal is compared with the original data to calculate the bit error rate (BER). The performance improvement of the system is verified by calculating the bit error rate.
[0062] Compared with existing technologies, this method divides the nonlinear damage of the entire optical fiber into multiple sub-parts and performs pre-compensation step by step. The specific operation steps are as follows:
[0063] ① Calculate the corresponding coefficients in advance based on the preset series and the length of each nonlinear perturbation coefficient.
[0064] The amplitude and phase factor of each level are obtained based on the training data.
[0065] ② Using x and y polarization signals, generate four random information sequences and modulate them.
[0066] ③ Perform multi-level pre-compensation based on perturbation on the modulated signal, and load the pre-compensated signal onto the carrier wave through an analog laser and send it into the nonlinear channel for transmission.
[0067] ④ After transmission through optical fiber, the signal undergoes coherent detection and receiver filtering before being sent to the digital signal processing module.
[0068] ⑤ The signal is synchronized and doubled in sampling. A zero-forcing equalizer is used to compensate for chromatic dispersion, a constant modulus algorithm is used to compensate for polarization mode dispersion, and the Viterbi-Viterbi algorithm is used to estimate the carrier phase.
[0069] ⑥ Demodulate the signal and calculate the bit error rate.
[0070] Nonlinear compensation based on perturbation can be operated at the symbol level, which is less complex and easier to operate than double sampling. Since the total nonlinear effect is closely related to the fiber length, dividing the nonlinearity of the fiber into multiple segments will significantly reduce the length of the nonlinear perturbation coefficients required to compensate for the nonlinear effect of each segment. Therefore, adopting a multi-level pre-compensation method can effectively reduce the length of the nonlinear coefficients, thereby reducing the computational complexity. The error backpropagation algorithm can accurately calculate the amplitude and phase factor of each level.
[0071] like Figure 3 The bit error rate (BER) of the proposed method was compared after applying different levels of pre-compensation algorithms, and the performance of the method under different transmission powers was verified. Simulation results show that when the power is low, the performance difference between different algorithms is small because the main source of the error is Gaussian noise, and the influence of nonlinear effects is minimal. As the transmit power increases, the BER decreases with the increase of the number of pre-compensation levels, indicating that multi-level pre-compensation performs better in scenarios with strong nonlinear effects. The algorithm shows the greatest performance improvement when the power is around 0dBm to 1dBm. Further increases in power lead to a decrease in the performance improvement of multi-level pre-compensation, indicating that the nonlinear effect is too large, requiring higher-level or longer nonlinear pre-compensation coefficients for compensation. Single-level pre-compensation, while achieving the same performance, requires much longer nonlinear perturbation coefficients, exponentially increasing computational complexity.
[0072] This method combines nonlinear pre-compensation algorithms with machine learning in high-speed, long-distance optical communication systems. It primarily aims to eliminate the effects of nonlinearity in optical fiber channels to optimize system performance. Simulation results demonstrate that this invention can significantly reduce the bit error rate, thereby extending the transmission distance and increasing transmission capacity.
[0073] Example 2
[0074] This embodiment provides an electronic device, including: one or more processors and a memory, wherein the memory stores one or more programs, the one or more programs including instructions for executing the optical communication method based on multi-level nonlinear pre-compensation as described in Embodiment 1.
[0075] Example 3
[0076] This embodiment provides a computer-readable storage medium including one or more programs executed by one or more processors of the provided electronic device, the one or more programs including instructions for executing the optical communication method based on multi-level nonlinear pre-compensation described in Embodiment 1.
[0077] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.
Claims
1. An optical communication method based on multi-level nonlinear pre-compensation, characterized in that, Applied to the sending end, the method includes the following steps: The system acquires and modulates the input signal, performs multi-level nonlinear pre-compensation on the modulated signal, acquires the compensated signal, and obtains the transmission signal based on the compensated signal through pre-processing before transmission. The signal is then transmitted to the receiving end via optical fiber. The parameters of each level of nonlinear pre-compensation have been pre-trained. After receiving the transmission signal, the receiving end performs post-processing and demodulation to obtain the target signal, thereby realizing communication between the sending end and the receiving end. The process of obtaining the pre-trained parameters for each level of nonlinear pre-compensation includes the following steps: Obtain a training sample set, and train the parameters of each level of nonlinear pre-compensation based on the training sample set. When the cost function value reaches the preset convergence condition, obtain the pre-trained parameters of each level of nonlinear pre-compensation. The acquisition of the training sample set includes the following steps: A symbol sequence is randomly generated at the transmitting end and transmitted to the receiving end through the nonlinear channel of the optical fiber. After compensation, the receiving end obtains the first bit error rate. The symbol sequence is then transmitted to the receiving end through the dispersion channel of the optical fiber. The receiving end uses a digital backpropagation algorithm to compensate and obtain the second bit error rate. Based on the first bit error rate and the second bit error rate, the parameters of the preset signal compensation algorithm at the receiving end are adjusted so that the first bit error rate and the second bit error rate are the same. Based on the nonlinear impairment of the output of the digital backpropagation algorithm at this time, training samples are established. The training sample set is obtained based on multiple training samples; The multi-level nonlinear pre-compensation is achieved using the following formula: In the formula, M is the number of pre-compensation levels. and These are the amplitude and phase factor of the k-th level, respectively. The initial signal after modulation. This represents the corresponding perturbation term.
2. The optical communication method based on multi-level nonlinear pre-compensation according to claim 1, characterized in that, The parameters for each level of nonlinear precompensation include the amplitude factor and the phase factor.
3. The optical communication method based on multi-level nonlinear pre-compensation according to claim 1, characterized in that, The process of training the parameters of each level of nonlinear pre-compensation based on the training sample set includes the following steps: Calculate the partial derivatives of the cost function with respect to the parameters of each nonlinear pre-compensation stage, and update the parameters using the gradient descent algorithm.
4. The optical communication method based on multi-level nonlinear pre-compensation according to claim 1, characterized in that, The cost function value is obtained based on the compensation term obtained from the nonlinear pre-compensation and the nonlinear damage in the training samples.
5. The optical communication method based on multi-level nonlinear pre-compensation according to claim 1, characterized in that, The process of obtaining the first bit error rate includes the following steps: Based on the symbol sequence, it is transmitted to the receiving end through the nonlinear channel of the optical fiber. The receiving end performs dispersion compensation and polarization mode dispersion compensation to obtain the first bit error rate.
6. The optical communication method based on multi-level nonlinear pre-compensation according to claim 1, characterized in that, The modulation is DQPSK modulation.
7. The optical communication method based on multi-level nonlinear pre-compensation according to claim 1, characterized in that, The preprocessing before transmission includes the following steps: The pre-compensated signal is then processed through pulse shaping, transmitter filtering, and IQ modulation to obtain the transmitted signal.