WO2026001072A1 - 一种计算大带宽光纤通信系统非线性噪声的方法及装置 - Google Patents

一种计算大带宽光纤通信系统非线性噪声的方法及装置

Info

Publication number
WO2026001072A1
WO2026001072A1 PCT/CN2025/081246 CN2025081246W WO2026001072A1 WO 2026001072 A1 WO2026001072 A1 WO 2026001072A1 CN 2025081246 W CN2025081246 W CN 2025081246W WO 2026001072 A1 WO2026001072 A1 WO 2026001072A1
Authority
WO
WIPO (PCT)
Prior art keywords
noise
channel
xpm
nonlinear
monitoring channel
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
PCT/CN2025/081246
Other languages
English (en)
French (fr)
Inventor
吴学锋
邵高琦
刘锦秋
姚殊畅
尹山
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Fiberhome Telecommunication Technologies Co Ltd
Original Assignee
Fiberhome Telecommunication Technologies Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Fiberhome Telecommunication Technologies Co Ltd filed Critical Fiberhome Telecommunication Technologies Co Ltd
Publication of WO2026001072A1 publication Critical patent/WO2026001072A1/zh
Pending legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B10/00—Transmission systems employing electromagnetic waves other than radio-waves, e.g. infrared, visible or ultraviolet light, or employing corpuscular radiation, e.g. quantum communication
    • H04B10/07—Arrangements for monitoring or testing transmission systems; Arrangements for fault measurement of transmission systems
    • H04B10/075—Arrangements for monitoring or testing transmission systems; Arrangements for fault measurement of transmission systems using an in-service signal
    • H04B10/079—Arrangements for monitoring or testing transmission systems; Arrangements for fault measurement of transmission systems using an in-service signal using measurements of the data signal
    • H04B10/0795—Performance monitoring; Measurement of transmission parameters

Definitions

  • This invention belongs to the field of communication technology, and more specifically, relates to a method and apparatus for calculating nonlinear noise in a high-bandwidth optical fiber communication system.
  • DWDM dense wavelength division multiplexing
  • Nonlinear impairments in optical communication transmission systems are increasingly becoming a major factor limiting signal transmission capabilities.
  • the Gaussian Noise Model (GN) is a commonly used approach.
  • the conventional GN model integration algorithm for solving large-bandwidth scenarios is complex, computationally intensive, time-consuming, and difficult to apply in engineering.
  • the GN model performs integration calculations based on information such as the input optical power, fiber type, and input optical signal spectrum.
  • the integration is a triple integration of the signal spectrum over frequency; the wider the signal spectrum bandwidth, the longer the calculation time.
  • the computation time of the GN model is also related to the number of fiber segments being calculated; the more segments, the longer the calculation time.
  • a major drawback of the conventional GN model is its low computational efficiency, which decreases further with wider bands.
  • calculating the same link scenario in the C+L band takes more than four times longer.
  • current solutions for calculating the nonlinear noise of a monitoring channel in the C+L band takes several minutes, making it difficult to apply in engineering.
  • the present invention provides a method and apparatus for calculating nonlinear noise in a high-bandwidth optical fiber communication system. Its purpose is to simulate and calculate the nonlinear noise of each channel in a high-bandwidth optical fiber communication system with high accuracy and as fast as possible, so as to evaluate the performance of the system, thereby solving the technical problem of long time consumption in calculating and monitoring channel nonlinear noise.
  • a method for calculating nonlinear noise in a high-bandwidth optical fiber communication system comprising:
  • the sum of the second XPM noise caused by crosstalk channels with a frequency interval greater than or equal to a preset frequency threshold on the monitoring channel is calculated.
  • the sum of the SPM noise, the first XPM noise, and the second XPM noise is added together to obtain the nonlinear noise corresponding to the monitoring channel.
  • the calculation of the sum of second XPM noise caused by crosstalk channels with a frequency interval greater than or equal to a preset frequency threshold on the monitoring channel, incorporating the relative nonlinear noise figure includes:
  • the sum of the second XPM noise caused by crosstalk channels with a frequency interval greater than or equal to a preset frequency threshold on the monitoring channel is obtained.
  • PLargeBd is the sum of the second XPM noise
  • i represents the channel index difference between the crosstalk channel and the monitoring channel
  • k represents the channel index difference between the crosstalk channel and the monitoring channel whose frequency interval with the monitoring channel is equal to a preset frequency threshold
  • M is the channel index difference between the crosstalk channel to the left of the monitoring channel and the monitoring channel
  • N is the channel index difference between the crosstalk channel to the right of the monitoring channel and the monitoring channel whose frequency interval with the monitoring channel is equal to a preset frequency threshold
  • PNL_ref is the reference XPM noise
  • G_i represents the relative nonlinear noise figure of the i-th channel
  • G_ref represents the relative nonlinear noise figure of the crosstalk channel whose frequency interval with the monitoring channel is equal to a preset frequency threshold.
  • each crosstalk channel whose frequency interval with the monitoring channel is greater than or equal to a preset frequency threshold is calculated according to the following formula:
  • G is the relative nonlinear noise figure caused by the crosstalk channel on the monitoring channel
  • ⁇ is the nonlinear coefficient
  • Lw is the walk-off length
  • L is the fiber length
  • ⁇ is the attenuation coefficient
  • t is the time
  • C ⁇ ( t′1 , t′2 ) is a trigonometric function.
  • the method for calculating the nonlinear noise of a high-bandwidth optical fiber communication system further includes:
  • the relative nonlinear noise figure of each crosstalk channel is obtained by table lookup or interpolation calculation.
  • the simplified GN model is as follows:
  • G NLI (f) represents the nonlinear noise power density spectrum
  • ⁇ is the nonlinear coefficient
  • Leff is the effective length of the optical fiber
  • GWDM is the signal light power density spectrum
  • f, f1 , f2 are the frequencies
  • ⁇ is the four-wave mixing transfer function
  • ⁇ is the four-wave mixing enhancement factor.
  • Integrating G NLI (f) at the corresponding frequency yields the nonlinear noise of the corresponding channel.
  • the step of adding the SPM noise, the sum of the first XPM noise, and the sum of the second XPM noise to obtain the nonlinear noise corresponding to the monitoring channel includes:
  • the nonlinear noise corresponding to the monitoring channel is obtained by summing the SPM noise, the optimized first XPM noise, and the optimized second XPM noise.
  • the preset frequency threshold is 500GHz ⁇ 10GHz.
  • the transmission band of the high-bandwidth optical fiber communication system is C-band, L-band, C+L-band, or C+L+S-band.
  • an apparatus for calculating nonlinear noise in a high-bandwidth optical fiber communication system comprising at least one processor and a memory, wherein the at least one processor and the memory are connected via a data bus, and the memory stores instructions executable by the at least one processor, wherein the instructions, after being executed by the processor, are used to perform the method for calculating nonlinear noise in a high-bandwidth optical fiber communication system.
  • Figure 2 is a flowchart of the method for calculating nonlinear noise in a high-bandwidth optical fiber communication system provided in an embodiment of the present invention.
  • Figure 3 is a schematic diagram of the variation curves of single-wave calculation time and calculation error provided in the embodiment of the present invention.
  • Figure 5 is a three-dimensional schematic diagram of relative nonlinear noise figure, channel spacing, and channel step provided in an embodiment of the present invention
  • FIG. 7 is a schematic flowchart of step 40 provided in an embodiment of the present invention.
  • Figure 8 is a schematic diagram of a 100G QPSK code pattern, C120+L120 waveband, 11-span 100km G.652 long fiber system provided in an embodiment of the present invention
  • Figure 9 shows a comparison of simulation results (computational cost comparison) before and after algorithm optimization provided in the embodiments of the present invention.
  • Figure 10 shows a comparison of simulation results before and after algorithm optimization (comparison of computation time) provided in the embodiment of the present invention
  • Figure 12 is a schematic diagram of the structure of a device for calculating nonlinear noise in a high-bandwidth optical fiber communication system provided in an embodiment of the present invention.
  • This embodiment provides a method for calculating nonlinear noise in a high-bandwidth optical fiber communication system, wherein the transmission band of the high-bandwidth optical fiber communication system is C-band, L-band, C+L-band, or C+L+S-band.
  • the transmission band of the high-bandwidth optical fiber communication system is a C-band, such as C80 band, C96 band, C120 band; an L-band, such as L96 band, L120 band; a C+L-band, such as C96+L96 band, C120+L20 band; and a C+L+S-band, etc.
  • methods for calculating nonlinear noise in high-bandwidth optical fiber communication systems include:
  • the SPM noise of the monitoring channel in a high-bandwidth optical fiber communication system is obtained based on the simplified GN model.
  • G NLI (f) represents the nonlinear noise power density spectrum
  • ⁇ is the nonlinear coefficient
  • Leff is the effective length of the optical fiber
  • GWDM is the signal light power density spectrum
  • f, f1 , and f2 are the frequencies
  • ⁇ is the four-wave mixing transfer function
  • ⁇ is the four-wave mixing enhancement factor.
  • the nonlinear noise power density spectrum G ⁇ sub>NLI ⁇ /sub> (f) at each frequency is calculated based on the simplified GN model. Integrating G ⁇ sub>NLI ⁇ /sub> (f) at the corresponding frequency yields the nonlinear noise of the corresponding channel.
  • the values of f ⁇ sub>1 ⁇ /sub> and f ⁇ sub>2 ⁇ /sub> traverse from negative infinity to positive infinity. For a specific scenario, this means covering the entire waveband of the scenario.
  • determining the fiber type also clarifies its loss and dispersion characteristics for different frequencies of light. Therefore, the loss parameters corresponding to the loss characteristics and the dispersion parameters related to the dispersion characteristics are not used as input parameters.
  • the GN model calls typical parameters internally based on the fiber type, replacing the actual loss and dispersion parameters with typical coefficients.
  • Step 20 Calculate the sum of the first cross phase modulation (XPM) noise caused by crosstalk channels with a frequency interval less than a preset frequency threshold on the monitoring channel.
  • XPM cross phase modulation
  • Crosstalk channels refer to all channels in the entire band except for the monitoring channel.
  • the XPM noise of each crosstalk channel with a frequency interval less than a preset frequency threshold from the monitoring channel is obtained, and the calculated XPM noise is added together to obtain the first XPM noise sum.
  • the preset frequency threshold can be 500GHz ⁇ 10GHz, or more precisely, 500GHz, thus achieving a relative balance between computational efficiency and computational accuracy.
  • computational efficiency is a greater priority
  • other values can be selected for higher efficiency
  • computational accuracy is a greater priority
  • other values can be selected for higher accuracy.
  • This embodiment achieves speedup by simplifying the integration calculation of the large interval section.
  • GB_ref the preset frequency threshold
  • the noise proportion of the simplified calculation part is larger, so the simplification error introduced will be larger.
  • the larger the preset frequency threshold GB_ref the larger the spectral bandwidth that needs to be integrated, and therefore the longer the calculation time; it also means that the noise proportion of the simplified calculation part is smaller, so the simplification error introduced will be smaller.
  • Figure 3 shows the curves of single-wavelength computation time and computational error when the preset frequency threshold GB_ref takes different values. From the curves, we can see that when GB_ref is greater than 10 times the bandwidth of 50 GHz, the computational error is less than 4%, and it begins to decrease slowly as GB_ref increases. The computation time changes approximately linearly with GB_ref; the larger GB_ref is, the longer the computation time. Therefore, based on the balance between computational efficiency and accuracy, GB_ref is set to 500 GHz.
  • the horizontal axis represents the boundary between the selected large-interval and small-interval scenarios. Each scale represents 50 GHz, so 10 on the horizontal axis represents 500 GHz.
  • Step 30 Combine the relative nonlinear noise figure to calculate the sum of the second XPM noise caused by crosstalk channels with a frequency interval greater than or equal to a preset frequency threshold on the monitoring channel.
  • the nonlinear noise caused by a large-interval crosstalk channel i.e., a crosstalk channel with a frequency interval greater than or equal to a preset frequency threshold from the monitoring channel
  • the nonlinear noise of the large-interval crosstalk channel to the monitoring channel mainly consists of XPM, and other complex terms can be ignored.
  • the nonlinear noise caused by each crosstalk channel to the monitoring channel is also independent and can be calculated independently and linearly added.
  • the relative nonlinear noise coefficients between each crosstalk channel are introduced, eliminating the need to calculate the XPM noise of large-interval crosstalk channels using the GN model. This simplifies the calculation of XPM noise generated by crosstalk channels with an interval greater than a preset frequency threshold from the monitoring channel.
  • the nonlinear noise of the large-bandwidth optical fiber communication system to be calculated is divided into two parts: the first sum of XPM noise obtained in step 20 and the second sum of XPM noise obtained in step 30.
  • Step 40 Add the sum of the SPM noise, the first XPM noise, and the second XPM noise to obtain the nonlinear noise corresponding to the monitoring channel.
  • the SPM noise of the monitoring channel calculated by the GN model based on the assumption that the nonlinear noise of each channel is linearly independent, the SPM noise of the monitoring channel calculated by the GN model, the XPM noise caused by the small-interval crosstalk channel calculated by the GN model (i.e., the sum of the first XPM noise), and the XPM noise caused by the large-interval crosstalk channel obtained by combining the relative nonlinear noise coefficient (i.e., the sum of the second XPM noise) are linearly superimposed to obtain the total nonlinear noise.
  • this embodiment simplifies the calculation process of the integral GN model, simplifying the calculation of XPM noise caused by small-interval crosstalk channels. Furthermore, since large-interval crosstalk channels are farther from the monitoring channel, their impact on the nonlinear noise of the monitoring channel is smaller, and the correlation of nonlinear noise generated by each channel in the monitoring channel is weak. Therefore, a relative nonlinear coefficient can be introduced, and the XPM noise caused by large-interval crosstalk channels can be calculated in conjunction with the relative nonlinear noise coefficient. This allows for the separate calculation of the large-interval crosstalk noise component, which is computationally time-consuming but has a small noise impact. This improves computational efficiency without significantly affecting calculation accuracy, thus addressing the shortcomings of the integral GN model.
  • step 30 the calculation of the sum of the second XPM noise caused by crosstalk channels with a frequency interval greater than or equal to a preset frequency threshold on the monitoring channel includes:
  • Step 301 Calculate the reference XPM noise caused by the crosstalk channel on the monitoring channel when the frequency interval between the monitoring channel and the monitoring channel is equal to the preset frequency threshold.
  • the reference XPM noise caused by a crosstalk channel with a frequency interval equal to a preset frequency threshold on the monitoring channel is calculated based on a simplified GN model.
  • Step 302 Calculate the relative nonlinear noise figure of crosstalk channels with frequency intervals greater than or equal to a preset frequency threshold.
  • the XPM effect caused by a crosstalk channel can be calculated by calculating the nonlinear phase induced by the crosstalk channel. This nonlinear phase can then be obtained through time-domain power integration.
  • B is the XPM parameter, which is 3/2 in a typical scenario; ⁇ is the nonlinear coefficient; L is the fiber length; ⁇ is the attenuation coefficient; P2 is the power of the crosstalk light; d12 is the walk-off parameter characterizing the GVD mismatch between channels; t is time; and z is the integral variable over length.
  • ⁇ is nonlinear noise
  • B is the XPM parameter, which is 3/2 in a typical scenario
  • ⁇ is the nonlinear coefficient
  • L is the fiber length
  • ⁇ is the attenuation coefficient
  • Pp is the power of the crosstalk light
  • Lw is the walk-off length
  • t is the time
  • C ⁇ ( t′1 , t′2 ) is a trigonometric function.
  • G is the relative nonlinear noise figure caused by the crosstalk channel on the monitoring channel
  • ⁇ is the nonlinear coefficient
  • Lw is the walk-off length
  • L is the fiber length
  • ⁇ is the attenuation coefficient
  • t is the time
  • C ⁇ ( t′1 , t′2 ) is a trigonometric function.
  • the relative nonlinear noise figure varies depending on the code pattern, fiber type, and fiber length.
  • the relative nonlinear noise figure also varies depending on the monitoring channel and channel spacing.
  • the relative nonlinear noise figure of a crosstalk channel whose frequency interval with the monitoring channel is greater than or equal to a preset frequency threshold can be calculated according to the aforementioned formula.
  • this equivalent nonlinear noise G once the XPM noise of a certain crosstalk channel is known, it can be quickly converted into the XPM noise of other crosstalk channels.
  • Step 303 Based on the reference XPM noise and the relative nonlinear noise coefficients of each channel, obtain the sum of the second XPM noise caused by crosstalk channels with a frequency interval greater than or equal to a preset frequency threshold on the monitoring channel.
  • PLargeBd is the sum of the second XPM noise
  • i represents the channel index difference between the crosstalk channel and the monitoring channel
  • k represents the channel index difference between the crosstalk channel and the monitoring channel whose frequency interval with the monitoring channel is equal to a preset frequency threshold
  • M is the channel index difference between the crosstalk channel to the left of the monitoring channel and the monitoring channel
  • N is the channel index difference between the crosstalk channel to the right of the monitoring channel and the monitoring channel whose frequency interval with the monitoring channel is equal to a preset frequency threshold
  • PNL_ref is the reference XPM noise
  • G_i represents the relative nonlinear noise figure of the i-th channel
  • G_ref represents the relative nonlinear noise figure of the crosstalk channel whose frequency interval with the monitoring channel is equal to a preset frequency threshold.
  • the XPM noise caused by large-interval crosstalk channels in the current scenario can be quickly calculated using the aforementioned method.
  • the relative nonlinearity coefficient is related to parameters such as fiber type, fiber length, code pattern, and monitoring channel. Therefore, once these parameters are determined, the relative nonlinearity coefficient of the crosstalk channel can also be determined. In actual use, the relative nonlinearity coefficient of each crosstalk channel under the current condition can be obtained by looking up a table.
  • the method for calculating the nonlinear noise of a large-bandwidth optical fiber communication system further includes:
  • At least one relative nonlinear noise figure table should be established based on the fiber type, fiber length, code pattern, and monitoring channel that may be involved in a high-bandwidth optical fiber communication system.
  • the fiber type, fiber length, code pattern, and monitoring channel that may be involved in the actual scenario can be pre-summarized, and at least one relative nonlinear noise figure table should be proposed based on these parameters.
  • the relative nonlinear noise figure of each crosstalk channel is obtained by table lookup or interpolation calculation.
  • the relative nonlinear noise figure of each crosstalk channel is obtained directly by looking up the table. If the selected relative nonlinear noise figure table does not perfectly match the large-bandwidth fiber optic communication system to be calculated (meaning the fiber type, code pattern, and monitoring channel are consistent, but the fiber length is inconsistent), then the relative nonlinear noise figure of each crosstalk channel is obtained by interpolation.
  • the explanation of the interpolation method is as follows:
  • parameters such as fiber type and code pattern in high-bandwidth fiber optic communication systems can be preset and do not change with the actual scenario.
  • fiber length varies with the actual scenario and is less controllable. Therefore, in a more preferred embodiment, a table of relative nonlinear noise figures for classic fiber lengths relative to different fiber lengths and code patterns can be pre-established. If no table of relative nonlinear noise figures with the same fiber length as the high-bandwidth fiber optic communication system to be calculated exists, then the two table of relative nonlinear noise figures with the smallest difference in fiber length from the high-bandwidth fiber optic communication system to be calculated are selected and denoted as Table A and Table B, respectively.
  • the fiber type, code pattern, and monitoring channel corresponding to Table A and Table B are consistent with the fiber type, code pattern, and monitoring channel of the high-bandwidth fiber optic communication system to be calculated.
  • the table of relative nonlinear noise figures corresponding to the large-bandwidth optical fiber communication system to be calculated has been established in advance, the relative nonlinear noise figures of each crosstalk channel are obtained by looking up the table; if the nonlinear noise figures corresponding to the actual optical fiber length of the large-bandwidth optical fiber communication system to be calculated have not been established in advance, the corresponding relative nonlinear noise figures are obtained by interpolation.
  • the first dimension is the monitoring signal channel number (SigCH#); the second dimension is the relative nonlinear noise figure (G) generated by the crosstalk channel in the corresponding monitoring channel at frequency intervals from 50GHz to 12300GHz, with a 50GHz step.
  • Figure 6 provides the relative nonlinear coefficients of the first monitoring channel after normalization for a 10km G.652 fiber at various channel intervals (i.e., the channel spacing in Figure 6).
  • the XPM noise is corrected using a simplified Raman correction factor to improve calculation accuracy.
  • the inter-band Raman effect cannot be ignored.
  • the Raman correction factor for each channel can be calculated in advance. The expression for the Raman correction factor is as follows:
  • LAMAN is the calculated Raman effect correction coefficient
  • Crk is the polarization-related factor, which is 2 in conventional scenarios
  • toopPower is the total power of the entire waveband
  • Cr is the Raman gain coefficient
  • ⁇ is the fiber attenuation coefficient
  • L is the fiber length
  • Freq is the signal frequency.
  • step 40 the sum of the SPM noise, the first XPM noise, and the second XPM noise to obtain the nonlinear noise corresponding to the monitoring channel includes:
  • Step 401 Obtain a simplified Raman correction factor, and optimize the first XPM noise sum using the Raman correction factor to obtain the optimized first XPM noise sum.
  • the nonlinear noise P NL_i (GB ⁇ GB_ref) caused by the small-interval crosstalk channel is calculated using the simplified GN model.
  • the sum of the first XPM noise of the small-interval crosstalk channel on the monitoring channel can then be calculated.
  • Step 402 Obtain a simplified Raman correction factor, and optimize the second XPM noise sum using the Raman correction factor to obtain the optimized second XPM noise sum.
  • the nonlinear noise PNL_ref of the crosstalk channel with a frequency interval of GB_ref with the monitoring channel calculated by the GN model, combined with the relative nonlinear noise coefficient Gi of other crosstalk channels and the relative nonlinear noise coefficient Gk of the crosstalk channel at GB_ref, can be calculated to obtain the second XPM noise sum of the large-interval crosstalk channel.
  • the optimized second XPM noise sum PNL_LargeBd P LargeBd *LAMAN i ;
  • i represents the channel number
  • k represents the channel number of the crosstalk channel at GB_ref
  • N is the total number of channels.
  • Step 403 Add the SPM noise, the optimized first XPM noise, and the optimized second XPM noise together to obtain the nonlinear noise corresponding to the monitoring channel.
  • P NL represents the total nonlinear noise power caused by the system on the monitoring channel
  • Span represents the number of segments, ranging from 1 to NSpan.
  • this embodiment has at least the following beneficial effects:
  • the practical nonlinear noise calculation method established according to the method of the present invention has a simple structure and is easy to implement.
  • This invention can solve the problems of traditional theoretical models being too complex, difficult to solve, and time-consuming, making it more practical.
  • the present invention has a significant advantage in terms of accuracy, but does not significantly improve complexity.
  • this embodiment provides a comparison of the simulation results of the noise calculation scheme of Embodiment 1 with existing noise schemes in a specific scenario.
  • the high-bandwidth optical fiber communication system has 11 spans of G.652 optical fibers, with each span having a fiber length of 100km; the transmission band is C++ plus L++ band, with a total of 240 50GHz signals; the monitoring signal code is 100G QPSK, the signal is output from the transmitting OTU, coupled into the transmitting optical amplifier through the transmitting MUX, and then transmitted through 5 spans in sequence, after passing through a WSS equalization, it is transmitted through the last 6 optical fiber spans in sequence, and finally down-waved through the receiving DEMUX and enters the receiving end.
  • Figure 9 compares the computational costs of each monitoring channel before and after the implementation of the proposed solution.
  • Each point in Figure 9, labeled "After Speed Up,” represents the computational result of one monitoring channel. Specifically, each point requires a nonlinear noise calculation.
  • the 185THz channel is the monitoring channel, while all other channels from 185.05THz to 196.6THz are crosstalk channels.
  • the noise generated by the 185THz channel itself is SPM noise.
  • the noise generated by channels from 185.05 to 185.5THz on the 185THz channel is XPM noise (small-interval channel noise), and the noise generated by channels from 185.55THz to 196.6THz on the 185THz channel is large-interval XPM noise.
  • Figure 10 compares the computational time of each monitoring channel before and after the implementation of the proposed solution. As shown in Figure 9, the solution of this invention has minimal impact on the calculation accuracy, remaining at two decimal places. Meanwhile, as shown in Figure 10, the solution of this invention significantly improves the calculation efficiency, increasing it by nearly four times. This effectively solves the problem of long calculation times for nonlinear noise in high-bandwidth optical fiber transmission systems.
  • this invention also provides an apparatus for calculating nonlinear noise in a high-bandwidth optical fiber communication system, as shown in Figure 12, which is a schematic diagram of the apparatus architecture of an embodiment of this invention.
  • the apparatus of this embodiment includes one or more processors 21 and a memory 22.
  • Figure 12 shows an example with one processor 21.
  • the processor 21 and the memory 22 can be connected via a bus or other means.
  • Figure 12 shows an example of a connection via a bus.
  • the memory 22 is a non-volatile computer-readable storage medium serving as a method for calculating nonlinear noise in a high-bandwidth optical fiber communication system. It can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules, such as the method for calculating nonlinear noise in a high-bandwidth optical fiber communication system in Embodiment 1.
  • the processor 21 executes various functional applications and data processing of the device by running the non-volatile software programs, instructions, and modules stored in the memory 22, thereby implementing the method for calculating nonlinear noise in a high-bandwidth optical fiber communication system as described in the embodiment.
  • the memory 22 may include high-speed random access memory and non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device.
  • the memory 22 may optionally include memory remotely located relative to the processor 21, and these remote memories may be connected to the processor 21 via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.
  • the program instructions/modules are stored in the memory 22 and, when executed by one or more processors 21, perform the method for calculating nonlinear noise in a high-bandwidth optical fiber communication system as described in the above embodiments.
  • the program can be stored in a computer-readable storage medium, which may include: read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk, etc.

Landscapes

  • Physics & Mathematics (AREA)
  • Electromagnetism (AREA)
  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Optical Communication System (AREA)

Abstract

本发明公开了一种计算大带宽光纤通信系统非线性噪声的方法及装置，包括：计算大带宽光纤通信系统中监测信道的SPM噪声；计算与监测信道的频率间隔小于预设频率阈值的串扰信道在监测信道上引起的第一XPM噪声总和；结合相对非线性噪声系数，计算与监测信道的频率间隔大于等于预设频率阈值的串扰信道在监测信道上引起的第二XPM噪声总和；将所述SPM噪声、所述第一XPM噪声总和以及所述第二XPM噪声总和相加，得到监测信道所对应的非线性噪声。将计算耗时严重但噪声影响占比小的大间隔串扰噪声部分单独计算。既提升了计算效率，又不对计算精度造成太大的影响，补足了积分法GN模型的缺陷。

Description

一种计算大带宽光纤通信系统非线性噪声的方法及装置 技术领域
本发明属于通信技术领域，更具体地，涉及一种计算大带宽光纤通信系统非线性噪声的方法及装置。
背景技术
随着视频业务、云端技术等不断发展，通信流量呈持续爆发式增长，对光传送网提出了高速率、大容量传输且建网成本低的要求。为进一步扩大系统容量，需要采用密集波分复用(Dense Wavelength-Division Multiplexing，简称为：DWDM)扩充信道数，同时尽可能扩宽信道工作的波长范围。信号传输波段由原先的C波段向C+L波段、C+L+S波段等扩展。
光通信传输系统中的非线性损伤越来越成为限制信号传输能力的主要因数。在实际网络中考虑到成本、安全性、可行性等因素，需在规划设计、系统维护等情况下预知非线性损伤性能，当评估或者计算一个信道的非线性噪声时，使用高斯噪声模型(Gaussian Noise Model，简称为GN)是一个常用的方案。而常规的GN模型积分算法求解大带宽场景的过程复杂，计算量大，耗时长，工程应用困难。GN模型计算系统非线性噪声时，基于入纤光功率、光纤类型、入纤光信号谱等信息进行积分计算，而且积分是针对信号光谱在频率上进行三重积分，信号谱带宽越宽，耗时越长；GN模型的计算耗时还与计算的光纤跨段数相关，跨段数越多，耗时也越长。
综上，常规的GN模型的一大缺点是计算效率低，且波带越宽计算效率越低。相比于计算C波段的系统，计算C+L波段相同链路的场景耗时是原先的4倍以上。以11跨段的G.652长纤系统为例，计算C+L波带用例，当前已有方案计算一个监测信道非线性噪声的耗时在几分钟量级，工程应用困难。
为此，需要建立一套快速准确的计算方法，计算大带宽光传输系统的非线性噪声，在不影响计算精度的前提下，提升算法的计算效率，让复杂的评估过程能够在仿真中得以快速实现，保证传输系统的可靠性。
发明内容
针对现有技术的以上缺陷或改进需求，本发明提供了一种计算大带宽光纤通信系统非线性噪声的方法及装置，其目的在于以较高精度尽可能快地仿真计算大带宽光纤通信系统中各个信道的光纤非线性噪声，用于评估系统的性能，由此解决计算监测信道非线性噪声的耗时较长的技术问题。
为实现上述目的，按照本发明的一个方面，提供了一种计算大带宽光纤通信系统非线性噪声的方法，包括：
计算大带宽光纤通信系统中监测信道的SPM噪声；
计算与监测信道的频率间隔小于预设频率阈值的串扰信道在监测信道上引起的第一XPM噪声总和；
结合相对非线性噪声系数，计算与监测信道的频率间隔大于等于预设频率阈值的串扰信道在监测信道上引起的第二XPM噪声总和；
将所述SPM噪声、所述第一XPM噪声总和以及所述第二XPM噪声总和相加，得到监测信道所对应的非线性噪声。
进一步地，所述结合相对非线性噪声系数，计算与监测信道的频率间隔大于等于预设频率阈值的串扰信道在监测信道上引起的第二XPM噪声总和包括：
计算与监测信道的频率间隔等于预设频率阈值的串扰信道在监测信道上引起的参考XPM噪声；
计算与监测信道的频率间隔大于等于预设频率阈值的各个串扰信道的相对非线性噪声系数；
根据所述参考XPM噪声和各个串扰信道的相对非线性噪声系数，得到与监测信道的频率间隔大于等于预设频率阈值的串扰信道在监测信道上引起的第二XPM噪声总和。
进一步地，按照下述公式计算第二XPM噪声总和：
其中，PLargeBd为第二XPM噪声总和；i表示串扰信道离监测信道的信道序号差值；k表示与监测信道的频率间隔等于预设频率阈值的串扰信道与监测信道之间的信道序号差值；M为监测信道左边且离监测信道最远的串扰信道与监测信道之间的信道序号差；N为监测信道右边且离监测信道最远的串扰信道与监测信道的信道序号差，PNL_ref为参考XPM噪声，G_i表示第i个信道的相对非线性噪声系数，G_ref表示与监测信道的频率间隔等于预设频率阈值的串扰信道的相对非线性噪声系数。
进一步地，按照下述公式计算与监测信道的频率间隔大于等于预设频率阈值的各个串扰信道的相对非线性噪声系数：
其中，G为串扰信道在监测信道上引起的相对非线性噪声系数，γ为非线性系数，Lw为走离长度，L为光纤长度，α为衰减系数，t为时间；CΔ(t′1，t′2)为三角函数。
进一步地，所述计算大带宽光纤通信系统非线性噪声的方法还包括：
基于大带宽光纤通信系统所可能涉及的光纤类型、光纤长度、码型和监测信道建立至少一个相对非线性噪声系数表；
根据待计算的大带宽光纤通信系统实际使用的光纤类型、光纤长度、码型和监测信道选择相应的相对非线性噪声系数表；
以所选择的相对非线性噪声系数表为依据，采用查表或者插值计算的方式获取各个串扰信道的相对非线性噪声系数。
进一步地，根据简化后的GN模型计算SPM噪声和第一XPM噪声总和，简化后的GN模型为：
其中，GNLI(f)表示非线性噪声功率密度谱，γ为非线性系数，Leff为光纤的有效长度，GWDM为信号光功率密度谱，f、f1、f2为频率，ρ为四波混频传输函数，χ为四波混频增强因子；
对GNLI(f)在相应的频率上积分得到相应信道的非线性噪声。
进一步地，所述将所述SPM噪声、所述第一XPM噪声总和以及所述第二XPM噪声总和相加，得到监测信道所对应的非线性噪声包括：
获取简化的拉曼修正因子，通过拉曼修正因子对所述第一XPM噪声总和进行优化，得到优化后的第一XPM噪声总和；
通过拉曼修正因子对所述第二XPM噪声总和进行优化，得到优化后的第二XPM噪声总和；
将所述SPM噪声、所述优化后的第一XPM噪声总和以及所述优化后的第二XPM噪声总和相加，得到监测信道所对应的非线性噪声。
进一步地，所述预设频率阈值为500GHz±10GHz。
进一步地，所述大带宽光纤通信系统的传输波带为C波段、L波段、C+L波段或C+L+S波段。
为实现上述目的，按照本发明的一个方面，提供了一种计算大带宽光纤通信系统非线性噪声的装置，包括至少一个处理器和存储器，所述至少一个处理器和存储器之间通过数据总线连接，所述存储器存储有可被所述至少一个处理器执行的指令，所述指令在被所述处理器执行后，用于完成所述的计算大带宽光纤通信系统非线性噪声的方法。
总体而言，通过本发明所构思的以上技术方案与现有技术相比，具有如下有益效果：由于大间隔串扰信道离监测信道间隔较远，对监测信道的非线性噪声影响越小，且各信道在监测信道产生的非线性噪声相关性弱，因此可以引入相对非线性系数，结合相对非线性噪声系数计算大间隔串扰信道所引起的XPM噪声，无需通过GN模型计算大间隔串扰信道的XPM噪声，从而将计算耗时严重但噪声影响占比小的大间隔串扰噪声部分单独计算。既提升了计算效率，又不对计算精度造成太大的影响，补足了积分法GN模型的缺陷。
另一方面，将积分法GN模型的计算过程进行了简化，简化了小间隔串扰信道所引起的XPM噪声的计算过程。
附图说明
图1是本发明实施例提供的一种计算大带宽光纤通信系统非线性噪声的方法的流程示意图；
图2是本发明实施例提供的大带宽光纤通信系统非线性噪声的计算方法方法的流程框图；
图3是本发明实施例提供的单波计算耗时及计算误差的变化曲线示意图；
图4是本发明实施例提供的步骤30的具体流程示意图；
图5是本发明实施例提供的一种相对非线性噪声系数、信道间距以及信道步进的三维示意图；
图6是本发明实施例提供的一种信道间距与相对非线性噪声系数的关系二维示意图；
图7是本发明实施例提供的步骤40的具体流程示意图；
图8是本发明实施例提供的一种100G QPSK码型、C120+L120波带、11跨100km G.652长纤系统的场景示意图；
图9是本发明实施例提供的算法优化前后的仿真结果对比(计算代价比较)；
图10是本发明实施例提供的算法优化前后的仿真结果对比(计算耗时比较)；
图11是本发明实施例提供的算法优化前后的积分范围示意图；
图12是本发明实施例提供的一种计算大带宽光纤通信系统非线性噪声的装置的结构示意图。
具体实施方式
为了使本发明的目的、技术方案及优点更加清楚明白，以下结合附图及实施例，对本发明进行进一步详细说明。应当理解，此处所描述的具体实施例仅仅用以解释本发明，并不用于限定本发明。此外，下面所描述的本发明各个实施方式中所涉及到的技术特征只要彼此之间未构成冲突就可以相互组合。
实施例1：
本实施例提供了一种计算大带宽光纤通信系统非线性噪声的方法，所述大带宽光纤通信系统的传输波带为C波段、L波段、C+L波段或C+L+S波段。具体地，所述大带宽光纤通信系统的传输波带为C波带，如C80波段，C96波段，C120波段；L波带，如L96波段，L120波段；C+L波带，如C96+L96波段，C120+L20波段；以及C+L+S波段等。
参阅图1和图2，计算大带宽光纤通信系统非线性噪声的方法包括：
步骤10：计算大带宽光纤通信系统中监测信道的自相位调制(Self-Phase Modulation，简称为SPM)噪声。
在一个实施例中，根据简化后的GN模型，得到大带宽光纤通信系统中监测信道的SPM噪声。
其中，简化后的GN模型为：
其中，GNLI(f)表示非线性噪声功率密度谱，γ为非线性系数，Leff为光纤的有效长度，GWDM为信号光功率密度谱，f、f1、f2为频率，ρ为四波混频传输函数，χ为四波混频增强因子。
根据简化后的GN模型计算各频率下的非线性噪声功率密度谱GNLI(f)，对GNLI(f)在相应的频率上积分得到相应信道的非线性噪声。其中，计算某个频率f下的非线性噪声功率密度谱GNLI(f)时，f1、f2的取值从理论上说就都是负无穷到正无穷遍历。针对具体的场景，就是覆盖场景的整个波带即可。
其中，计算非线性噪声之前，首先确定需要仿真的系统的各项参数，各项参数包括仿真过程中的可变量以及常数量，其中，可变量包括：入纤信号光功率Pi、入纤光功率谱SP和监测信道序号；常数量包括：光纤类型、光纤长度L和光纤跨段数。
虽然非线性噪声也与损耗参数及色散参数相关，但是确定光纤类型的同时也明确了其对不同频率光的损耗特性和色散特性。因此，与损耗特性相对应的损耗参数，以及与色散特性相关的色散参数并不作为输入量，GN模型根据光纤类型在算法内部调用典型参数，以典型系数替代实际的损耗参数和色散参数。
步骤20：计算与监测信道的频率间隔小于预设频率阈值的串扰信道在监测信道上引起的第一交叉相位调制(Cross Phase Modulation，简称为XPM)噪声总和。
其中，监测信道指的是：当前需要计算非线性噪声的信道，整个波带中任一个信道均可以为监测信道。
串扰信道指的是：整个波带中，除了监测信道之外的所有信道均是串扰信道。
例如，波带中有信道1～信道40，共40个信道。假设需要知道信道3的非线性噪声，那么信道3即为监测信道，信道1到信道2、信道4到信道40波都属于串扰信道；同理，如果需要计算信道40的非线性噪声，那么信道40为监测信道，信道1到信道39均为串扰信道。
在一个实施例中，根据简化后的GN模型，得到与监测信道的频率间隔小于预设频率阈值的各个串扰信道的XPM噪声，将计算得到的XPM噪声相加得到第一XPM噪声总和。
其中，所述预设频率阈值的选取主要取决于计算效率及计算精度，根据不同侧重点，可以取不同值。
在一个实施例中，所述预设频率阈值可以为500GHz±10GHz，更精确地，所述预设频率阈值可以为500GHz，如此可以使计算效率及计算精度达到一个相对的平衡。当然，如果更侧重于计算效率，则可以选取其他值，使得计算效率更高；如果更侧重于计算精度，则可以选取其他值，使得计算精度更高。
关于预设频率阈值设置为500GHz的设计意图如下介绍：
本实施例是通过简化大间隔部分的积分计算来实现提速，预设频率阈值GB_ref越小，代表需要积分的频谱带宽越小，所以计算的耗时越少；但同时说明简化计算部分的噪声占比越大，所以引入的简化误差将越大。
同理，预设频率阈值GB_ref越大，代表需要积分的频谱带宽越大，所以计算的耗时越大；同时也说明简化计算部分的噪声占比越少，所以引入的简化误差将越小。
图3是预设频率阈值GB_ref取不同值时，单波计算耗时及计算误差的变化曲线，从变化曲线上可以得到：在GB_ref大于10个50GHz带宽以上时，计算误差小于4％，且开始随GB_ref增大缓慢变小；计算耗时随GB_ref近似线性变化，GB_ref越大计算耗时越长。因此，根据计算效率和计算精度的平衡，GB_ref取500GHz。其中，在图3中，横坐标指的是所选择的大间隔场景和小间隔场景的区分边界。每个刻度代表50GHz，所以在横坐标10代表500GHz。
步骤30：结合相对非线性噪声系数，计算与监测信道的频率间隔大于等于预设频率阈值的串扰信道在监测信道上引起的第二XPM噪声总和。
即，计算±GB_ref(预设频率阈值)间隔范围外信道所引起的噪声总和。
基于各串扰信道的非线性串扰噪声是相互独立的假设，大间隔串扰信道(即，与监测信道的频率间隔大于等于预设频率阈值的串扰信道)所引起的非线性噪声可简化为相应串扰信道XPM噪声的叠加。在无色散补偿系统中，大间隔的串扰信道对监测信道的非线性噪声主要由XPM组成，其他复杂项可以忽略；同时，由于各串扰信道所传递的信息是相互独立的，所以各串扰信道对监测信道引起的非线性噪声也是相互独立的，可以进行独立计算和线性相加。
在本实施例中，引入各串扰信道间的相对非线性噪声系数，无需通过GN模型计算大间隔串扰信道的XPM噪声，简化计算离监测信道间隔大于预设频率阈值的串扰信道产生的XPM噪声。
在本实施例中，将待计算的大带宽光纤通信系统非线性噪声分成两部分：步骤20所得的第一XPM噪声总和、步骤30所得的第二XPM噪声总和。
步骤40：将所述SPM噪声、所述第一XPM噪声总和以及所述第二XPM噪声总和相加，得到监测信道所对应的非线性噪声。
在本实施例中，基于各信道非线性噪声线性无关的假设，将通过GN模型计算得到的监测信道的SPM噪声、通过GN模型计算得到的小间隔串扰信道引起的XPM噪声(即，第一XPM噪声总和)和结合相对非线性噪声系数得到的大间隔串扰信道引起的XPM噪声(即，第二XPM噪声总和)线性叠加起来，得到总的非线性噪声。
区别于现有技术，本实施例将积分法GN模型的计算过程进行了简化，简化了小间隔串扰信道所引起的XPM噪声的计算过程；另外，由于大间隔串扰信道离监测信道间隔较远，对监测信道的非线性噪声影响越小，且各信道在监测信道产生的非线性噪声相关性弱，因此可以引入相对非线性系数，结合相对非线性噪声系数计算大间隔串扰信道所引起的XPM噪声，从而将计算耗时严重但噪声影响占比小的大间隔串扰噪声部分单独计算。既提升了计算效率，又不对计算精度造成太大的影响，补足了积分法GN模型的缺陷。
参阅图4，在步骤30中，所述计算与监测信道的频率间隔大于等于预设频率阈值的串扰信道在监测信道上引起的第二XPM噪声总和包括：
步骤301：计算与监测信道的频率间隔等于预设频率阈值的串扰信道在监测信道上引起的参考XPM噪声。
在一个实施例中，根据简化后的GN模型计算与监测信道的频率间隔等于预设频率阈值的串扰信道在监测信道上引起的参考XPM噪声。
步骤302：计算与监测信道的频率间隔大于等于预设频率阈值的串扰信道的相对非线性噪声系数。
对于单段光纤，计算某一串扰信道引起的XPM效应可以通过计算串扰信道引起的非线性相位实现。串扰信道引起的非线性相位又可以通过时域功率积分得到：
其中，B为XPM参数，常规场景取3/2；γ为非线性系数；L为光纤长度；α为衰减系数；P2为串扰光的功率；d12为走离参数表征通道之间的GVD失配；t为时间；z为长度上的积分变量。
通过计算非线性相位的均方差，即可得到相应的等效的XPM噪声功率：
其中，σ为非线性噪声；B为XPM参数，常规场景取3/2；γ为非线性系数；L为光纤长度；α为衰减系数；Pp为串扰光的功率，Lw为走离长度，t为时间；CΔ(t′1，t′2)为三角函数。
针对不同的串扰信道，只需要知道他们间的相对值即可，所以上式可以简化为相对非线性噪声计算，则计算与监测信道的频率间隔大于等于预设频率阈值的串扰信道的相对非线性噪声系数的具体公式为：
其中，G为串扰信道在监测信道上引起的相对非线性噪声系数，γ为非线性系数，Lw为走离长度，L为光纤长度，α为衰减系数，t为时间；CΔ(t′1，t′2)为三角函数。
对于不同码型、不同光纤类型、不同光纤长度相对非线性噪声系数不同。不同监测信道、不同信道间隔相对非线性噪声系数不同。
综上，在本实施例中，可以按照前述公式计算与监测信道的频率间隔大于等于预设频率阈值的串扰信道的相对非线性噪声系数。通过该等效非线性噪声G，只要知道了某串扰信道的XPM噪声，就能快速换算成其他串扰信道的XPM噪声。
步骤303：根据所述参考XPM噪声和各个信道的相对非线性噪声系数，得到与监测信道的频率间隔大于等于预设频率阈值的串扰信道在监测信道上引起的第二XPM噪声总和。
按照下述公式计算第二XPM噪声总和：
其中，PLargeBd为第二XPM噪声总和；i表示串扰信道离监测信道的信道序号差值；k表示与监测信道的频率间隔等于预设频率阈值的串扰信道与监测信道之间的信道序号差值；M为监测信道左边且离监测信道最远的串扰信道与监测信道之间的信道序号差；N为监测信道右边且离监测信道最远的串扰信道与监测信道的信道序号差，PNL_ref为参考XPM噪声，G_i表示第i个信道的相对非线性噪声系数，G_ref表示与监测信道的频率间隔等于预设频率阈值的串扰信道的相对非线性噪声系数。
即，表示位于监测信道左边的所有大间隔串扰信道的噪声和，表示位于监测信道右边的所有大间隔串扰信道的噪声和，将这个两个噪声和相加即可得到第二XPM噪声总和。
其中，位于监测信道左边的串扰信道指的是频率小于监测信道的频率的串扰信道；位于监测信道右边的串扰信道指的是频率大于监测信道的频率的串扰信道。
按照前述方式可以快速地计算出当前场景下大间隔串扰信道引起的XPM噪声。
在实际应用场景下，相对非线性系数与光纤类型、光纤长度、码型、监测信道等参量有关。所以，当以上参量确定了，串扰信道的相对非线性系数也就可以确定了，实际使用中可以采用查表的方式获取当前情况下的各个串扰信道的相对非线性系数。
因此，为了提高获取串扰信道的相对非线性系数的效率，所述计算大带宽光纤通信系统非线性噪声的方法还包括：
基于大带宽光纤通信系统所可能涉及的光纤类型、光纤长度、码型和监测信道建立至少一个相对非线性噪声系数表。其中，可以预先汇总实际场景下可能会涉及的光纤类型、光纤长度、码型和监测信道，基于以上参量建议至少一个相对非线性噪声系数表。
根据待计算的大带宽光纤通信系统实际使用的光纤类型、光纤长度、码型和监测信道选择相应的相对非线性噪声系数表。在进行计算之前，获取待计算的大带宽光纤通信系统所使用的光纤类型、光纤长度、码型和监测信道，从预先建立的至少一个相对非线性噪声系数表中获取与待计算的大带宽光纤通信系统对应的相对非线性噪声系数表。
以所选择的相对非线性噪声系数表为依据，采用查表或者插值计算的方式获取各个串扰信道的相对非线性噪声系数。
如果所选择的相对非线性噪声系数表与待计算的大带宽光纤通信系统完全匹配(指的是光纤类型、光纤长度、码型和监测信道均一致)，则直接采用查表的方式获取各个串扰信道的相对非线性噪声系数；如果如果所选择的相对非线性噪声系数表与待计算的大带宽光纤通信系统并不完全匹配(指的是光纤类型、码型和监测信道均一致，但光纤长度不一致)，则采用插值计算的方式获取各个串扰信道的相对非线性噪声系数，插值计算的方式的解释说明如下：
一般而言，实际场景下，大带宽光纤通信系统的光纤类型和码型这些参量是可以预先设定的，并不会跟随实际的场景而变。但是光纤长度会跟随实际场景而变动，不太可控。因此，在更为优选的实施例中，可以预先建立经典光纤长度相对于不同光纤长度、不同码型的相对非线性噪声系数表，如果并不存在与待计算的大带宽光纤通信系统所使用的光纤长度一致的相对非线性噪声系数表；则选取与待计算的大带宽光纤通信系统所使用的光纤长度差异最小的两个相对非线性噪声系数表，分别记为相对非线性噪声系数表A和相对非线性噪声系数表B，其中，相对非线性噪声系数表A和相对非线性噪声系数表B所对应的光纤类型、码型和监测信道与待计算的大带宽光纤通信系统光纤类型、码型和监测信道一致。
以相对非线性噪声系数表A和相对非线性噪声系数表B为基准，进行插值计算，获取待计算的大带宽光纤通信系统所对应的相对非线性噪声系数表，再采用查表的方式获取取各个串扰信道的相对非线性噪声系数。
即，如果待计算的大带宽光纤通信系统所对应的相对非线性噪声系数表已经被预先建立，则采用查表的方式获取各个串扰信道的相对非线性噪声系数；如果待计算的大带宽光纤通信系统的实际光纤长度所对应的非线性噪声系数没有被预先建立，则采用插值计算的方式获取相应的相对非线性噪声系数。
例如，对某一光纤建模时，为了适用任何的光纤长度，设置了10km、20km、30km、40km、60km、100km、200km、300km、400km等9个典型光纤长度作为基准，提前计算好这9个长度下的相对非线性噪声系数表。如图5所示，每个表格，第一维是监测信号信道序号(SigCH#)；第二维是以50GHz为步进，50GHz到12300GHz频率间隔下串扰信道在对应监测信道产生的相对非噪声系数(G)。如图6所示，提供了10km G.652光纤，归一化后，第一个监测信道在各个信道间隔(即图6中的信道间距)下的相对非线性系数。
在本实施例中，通过简化后的拉曼修正因子对XPM噪声进行修正，提高计算的精度。对于大带宽场景，带间拉曼效应无法忽略，为了考虑拉曼效应对非线性的影响，可以提前计算出各信道的拉曼修正因子。其中，拉曼修正因子的表达式如下：
其中，LAMAN为计算的到拉曼效应修正系数；Crk为偏振相关的因子，常规场景取2；toopPower为整个波带的总功率；Cr为拉曼增益系数；α为光纤衰减系数；L为光纤长度；Freq为信号频率。
参阅图7，在步骤40中，所述将所述SPM噪声、所述第一XPM噪声总和以及所述第二XPM噪声总和相加，得到监测信道所对应的非线性噪声包括：
步骤401：获取简化的拉曼修正因子，通过拉曼修正因子对所述第一XPM噪声总和进行优化，得到优化后的第一XPM噪声总和。
在本实施例中，通过简化后的GN模型计算出小间隔串扰信道引起的非线性噪声PNL_i(GB＜GB_ref)，即可计算得到小间隔串扰信道在监测信道上的第一XPM噪声总和，再结合拉曼修正因子LAMANi，得到优化后的第一XPM噪声总和PNL_SmallBd，即，PNL_SmallBd＝∑PNL_i(GB＜GB_ref)*LAMANi，其中，i表示信道序号。
步骤402：获取简化的拉曼修正因子，通过拉曼修正因子对所述第二XPM噪声总和进行优化，得到优化后的第二XPM噪声总和。
在本实施例中，计算大间隔串扰信道引起的非线性噪声时，根据GN模型计算得到的与监测信道的频率间隔为GB_ref的串扰信道的非线性噪声PNL_ref，结合其他串扰信道的相对非线性噪声系数Gi和GB_ref处串扰信道的相对非线性噪声系数Gk，就可以计算出大间隔串扰信道的第二XPM噪声总和，并考虑拉曼修正系数LAMANi，得到优化后的第二XPM噪声总和PNL_LargeBd；
PNL_LargeBd＝PLargeBd*LAMANi；
其中，i表示信道序号，k表示GB_ref处串扰信道的信道序号，N为总的信道数。
步骤403：将所述SPM噪声、所述优化后的第一XPM噪声总和以及所述优化后的第二XPM噪声总和相加，得到监测信道所对应的非线性噪声。
最后，对系统各个跨段的非线性性噪声进行累加，计算系统总的非线性噪声具体包括：
PNL＝∑Span(PNL_SmallBd+PNL_LargeBd+SPM噪声)
其中，PNL表示系统在监测信道上引起的总非线性噪声功率；Span表示跨段数，范围从1到NSpan。
区别于现有技术，本实施例至少具有如下有益效果：
(1)将积分法GN模型的计算过程进行了简化，如图11所示，大大缩小了积分范围，同时引入相对非线性系数，从而将计算耗时严重但噪声影响占比小的大间隔串扰噪声部分单独计算。既提升了计算效率，又不对计算精度造成太大的影响，补足了积分法GN模型的缺陷。
(2)依据本发明方法建立的实用化的非线性噪声计算方法结构简单，易于实现。
(3)本发明可解决传统理论模型过于复杂，求解困难，耗时长的问题，更实用。
(4)依据本发明方法建立的模型需要的参数容易确定，很多参数可直接查阅产品手册获得。
(5)本发明相比于其他简化计算方法，在精度方面有显著的优势，但复杂度没有特别的提升。
实施例2：
基于前述实施例1，本实施例提供在一具体场景下，实施例1的噪声计算方案相对于现有噪声方案的仿真结果对比。
如图8所示，在本实施例中，大带宽光纤通信系统共有11跨G.652光纤，每跨段的光纤长度为100km；传输波带为C++加L++波带，共240路50GHz的信号；监测信号码型为100G QPSK，信号由发端OTU输出，经过发端MUX耦合进入发端光放，然后依次经过5个跨段传输，在经过一个WSS均衡后再依次经过后6个光纤跨段传输，最后经过收端DEMUX下波并进入接收端。
针对如图8所示的11跨大带宽光纤通信系统的场景进行仿真，得到如图9和图10所示的算法优化前后的仿真结果对比，其中，图9为本发明方案实施前后的各监测信道计算代价比较。图9中“提速后”的每个点都代表一个监测信道的计算结果。具体的，每个点都需要进行一次非线性噪声计算，以频率为185THz的点(曲线上第一个点)为例，计算时，频率为185THz的信道是监测信道，其他频率从185.05THz到196.6THz的所有信道都是串扰信道。频率为185THz的信道自身产生的噪声为SPM噪声，频率为185.05到185.5THz的信道对185THz信道产生的噪声为小间隔信道的XPM噪声，185.55THz到196.6THz信道在185THz信道上产生的噪声为大间隔XPM噪声。图10为本发明方案实施前后的各监测信道计算耗时比较。从图9可以看到，本发明方案对计算的精度影响很小，在小数点后两位；同时，从图10中可以看到，本发明方案对计算的效率有显著的提升，效率提升近4倍。较好的解决了大带宽光纤传输系统非线性噪声计算过程耗时长的难题。
实施例3：
在上述实施例1提供的计算大带宽光纤通信系统非线性噪声的方法的基础上，本发明还提供了一种计算大带宽光纤通信系统非线性噪声的装置，如图12所示，是本发明实施例的装置架构示意图。本实施例的装置包括一个或多个处理器21以及存储器22。其中，图12中以一个处理器21为例。
所述处理器21和所述存储器22可以通过总线或者其他方式连接，图12中以通过总线连接为例。
所述存储器22作为一种计算大带宽光纤通信系统非线性噪声的方法的非易失性计算机可读存储介质，可用于存储非易失性软件程序、非易失性计算机可执行程序以及模块，如实施例1中的计算大带宽光纤通信系统非线性噪声的方法。所述处理器21通过运行存储在所述存储器22中的非易失性软件程序、指令以及模块，从而执行装置的各种功能应用以及数据处理，即实现实施例的计算大带宽光纤通信系统非线性噪声的方法。
所述存储器22可以包括高速随机存取存储器，还可以包括非易失性存储器，例如至少一个磁盘存储器件、闪存器件、或其他非易失性固态存储器件。在一些实施例中，所述存储器22可选包括相对于所述处理器21远程设置的存储器，这些远程存储器可以通过网络连接至所述处理器21。上述网络的实例包括但不限于互联网、企业内部网、局域网、移动通信网及其组合。
所述程序指令/模块存储在所述存储器22中，当被所述一个或者多个处理器21执行时，执行上述实施例中的计算大带宽光纤通信系统非线性噪声的方法。
本领域普通技术人员可以理解实施例的各种方法中的全部或部分步骤是可以通过程序来指令相关的硬件来完成，该程序可以存储于一计算机可读存储介质中，存储介质可以包括：只读存储器(ROM，Read Only Memory)、随机存取存储器(RAM，Random Access Memory)、磁盘或光盘等。
本领域的技术人员容易理解，以上所述仅为本发明的较佳实施例而已，并不用以限制本发明，凡在本发明的精神和原则之内所作的任何修改、等同替换和改进等，均应包含在本发明的保护范围之内。

Claims (10)

  1. 一种计算大带宽光纤通信系统非线性噪声的方法，其特征在于，包括：
    计算大带宽光纤通信系统中监测信道的SPM噪声；
    计算与监测信道的频率间隔小于预设频率阈值的串扰信道在监测信道上引起的第一XPM噪声总和；
    结合相对非线性噪声系数，计算与监测信道的频率间隔大于等于预设频率阈值的串扰信道在监测信道上引起的第二XPM噪声总和；
    将所述SPM噪声、所述第一XPM噪声总和以及所述第二XPM噪声总和相加，得到监测信道所对应的非线性噪声。
  2. 根据权利要求1所述的计算大带宽光纤通信系统非线性噪声的方法，其特征在于，所述结合相对非线性噪声系数，计算与监测信道的频率间隔大于等于预设频率阈值的串扰信道在监测信道上引起的第二XPM噪声总和包括：
    计算与监测信道的频率间隔等于预设频率阈值的串扰信道在监测信道上引起的参考XPM噪声；
    计算与监测信道的频率间隔大于等于预设频率阈值的各个串扰信道的相对非线性噪声系数；
    根据所述参考XPM噪声和各个串扰信道的相对非线性噪声系数，得到与监测信道的频率间隔大于等于预设频率阈值的串扰信道在监测信道上引起的第二XPM噪声总和。
  3. 根据权利要求2所述的计算大带宽光纤通信系统非线性噪声的方法，其特征在于，按照下述公式计算第二XPM噪声总和：
    其中，PLargeBd为第二XPM噪声总和；i表示串扰信道离监测信道的信道序号差值；k表示与监测信道的频率间隔等于预设频率阈值的串扰信道与监测信道之间的信道序号差值；M为监测信道左边且离监测信道最远的串扰信道与监测信道之间的信道序号差；N为监测信道右边且离监测信道最远的串扰信道与监测信道的信道序号差，PNL_ref为参考XPM噪声，G_i表示第i个信道的相对非线性噪声系数，G_ref表示与监测信道的频率间隔等于预设频率阈值的串扰信道的相对非线性噪声系数。
  4. 根据权利要求2所述的计算大带宽光纤通信系统非线性噪声的方法，其特征在于，按照下述公式计算与监测信道的频率间隔大于等于预设频率阈值的各个串扰信道的相对非线性噪声系数：
    其中，G为串扰信道在监测信道上引起的相对非线性噪声系数，γ为非线性系数，Lw为走离长度，L为光纤长度，α为衰减系数，t为时间；CΔ(t′1，t′2)为三角函数。
  5. 根据权利要求1所述的计算大带宽光纤通信系统非线性噪声的方法，其特征在于，所述计算大带宽光纤通信系统非线性噪声的方法还包括：
    基于大带宽光纤通信系统所可能涉及的光纤类型、光纤长度、码型和监测信道建立至少一个相对非线性噪声系数表；
    根据待计算的大带宽光纤通信系统实际使用的光纤类型、光纤长度、码型和监测信道选择相应的相对非线性噪声系数表；
    以所选择的相对非线性噪声系数表为依据，采用查表或者插值计算的方式获取各个串扰信道的相对非线性噪声系数。
  6. 根据权利要求1-5任一项所述的计算大带宽光纤通信系统非线性噪声的方法，其特征在于，根据简化后的GN模型计算SPM噪声和第一XPM噪声总和，简化后的GN模型为：
    其中，GNLI(f)表示非线性噪声功率密度谱，γ为非线性系数，Leff为光纤的有效长度，GWDM为信号光功率密度谱，f、f1、f2为频率，ρ为四波混频传输函数，χ为四波混频增强因子；
    对GNLI(f)在相应的频率上积分得到相应信道的非线性噪声。
  7. 根据权利要求1-5任一项所述的计算大带宽光纤通信系统非线性噪声的方法，其特征在于，所述将所述SPM噪声、所述第一XPM噪声总和以及所述第二XPM噪声总和相加，得到监测信道所对应的非线性噪声包括：
    获取简化的拉曼修正因子，通过拉曼修正因子对所述第一XPM噪声总和进行优化，得到优化后的第一XPM噪声总和；
    通过拉曼修正因子对所述第二XPM噪声总和进行优化，得到优化后的第二XPM噪声总和；
    将所述SPM噪声、所述优化后的第一XPM噪声总和以及所述优化后的第二XPM噪声总和相加，得到监测信道所对应的非线性噪声。
  8. 根据权利要求1-5任一项所述的计算大带宽光纤通信系统非线性噪声的方法，其特征在于，所述预设频率阈值为500GHz±10GHz。
  9. 根据权利要求1-5任一项所述的计算大带宽光纤通信系统非线性噪声的方法，其特征在于，所述大带宽光纤通信系统的传输波带为C波段、L波段、C+L波段或C+L+S波段。
  10. 一种计算大带宽光纤通信系统非线性噪声的装置，其特征在于，包括至少一个处理器和存储器，所述至少一个处理器和存储器之间通过数据总线连接，所述存储器存储有可被所述至少一个处理器执行的指令，所述指令在被所述处理器执行后，用于完成权利要求1-9任一所述的计算大带宽光纤通信系统非线性噪声的方法。
PCT/CN2025/081246 2024-06-28 2025-03-07 一种计算大带宽光纤通信系统非线性噪声的方法及装置 Pending WO2026001072A1 (zh)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN202410850290.2 2024-06-28
CN202410850290.2A CN118764084B (zh) 2024-06-28 2024-06-28 一种计算大带宽光纤通信系统非线性噪声的方法及装置

Publications (1)

Publication Number Publication Date
WO2026001072A1 true WO2026001072A1 (zh) 2026-01-02

Family

ID=92940713

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2025/081246 Pending WO2026001072A1 (zh) 2024-06-28 2025-03-07 一种计算大带宽光纤通信系统非线性噪声的方法及装置

Country Status (2)

Country Link
CN (1) CN118764084B (zh)
WO (1) WO2026001072A1 (zh)

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN118764084B (zh) * 2024-06-28 2025-12-02 烽火通信科技股份有限公司 一种计算大带宽光纤通信系统非线性噪声的方法及装置

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8787754B1 (en) * 2012-10-31 2014-07-22 Ciena Corporation Rapid calculation of nonlinear optical transmission impairments
CN106452593A (zh) * 2016-05-30 2017-02-22 北京邮电大学 滤波器的构建方法和装置、及非线性噪声抑制方法和系统
CN117768050A (zh) * 2023-12-05 2024-03-26 重庆邮电大学 宽谱域光通信系统中波长选择与功率分配的联合优化方法
CN118764084A (zh) * 2024-06-28 2024-10-11 武汉烽火技术服务有限公司 一种计算大带宽光纤通信系统非线性噪声的方法及装置

Family Cites Families (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8472809B2 (en) * 2011-04-13 2013-06-25 Mitsubishi Electric Research Laboratories, Inc. Adaptive cross-polarization modulation cancellers for coherent optical communication systems
WO2012119364A1 (en) * 2011-07-27 2012-09-13 Huawei Technologies Co., Ltd. Method and apparatus for determining an optical signal-to-noise ratio (OSNR) penalty
CN103368644B (zh) * 2012-04-06 2016-03-30 富士通株式会社 一种交叉相位调制损伤的估计方法和装置
US8861960B2 (en) * 2012-08-20 2014-10-14 Ciena Corporation Systems and methods for the compensation of nonlinear cross polarization and cross phase modulation in dual polarization coherent channels
CN106571867B (zh) * 2015-10-12 2019-02-22 富士通株式会社 光信噪比的监测装置以及接收机
CN107800474A (zh) * 2016-09-07 2018-03-13 富士通株式会社 噪声强度的检测装置、方法以及相干光接收机
CN107147440B (zh) * 2017-02-22 2019-09-24 北京理工大学 一种测量wdm光纤通信系统信道间非线性效应的装置和方法
US10965371B2 (en) * 2019-04-12 2021-03-30 Huawei Technologies Co., Ltd. Optical performance monitoring based on fast bit error rate (BER) statistics
US10903912B2 (en) * 2019-05-17 2021-01-26 Huawei Technologies Co., Ltd. Method and apparatus for nonlinear compensation in coherent optical links
CN111884716B (zh) * 2020-06-30 2021-10-15 中国南方电网有限责任公司 一种基于神经网络的光纤通信系统性能评估方法
CN116599580B (zh) * 2023-05-04 2025-11-04 烽火通信科技股份有限公司 一种计算拉曼放大场景非线性噪声的方法和装置

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8787754B1 (en) * 2012-10-31 2014-07-22 Ciena Corporation Rapid calculation of nonlinear optical transmission impairments
CN106452593A (zh) * 2016-05-30 2017-02-22 北京邮电大学 滤波器的构建方法和装置、及非线性噪声抑制方法和系统
CN117768050A (zh) * 2023-12-05 2024-03-26 重庆邮电大学 宽谱域光通信系统中波长选择与功率分配的联合优化方法
CN118764084A (zh) * 2024-06-28 2024-10-11 武汉烽火技术服务有限公司 一种计算大带宽光纤通信系统非线性噪声的方法及装置

Also Published As

Publication number Publication date
CN118764084B (zh) 2025-12-02
CN118764084A (zh) 2024-10-11

Similar Documents

Publication Publication Date Title
D’Amico et al. Scalable and disaggregated GGN approximation applied to a C+ L+ S optical network
CN111587545B (zh) 分析光传输网络的系统和方法
US11290185B2 (en) Remote optical fiber dispersion compensation device and method, remote access device, electronic device and non-transient computer-readable storage medium
WO2026001072A1 (zh) 一种计算大带宽光纤通信系统非线性噪声的方法及装置
CN109714100B (zh) 一种多波长信道的光纤非线性串扰计算方法
CN114499679A (zh) 多波段拉曼放大器设计方法及系统
Escobar-Landero et al. S+ C+ L high-capacity transmission systems: Challenges and opportunities
CN109462435B (zh) 一种光纤通信系统的系统中非线性效应分析模型及方法
Zefreh et al. A closed-form nonlinearity model for forward-Raman-amplified WDM optical links
D'Amico et al. Introducing the perturbative solution of the inter-channel stimulated Raman scattering in single-mode optical fibers
CN116599580B (zh) 一种计算拉曼放大场景非线性噪声的方法和装置
CN119483754A (zh) 一种分布式拉曼光纤放大器的参数确定方法、装置、设备及介质
US12597997B2 (en) Determination of optical performance of distributed Raman amplifiers using deep neural networks
CN117811652A (zh) 长途光网络的性能监控系统、方法、设备及存储介质
CN104410452A (zh) 一种长距离相干光通信系统传输性能优化方法
Staschuk et al. Method of determining the length of the amplifying section by four-wave mixing for the line based on the standard fiber
Nadal et al. Crosstalk Analysis in MBoSDM Optical Networks with Programmable Transceiver and Switching Solutions
Venda et al. Assessing the quality of transmission of lightpaths in multiband C+ L networks through Gaussian noise models
Xia et al. Acceleration and parallelization methods for the ISRS-EGN model
Gorbashova et al. Optimization of Equalizer Spacing Period in Multispan Fiber-Optic Lines
Liu et al. Multi-stage Programmable Raman Amplifier-based Online Transmission Optimization for Multi-band Dynamic Optical Networks
US20130253896A1 (en) Optimizing optical network simulations
Seki et al. Channel power pre-equalization for photonic exchange node in heterogeneous multi-band transmission networks
Lin et al. Multi-Hop Power-Spectrum and GSNR Prediction based on Cascade Learning
Gao et al. Power Control with Online Gain Optimization for Multi-Band Optical Networks

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 25824467

Country of ref document: EP

Kind code of ref document: A1