Hollow core fiber performance cost prediction method, electronic device, medium, and product

Abstract

The embodiment of the application provides a kind of hollow optical fiber performance cost prediction method, electronic equipment, medium and product, it is related to optical communication field, method includes: determining the reference performance cost of reference hollow optical fiber to target transmission system under gas absorption effect;Obtain first characteristic parameter and second characteristic parameter, first characteristic parameter indicates the target performance characteristic quantity of reference hollow optical fiber, and second characteristic parameter indicates the target performance characteristic quantity of hollow optical fiber to be measured;Based on the corresponding relationship between first characteristic parameter and second characteristic parameter, adjust reference performance cost, to determine the predicted performance cost of hollow optical fiber to be measured to target transmission system under gas absorption effect.The application utilizes linear superposition principle to scale transformation or reconstruction reference performance cost, without the full frequency band frequent test to hollow optical fiber to be measured, can calculate the performance cost distribution generated by its GLA effect, to greatly save test time, improve performance evaluation efficiency after present network deployment.

Description

Technical Field

[0001] This application relates to the field of optical communication technology, and in particular to a method for predicting the performance cost of hollow optical fibers, electronic devices, media, and products. Background Technology

[0002] Anti-resonant hollow-core fiber (AR-HCF) has shown great promise for deployment in data center interconnects and other scenarios due to its advantages such as ultra-low loss, latency, and nonlinearity. However, residual or intrusive gases such as carbon dioxide within the fiber core can induce frequency-selective gas absorption effects. Furthermore, different optical transceivers (TPDs) exhibit varying performance under the same GLA conditions due to differences in modulation formats and digital signal processing (DSP) algorithms. This coupling between the fiber's gas absorption characteristics and the device's response characteristics presents a significant challenge to assessing the performance cost of hollow-core fiber in transmission systems. Summary of the Invention

[0003] In a first aspect, embodiments of this application provide a method for predicting the performance cost of hollow-core optical fiber, comprising: determining a reference performance cost of a reference hollow-core optical fiber for a target transmission system under gas absorption effect; obtaining a first characteristic parameter and a second characteristic parameter, wherein the first characteristic parameter represents a target performance characterization quantity of the reference hollow-core optical fiber, and the second characteristic parameter represents a target performance characterization quantity of the hollow-core optical fiber to be tested; and adjusting the reference performance cost based on the correspondence between the first characteristic parameter and the second characteristic parameter to determine the predicted performance cost of the hollow-core optical fiber to be tested for the target transmission system under gas absorption effect.

[0004] Secondly, embodiments of this application provide a method for predicting the performance cost of hollow-core optical fiber, comprising: obtaining a performance cost response function corresponding to a single absorption spectral line of the hollow-core optical fiber, wherein the performance cost response function represents the relationship between the performance cost of the target optical transceiver and the corresponding frequency offset at a target absorption depth, wherein the corresponding frequency offset is the frequency offset of the target optical transceiver relative to the corresponding absorption spectral line; obtaining a performance cost transformation function corresponding to a single absorption spectral line of the hollow-core optical fiber, wherein the performance cost transformation function represents the relationship between the performance cost of the target optical transceiver and the absorption depth of the corresponding absorption spectral line at a first target frequency; obtaining the performance cost of a single absorption spectral line of the hollow-core optical fiber based on the product of the performance cost response function and the performance cost transformation function, and obtaining the performance cost of the hollow-core optical fiber for a target transmission system based on the performance costs corresponding to multiple absorption spectral lines of the hollow-core optical fiber, wherein the target transmission system includes the target optical transceiver.

[0005] Thirdly, embodiments of this application provide a performance cost prediction method, comprising: determining hollow-core optical fibers corresponding to each transmission channel of a target optical communication link; predicting the performance cost of the target optical communication link based on the performance cost of each hollow-core optical fiber, wherein: at least one hollow-core optical fiber is a hollow-core optical fiber to be tested, the performance cost of the hollow-core optical fiber to be tested is the predicted performance cost, and the predicted performance cost is determined based on the method described in the first aspect; or, the performance cost of at least one hollow-core optical fiber is determined based on the method described in the second aspect.

[0006] Fourthly, embodiments of this application provide an electronic device, including a memory, a processor, and a computer program stored in the memory, wherein the processor implements any of the methods of embodiments of this application when executing the computer program.

[0007] Fifthly, embodiments of this application provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the method of any one of the embodiments of this application.

[0008] Sixthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements any of the methods described in the embodiments of this application.

[0009] According to the technical solution of this application embodiment, by obtaining the characteristic parameters of the reference hollow fiber and the hollow fiber under test and establishing their correspondence, and by utilizing the basic principle that the performance cost of different gas absorption spectral lines to the same target optical transceiver satisfies the linear superposition effect, the complex multi-absorption spectral line coupling problem is simplified into a linear combination of single absorption spectral line responses or a scaling transformation of the overall proportion. Thus, without the need for cumbersome full-band testing of the hollow fiber under test, only a few characteristic parameters are required to accurately calculate the performance cost of the hollow fiber under test under the gas absorption spectral line effect, greatly saving testing time and significantly improving the performance evaluation efficiency after deployment in the current network.

[0010] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application, it can be implemented according to the contents of the specification. In order to make the above and other objects, features and advantages of this application more easy to understand, specific embodiments of this application are given below. Attached Figure Description

[0011] In the accompanying drawings, unless otherwise specified, the same reference numerals throughout the various drawings denote the same or similar parts or elements. These drawings are not necessarily drawn to scale. It should be understood that these drawings depict only some embodiments according to this application and should not be construed as limiting the scope of this application.

[0012] Figure 1 A schematic diagram of the gas absorption effect in hollow optical fiber is shown.

[0013] Figure 2 and Figure 3 The simulation and measured results show the optical signal-to-noise ratio loss caused by gas absorption effect in the range of 189.5 to 190.2 terahertz after a section of hollow fiber is transmitted.

[0014] Figure 4 A flowchart is shown for the performance cost prediction method 400 for hollow optical fibers provided in an embodiment of this application.

[0015] Figure 5 The flowchart illustrates a method for predicting performance costs by scaling the reference performance cost based on the proportional relationship between the first and second feature parameters.

[0016] Figure 6 The diagram illustrates an application example of the performance cost prediction method for a single absorption spectral line scenario provided in this application embodiment.

[0017] Figure 7 The diagram illustrates an application example of the performance cost prediction method for multiple absorption spectral lines provided in the embodiments of this application.

[0018] Figure 8 The figure shows an application example of a performance cost prediction method based on actual measurement calibration at a specific frequency.

[0019] Figure 9 A flowchart is shown for the performance cost prediction method 900 for hollow optical fibers provided in an embodiment of this application.

[0020] Figure 10 A flowchart of the performance cost prediction method 1000 provided in an embodiment of this application is shown.

[0021] Figure 11 A block diagram of an electronic device provided in an embodiment of this application is shown. Detailed Implementation

[0022] In the following description, only certain exemplary embodiments are briefly described. As those skilled in the art will recognize, the described embodiments can be modified in various ways without departing from the concept or scope of this application. Therefore, the drawings and description are considered to be exemplary in nature and not restrictive.

[0023] To facilitate understanding of the technical solutions of the embodiments of this application, the relevant technologies of the embodiments of this application are described below. The following relevant technologies are optional solutions and can be combined with the technical solutions of the embodiments of this application in any way, and all of them fall within the protection scope of the embodiments of this application.

[0024] First, let's explain the terms used below.

[0025] Anti-resonant hollow fiber (AR-HCF): A type of optical fiber that uses the principle of anti-resonance reflection to confine light within an air core for transmission. It has excellent characteristics such as ultra-low loss, ultra-low latency, and ultra-low nonlinear effects.

[0026] Single-mode fiber (SMF): A type of optical fiber that can only transmit one mode (fundamental mode). It is the most commonly used transmission medium in traditional optical communication systems, and its optical signals are mainly transmitted in solid glass.

[0027] Datacenter Interconnect (DCI): A high-speed optical communication network scenario that connects data centers in different geographical locations, with extremely high requirements for transmission capacity, latency and power consumption.

[0028] Gas line absorption (GLA): When light propagates in a hollow optical fiber, the discrete loss characteristics caused by the absorption of light energy by residual gas molecules (such as carbon dioxide and water vapor) in the fiber core at specific resonant frequencies have strong frequency selectivity.

[0029] Optical transceiver (TPD): A key electrical layer device in an optical communication system, responsible for the conversion between electrical and optical signals. It includes a transmitter, a receiver, and a digital signal processing unit. Its performance is affected by the modulation format and algorithm.

[0030] Optical Signal-to-Noise Ratio (OSNR) is a core parameter for measuring the signal quality of optical networks. It is defined as the ratio of signal power to noise power. This parameter is related to the prediction of the system's bit error rate and is of great significance for evaluating the transmission performance of optical networks.

[0031] Back-to-Back (BTB): A test configuration for optical communication systems, in which the transmitter and receiver are directly connected (without fiber optic transmission or only connected via a very short jumper) to obtain the system's baseline performance curve under ideal channel conditions.

[0032] Anti-resonant hollow fiber (AR-HCF) has attracted widespread attention in recent years due to its superior transmission performance. Compared to traditional solid-core single-mode fiber (SMF), AR-HCF exhibits significant advantages in key transmission characteristics, including ultra-low loss (less than 0.1 dB / km), ultra-low latency (reduced by more than 30%), ultra-low nonlinear effects (reduced by more than 30 dB), ultra-low Rayleigh scattering (reduced by more than 40 dB), and ultra-wide transmission bandwidth (more than 400 nanometers). Increasing experimental results demonstrate that the transmission capacity and distance based on AR-HCF can be significantly improved, thereby promoting its practical deployment in scenarios such as data center interconnect (DCI).

[0033] However, the gas absorption line (GLA) effect in hollow-core optical fibers poses a potential challenge to their practical applications. Atmospheric gases such as carbon dioxide, carbon monoxide, and water vapor remaining within the fiber core introduce significant absorption characteristics within the transmission window, with carbon dioxide absorption being the most prominent. The intensity of carbon dioxide absorption depends primarily on the fiber length and the gas concentration within the core. Although current fabrication processes aim to purify residual gas contamination within hollow-core fibers, completely eliminating these residual gases during manufacturing remains a significant challenge. Furthermore, when the fiber end face is exposed (e.g., due to fiber breakage), ambient gases may gradually enter the hollow-core fiber, introducing new residual gases. Because these residual gases exhibit frequency selectivity, it is necessary to assess the performance cost of the gas absorption effect on the transmission system. Simultaneously, different electrical layer devices, such as optical transceivers (TPDs), often exhibit different transmission costs under the same gas absorption line conditions due to differences in modulation formats and digital signal processing (DSP) algorithms. This presents a significant challenge to evaluating the system capacity of hollow-core fibers, especially those already deployed in existing networks.

[0034] Gas absorption effect assessment techniques in related technologies show that the gas absorption effect is most pronounced in the long-wavelength (L) band. Figure 1 A schematic diagram illustrating the gas absorption effect in hollow-core optical fiber is shown. Figure 1 As shown, the horizontal axis represents frequency in terahertz (THz), and the vertical axis represents the loss of 100 km of hollow-core fiber (HCF) in decibels (dB). The curve extending along the horizontal axis shows the overall trend of the loss of hollow-core fiber with frequency, while the vertical line extending along the vertical axis vividly represents the distribution of discrete absorption lines caused by residual gas in the U-band, L-band, and conventional (C) band. It can be seen that the absorption lines are particularly dense and have high intensity in the L-band.

[0035] Gas absorption is a frequency-dependent physical phenomenon. The full width at half maximum (FWHM) of each absorption line is approximately 1 GHz, and the frequency spacing between adjacent absorption lines ranges from approximately 30 GHz to 60 GHz. For coherent transmission systems, a typical high-speed optical transceiver occupies a spectral width of approximately 50 GHz to 200 GHz. Within the gas absorption spectral region, an optical transceiver is generally affected by one or more absorption lines, and the frequency shift of the transceiver's center frequency relative to the absorption line frequency will result in different performance costs. Therefore, a complete performance evaluation of gas absorption spectral lines requires bit error rate-optical signal-to-noise ratio (BER-OSNR) curve measurements at different frequencies with relatively fine step sizes.

[0036] Figure 2 and Figure 3 The simulation and measured results show the optical signal-to-noise ratio loss caused by gas absorption effect in the range of 189.5 to 190.2 terahertz after a section of hollow fiber is transmitted. Figure 2 and Figure 3 In the graph, the horizontal axis represents frequency in terahertz (THz), and the vertical axis represents optical signal-to-noise ratio (OSNR) cost in decibels (dB). Figure 2 and Figure 3 In this model, a discrete point represents a test data point. Each test data point represents a specific performance value obtained from testing at different frequencies. The horizontal axis interval between two adjacent test data points is approximately 0.5 GHz, totaling over 1400 test data points. Figure 2 and Figure 3 It can be seen that the OSNR cost fluctuates significantly with frequency, exhibiting a periodic fluctuation characteristic highly correlated with the absorption spectral distribution. Therefore, a complete GLA performance evaluation requires BER-OSNR curve measurements at different frequency points with relatively fine step sizes. Each measurement is a complex and time-consuming process; thus, it is virtually impossible to perform complete GLA performance testing and evaluation for every hollow fiber in a live network deployment.

[0037] In view of this, the embodiments of this application aim to provide a performance cost prediction method for the GLA effect of hollow fiber (HCF). By obtaining the characteristic parameters of the reference hollow fiber and the hollow fiber under test and establishing their correspondence, the performance cost of the reference is scaled or reconstructed using the principle of linear superposition. The performance cost distribution caused by the GLA effect can be accurately calculated without the need for frequent testing of the hollow fiber under test across the entire frequency band, thereby significantly saving testing time and significantly improving the efficiency of performance evaluation after deployment in the current network.

[0038] The technical solution of this application and how it solves the aforementioned technical problems are described in detail below with specific embodiments. The listed specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0039] Figure 4 A flowchart illustrating the performance cost prediction method 400 for hollow-core optical fiber provided in an embodiment of this application is shown. Figure 4 As shown, the method may include steps S401 to S403.

[0040] Step S401: Determine the reference performance cost of the reference hollow fiber to the target transmission system under the gas absorption effect.

[0041] The target transmission system refers to a coherent optical communication link containing a specific type of optical transceiver (TPD). The modulation format, digital signal processing (DSP) algorithm, and spectral response characteristics of the TPD determine the system's sensitivity to gas absorption lines. The reference hollow fiber can be a section of known fiber already deployed in the target transmission system (i.e., the fiber under test, DUT HCF). In this embodiment, the performance cost can be characterized by the optical signal-to-noise ratio (OSNR). The reference performance cost refers to the distribution data of the OSNR cost as a function of frequency caused by the gas absorption effect in the target transmission system of a reference hollow fiber. In one example, when using a prediction method based on a physical model, the reference performance cost can be the performance cost in a single absorption spectral line scenario, such as the performance cost response function in a single absorption spectral line scenario; in another example, when using a prediction method based on actual measurement calibration at a specific frequency, the reference performance cost can also be the overall performance cost distribution in a multi-absorption spectral line scenario.

[0042] Step S402: Obtain the first characteristic parameter and the second characteristic parameter. The first characteristic parameter represents the target performance characterization quantity of the reference hollow fiber, and the second characteristic parameter represents the target performance characterization quantity of the hollow fiber to be tested.

[0043] Among them, the hollow-core optical fiber to be tested refers to another optical fiber whose gas absorption effect performance needs to be evaluated, which is intended to be deployed in or applicable to the same target transmission system mentioned above.

[0044] The target performance characterization quantity is a key physical quantity or performance index used to quantify the intensity of the response of hollow-core optical fibers to gas absorption effects. In one example, when using a prediction method based on a physical model, the target performance characterization quantity is the set of absorption spectral depths, that is, the absorption depth of each gas absorption spectral line (such as carbon dioxide, water vapor, etc.) in the reference hollow-core fiber and the hollow-core fiber under test at a specific frequency. In another example, when using a prediction method based on actual measurement calibration at a specific frequency, the target performance characterization quantity can be the system performance cost at a specific frequency, that is, the OSNR cost obtained by actual measurement of the reference hollow-core fiber and the hollow-core fiber under test at a fixed frequency.

[0045] Step S403: Based on the correspondence between the first characteristic parameter and the second characteristic parameter, adjust the reference performance cost to determine the predicted performance cost of the hollow-core optical fiber under test for the target transmission system under the gas absorption effect.

[0046] In other words, the ratio or mapping relationship between the reference hollow fiber and the hollow fiber under test in terms of target performance characterization quantities can be used to perform mathematical transformations based on the reference performance cost.

[0047] In one example, the target performance characterization quantity is the set of absorption spectral depths. The performance cost response function under the single absorption spectral scenario can be weighted according to the ratio of the absorption depth of the reference hollow fiber and the hollow fiber under test. Based on the principle of linear superposition, the performance cost of each absorption spectral line of the hollow fiber under test is re-accumulated, thereby reconstructing the predicted performance cost of the hollow fiber under test.

[0048] In another example, the target performance characterization quantity can be the performance cost at a specific frequency. The overall performance cost of the reference hollow fiber can be scaled based on the ratio of the performance cost of the reference hollow fiber to that of the hollow fiber under test at a specific frequency to obtain the predicted performance cost of the hollow fiber under test.

[0049] The final predicted performance cost is the complete distribution of the OSNR cost of the hollow fiber under test under the same target transmission system, which is affected by the gas absorption effect, as a function of frequency.

[0050] According to the technical solution of this application embodiment, by constructing a performance evaluation model of gas absorption spectral line effect for a target optical transceiver (TPD), characteristic parameters of a reference hollow fiber and the hollow fiber under test are obtained and their correspondence is established. By utilizing the basic principle that the performance cost of different gas absorption spectral lines to the same target optical transceiver satisfies the linear superposition effect, the complex multi-absorption spectral line coupling problem is simplified into a linear combination of single absorption spectral line responses or a scaling transformation of the overall proportion. Thus, without the need for cumbersome full-band testing of the hollow fiber under test, only a few characteristic parameters are required to accurately calculate the performance cost of the hollow fiber under test under the GLA effect, greatly saving testing time and significantly improving the performance evaluation efficiency after deployment in the current network.

[0051] In one implementation, the reference hollow fiber and the hollow fiber under test are the same target hollow fiber. The reference performance cost and the predicted performance cost are the performance costs corresponding to different absorption spectral lines in the target hollow fiber. That is, the reference hollow fiber and the hollow fiber under test are the same hollow fiber, differing only in the absorption spectral regions of interest. For example, the reference performance cost of the reference hollow fiber represents the absorption peak performance cost of a measured portion of the spectrum in the fiber (i.e., the target hollow fiber), while the predicted performance cost of the hollow fiber under test represents the absorption peak performance cost of other spectral regions in the fiber that have not yet been directly measured but can be predicted using the prediction method of this application.

[0052] In one embodiment, the reference hollow fiber and the hollow fiber under test are different hollow fibers with the same gas absorption effect. That is, although the reference and the hollow fiber under test are physically different (e.g., produced in different batches or with different reel numbers), the types of residual gas within their cores, the center frequency positions of the absorption spectra, and their distribution patterns are consistent; the only difference is the absorption depth of each absorption spectrum due to variations in fiber length or gas concentration. In other words, the performance cost distribution data of a deployed or tested reference hollow fiber can be used to predict the performance cost distribution of a newly laid or evaluated hollow fiber under the gas absorption effect.

[0053] As mentioned above, this application provides two performance cost evaluation methods based on two principles: a prediction method based on a physical model and a prediction method based on actual measurement calibration at a specific frequency. These will be described in detail below.

[0054] In a prediction method based on a physical model, the target performance characterization quantity includes the absorption depth of at least one absorption line, i.e., the set of absorption line depths, which can be denoted as... ,in, A positive integer, representing the first... Absorption spectral lines that contribute to performance costs (such as optical signal-to-noise ratio (OSNR) costs).

[0055] The first characteristic parameter, namely the set of absorption spectral depths of the reference hollow fiber, can be denoted as: ,in, This represents the reference hollow-core fiber. The second characteristic parameter is the set of absorption spectral depths of the hollow-core fiber under test, which can be denoted as... ,in, This indicates the hollow-core optical fiber to be tested. For example, and The results can be obtained by measuring the corresponding optical fibers using a spectral loss tester.

[0056] Further, in step S403, based on the correspondence between the first characteristic parameter and the second characteristic parameter, the reference performance cost is adjusted to determine the predicted performance cost of the hollow fiber under test for the target transmission system under the gas absorption effect. Specifically, this may include: based on the proportional relationship between the first characteristic parameter and the second characteristic parameter, the reference performance cost is proportionally transformed to obtain the predicted performance cost, that is, the performance cost distribution of the hollow fiber under test for the target transmission system.

[0057] In this embodiment, the reference performance cost is the overall performance cost of each absorption line in the reference hollow fiber to the target transmission system, reflecting the actual transmission impairment of the reference hollow fiber in the target transmission system. The first characteristic parameter (i.e., the set of absorption line depths of the reference hollow fiber) ) and the second characteristic parameter (i.e., the set of absorption spectral depths of the hollow fiber under test) The proportional relationship between () and (), i.e., the absorption depth ratio.

[0058] Due to the inherent response characteristics of the target transmission system (including the target optical transceiver), these characteristics remain unchanged under both the reference scenario of the reference hollow fiber and the test scenario of the hollow fiber under test. The performance difference between the reference hollow fiber and the hollow fiber under test mainly stems from the difference in absorption depth of the absorption spectral lines caused by differences in the internal residual gas concentration or fiber length. Therefore, based on the principle of linear superposition of performance costs due to gas absorption spectral line effects, by calculating the ratio of the absorption depth of the hollow fiber under test and the reference hollow fiber on the corresponding absorption spectral lines, a proportionality coefficient for the performance cost can be constructed. Then, by proportionally varying the known reference performance cost of the reference hollow fiber according to this proportionality coefficient, the performance cost distribution of the hollow fiber under test under the same target transmission system can be directly derived, i.e., the predicted performance cost.

[0059] Based on this, the absorption spectral depth set of the hollow fiber under test can be obtained through a simple spectral loss test, thereby quickly adapting to hollow fibers of the same type with arbitrary length or gas concentration. There is no need to repeatedly build an expensive coherent transmission test environment for each new hollow fiber, which greatly reduces the test complexity and cost. The evaluation test that originally required traversing the entire frequency band and was time-consuming has been simplified into a fast spectral loss test and simple mathematical ratio calculation. This allows for the real-time evaluation of the gas absorption effect performance of newly laid hollow fibers at extremely low cost during the network deployment phase, which significantly improves the efficiency of network planning and optimization.

[0060] In another prediction approach based on physical models, the target performance characterization includes the absorption depth of at least one absorption line, i.e., the set of absorption line depths. The first characteristic parameter is the set of absorption spectral depths of the reference hollow fiber. The second characteristic parameter is the set of absorption spectral depths of the hollow fiber under test. The reference performance cost is characterized based on the performance cost response function corresponding to a single absorption spectral line of the reference hollow fiber.

[0061] The performance-cost response function can be written as: This represents the relationship between the performance cost of the target optical transceiver and the corresponding frequency shift at the target absorption depth, where the corresponding frequency shift is the frequency shift of the target optical transceiver relative to the corresponding absorption spectral line. This indicates the center frequency of the target optical transceiver. Indicates the first The center frequency of the absorption spectral lines that contribute to the performance cost, therefore... This indicates that the target optical transceiver is compared to the first... The frequency shift of each absorption spectral line. The target absorption depth refers to the absorption depth of the corresponding absorption spectral line fixed at a specific depth during the current traversal; this specific absorption depth is the target absorption depth and can be configured according to actual needs.

[0062] Furthermore, such as Figure 5 As shown, based on the proportional relationship between the first feature parameter and the second feature parameter, the reference performance cost is proportionally transformed to obtain the predicted performance cost, which may include steps S501 to S503.

[0063] Step S501: Obtain the reference performance cost transformation function corresponding to a single absorption spectral line of the reference hollow fiber.

[0064] The reference performance cost transformation function can be denoted as: This represents the relationship between the performance cost of the target optical transceiver and the absorption depth of the corresponding absorption line of the reference hollow fiber at a fixed frequency (i.e., the first target frequency). Indicates the reference hollow fiber (denoted as ). The set of absorption spectral depths. This indicates the corresponding absorption spectral line involved in the measurement at an absorption depth of [insert depth here]. The transformation function corresponding to the time. Generally, based on simulation results, within a specific absorption depth range, along with It exhibits a linear relationship, that is , This represents the normalization coefficient.

[0065] Step S502: Based on the proportional relationship between the first characteristic parameter and the second characteristic parameter, the reference performance cost transformation function is proportionally transformed to obtain the predicted performance cost transformation function corresponding to the hollow fiber under test.

[0066] The prediction performance cost transformation function can be denoted as: This represents the relationship between the performance cost of the target optical transceiver and the absorption depth of the corresponding absorption line of the hollow-core optical fiber under test at a fixed frequency (i.e., the first target frequency). Indicates the reference hollow fiber (denoted as ). The set of absorption spectral depths. This indicates the corresponding absorption spectral line involved in the measurement at an absorption depth of [insert depth here]. The transformation function corresponding to the time.

[0067] Performance cost distribution of reference hollow fiber This can be expressed as Formula 1: 。

[0068] Performance cost distribution of the hollow-core optical fiber under test This can be expressed as Formula 2: 。

[0069] The performance cost of gas absorption spectral line effects satisfies the principle of linear superposition, including Equation 3: 。

[0070] Therefore, given the known reference performance cost transformation function The first characteristic parameter (i.e., the set of absorption spectral depths of the reference hollow fiber) The second characteristic parameter (i.e., the set of absorption spectral depths of the hollow fiber under test) In the case of ), the reference performance cost transformation function is adjusted according to the ratio between the first characteristic parameter and the second characteristic parameter. By performing a proportional transformation of the corresponding value, the predicted performance cost transformation function can be derived. .

[0071] Step S503: Based on the product of the performance cost response function and the predicted performance cost transformation function, the performance cost of a single absorption spectral line of the hollow fiber under test is obtained, and the predicted performance cost is obtained based on the performance costs corresponding to multiple absorption spectral lines of the hollow fiber under test.

[0072] As shown above, based on formulas 1, 2, and 3, the performance cost distribution of the hollow-core optical fiber under test can be determined. Converted to Formula 4: 。

[0073] Based on this, the overall performance cost (performance cost distribution of multiple absorption lines) caused by the GLA effect is decoupled into the frequency response characteristics of the target optical transceiver for the GLA (i.e., the performance cost response function). ) and the fiber-specific depth response characteristics (i.e., the reference performance cost transformation function) / Predicting performance cost transformation function Two independent components. Since the hardware configuration and digital signal processing algorithm of the target optical transceiver remain constant in both the reference and test scenarios, its inherent frequency response characteristics do not change with the hollow fiber; however, the performance differences between different hollow fibers are only reflected in the different absorption depths of each absorption line, thus leading to a change in the transformation function (reference performance cost transformation function). / Predicting performance cost transformation function The numerical change of the transform function. Therefore, utilizing the principle of linear superposition and the characteristic that the transform function is proportional to the absorption depth within a specific absorption depth range, the prediction method of this application embodiment does not require re-measuring or simulating complex frequency response functions. It only needs to obtain the set of absorption spectral depths of the hollow fiber under test through a simple spectral loss test, and combine it with known reference data to quickly reconstruct the transform function (predictive performance cost transform function) corresponding to the hollow fiber under test through mathematical proportional calculations. ).

[0074] In other words, this prediction method, based on the linear superposition mechanism of gas absorption spectral line effects, achieves high-precision reuse and transfer of physical models. It simplifies the complex process of full-band, multi-parameter modeling for each new optical fiber into a linear scaling calculation of a single variable (absorption depth), greatly reducing computational complexity and significantly improving the evaluation efficiency of hollow optical fibers of different lengths and gas concentrations. This provides efficient and reliable theoretical basis and data support for the deployment of hollow optical fibers in the current network, wavelength planning, and system margin design.

[0075] In one implementation, the performance cost response function is determined by at least one of the following methods: method A, method B, and method C.

[0076] Method A: Construct a test environment to simulate the filtering effect of a single absorption spectral line. In the test environment, set the absorption depth of the corresponding absorption spectral line as the target absorption depth, and measure the bit error rate-optical signal-noise ratio curves at different frequency offsets. Compare each bit error rate-optical signal-noise ratio curve with the same reference curve to obtain the performance-cost response function.

[0077] In other words, Method A is an experimental measurement method. For example, it involves: using specific optical devices (such as programmable optical filters) to simulate the filtering effect of a single absorption spectral line and constructing a test link; adjusting the center frequency of the target optical transceiver to near the simulated absorption spectral line to build a bit error rate-optical signal-to-noise ratio (BER-OSNR) test environment; iteratively adjusting the center frequency of the target optical transceiver to obtain different frequency offsets, and measuring the BER-OSNR curve at each frequency offset; comparing each BER-OSNR curve with a baseline curve in a back-to-back (BTB) transmission scenario to calculate the optical signal-to-noise ratio cost at each frequency offset; and combining the optical signal-to-noise ratio costs corresponding to each frequency offset to obtain the performance cost response function characterizing the inherent characteristics of the target optical transceiver. .

[0078] Method B: The performance cost of the target optical transceiver under different frequency offsets is calculated by a simulation system to obtain the performance cost response function. The simulation system is used to input the transmit response characteristics and receive response characteristics of the target optical transceiver, and the absorption depth of the corresponding absorption spectral lines is set as the target absorption depth.

[0079] In other words, Method B is a simulation approach. For example: A numerical simulation system containing a target optical transceiver model is established. The transmitter response function (e.g., modulator bandwidth, chirp characteristics) and receiver response function (e.g., photodetector response, electrical filter shape) of the target optical transceiver, along with digital signal processing (DSP) algorithm parameters, are input into the simulation system. A filtering effect model simulating a single absorption line is loaded into the simulation system, and the absorption depth of this simulated absorption line is fixed to the target absorption depth. The center frequency of the virtual target optical transceiver in the simulation system is controlled to scan relative to the center frequency of the simulated absorption line to cover different frequency offsets. The bit error rate performance under each frequency offset is simulated and calculated, and compared with the baseline performance under ideal channel conditions to obtain the optical signal-to-noise ratio cost. The simulation results under all frequency offsets are then summarized to obtain the performance cost response function. .

[0080] Method C: Derive the performance cost response function based on the performance cost measurements of the target optical transceiver at multiple frequency offsets and the first characteristic parameter.

[0081] In other words, method C is an indirect calculation method, which is based on the overall optical signal-to-noise ratio cost measurement results under the combined effect of multiple absorption spectral lines, combined with the known first characteristic parameter (referring to the absorption spectral depth set of hollow fiber), and decouples the performance cost response curve under a single absorption spectral line through a mathematical inversion algorithm.

[0082] For example: connect the target optical transceiver to a given section of reference hollow fiber (i.e., DUT HCF), and adjust the center frequency of the target optical transceiver. In regions where gas absorption spectral line effects are significant, a BER-OSNR testing environment was set up and the overall optical signal-to-noise ratio cost was measured. .

[0083] At different center frequencies At that point, the overall optical signal-to-noise ratio cost measured This can be expressed as Formula 1: 。

[0084] The absorption depth of each absorption line can be independently measured using a spectral loss meter. As mentioned earlier, within a specific absorption depth range, along with It exhibits a linear relationship, that is Therefore, Formula 1 can be simplified to Formula 5: 。

[0085] For example, since the unknowns in this equation are To avoid random errors and improve solution accuracy, the center frequencies of the target optical transceiver can be traversed to obtain multiple sets of solutions at different center frequencies. Based on the measurement data, a system of equations is constructed, and then the normalization coefficients are calculated by back-calculating using the Least Mean Square Error algorithm. and performance cost response function The fitted curve.

[0086] In one implementation, the reference performance cost transformation function is determined in a manner that includes at least one of the following methods: method D, method E, and method F.

[0087] Method D: Construct a test environment to simulate the filtering effect of a single absorption spectral line. In the test environment, set the center frequency of the target optical transceiver to the first target frequency, measure the bit error rate-optical signal-noise ratio curves at different absorption depths, and compare each bit error rate-optical signal-noise ratio curve with the same reference curve to obtain the reference performance cost transformation function.

[0088] In other words, Method D is an experimental measurement method. For example, it involves: using specific optical devices to simulate the filtering effect of a single absorption spectral line and constructing a test link; fixing the center frequency of the target optical transceiver to the first target frequency corresponding to the simulated absorption spectral line to build a bit error rate-optical signal-to-noise ratio (BER-OSNR) test environment; traversing and setting different absorption depths of gas absorption spectral lines by changing the attenuation of the simulated filter or replacing fiber samples of different lengths / gas concentrations; measuring the BER-OSNR curves at each absorption depth and comparing each curve with the baseline curve in a back-to-back (BTB) transmission scenario to calculate the optical signal-to-noise ratio cost corresponding to each absorption depth; combining the optical signal-to-noise ratio costs corresponding to each absorption depth to obtain the reference performance cost transformation function characterizing the target optical transceiver's response characteristics to the absorption depth at that frequency. .

[0089] Method E: The performance cost of the target optical transceiver at different absorption depths is calculated by a simulation system to obtain a reference performance cost transformation function. The simulation system is used to input the transmit response characteristics and receive response characteristics of the target optical transceiver, and the center frequency of the target optical transceiver is set as the first target frequency.

[0090] In other words, Method E is a simulation approach. For example, it involves establishing a numerical simulation system containing a target optical transceiver model, inputting the transmitter response function (e.g., modulator bandwidth, chirp characteristics) and receiver response function (e.g., photodetector response, electrical filter shape) of the target optical transceiver, as well as digital signal processing (DSP) algorithm parameters into the simulation system; loading a filtering effect model simulating a single absorption line into the simulation system, and fixing the center frequency of the virtual target optical transceiver to the first target frequency; controlling the absorption depth of the gas absorption line in the simulation model to vary within a certain range, simulating and calculating the bit error rate performance at each absorption depth, and comparing it with the baseline performance under ideal channel conditions to obtain the optical signal-to-noise ratio cost; summarizing the simulation results at all absorption depths to obtain the reference performance cost transformation function. .

[0091] Method F: Based on the performance cost measurements of the target optical transceiver at at least one frequency offset and the first characteristic parameter, derive the reference performance cost transformation function.

[0092] In other words, method F is an indirect calculation method. It uses the overall optical signal-to-noise ratio cost measured in the actual transmission link, combined with known first characteristic parameters (referencing the absorption spectral depth set of hollow-core optical fibers), to decouple the performance cost transformation function under a single absorption spectral line through a mathematical inversion algorithm. For example, referring to the aforementioned method C, based on one or more actual measurements... The normalization coefficient can then be calculated by reverse deduction. ; and then use the formula The reference performance cost transformation function at any absorption depth is calculated, avoiding tedious depth traversal testing. Only a small number of test points are needed to quickly calibrate the reference performance cost transformation function.

[0093] Understandably, methods A and D are experimental measurement methods that can obtain high-precision response data based on real physical environments, effectively reflecting the non-ideal characteristics and hardware damage of actual devices, and ensuring the physical authenticity of model parameters. Methods B and E are simulation methods that do not require building complex physical test links, can flexibly configure arbitrary absorption depth and frequency shift scenarios, significantly reducing test costs and improving parameter acquisition efficiency. Methods C and F are indirect calculation methods based on a finite number of measurements and back-calculation. They only need to use existing network or single-point measured data combined with known spectral characteristics to decouple the key model functions, avoiding full-band scanning or deep traversal testing, greatly simplifying the evaluation process and improving the adaptability of existing network deployment.

[0094] Figure 6 This diagram illustrates an application example of the performance cost prediction method for a single absorption spectral line scenario provided in this application. For example... Figure 6As shown in the example application, the performance cost prediction method includes steps S601 to S607.

[0095] Step S601: Simulate the filtering effect of a single absorption spectral line based on experimental measurement or simulation.

[0096] Step S602: Set up the BER-OSNR test environment, adjust the center frequency of the target optical transceiver near the corresponding absorption spectral line, and measure the BER-OSNR curve.

[0097] Step S603: At the target absorption depth, traverse different frequency shifts to obtain the performance cost of the target optical transceiver at the corresponding absorption spectral line, i.e., the performance cost response function, denoted as... .

[0098] Step S604: At the first target frequency, traverse different absorption depths, and determine the performance cost of the target optical transceiver at the corresponding absorption spectral line, i.e., the reference performance cost transformation function, denoted as... .

[0099] Step S605: Measure the first characteristic parameter (the set of absorption line depths of the reference hollow fiber) and the second characteristic parameter (the set of absorption line depths of the hollow fiber under test) using a spectral loss tester, and label them as follows: and .

[0100] Step S606: Based on the ratio between the first feature parameter and the second feature parameter, perform a proportional transformation on the reference performance cost transformation function to deduce the predicted performance cost transformation function, denoted as... .

[0101] Step S607: Based on the product of the performance cost response function and the predicted performance cost transformation function, the performance cost of a single absorption line of the hollow fiber under test is obtained. Then, based on the performance costs corresponding to multiple absorption lines of the hollow fiber under test, the predicted performance cost is obtained, i.e., the performance cost distribution of multiple absorption lines of the hollow fiber under test, expressed as: .

[0102] Figure 7 This diagram illustrates an application example of the performance cost prediction method for multiple absorption spectral lines provided in this application. For example... Figure 7 As shown in the example application, the performance cost prediction method includes steps S701 to S709.

[0103] Step S701: Simulate the filtering effect of a single absorption spectral line based on experimental measurement or simulation, build a BER-OSNR test environment, adjust the center frequency of the target optical transceiver near the corresponding absorption spectral line, and measure the BER-OSNR curve.

[0104] Step S702: Obtain the performance cost measurement values ​​of the target optical transceiver at multiple frequency offsets, as well as the first characteristic parameter, and calculate the normalization coefficient based on the minimum mean square error.

[0105] Step S703: Calculate the performance-cost response function of the target optical transceiver under a single absorption spectral line, denoted as... .

[0106] Step S704: Calculate the reference performance cost transformation function of the target optical transceiver in the reference hollow fiber scenario, denoted as... .

[0107] Among them, steps S702 to S704 are, according to formula 5: Derivation of normalization coefficients Performance-cost response function and .

[0108] Step S705: Measure the first characteristic parameter (the set of absorption line depths of the reference hollow fiber) and the second characteristic parameter (the set of absorption line depths of the hollow fiber under test) using a spectral loss tester, and label them as follows: and .

[0109] Step S706: Based on the ratio between the first feature parameter and the second feature parameter, perform a proportional transformation on the reference performance cost transformation function to deduce the predicted performance cost transformation function, denoted as... .

[0110] Step S707: Based on the product of the performance cost response function and the predicted performance cost transformation function, the performance cost of a single absorption line of the hollow fiber under test is obtained. Then, based on the performance costs corresponding to multiple absorption lines of the hollow fiber under test, the predicted performance cost is obtained, i.e., the performance cost distribution of multiple absorption lines of the hollow fiber under test, expressed as: .

[0111] The following describes a prediction method based on actual measurement calibration at a specific frequency. Specifically, the target performance characterization quantity is the system performance cost at a specific frequency point (i.e., the second target frequency), and the reference performance cost is the overall performance cost distribution of multiple absorption spectral lines of the reference hollow fiber.

[0112] In other words, this method no longer relies on the deep decomposition and reconstruction of a single absorption spectral line. Instead, it directly uses the performance cost curve of the complete gas absorption spectral line effect obtained by the reference hollow fiber in the target transmission system as a benchmark. The performance cost distribution of the hollow fiber under test is calibrated and calculated by the ratio of the single measured performance cost of the hollow fiber under test at a specific frequency point to the performance cost value of the reference hollow fiber at the same frequency point.

[0113] Specifically, in step S403, based on the correspondence between the first characteristic parameter and the second characteristic parameter, the reference performance cost is adjusted to determine the predicted performance cost of the hollow fiber under test for the target transmission system under the gas absorption effect. This may include: calculating the ratio of the second characteristic parameter to the first characteristic parameter to obtain the performance cost scaling factor; using the performance cost scaling factor to scale the overall performance cost of the reference hollow fiber to obtain the predicted performance cost, i.e., the performance cost distribution of the hollow fiber under test.

[0114] Among them, the known reference hollow fiber (denoted as) The overall performance cost of the DUT HCF in the target transmission system is expressed as: The overall performance cost incorporates the combined effects of multiple absorption lines. Assume the reference hollow fiber operates at a specific frequency (i.e., the second target frequency). An optical signal-to-noise ratio (SNR) cost test was conducted at the location, and the performance cost test results were as follows: (i.e., the first characteristic parameter). The hollow-core fiber under test (denoted as...) (i.e., the new HCF), at the same specific frequency point An optical signal-to-noise ratio (SNR) cost test was conducted at the location, and the performance cost test results were as follows: (i.e., the second characteristic parameter). Based on the principle that the gas absorption spectral line effect has an overall scaling characteristic under the same optical transceiver configuration, a performance cost scaling factor K is constructed: .

[0115] Furthermore, the performance cost scaling factor K is applied to the performance cost distribution of the reference hollow fiber. The performance cost distribution of the hollow-core optical fiber under test can then be calculated. This is represented by Formula 6: 。

[0116] Using the above formula, a single-point test of the hollow-core fiber under test is sufficient to map its performance cost at a specific frequency point to the entire frequency band through a linear scaling transformation, thereby quickly reconstructing the complete performance cost distribution of the hollow-core fiber under test under the gas absorption effect. This prediction method greatly simplifies the testing process and reduces costs, reducing the complex bit error rate-optical signal-to-noise ratio curve scanning test that originally required traversing the entire frequency band (e.g., 1400 frequency points) to a rapid field test at a single specific frequency point. Furthermore, this prediction method can be derived directly based on the proportional relationship of single-point measured data when the GLA effect and distribution are similar, exhibiting strong engineering practicality and robustness, and is suitable for rapid performance evaluation of existing fiber optic networks and rapid access verification of new fiber optic networks.

[0117] In one implementation, the target transmission system includes a target optical transceiver, and the reference performance cost (i.e., the overall performance cost of the reference hollow fiber in the target transmission system is expressed as follows) The determination method may include: adjusting the center frequency of the target optical transceiver to multiple different frequencies, and measuring the bit error rate-optical signal-noise ratio curves of the reference hollow fiber at each different frequency; comparing the bit error rate-optical signal-noise ratio curves at each different frequency with the same reference curve to determine the performance cost of the reference hollow fiber at each different frequency, and obtaining the reference performance cost.

[0118] For example, a reference hollow fiber is connected to a test link containing the target optical transceiver. The center frequency of the target optical transceiver is traversed and adjusted within a frequency band where gas absorption spectral effects are significant, using a preset fine frequency step size. At each frequency point, the bit error rate (BER) versus optical signal-to-noise ratio (OSNR) curve is measured by changing the input optical power or adding noise. Simultaneously, under the same test environment, the reference hollow fiber is removed or the link is configured in back-to-back (BTB) mode to obtain a baseline curve under ideal channel conditions. For each frequency point, the difference between the OSNR required for the reference hollow fiber link and the OSNR required for the baseline link at the target BER is calculated, and this difference is recorded as the OSNR cost at that frequency point. By arranging the OSNR costs corresponding to each frequency point in frequency order, a continuous overall performance cost distribution curve can be fitted and generated. .

[0119] This implementation method can provide a benchmark for subsequent scaling calculations, laying a solid data foundation for quickly evaluating the performance of new optical fibers without repeating full-band testing.

[0120] Figure 8 An application example diagram is shown, illustrating a performance cost prediction method based on actual measurement calibration at a specific frequency. (Example:) Figure 8As shown in the example application, the performance cost prediction method may include steps S801 to S805.

[0121] Step S801: Simulate the filtering effect of a single absorption spectral line based on experimental measurement or simulation methods.

[0122] Step S802: Set up the BER-OSNR test environment, adjust the center frequency of the target optical transceiver near the corresponding absorption spectral line, and measure the BER-OSNR curve.

[0123] Step S803: Perform an optical signal-to-noise ratio (SNR) test on the reference hollow fiber at a specific frequency point (i.e., the second target frequency), and mark the resulting performance cost test result (i.e., the first characteristic parameter) as... .

[0124] Step S804: Perform an optical signal-to-noise ratio (SNR) penalty test at the same specific frequency point on the hollow-core fiber under test (i.e., the new HCF). The performance penalty test result (i.e., the second characteristic parameter) is marked as... .

[0125] Step S805: Calculate the performance cost distribution of the hollow fiber under test based on Formula 6.

[0126] Figure 9 A flowchart illustrating the performance cost prediction method 900 for hollow-core optical fibers provided in an embodiment of this application is shown. Figure 9 As shown, the method may include: Step S901, obtaining the performance cost response function corresponding to a single absorption spectral line of the hollow fiber, the performance cost response function representing the relationship between the performance cost of the target optical transceiver and the corresponding frequency offset at the target absorption depth, the corresponding frequency offset being the frequency offset of the target optical transceiver relative to the corresponding absorption spectral line; Step S902, obtaining the performance cost transformation function corresponding to a single absorption spectral line of the hollow fiber, the performance cost transformation function representing the relationship between the performance cost of the target optical transceiver and the absorption depth of the corresponding absorption spectral line at the first target frequency; Step S903, obtaining the performance cost of a single absorption spectral line of the hollow fiber based on the product of the performance cost response function and the performance cost transformation function, and obtaining the performance cost of the hollow fiber for the target transmission system based on the performance costs corresponding to multiple absorption spectral lines of the hollow fiber, the target transmission system including the target optical transceiver.

[0127] The hollow fiber can be a reference hollow fiber or a hollow fiber under test. For specific implementation methods and technical effects, please refer to the above text.

[0128] In one implementation, the performance cost response function is determined by at least one of the following methods: constructing a test environment to simulate the filtering effect of a single absorption spectral line; setting the absorption depth of the corresponding absorption spectral line as the target absorption depth in the test environment; measuring the bit error rate-optical signal-to-noise ratio curves at different frequency offsets; and comparing each bit error rate-optical signal-to-noise ratio curve with the same reference curve to obtain the performance cost response function; calculating the performance cost of the target optical transceiver at different frequency offsets using a simulation system to obtain the performance cost response function; the simulation system is used to input the transmit response characteristics and receive response characteristics of the target optical transceiver, and setting the absorption depth of the corresponding absorption spectral line as the target absorption depth; and deriving the performance cost response function based on the measured performance cost values ​​of the target optical transceiver at multiple frequency offsets and the first characteristic parameter. For details, please refer to the previous descriptions of methods A, B, and C.

[0129] In one implementation, the determination of the performance cost transformation function includes at least one of the following: constructing a test environment to simulate the filtering effect of a single absorption spectral line; setting the center frequency of the target optical transceiver to a first target frequency in the test environment; measuring the bit error rate-optical signal-to-noise ratio curves at different absorption depths; and comparing each bit error rate-optical signal-to-noise ratio curve with the same reference curve to obtain the performance cost transformation function; calculating the performance cost of the target optical transceiver at different absorption depths using a simulation system to obtain the performance cost transformation function; the simulation system is used to input the transmit response characteristics and receive response characteristics of the target optical transceiver, and setting the center frequency of the target optical transceiver to the first target frequency; setting the center frequency of the target optical transceiver to the first target frequency, and deriving the performance cost transformation function based on at least one performance cost measurement value of the target optical transceiver and a first characteristic parameter. For details, please refer to the previous descriptions of methods D, E, and F.

[0130] Figure 10 A flowchart illustrating the performance cost prediction method 1000 provided in an embodiment of this application is shown. Figure 10 As shown, the method may include: step S1001, determining the hollow fiber corresponding to each transmission channel of the target optical communication link; step S1002, predicting the performance cost of the target optical communication link based on the performance cost of each hollow fiber, wherein: at least one hollow fiber is the hollow fiber to be tested, the performance cost of the hollow fiber to be tested is the predicted performance cost, and the predicted performance cost is determined based on method 400; or, the performance cost of at least one hollow fiber is determined based on method 900.

[0131] In the planning and deployment of optical transmission networks, transmission quality estimation based on optical signal-to-noise ratio (SNR) plays a crucial role. Depending on the availability and richness of actual data for optical transmission networks, the planning and deployment process is typically divided into three phases: preliminary planning, construction, and expansion. Method 1000 can be used to evaluate the performance cost of target optical communication links that are to be planned, planned, to be deployed, or already deployed.

[0132] Corresponding to the application scenario and method 400 provided in the embodiments of this application, the embodiments of this application also provide a performance cost prediction device for hollow optical fiber, including: a reference performance cost determination module, used to determine the reference performance cost of a reference hollow optical fiber to a target transmission system under the gas absorption effect; a feature parameter acquisition module, used to acquire a first feature parameter and a second feature parameter, wherein the first feature parameter represents the target performance characterization quantity of the reference hollow optical fiber, and the second feature parameter represents the target performance characterization quantity of the hollow optical fiber to be tested; and a prediction performance cost determination module, used to adjust the reference performance cost based on the correspondence between the first feature parameter and the second feature parameter, so as to determine the prediction performance cost of the hollow optical fiber to be tested to the target transmission system under the gas absorption effect.

[0133] In one embodiment, the target transmission system includes a target optical transceiver; the target performance characterization quantity includes the absorption depth of at least one absorption spectral line; the predicted performance cost determination module is specifically used to: perform a proportional transformation on the reference performance cost based on the proportional relationship between the first characteristic parameter and the second characteristic parameter to obtain the predicted performance cost.

[0134] In one embodiment, the reference performance cost is characterized based on the performance cost response function corresponding to a single absorption spectral line of the reference hollow fiber. The performance cost response function represents the relationship between the performance cost of the target optical transceiver and the corresponding frequency offset at a target absorption depth, where the corresponding frequency offset is the frequency offset of the target optical transceiver relative to the corresponding absorption spectral line. The predicted performance cost determination module is specifically used for: obtaining a reference performance cost transformation function corresponding to a single absorption spectral line of the reference hollow fiber, where the reference performance cost transformation function represents the relationship between the performance cost of the target optical transceiver and the absorption depth of the corresponding absorption spectral line at a first target frequency; performing a proportional transformation on the reference performance cost transformation function based on the proportional relationship between the first characteristic parameter and the second characteristic parameter to obtain a predicted performance cost transformation function corresponding to the hollow fiber under test; obtaining the performance cost of a single absorption spectral line of the hollow fiber under test based on the product of the performance cost response function and the predicted performance cost transformation function; and obtaining the predicted performance cost based on the performance costs corresponding to multiple absorption spectral lines of the hollow fiber under test.

[0135] In one embodiment, the target performance characterization quantity is the performance cost at the second target frequency, the reference performance cost is the overall performance cost of multiple absorption spectral lines of the reference hollow fiber, and the predicted performance cost determination module is specifically used to: calculate the ratio of the second characteristic parameter to the first characteristic parameter to obtain a performance cost scaling factor; and use the performance cost scaling factor to scale the overall performance cost of the reference hollow fiber to obtain the predicted performance cost.

[0136] In one embodiment, the target transmission system includes a target optical transceiver; the method for determining the reference performance cost includes: adjusting the center frequency of the target optical transceiver to multiple different frequencies, and measuring the bit error rate-optical signal-to-noise ratio curves of the reference hollow fiber at each of the different frequencies; comparing the bit error rate-optical signal-to-noise ratio curves at each of the different frequencies with the same reference curve to determine the performance cost of the reference hollow fiber at each of the different frequencies, and obtaining the reference performance cost, wherein the reference performance cost includes the performance cost of the reference hollow fiber at each of the different frequencies.

[0137] In one embodiment, the reference hollow fiber and the hollow fiber under test are the same target hollow fiber, and the reference performance cost and the predicted performance cost are the performance costs corresponding to different absorption spectral lines in the target hollow fiber; or, the reference hollow fiber and the hollow fiber under test are different hollow fibers with the same gas absorption effect.

[0138] Corresponding to the application scenario and method 900 provided in the embodiments of this application, the embodiments of this application also provide a performance cost prediction device for hollow optical fiber, comprising: a performance cost response function acquisition module, used to acquire the performance cost response function corresponding to a single absorption spectral line of the hollow optical fiber, wherein the performance cost response function represents the relationship between the performance cost of the target optical transceiver and the corresponding frequency offset at a target absorption depth, wherein the corresponding frequency offset is the frequency offset of the target optical transceiver relative to the corresponding absorption spectral line; a performance cost transformation function acquisition module, used to acquire the performance cost transformation function corresponding to a single absorption spectral line of the hollow optical fiber, wherein the performance cost transformation function represents the relationship between the performance cost of the target optical transceiver and the absorption depth of the corresponding absorption spectral line at a first target frequency; and a performance cost determination module, used to obtain the performance cost of a single absorption spectral line of the hollow optical fiber based on the product of the performance cost response function and the performance cost transformation function, and to obtain the performance cost of the hollow optical fiber for a target transmission system based on the performance costs corresponding to multiple absorption spectral lines of the hollow optical fiber, wherein the target transmission system includes the target optical transceiver.

[0139] In one embodiment, the determination of the performance cost transformation function includes at least one of the following: constructing a test environment to simulate the filtering effect of a single absorption spectral line; setting the center frequency of the target optical transceiver to the first target frequency in the test environment; measuring the bit error rate-optical signal-to-noise ratio curves at different absorption depths; and comparing each bit error rate-optical signal-to-noise ratio curve with the same reference curve to obtain the performance cost transformation function; calculating the performance cost of the target optical transceiver at different absorption depths using a simulation system to obtain the performance cost transformation function; wherein the simulation system is used to input the transmit response characteristics and receive response characteristics of the target optical transceiver, and setting the center frequency of the target optical transceiver to the first target frequency; and deriving the performance cost transformation function based on the performance cost measurements of the target optical transceiver at at least one frequency offset and the first characteristic parameter.

[0140] Corresponding to the application scenario and method 1000 provided in the embodiments of this application, the embodiments of this application also provide a performance cost prediction device, including: a hollow fiber determination module, used to determine the hollow fiber corresponding to each transmission channel of the target optical communication link; and a performance cost prediction module, used to predict the performance cost of the target optical communication link based on the performance cost of each hollow fiber, wherein: at least one of the hollow fibers is a hollow fiber to be tested, the performance cost of the hollow fiber to be tested is the predicted performance cost, and the predicted performance cost is determined based on method 400; or, the performance cost of at least one hollow fiber is determined based on method 900.

[0141] The functions of each module in each device in the embodiments of this application can be found in the corresponding description in the above method, and they have corresponding beneficial effects, which will not be repeated here.

[0142] Figure 11 This is a block diagram of an electronic device used to implement embodiments of this application. For example... Figure 11 As shown, the electronic device includes a memory 1101 and a processor 1102. The memory 1101 stores a computer program that can run on the processor 1102. When the processor 1102 executes the computer program, it implements the method described in the above embodiments. The number of memories 1101 and processors 1102 can be one or more. In a specific implementation, the electronic device may also include a communication interface 1103 for communicating with external devices and performing data exchange and transmission.

[0143] In practical implementation, if the memory 1101, processor 1102, and communication interface 1103 are implemented independently, they can be interconnected via a bus to communicate with each other. This bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. This bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 11 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0144] Optionally, in a specific implementation, if the memory 1101, processor 1102, and communication interface 1103 are integrated on a single chip, then the memory 1101, processor 1102, and communication interface 1103 can communicate with each other through an internal interface.

[0145] This application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method provided in this application.

[0146] This application provides a computer program product, including a computer program that, when executed by a processor, implements the method provided in this application.

[0147] This application also provides a chip including a processor for calling and executing instructions stored in a memory, causing a communication device with the chip installed to perform the method provided in this application.

[0148] This application also provides a chip, including: an input interface, an output interface, a processor, and a memory. The input interface, output interface, processor, and memory are connected through an internal connection path. The processor is used to execute code in the memory. When the code is executed, the processor is used to execute the method provided in the application embodiment.

[0149] It should be understood that the aforementioned processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. General-purpose processors can be microprocessors or any conventional processor. It is worth noting that the processor can be a processor supporting Advanced Reduced Instruction Set Machines (ARM) architecture.

[0150] Further, optionally, the aforementioned memory may include read-only memory and random access memory. The memory may be volatile memory or non-volatile memory, or may include both. Non-volatile memory may include read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory may include random access memory (RAM), which serves as an external cache. By way of example, but not limitation, many forms of RAM are available. Examples include Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDR SDRAM), Enhanced Synchronous DRAM (ESDRAM), Sync Link DRAM (SLDRAM), and Direct Rambus RAM (DR RAM).

[0151] It should be noted that the application scenarios or examples provided in the embodiments of this application are for ease of understanding, and the embodiments of this application do not specifically limit the application of the technical solutions. In addition, all information and data involved in this application are information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of related data must comply with the relevant laws, regulations and standards of relevant countries and regions, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0152] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions according to this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another.

[0153] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of those different embodiments or examples.

[0154] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "a plurality of" means two or more, unless otherwise explicitly specified.

[0155] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process. Furthermore, the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functionality involved.

[0156] The logic and / or steps described in the flowchart or otherwise herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus or device (such as a computer-based system, a processor-included system or other system that can fetch and execute instructions from, an instruction execution system, apparatus or device).

[0157] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. All or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware, the program being stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiments.

[0158] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. This storage medium can be a read-only memory, a disk, or an optical disk, etc.

[0159] The above description is merely an exemplary embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various variations or substitutions within the technical scope described in this application, and these should all be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for predicting the performance cost of hollow-core optical fiber, comprising: Determine the reference performance cost of the reference hollow fiber to the target transmission system under the gas absorption effect; Obtain a first characteristic parameter and a second characteristic parameter, wherein the first characteristic parameter represents the target performance characterization quantity of the reference hollow fiber, and the second characteristic parameter represents the target performance characterization quantity of the hollow fiber to be tested. Based on the correspondence between the first characteristic parameter and the second characteristic parameter, the reference performance cost is adjusted to determine the predicted performance cost of the hollow-core optical fiber under test for the target transmission system under the gas absorption effect.

2. The method according to claim 1, wherein, The target transmission system includes a target optical transceiver; the target performance characterization quantity includes the absorption depth of at least one absorption line; adjusting the reference performance cost based on the correspondence between the first characteristic parameter and the second characteristic parameter to determine the predicted performance cost of the hollow-core fiber under test for the target transmission system under gas absorption effect includes: Based on the proportional relationship between the first feature parameter and the second feature parameter, the reference performance cost is proportionally transformed to obtain the predicted performance cost.

3. The method according to claim 2, wherein, The reference performance cost is characterized based on the performance cost response function corresponding to a single absorption spectral line of the reference hollow fiber. The performance cost response function represents the relationship between the performance cost of the target optical transceiver and the corresponding frequency shift at the target absorption depth. The corresponding frequency shift is the frequency shift of the target optical transceiver relative to the corresponding absorption spectral line. The step of scaling the reference performance cost based on the proportional relationship between the first feature parameter and the second feature parameter to obtain the predicted performance cost includes: Obtain the reference performance cost transformation function corresponding to a single absorption spectral line of the reference hollow fiber. The reference performance cost transformation function represents the relationship between the performance cost of the target optical transceiver and the absorption depth of the corresponding absorption spectral line at the first target frequency. Based on the proportional relationship between the first feature parameter and the second feature parameter, the reference performance cost transformation function is proportionally transformed to obtain the predicted performance cost transformation function corresponding to the hollow fiber under test. The performance cost of a single absorption line of the hollow fiber under test is obtained by multiplying the performance cost response function and the predicted performance cost transformation function. The predicted performance cost is obtained based on the performance costs corresponding to multiple absorption lines of the hollow fiber under test.

4. The method according to claim 3, wherein, The method for determining the performance cost response function includes at least one of the following: A test environment is constructed to simulate the filtering effect of a single absorption spectral line. In the test environment, the absorption depth of the corresponding absorption spectral line is set as the target absorption depth, and the bit error rate-optical signal-noise ratio curves at different frequency offsets are measured. Each bit error rate-optical signal-noise ratio curve is compared with the same reference curve to obtain the performance cost response function. The performance cost of the target optical transceiver under different frequency offsets is calculated by the simulation system to obtain the performance cost response function. The simulation system is used to input the transmit response characteristics and receive response characteristics of the target optical transceiver, and the absorption depth of the corresponding absorption spectral line is set as the target absorption depth. Based on the performance cost measurements of the target optical transceiver at multiple frequency offsets and the first characteristic parameter, the performance cost response function is derived.

5. The method according to claim 3, wherein, The method for determining the reference performance cost transformation function includes at least one of the following: A test environment is constructed to simulate the filtering effect of a single absorption spectral line. In the test environment, the center frequency of the target optical transceiver is set to the first target frequency, and the bit error rate-optical signal-noise ratio curves at different absorption depths are measured. Each bit error rate-optical signal-noise ratio curve is compared with the same reference curve to obtain the reference performance cost transformation function. The simulation system calculates the performance cost of the target optical transceiver at different absorption depths to obtain the reference performance cost transformation function. The simulation system is used to input the transmit response characteristics and receive response characteristics of the target optical transceiver, and the center frequency of the target optical transceiver is set to the first target frequency. Based on the performance cost measurements of the target optical transceiver at multiple frequency offsets and the first feature parameter, the reference performance cost transformation function is derived.

6. The method according to claim 1, wherein, The target performance characterization quantity is the performance cost at the second target frequency, and the reference performance cost is the overall performance cost of multiple absorption spectral lines of the reference hollow fiber. Adjusting the reference performance cost based on the correspondence between the first characteristic parameter and the second characteristic parameter to determine the predicted performance cost of the hollow fiber under test for the target transmission system under gas absorption effect includes: Calculate the ratio of the second feature parameter to the first feature parameter to obtain the performance cost scaling factor; The overall performance cost of the reference hollow fiber is scaled using the performance cost scaling factor to obtain the predicted performance cost.

7. The method according to claim 6, wherein, The target transmission system includes a target optical transceiver; The methods for determining the reference performance cost include: The center frequency of the target optical transceiver is adjusted to multiple different frequencies, and the bit error rate-optical signal-noise ratio curves of the reference hollow fiber are measured at each of the different frequencies. The bit error rate-optical signal-to-noise ratio curves at each of the different frequencies are compared with the same reference curve to determine the performance cost of the reference hollow fiber at each of the different frequencies, and the reference performance cost is obtained, which includes the performance cost of the reference hollow fiber at each of the different frequencies.

8. The method according to any one of claims 1 to 7, wherein, The reference hollow fiber and the hollow fiber under test are the same target hollow fiber, and the reference performance cost and the predicted performance cost are the performance costs corresponding to different absorption spectral lines in the target hollow fiber; or, the reference hollow fiber and the hollow fiber under test are different hollow fibers with the same gas absorption effect.

9. A method for predicting the performance cost of hollow-core optical fiber, comprising: Obtain the performance cost response function corresponding to a single absorption spectral line of the hollow-core optical fiber. The performance cost response function represents the relationship between the performance cost of the target optical transceiver and the corresponding frequency shift at the target absorption depth. The corresponding frequency shift is the frequency shift of the target optical transceiver relative to the corresponding absorption spectral line. Obtain the performance cost transformation function corresponding to a single absorption spectral line of the hollow optical fiber. The performance cost transformation function represents the relationship between the performance cost of the target optical transceiver and the absorption depth of the corresponding absorption spectral line at the first target frequency. The performance cost of a single absorption line of the hollow fiber is obtained by multiplying the performance cost response function and the performance cost transformation function. Based on the performance costs corresponding to multiple absorption lines of the hollow fiber, the performance cost of the hollow fiber for the target transmission system is obtained, whereby the target transmission system includes the target optical transceiver.

10. The method according to claim 9, wherein, The method for determining the performance cost response function includes at least one of the following: A test environment is constructed to simulate the filtering effect of a single absorption spectral line. In the test environment, the absorption depth of the corresponding absorption spectral line is set as the target absorption depth, and the bit error rate-optical signal-noise ratio curves at different frequency offsets are measured. Each bit error rate-optical signal-noise ratio curve is compared with the same reference curve to obtain the performance cost response function. The performance cost of the target optical transceiver under different frequency offsets is calculated by the simulation system to obtain the performance cost response function. The simulation system is used to input the transmit response characteristics and receive response characteristics of the target optical transceiver, and the absorption depth of the corresponding absorption spectral line is set as the target absorption depth. Based on the performance cost measurements of the target optical transceiver at multiple frequency offsets and the first characteristic parameter, the performance cost response function is derived.

11. The method according to claim 9, wherein, The method for determining the performance cost transformation function includes at least one of the following: A test environment is constructed to simulate the filtering effect of a single absorption spectral line. In the test environment, the center frequency of the target optical transceiver is set to the first target frequency, and the bit error rate-optical signal-noise ratio curves at different absorption depths are measured. Each bit error rate-optical signal-noise ratio curve is compared with the same reference curve to obtain the performance cost transformation function. The performance cost of the target optical transceiver at different absorption depths is calculated by a simulation system to obtain the performance cost transformation function. The simulation system is used to input the transmit response characteristics and receive response characteristics of the target optical transceiver, and the center frequency of the target optical transceiver is set to the first target frequency. Based on the performance cost measurements of the target optical transceiver at at least one frequency offset, and the first characteristic parameter, the performance cost transformation function is derived.

12. A performance cost prediction method, comprising: Determine the hollow fiber corresponding to each transmission channel of the target optical communication link; Based on the performance cost of each of the hollow optical fibers, the performance cost of the target optical communication link is predicted, wherein: at least one of the hollow optical fibers is a hollow optical fiber under test, the performance cost of the hollow optical fiber under test is a predicted performance cost, and the predicted performance cost is determined based on the method of any one of claims 1 to 8; or, the performance cost of at least one of the hollow optical fibers is determined based on the method of any one of claims 9 to 11.

13. An electronic device comprising a memory, a processor, and a computer program stored in the memory, wherein the processor, when executing the computer program, implements the method of any one of claims 1 to 12.

14. A computer-readable storage medium storing a computer program that, when executed by a processor, implements the method of any one of claims 1 to 12.

15. A computer program product comprising a computer program that, when executed by a processor, implements the method according to any one of claims 1 to 12.

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