Performance analysis method, apparatus, terminal equipment, and storage medium based on hard-isolated passive optical fiber networks

Abstract

This invention discloses a performance analysis method, apparatus, terminal device, and storage medium for hard-isolated passive optical fiber (POB) networks. The method constructs a simulation model characterizing the propagation of optical signals in each channel of the POB network based on the group velocity dispersion parameters, preset attenuation coefficients, and preset nonlinear coefficients. The simulation model is then solved to obtain an approximate analytical solution characterizing the temporal and spatial variations of the optical signal in each channel. Based on this approximate analytical solution, the bit error rate (BER) and achievable information rate (RI) of each channel in the POB network are calculated. Finally, the performance analysis results of the POB network are generated based on the BER and RI. Compared to existing laboratory testing methods, this invention achieves the goal of low-cost and efficient performance analysis of hard-isolated POB networks.

Description

Technical Field

[0001] This invention relates to the field of communication technology, and in particular to a performance analysis method, apparatus, terminal equipment, and storage medium based on hard-isolated passive optical fiber networks. Background Technology

[0002] GPON (Gigabit Passive Optical Network) is a network architecture based on optical fiber communication technology, used to provide high-speed data transmission, video, and voice services. It transmits data to end users via optical fiber, offering advantages such as high bandwidth, low latency, and long transmission distances. Hard isolation technology is a technique in optical communication systems that physically isolates different wavelength channels to prevent interference and crosstalk, thereby mitigating nonlinear effects. This method is particularly important in environments with high data rates and extended transmission distances, as nonlinear effects such as cross-phase modulation (XPM) and four-wave mixing (FWM) are more pronounced. By applying hard isolation technology in GPON, the interaction between different wavelength channels can be minimized, effectively reducing the overall nonlinear effects experienced by each channel. This isolation helps maintain the integrity of the optical signal and preserve high data transmission quality.

[0003] Performance testing of passive fiber optic networks employing hard isolation technology typically utilizes laboratory testing methods. These methods simulate real-world network conditions in a controlled environment to conduct various performance tests on the passive fiber optic network. This approach allows for precise control of test conditions, including optical power, wavelength, fiber type, and the intensity of nonlinear effects, thereby accurately measuring and evaluating network performance indicators. However, this method requires a dedicated test platform and specialized testing equipment, resulting in relatively high costs and a lengthy testing cycle. Summary of the Invention

[0004] This invention provides a performance analysis method, apparatus, terminal device, and storage medium based on hard-isolated passive optical fiber networks. By constructing a simulation model of optical signal propagation in each channel of the passive optical fiber network, the goal of low-cost and efficient performance analysis of hard-isolated passive optical fiber networks can be achieved.

[0005] An embodiment of the present invention provides a performance analysis method for hard-isolated passive optical fiber networks, comprising:

[0006] Obtain the group velocity dispersion parameters, preset attenuation coefficient, and preset nonlinear coefficient of each isolated channel in the passive optical fiber network to be analyzed.

[0007] An optical signal is input into each of the channels, and the propagation process of the optical signal in each channel is modeled based on the group velocity dispersion parameter, attenuation coefficient, and nonlinear coefficient of the channel, so as to obtain a simulation model of each channel.

[0008] The optical signal is converted into a power series based on the nonlinear coefficients, and the simulation model is solved to obtain an approximate analytical solution for each simulation model; wherein, the approximate analytical solution is a function characterizing the change of the optical signal in the channel with time and space;

[0009] Based on the approximate analytical solution, calculate the bit error rate and achievable information rate of each channel in the passive optical fiber network;

[0010] Based on the bit error rate and the achievable information rate, the performance analysis results of the passive optical fiber network are generated.

[0011] Furthermore, based on the group velocity dispersion parameters, attenuation coefficients, and nonlinear coefficients of the channels, the simulation process of optical signal propagation in each channel is established, and a simulation model for each channel is constructed, including:

[0012] Using the nonlinear Schrödinger equation, and based on the group velocity dispersion parameter, attenuation coefficient, and nonlinear coefficient of the channels, a simulation model is constructed to characterize the propagation of optical signals in each channel:

[0013]

[0014] Among them, A i (t,z) represents the simulated optical signal in the i-th channel, where t is the time delay frame of the optical signal, z is the propagation distance of the optical signal, and β... 2,i Let γ be the group velocity dispersion parameter of the i-th channel. i Let α be the nonlinear coefficient of the i-th channel. i Let be the attenuation coefficient of the i-th channel, and j be the imaginary unit.

[0015] Furthermore, the process of converting the optical signal into a power series based on the nonlinear coefficients and solving the simulation model to obtain approximate analytical solutions for each simulation model includes:

[0016] The optical signal is converted into a power series based on the nonlinear coefficients:

[0017]

[0018] Where k is the number of terms in the series summation.

[0019] Based on the first two terms of the power series, approximate analytical solutions for each simulation model are obtained:

[0020]

[0021] in, This is the approximate analytical solution output by the simulation model for the i-th channel. The first term of the power series is... This is the second term of the power series.

[0022] Furthermore, before calculating the bit error rate and achievable information rate of each channel in the passive optical fiber network based on the approximate analytical solution, the method further includes:

[0023] The normalized variance of each channel is calculated using the following formula:

[0024]

[0025] Among them, NSD i Let A′ be the normalized variance of the i-th channel. i (t,z) represents the actual measurement result that characterizes the change of the optical signal in the i-th channel with time and space.

[0026] Based on the normalized variance of each of the channels, determine whether each of the simulation models is accurate;

[0027] Once the accuracy of the simulation models is confirmed, the bit error rate and achievable information rate of each channel in the passive optical fiber network are calculated based on the approximate analytical solution.

[0028] Furthermore, based on the approximate analytical solution, the bit error rate of each channel in the passive optical fiber network is calculated, including:

[0029] Obtain the preset noise residual coefficient, preset noise spectral density, and bandwidth of each channel;

[0030] The signal-to-noise ratio of each channel is calculated based on the approximate analytical solution, the noise residual coefficient, the noise spectral density, and the bandwidth.

[0031] Based on the preset M-QAM modulation scheme, the signal-to-noise ratio per bit is calculated according to the signal-to-noise ratio.

[0032] Based on the signal-to-noise ratio per bit, the bit error rate of each channel is calculated using the following formula:

[0033]

[0034] Among them, BER i,M-QAM Let M be the bit error rate of the i-th channel, M be the modulation coefficient of the M-QAM modulation scheme, and SNR be the bit error rate. b The signal-to-noise ratio per bit.

[0035] Furthermore, the step of calculating the signal-to-noise ratio of each channel based on the approximate analytical solution, the noise residual coefficient, the noise spectral density, and the bandwidth includes:

[0036] The signal-to-noise ratio (SNR) of each channel is calculated using the following formula, based on the approximate analytical solution, the noise residual coefficient, the noise spectral density, and the bandwidth:

[0037]

[0038] Among them, SNR i Let n be the signal-to-noise ratio of the i-th channel, n0 be the noise spectral density, T be the preset observation period length, and B be the signal-to-noise ratio of the i-th channel. i Let α be the bandwidth of the i-th channel, and α be the noise spectral density.

[0039] Furthermore, calculating the achievable information rate of each channel in the passive optical fiber network based on the approximate analytical solution includes:

[0040] Based on the bandwidth and signal-to-noise ratio of each channel, the achievable information rate of each channel is calculated using the following formula:

[0041] C i =B i log2(1+SNR i );

[0042] Among them, C i Let B be the achievable information rate of the i-th channel. i Let SNR be the bandwidth of the i-th channel. i Let be the signal-to-noise ratio of the i-th channel.

[0043] Another embodiment of the present invention provides a performance analysis device based on a hard-isolated passive optical fiber network, comprising:

[0044] The data acquisition module includes: acquiring the group velocity dispersion parameters, preset attenuation coefficients, and preset nonlinear coefficients of each isolated channel in the passive optical fiber network to be analyzed.

[0045] The model building module is used to input optical signals into each of the channels and model the propagation process of optical signals in each channel based on the group velocity dispersion parameters, attenuation coefficients, and nonlinear coefficients of the channels, so as to obtain the simulation model of each channel.

[0046] The model solving module is used to convert the optical signal into a power series based on the nonlinear coefficients and solve the simulation model to obtain an approximate analytical solution for each simulation model; wherein, the approximate analytical solution is a function characterizing the change of the optical signal in the channel with time and space;

[0047] The data calculation module is used to calculate the bit error rate and achievable information rate of each channel in the passive optical fiber network based on the approximate analytical solution.

[0048] The performance analysis module is used to generate performance analysis results for the passive optical fiber network based on the bit error rate and the achievable information rate.

[0049] Another embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a performance analysis method based on a hard-isolated passive optical fiber network as described in any of the embodiments.

[0050] Another embodiment of the present invention provides a storage medium, characterized in that the storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device where the storage medium is located to execute a performance analysis method based on a hard-isolated passive optical fiber network as described in any of the above embodiments.

[0051] The following benefits can be obtained by implementing the present invention:

[0052] This invention discloses a performance analysis method, apparatus, terminal device, and storage medium for hard-isolated passive optical fiber (POB) networks. The method constructs a simulation model characterizing the propagation of optical signals in each channel of the POB network based on the group velocity dispersion parameters, preset attenuation coefficients, and preset nonlinear coefficients. The simulation model is then solved to obtain an approximate analytical solution characterizing the temporal and spatial variations of the optical signal in each channel. Based on this approximate analytical solution, the bit error rate (BER) and achievable information rate (RI) of each channel in the POB network are calculated. Finally, the performance analysis results of the POB network are generated based on the BER and RI. Compared to existing laboratory testing methods, this invention achieves the goal of low-cost and efficient performance analysis of hard-isolated POB networks. Attached Figure Description

[0053] Figure 1 This is a flowchart illustrating a performance analysis method for a hard-isolated passive optical fiber network according to an embodiment of the present invention.

[0054] Figure 2 This is a schematic diagram of the structure of a performance analysis device based on a hard-isolated passive optical fiber network provided in an embodiment of the present invention.

[0055] Figure 3 This is a schematic diagram illustrating the relationship between NSD and input optical signal power provided by an embodiment of the present invention.

[0056] Figure 4 This is a schematic diagram comparing the bit error rates of hard-isolated GPON under different modeling methods provided in an embodiment of the present invention.

[0057] Figure 5This is a schematic diagram comparing the AIR of hard-isolated GPON under different modeling methods provided in an embodiment of the present invention. Detailed Implementation

[0058] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0059] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned figure descriptions are intended to cover non-exclusive inclusions.

[0060] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0061] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0062] In the description of the embodiments of this application, the term "and / or" is simply a description of the association relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent the following three situations: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this document generally indicates that the associated objects are in an "or" relationship.

[0063] In the description of the embodiments of the present application, the term "multiple" refers to more than two (including two). Similarly, "multiple groups" refers to more than two groups (including two groups), and "multiple pieces" refers to more than two pieces (including two pieces).

[0064] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0065] See Figure 1 This is a flowchart illustrating a performance analysis method for hard-isolated passive optical fiber networks according to an embodiment of the present invention, including:

[0066] S1. Obtain the group velocity dispersion parameters, preset attenuation coefficient, and preset nonlinear coefficient of each isolated channel in the passive optical fiber network to be analyzed.

[0067] In a preferred embodiment of the present invention, the passive optical fiber network is configured with an "SMF-CBAND" fiber type, an attenuation coefficient of 0.2 dB / km, a nonlinear coefficient of 1.2 1 / (W km), and a group velocity dispersion of -21.67 ps² / km. The fiber span is 20 km, and the fiber length after the splitter is 1 km. The splitter divides the signal power by a ratio of 64. The modulation scheme used is QAM, with an M value of 4 and a symbol rate of 10 Gbaud. The simulation includes 217 symbols, with 16 samples per symbol. SSFM is performed in 30 steps, with a logarithmic step correction factor of 0.6. The perturbation method divides the fiber length into 20 km segments for Gaussian orthogonal integration, using two orthogonal points.

[0068] S2. Based on the group velocity dispersion parameter, attenuation coefficient, and nonlinear coefficient of the channel, simulate the propagation process of the optical signal in each channel and construct a simulation model for each channel.

[0069] Preferably, the step of simulating the propagation process of the optical signal in each channel based on the group velocity dispersion parameter, attenuation coefficient, and nonlinear coefficient of the channel, and constructing a simulation model for each channel, includes:

[0070] S21. Using the nonlinear Schrödinger equation, and based on the group velocity dispersion parameter, attenuation coefficient, and nonlinear coefficient of the channel, a simulation model is constructed to characterize the propagation of the optical signal in each channel:

[0071]

[0072] Among them, A i(t,z) represents the simulated optical signal in the i-th channel, where t is the time delay frame of the optical signal, z is the propagation distance of the optical signal, and β... 2,i Let γ be the group velocity dispersion parameter of the i-th channel. i Let α be the nonlinear coefficient of the i-th channel. i Let be the attenuation coefficient of the i-th channel, and j be the imaginary unit.

[0073] In a preferred embodiment of the present invention, the propagation of the optical signal in the optical fiber can be accurately described using the nonlinear Schrödinger equation (NLSE). For single polarization, the normalized NLSE (nonlinear Schrödinger equation) for the delay time frame t and distance z is:

[0074]

[0075] Where α is the attenuation coefficient, β2 is the GVD parameter (group velocity dispersion parameter), and γ is the nonlinear coefficient. The first term on the right represents dispersion, and the second term represents Kerr nonlinearity.

[0076] Therefore, in this embodiment, the effect of hard isolation is modeled by adding additional constraints and modifications to the NLSE (Nonlinear Schrödinger Equation), resulting in simulation models for each channel.

[0077] S3. Convert the optical signal into a power series based on the nonlinear coefficients, and solve the simulation model to obtain an approximate analytical solution for each simulation model; wherein, the approximate analytical solution is a function characterizing the change of the optical signal in the channel with time and space;

[0078] Preferably, the step of converting the optical signal into a power series based on the nonlinear coefficients and solving the simulation model to obtain approximate analytical solutions for each simulation model includes:

[0079] S31. Convert the optical signal into a power series based on the nonlinear coefficients:

[0080]

[0081] Where k is the number of terms in the series summation.

[0082] S32. Based on the first two terms of the power series, the approximate analytical solutions for each simulation model are obtained:

[0083]

[0084] in, This is the approximate analytical solution output by the simulation model for the i-th channel. The first term of the power series is... This is the second term of the power series.

[0085] In a preferred embodiment of the present invention, the RP method (regular perturbation method) is used to approximate the solution of NLSE by considering the nonlinear coefficient γ as a small perturbation.

[0086] Specifically, the signal A(t,z) is expanded into a power series of γ:

[0087]

[0088] An approximate solution can be obtained by considering only the first two terms:

[0089]

[0090] in

[0091]

[0092] The dispersion operator Dz is defined as:

[0093] D z {f}(t)=(f*h(·,z))(t),

[0094] Here, * denotes the convolution operation, and h(t,z) is the convolution kernel. The specific form of the convolution kernel h(t,z) is:

[0095]

[0096] Among them, D z The dispersion operator D at time {A(·,0)}(t) z {f}(t) is an operator acting on the function A(·,0) to describe the pulse broadening caused by dispersion in an optical fiber.

[0097] Preferably, before calculating the bit error rate and achievable information rate of each channel in the passive optical fiber network based on the approximate analytical solution, the method further includes:

[0098] S33. Calculate the normalized variance of each channel using the following formula:

[0099]

[0100] Among them, NSD i Let A′ be the normalized variance of the i-th channel. i (t,z) represents the actual measurement result that characterizes the change of the optical signal in the i-th channel with time and space.

[0101] S34. Determine whether each simulation model is accurate based on the normalized variance of each channel;

[0102] S35. When it is determined that the simulation models are all accurate, calculate the bit error rate and achievable information rate of each channel in the passive optical fiber network based on the approximate analytical solution.

[0103] In a preferred embodiment of the invention, the NSD index quantifies the accuracy of the simulation model by comparing the model's output with the actual fiber output obtained through numerical simulation. A lower NSD value indicates a more accurate model. The RP method's ability to predict optical signal behavior under nonlinear effects can significantly improve the prediction accuracy of nonlinear effects. By providing a more tractable approach, the RP model can reduce computational complexity while maintaining high accuracy, especially in highly nonlinear cases.

[0104] Furthermore, such as Figure 3 As shown, the NSD is calculated from the approximate analytical solution of the simulation model output obtained by simulation based on the parameters set in step S1. It can be seen that the NSD continuously decreases as the input power increases from 0 dBm to 20 dBm, indicating that the accuracy of the RP model improves with increasing input power. This trend shows that the RP method used in this embodiment effectively captures the nonlinear effects in optical fibers at high power levels, providing a more reliable approximation of the true signal behavior compared to the low-power case.

[0105] S4. Based on the approximate analytical solution, calculate the bit error rate and achievable information rate of each channel in the passive optical fiber network;

[0106] Preferably, the bit error rate of each channel in the passive optical fiber network is calculated based on the approximate analytical solution, including:

[0107] S41. Obtain the preset noise residual coefficient, preset noise spectral density, and bandwidth of each channel;

[0108] S42. Calculate the signal-to-noise ratio of each channel based on the approximate analytical solution, the noise residual coefficient, the noise spectral density, and the bandwidth;

[0109] Preferably, the step of calculating the signal-to-noise ratio of each channel based on the approximate analytical solution, the noise residual coefficient, the noise spectral density, and the bandwidth includes:

[0110] S421. Calculate the signal-to-noise ratio of each channel using the following formula, based on the approximate analytical solution, the noise residual coefficient, the noise spectral density, and the bandwidth:

[0111]

[0112] Among them, SNR i Let n be the signal-to-noise ratio of the i-th channel, n0 be the noise spectral density, T be the preset observation period length, and B be the signal-to-noise ratio of the i-th channel.i Let α be the bandwidth of the i-th channel, and α be the noise spectral density.

[0113] In a preferred embodiment of the present invention, given the amplitude of the canonical perturbation signal, i.e., under the premise of determining an approximate solution, the signal power P s Calculated by integrating the square of the signal amplitude over the observation period T:

[0114]

[0115] Noise power P under a given bandwidth B n Depend on:

[0116] P n =n0·B;

[0117] In the formula, n0 is the noise spectral density, with units of W / Hz. P NL The noise power introduced by nonlinear effects can be approximated as follows:

[0118]

[0119] Where α is the noise residual coefficient, which is smaller under hard isolation conditions.

[0120] Signal-to-noise ratio (SNR) is calculated as the ratio of signal power to noise power.

[0121]

[0122] S43. Based on the preset M-QAM modulation scheme, calculate the signal-to-noise ratio per bit according to the signal-to-noise ratio;

[0123] S44. Based on the signal-to-noise ratio per bit, calculate the bit error rate of each channel using the following formula:

[0124]

[0125] Among them, BER i,M-QAM Let M be the bit error rate of the i-th channel, M be the modulation coefficient of the M-QAM modulation scheme, and SNR be the bit error rate. b The signal-to-noise ratio per bit.

[0126] In a preferred embodiment of the present invention, such as Figure 4 As shown, the bit error rate calculated by the simulation model in this embodiment is essentially the same as that of other models. It exhibits very similar bit error rate performance across different input power ranges. This close consistency between the simulation model and other models indicates that both methods are highly effective in accurately capturing nonlinear effects and signal distortions in optical communication systems.

[0127] Furthermore, this consistency enhances the reliability of the simulation model as a performance analysis model, validating its use under computationally limited conditions without affecting the accuracy of BER predictions. Additionally, the observation of nearly identical BER trends for both models at different input power levels highlights the robustness of the system design and the adopted modeling methods. This consistency also provides a solid foundation for further optimization and development of advanced communication strategies that leverage the advantages of these modeling techniques to enhance overall system performance.

[0128] Preferably, calculating the achievable information rate of each channel in the passive optical fiber network based on the approximate analytical solution includes:

[0129] S45. Based on the bandwidth and signal-to-noise ratio of each channel, calculate the achievable information rate of each channel using the following formula:

[0130] C i =B i log2(1+SNR i );

[0131] Among them, C i Let B be the achievable information rate of the i-th channel. i Let SNR be the bandwidth of the i-th channel. i Let be the signal-to-noise ratio of the i-th channel.

[0132] In a preferred embodiment of the present invention, the achievable information rate is a fundamental indicator for evaluating the efficiency of a communication system. Using AIR to estimate the maximum data rate that can be achieved under given conditions provides valuable insights for system design and optimization.

[0133] Furthermore, such as Figure 5 As shown, this embodiment describes four scenarios: approximating AIR using RP modeling, approximating AIR using SSFM modeling, approximating AIR using RP modeling and hard isolation, and approximating AIR using SSFM modeling and hard isolation.

[0134] For the approximate AIR modeled using RP, AIR starts at approximately 5.5 × 10¹⁰ bits / s at 0 dBm and gradually increases, reaching around 5.7 × 10¹⁰ bits / s at 10 dBm. However, in the 10–20 dBm range, AIR tends to plateau and does not increase further, contrasting with the continuous increase observed in the SSFM model. This behavior indicates that RP modeling agrees well with SSFM modeling results at low SNR, but exhibits significant deviations at high SNR. These findings suggest that more accurate models are needed to effectively evaluate the communication performance of AIR in the high SNR region.

[0135] A significant improvement in AIR can be observed when hard isolation is introduced. Compared to the model without isolation, the approximate AIR of SSFM modeled with hard isolation increases much faster, starting at approximately 6.3 × 10¹⁰ bits / s and reaching approximately 9.1 × 10¹⁰ bits / s at 20 dBm. Without isolation, AIR only increases from 5.5 × 10¹⁰ to 8.2 × 10¹⁰ bits / s. This huge increase demonstrates that hard isolation is very effective in improving information rate.

[0136] Simulation results demonstrate that hard isolation plays a crucial role in improving the achievable information rate of communication systems. While the RP and SSFM models without isolation show limited improvement with increasing input power, introducing hard isolation yields significant gains, particularly in the SSFM model. This indicates that integrating hard isolation techniques is essential for achieving optimal performance and higher information rates, especially in high-power scenarios.

[0137] S5. Based on the bit error rate and the achievable information rate, generate the performance analysis results of the passive optical fiber network.

[0138] This embodiment provides a performance analysis method for hard-isolated passive optical fiber (POB) networks. Based on the group velocity dispersion parameters, preset attenuation coefficients, and preset nonlinear coefficients of each channel in the POB network, a simulation model is constructed to characterize the propagation of optical signals in each channel. The simulation model is then solved to obtain an approximate analytical solution characterizing the temporal and spatial variations of the optical signal in each channel. Based on this approximate analytical solution, the bit error rate (BER) and achievable information rate (RI) of each channel in the POB network are calculated. Finally, the performance analysis results of the POB network are generated based on the BER and RI. Compared to the laboratory testing methods used in existing technologies, this invention achieves the goal of low-cost and efficient performance analysis of hard-isolated POB networks.

[0139] See Figure 2 This is a schematic diagram of a performance analysis device based on a hard-isolated passive optical fiber network according to an embodiment of the present invention, comprising:

[0140] The data acquisition module includes: acquiring the group velocity dispersion parameters, preset attenuation coefficients, and preset nonlinear coefficients of each isolated channel in the passive optical fiber network to be analyzed.

[0141] The model building module is used to input optical signals into each of the channels and model the propagation process of optical signals in each channel based on the group velocity dispersion parameters, attenuation coefficients, and nonlinear coefficients of the channels, so as to obtain the simulation model of each channel.

[0142] The model solving module is used to convert the optical signal into a power series based on the nonlinear coefficients and solve the simulation model to obtain an approximate analytical solution for each simulation model; wherein, the approximate analytical solution is a function characterizing the change of the optical signal in the channel with time and space;

[0143] The data calculation module is used to calculate the bit error rate and achievable information rate of each channel in the passive optical fiber network based on the approximate analytical solution.

[0144] The performance analysis module is used to generate performance analysis results for the passive optical fiber network based on the bit error rate and the achievable information rate.

[0145] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0146] Those skilled in the art will clearly understand that, for convenience and brevity, the specific working process of the device described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0147] Another preferred embodiment of the present invention provides a terminal device including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a performance analysis method based on a hard-isolated passive optical fiber network as described in any of the foregoing embodiments.

[0148] The terminal device can be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0149] The 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. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.

[0150] The memory can be used to store the computer program. The processor implements various functions of the terminal device by running or executing the computer program stored in the memory and calling data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function, etc.; the data storage area may store data created based on the use of the mobile phone, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0151] Another preferred embodiment of the present invention provides a storage medium, which is a computer-readable storage medium. A computer program is stored in the computer-readable storage medium, and when executed by a processor, the computer program can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0152] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A performance analysis method based on hard-isolated passive optical fiber networks, characterized in that, include: Obtain the group velocity dispersion parameters, preset attenuation coefficient, and preset nonlinear coefficient of each isolated channel in the passive optical fiber network to be analyzed. Using the nonlinear Schrödinger equation, a simulation model is constructed to characterize the propagation of optical signals in each channel based on the group velocity dispersion parameter, attenuation coefficient, and nonlinear coefficient of the channel. The optical signal is converted into a power series based on the nonlinear coefficients, and the simulation model is solved to obtain an approximate analytical solution for each simulation model; wherein, the approximate analytical solution is a function characterizing the change of the optical signal in the channel with time and space; Based on the approximate analytical solution, calculate the bit error rate and achievable information rate of each channel in the passive optical fiber network; Based on the bit error rate and the achievable information rate, the performance analysis results of the passive optical fiber network are generated; The simulation model is as follows: ； in, For the simulated optical signal in the i-th channel, The time frame of the optical signal is the delay time frame. The propagation distance of the optical signal. Let i be the group velocity dispersion parameter of the i-th channel. Let be the nonlinear coefficient of the i-th channel. Let be the attenuation coefficient of the i-th channel. It is the imaginary unit.

2. The performance analysis method for a hard-isolated passive optical fiber network as described in claim 1, characterized in that, The process of converting the optical signal into a power series based on the nonlinear coefficients and solving the simulation model to obtain approximate analytical solutions for each simulation model includes: The optical signal is converted into a power series based on the nonlinear coefficients: ； Where k is the number of terms in the series summation; Based on the first two terms of the power series, approximate analytical solutions for each simulation model are obtained: ； in, This is the approximate analytical solution output by the simulation model for the i-th channel. The first term of the power series is... This is the second term of the power series.

3. The performance analysis method for a hard-isolated passive optical fiber network as described in claim 2, characterized in that, Before calculating the bit error rate and achievable information rate of each channel in the passive optical fiber network based on the approximate analytical solution, the method further includes: The normalized variance of each channel is calculated using the following formula: ； in, Let be the normalized variance of the i-th channel. The measurement results represent the temporal and spatial variations of the optical signal in the i-th channel as actually measured; Based on the normalized variance of each of the channels, determine whether each of the simulation models is accurate; Once the accuracy of the simulation models is confirmed, the bit error rate and achievable information rate of each channel in the passive optical fiber network are calculated based on the approximate analytical solution.

4. The performance analysis method for a hard-isolated passive optical fiber network as described in claim 3, characterized in that, Based on the approximate analytical solution, the bit error rate of each channel in the passive optical fiber network is calculated, including: Obtain the preset noise residual coefficient, preset noise spectral density, and bandwidth of each channel; The signal-to-noise ratio of each channel is calculated based on the approximate analytical solution, the noise residual coefficient, the noise spectral density, and the bandwidth. Based on the preset M-QAM modulation scheme, the signal-to-noise ratio per bit is calculated according to the signal-to-noise ratio. Based on the signal-to-noise ratio per bit, the bit error rate of each channel is calculated using the following formula: ； ； in, Let be the bit error rate of the i-th channel. The modulation coefficients of the M-QAM modulation scheme are... The signal-to-noise ratio per bit.

5. The performance analysis method for a hard-isolated passive optical fiber network as described in claim 4, characterized in that, The step of calculating the signal-to-noise ratio of each channel based on the approximate analytical solution, the noise residual coefficient, the noise spectral density, and the bandwidth includes: The signal-to-noise ratio (SNR) of each channel is calculated using the following formula, based on the approximate analytical solution, the noise residual coefficient, the noise spectral density, and the bandwidth: ； in, , The noise spectral density, The preset observation period length, Let be the bandwidth of the i-th channel. The noise spectral density is given.

6. The performance analysis method for a hard-isolated passive optical fiber network as described in claim 5, characterized in that, The step of calculating the achievable information rate of each channel in the passive optical fiber network based on the approximate analytical solution includes: Based on the bandwidth and signal-to-noise ratio of each channel, the achievable information rate of each channel is calculated using the following formula: ； in, Let be the achievable information rate of the i-th channel. Let be the bandwidth of the i-th channel. Let be the signal-to-noise ratio of the i-th channel.

7. A performance analysis device based on a hard-isolated passive optical fiber network, characterized in that, include: The data acquisition module includes: acquiring the group velocity dispersion parameters, preset attenuation coefficients, and preset nonlinear coefficients of each isolated channel in the passive optical fiber network to be analyzed. The model building module is used to construct a simulation model to characterize the propagation of optical signals in each channel by using the nonlinear Schrödinger equation, based on the group velocity dispersion parameter, attenuation coefficient, and nonlinear coefficient of the channel. The model solving module is used to convert the optical signal into a power series based on the nonlinear coefficients and solve the simulation model to obtain an approximate analytical solution for each simulation model; wherein, the approximate analytical solution is a function characterizing the change of the optical signal in the channel with time and space; The data calculation module is used to calculate the bit error rate and achievable information rate of each channel in the passive optical fiber network based on the approximate analytical solution. The performance analysis module is used to generate performance analysis results for the passive optical fiber network based on the bit error rate and the achievable information rate. The simulation model is as follows: ； in, For the simulated optical signal in the i-th channel, The time frame of the optical signal is the delay time frame. The propagation distance of the optical signal. Let i be the group velocity dispersion parameter of the i-th channel. Let be the nonlinear coefficient of the i-th channel. Let be the attenuation coefficient of the i-th channel. It is the imaginary unit.

8. A terminal device, characterized in that, The system includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements a performance analysis method for a hard-isolated passive optical fiber network as described in any one of claims 1 to 6.

9. A storage medium, characterized in that, The storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device where the storage medium is located to perform a performance analysis method based on a hard-isolated passive optical fiber network as described in any one of claims 1 to 6.

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