Performance analysis method and device based on hard isolation passive optical fiber network, terminal equipment and storage medium

Abstract

The invention discloses a performance analysis method and device based on a hard isolation passive optical fiber network, terminal equipment and a storage medium, and the method comprises the steps: constructing a simulation model used for representing the propagation of an optical signal in each channel according to a group velocity dispersion parameter, a preset attenuation coefficient and a preset nonlinear coefficient of each channel in the passive optical fiber network; and the simulation model is solved to obtain an approximate analytic solution used for representing the change of the optical signal in each channel along with time and space, so that the bit error rate and the achievable information rate of each channel in the passive optical network are calculated according to the approximate analytic solution, and finally, according to the bit error rate and the achievable information rate, the optical signal in the passive optical network is simulated according to the bit error rate and the achievable information rate. And generating a performance analysis result of the passive optical fiber network. Compared with a laboratory test method adopted in the prior art, the method has the advantage that the aim of performing performance analysis on the passive optical fiber network based on hard isolation efficiently at low cost can be achieved.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and in particular to a performance analysis method, device, terminal equipment and storage medium based on a hard-isolated passive optical fiber network. Background Art

[0002] GPON (Gigabit Passive Optical Network) is a network architecture based on fiber optic communication technology. It is used to provide high-speed data transmission, video and voice services. It can transmit data to end users through optical fiber. It has the advantages of high bandwidth, low latency and long transmission distance. Hard isolation technology is a technology that mitigates nonlinear effects in optical communication systems by physically isolating different wavelength channels to prevent interference and crosstalk. This approach is particularly important in environments with high data rates and extended transmission distances, because nonlinear effects such as cross-phase modulation (XPM) and four-wave mixing (FWM) are more obvious. 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 to maintain the integrity of the optical signal and maintain high data transmission quality.

[0003] For the performance test of passive optical fiber networks that use hard isolation technology, laboratory testing methods are mostly used to simulate the actual network environment in a controlled environment to perform various performance tests on the passive optical fiber network. This method can accurately control the test conditions, including optical power, wavelength, fiber type, nonlinear effect intensity, etc., so as to accurately measure and evaluate the performance indicators of the network. However, this method requires the construction of a dedicated test platform and equipped with professional test equipment, which is relatively costly and has a long test cycle. Summary of the invention

[0004] The embodiments of the present invention provide a performance analysis method, apparatus, terminal device and storage medium based on a hard-isolated passive optical fiber network, which achieves the goal of low-cost and efficient performance analysis of a hard-isolated passive optical fiber network by constructing a simulation model of the propagation of optical signals in each channel in the passive optical fiber network.

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

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

[0007] Inputting an optical signal into each of the channels, and modeling the propagation process of the optical signal in each channel according to the group velocity dispersion parameter, attenuation coefficient, and nonlinear coefficient of the channel to obtain a simulation model of each channel;

[0008] Converting the optical signal into a power series based on the nonlinear coefficient, and solving the simulation model to obtain an approximate analytical solution of each simulation model; wherein the approximate analytical solution is a function that characterizes the variation of the optical signal in the channel with time and space;

[0009] Calculating the bit error rate and achievable information rate of each channel in the passive optical fiber network according to the approximate analytical solution;

[0010] A performance analysis result of the passive optical fiber network is generated according to the bit error rate and the achievable information rate.

[0011] Furthermore, simulating the process of light signal propagation in each channel according to the group velocity dispersion parameter, attenuation coefficient, and nonlinear coefficient of the channel to construct a simulation model of each channel includes:

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

[0013]

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

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

[0016] Convert the optical signal into a power series based on the nonlinear coefficients:

[0017]

[0018] Here, k is the number of terms in the series to be summed.

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

[0020]

[0021] in, is the approximate analytical solution of the simulation model output of the ith channel, is the first term of the power series, 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 according to the approximate analytical solution, the method further includes:

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

[0024]

[0025] Among them, NSD i is the normalized variance of the ith channel, A′ i (t,z) is the actual measured result representing the variation of the optical signal in the i-th channel over time and space;

[0026] Determining whether each of the simulation models is accurate according to the normalized variance of each of the channels;

[0027] When it is determined that the simulation models are all accurate, the bit error rate and the achievable information rate of each channel in the passive optical fiber network are calculated according to the approximate analytical solution.

[0028] Further, according to the approximate analytical solution, the bit error rate of each channel in the passive optical fiber network is calculated, including:

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

[0030] Calculating the signal-to-noise ratio of each channel according to the approximate analytical solution, the noise residual coefficient, the noise spectral density, and the bandwidth;

[0031] Based on a preset M-QAM modulation scheme, and according to the signal-to-noise ratio, calculating a signal-to-noise ratio per bit;

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

[0033]

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

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

[0036] The signal-to-noise ratio of each channel is calculated according to the approximate analytical solution, the noise residual coefficient, the noise spectral density, and the bandwidth by the following formula:

[0037]

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

[0039] Further, the calculating the achievable information rate of each channel in the passive optical fiber network according to the approximate analytical solution includes:

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

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

[0042] Among them, C i is the achievable information rate of the ith channel, B i is the bandwidth of the ith channel, SNR i is the signal-to-noise ratio of the ith 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: obtaining the group velocity dispersion parameter, the preset attenuation coefficient, and the preset nonlinear coefficient of each isolated channel in the passive optical fiber network to be analyzed;

[0045] A model building module, used for inputting the optical signal into each of the channels, and modeling the process of the optical signal propagating in each channel according to the group velocity dispersion parameter, attenuation coefficient, and nonlinear coefficient of the channel to obtain a simulation model of each channel;

[0046] A model solving module, used for converting the optical signal into a power series based on the nonlinear coefficient, and solving the simulation model to obtain an approximate analytical solution of each simulation model; wherein the approximate analytical solution is a function that characterizes the variation of the optical signal in the channel with time and space;

[0047] A data calculation module, used for calculating the bit error rate and achievable information rate of each channel in the passive optical fiber network according to the approximate analytical solution;

[0048] A performance analysis module is used to generate a performance analysis result of the passive optical fiber network according to 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, wherein 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 one 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 running, the device where the storage medium is located is controlled to execute a performance analysis method based on a hard-isolated passive optical fiber network as described in any one of the above embodiments.

[0051] The following beneficial effects are achieved by implementing the present invention:

[0052] The present invention discloses a performance analysis method, device, terminal equipment and storage medium based on a hard-isolated passive optical fiber network. The method constructs a simulation model for characterizing the propagation of optical signals in each channel according to the group velocity dispersion parameters, preset attenuation coefficients and preset nonlinear coefficients of each channel in the passive optical fiber network, and solves the simulation model to obtain an approximate analytical solution for characterizing the variation of optical signals in each channel with time and space, so that the bit error rate and achievable information rate of each channel in the passive optical fiber network can be calculated according to the approximate analytical solution, and finally the performance analysis result of the passive optical fiber network is generated according to the bit error rate and achievable information rate. Compared with the laboratory test method used in the prior art, the present invention can achieve the goal of low-cost and efficient performance analysis of a hard-isolated passive optical fiber network. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 It is a flow chart of a performance analysis method based on a hard-isolated passive optical fiber network provided by an embodiment of the present invention.

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

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

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

[0057] Figure 5It is a schematic diagram of AIR comparison of hard-isolated GPON under different modeling methods provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0058] In order to make the purpose, technical solutions and advantages of this application clearer, the technical solutions in this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work 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 technicians in the technical field 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" in the specification and claims of this application and the above-mentioned figure descriptions and any variations thereof are intended to cover non-exclusive inclusions.

[0060] In the description of the embodiments of the present application, the technical terms "first", "second", etc. are only used to distinguish different objects, and cannot be understood as indicating or implying relative importance or implicitly indicating the number, specific order or primary and secondary relationship of the indicated technical features. In the description of the embodiments of the present application, the meaning of "multiple" is more than two, unless otherwise clearly and specifically defined.

[0061] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0062] In the description of the embodiments of the present application, the term "and / or" is only a description of the association relationship of the associated objects, indicating that there may be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after 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 the present application, unless otherwise clearly specified and limited, technical terms such as "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, and it can be the internal connection of two elements or the interaction relationship between two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the embodiments of the present application can be understood according to the specific circumstances.

[0065] See also Figure 1 , is a flow chart of a performance analysis method based on a hard-isolated passive optical fiber network provided by an embodiment of the present invention, comprising:

[0066] S1. Obtaining group velocity dispersion parameters, preset attenuation coefficients, and preset nonlinear coefficients of each isolated channel in a passive optical fiber network to be analyzed;

[0067] In a preferred embodiment of the present invention, the fiber type of the passive optical fiber network is set to "SMF-CBAND", the attenuation coefficient is 0.2dB / Km, the nonlinear coefficient is 1.2 1 / (W Km), and the group velocity dispersion is -21.67ps2 / Km. The fiber span is 20km, and the fiber length after the beam splitter is 1km. The distributor divides the signal power by a ratio of 64. The modulation scheme used is QAM, the M value is 4, and the symbol rate is set to 10Gbaud. The simulation includes 217 symbols, and 16 samples are sampled for each symbol. SSFM uses 30 steps, the correction factor of the logarithmic step is 0.6, and the perturbation method divides the fiber length into 20 km segments for Gaussian orthogonal integration, using 2 orthogonal points.

[0068] S2. According to the group velocity dispersion parameter, attenuation coefficient, and nonlinear coefficient of the channel, simulate the process of light signal propagation in each channel to construct a simulation model of each channel;

[0069] Preferably, simulating the process of light signal propagation in each channel according to the group velocity dispersion parameter, attenuation coefficient, and nonlinear coefficient of the channel to construct a simulation model of each channel includes:

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

[0071]

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

[0073] In a preferred embodiment of the present invention, the propagation of optical signals in optical fibers can be accurately described by NLSE. For single polarization, the normalized NLSE (nonlinear Schrödinger equation) of the delay time frame t and the 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 influence of hard isolation is modeled by adding additional constraints and modifications to the NLSE (nonlinear Schrödinger equation) to obtain a simulation model of each channel.

[0077] S3, converting the optical signal into a power series based on the nonlinear coefficient, and solving the simulation model to obtain an approximate analytical solution of each simulation model; wherein the approximate analytical solution is a function that characterizes the variation of the optical signal in the channel with time and space;

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

[0079] S31, converting the optical signal into a power series based on the nonlinear coefficient:

[0080]

[0081] Here, k is the number of terms in the series to be summed.

[0082] S32. According to the first two terms of the power series, an approximate analytical solution of each simulation model is obtained:

[0083]

[0084] in, is the approximate analytical solution of the simulation model output of the ith channel, is the first term of the power series, is the second term of the power series.

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

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

[0087]

[0088] Considering only the first two terms gives an approximate solution:

[0089]

[0090] in

[0091]

[0092] The dispersion operator Dz is defined as:

[0093] D z {f}(t)=(f*h(·,z))(t),

[0094] Among them, * represents 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 {A(·,0)}(t) dispersion operator D z {f}(t) is an operator acting on the function A(·,0) to describe the pulse broadening caused by the dispersion effect in the optical fiber.

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

[0098] S33, using the following formula to calculate the normalized variance of each channel:

[0099]

[0100] Among them, NSD i is the normalized variance of the ith channel, A′ i (t,z) is the actual measured result representing the variation of the optical signal in the i-th channel over time and space;

[0101] S34, judging whether each of the simulation models is accurate according to the normalized variance of each of the channels;

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

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

[0104] Further, such as Figure 3 As shown, the NSD calculated by the approximate analytical solution of the simulation model output obtained by simulating according to the parameters set in step S1, it can be seen that when the input power increases from 0dBm to 20dBm, the NSD continues to decrease, indicating that the accuracy of the RP model improves with the increase of input power. This trend shows that the RP method adopted in this embodiment effectively captures the nonlinear effects in the optical fiber at high power levels, and provides a more reliable approximation of the real signal behavior compared with the low power case.

[0105] S4. Calculate the bit error rate and achievable information rate of each channel in the passive optical fiber network according to the approximate analytical solution;

[0106] Preferably, calculating the bit error rate of each channel in the passive optical fiber network according to the approximate analytical solution includes:

[0107] S41, obtaining a preset noise residual coefficient, a preset noise spectrum density, and a bandwidth of each channel;

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

[0109] Preferably, calculating the signal-to-noise ratio of each channel according to 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 according to the approximate analytical solution, the noise residual coefficient, the noise spectral density, and the bandwidth using the following formula:

[0111]

[0112] Among them, SNR i is the signal-to-noise ratio of the ith channel, n0 is the noise spectral density, T is the preset observation period length, Bi is the bandwidth of the ith channel, and α is the noise spectral density.

[0113] In a preferred embodiment of the present invention, given the amplitude of the regular disturbance signal, that is, under the premise of determining the approximate solution, the signal power P s Calculated by integrating the square of the signal amplitude within the observation period T:

[0114]

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

[0116] P n =n0·B;

[0117] Where n0 is the noise spectral density, in W / Hz. NL is the noise power introduced by the nonlinear effect, which can be approximately calculated as

[0118]

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

[0120] The signal-to-noise ratio is calculated as the ratio of signal power to noise power

[0121]

[0122] S43, based on a preset M-QAM modulation scheme, according to the signal-to-noise ratio, calculating a signal-to-noise ratio per bit;

[0123] S44. Calculate the bit error rate of each channel according to the signal-to-noise ratio per bit by the following formula:

[0124]

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

[0126] In a preferred embodiment of the present invention, Figure 4 As shown, it can be seen that the bit error rate calculated by the simulation model of this embodiment is basically the same as the bit error rate of other models. Very similar bit error rate performance is shown in different input power ranges. This close agreement between the simulation model and other models shows that both methods are very effective in accurately capturing nonlinear effects and signal distortion in optical communication systems.

[0127] Furthermore, this consistency enhances the reliability of the simulation model as a performance analysis model and validates its use in situations where computational resources are limited without compromising the accuracy of BER predictions. Furthermore, nearly identical bit error rate (BER) trends were observed for both models at different input power levels, highlighting the robustness of the system design and modeling approach employed. This consistency also provides a solid foundation for further optimization and development of advanced communication strategies that leverage the benefits of these modeling techniques to enhance overall system performance.

[0128] Preferably, the calculating the achievable information rate of each channel in the passive optical fiber network according to the approximate analytical solution comprises:

[0129] S45. Calculate the achievable information rate of each channel according to the bandwidth and signal-to-noise ratio of each channel according to the following formula:

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

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

[0132] In a preferred embodiment of the present invention, the achievable information rate is a basic 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] Further, such as Figure 5 As shown, this embodiment describes four scenarios: using RP modeling to approximate AIR, using SSFM modeling to approximate AIR, using RP modeling and hard isolation to approximate AIR, and using SSFM modeling and hard isolation to approximate AIR.

[0134] For the approximate AIR using RP modeling, AIR starts from about 5.5×1010bits / s at 0dBm and gradually increases to reach about 5.7×1010bits / s at 10dBm. However, in the range of 10-20dBm, AIR tends to be stable and does not increase further, in contrast to the continuous increase of the SSFM model. This behavior shows that RP modeling is in good agreement with the SSFM modeling results at low SNR, but shows 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. The approximate AIR for SSFM modeling with hard isolation increases much faster than that for the model without isolation, starting at about 6.3×1010bits / s and reaching about 9.1×1010bits / s at 20dBm. Without isolation, AIR only increases from 5.5×1010 to 8.2×1010bits / s. This huge increase shows that hard isolation is very effective in increasing the information rate.

[0136] The simulation results show that hard isolation plays a vital role in improving the achievable information rate of the communication system. While the RP and SSFM models without isolation show limited improvements with increasing input power, the introduction of hard isolation leads to significant gains, especially in the SSFM model. This shows that integrating hard isolation technology is crucial to achieve the best performance and higher information rates, especially in high-power scenarios.

[0137] S5. Generate a performance analysis result of the passive optical fiber network according to the bit error rate and the achievable information rate.

[0138] This embodiment provides a performance analysis method based on a hard-isolated passive optical fiber network. According to the group velocity dispersion parameters, preset attenuation coefficients, and preset nonlinear coefficients of each channel in the passive optical fiber network, a simulation model for characterizing the propagation of optical signals in each channel is constructed, and the simulation model is solved to obtain an approximate analytical solution for characterizing the variation of optical signals in each channel over time and space, so that the bit error rate and achievable information rate of each channel in the passive optical fiber network can be calculated according to the approximate analytical solution, and finally the performance analysis result of the passive optical fiber network is generated according to the bit error rate and the achievable information rate. Compared with the laboratory test method used in the prior art, the present invention can achieve the goal of low-cost and efficient performance analysis of a hard-isolated passive optical fiber network.

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

[0140] The data acquisition module includes: obtaining the group velocity dispersion parameter, the preset attenuation coefficient, and the preset nonlinear coefficient of each isolated channel in the passive optical fiber network to be analyzed;

[0141] A model building module, used for inputting the optical signal into each of the channels, and modeling the process of the optical signal propagating in each channel according to the group velocity dispersion parameter, attenuation coefficient, and nonlinear coefficient of the channel to obtain a simulation model of each channel;

[0142] A model solving module, used for converting the optical signal into a power series based on the nonlinear coefficient, and solving the simulation model to obtain an approximate analytical solution of each simulation model; wherein the approximate analytical solution is a function that characterizes the variation of the optical signal in the channel with time and space;

[0143] A data calculation module, used for calculating the bit error rate and achievable information rate of each channel in the passive optical fiber network according to the approximate analytical solution;

[0144] A performance analysis module is used to generate a performance analysis result of the passive optical fiber network according to the bit error rate and the achievable information rate.

[0145] It should be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0146] Those skilled in the art can clearly understand that for the sake of convenience and brevity, the specific working process of the device described above can refer to the corresponding process in the aforementioned method embodiment, 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, wherein 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 one of the above embodiments.

[0148] The terminal device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0149] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the terminal device, and uses various interfaces and lines to connect various parts of the entire terminal device.

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

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

[0152] The above is a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the principle of the present invention. 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 network, characterized in that: include: Obtaining group velocity dispersion parameters, preset attenuation coefficients, and preset nonlinear coefficients of each isolated channel in the passive optical fiber network to be analyzed; According to the group velocity dispersion parameter, attenuation coefficient, and nonlinear coefficient of the channel, the process of light signal propagation in each channel is simulated to construct a simulation model of each channel; Converting the optical signal into a power series based on the nonlinear coefficient, and solving the simulation model to obtain an approximate analytical solution of each simulation model; wherein the approximate analytical solution is a function that characterizes the variation of the optical signal in the channel with time and space; Calculating the bit error rate and achievable information rate of each channel in the passive optical fiber network according to the approximate analytical solution; A performance analysis result of the passive optical fiber network is generated according to the bit error rate and the achievable information rate.

2. A performance analysis method based on hard-isolated passive optical fiber network as claimed in claim 1, characterized in that: The process of simulating the propagation of optical signals in each channel according to the group velocity dispersion parameter, attenuation coefficient, and nonlinear coefficient of the channel to construct a simulation model of each channel includes: Using the nonlinear Schrödinger equation, according to the group velocity dispersion parameter, attenuation coefficient, and nonlinear coefficient of the channel, a simulation model for characterizing the propagation of optical signals in each channel is constructed: Among them, A i (t,z) is the simulated optical signal in the i-th channel, t is the delay time frame of the optical signal, z is the propagation distance of the optical signal, β 2,i is the group velocity dispersion parameter of the ith channel, γ i is the nonlinear coefficient of the i-th channel, α i is the attenuation coefficient of the ith channel, and j is the imaginary unit.

3. A performance analysis method based on hard-isolated passive optical fiber network as claimed in claim 2, characterized in that: The step of converting the optical signal into a power series based on the nonlinear coefficient and solving the simulation model to obtain an approximate analytical solution of each simulation model includes: Convert the optical signal into a power series based on the nonlinear coefficients: Where k is the number of terms in the series summation; According to the first two terms of the power series, the approximate analytical solutions of each simulation model are obtained: in, is the approximate analytical solution of the simulation model output of the ith channel, is the first term of the power series, is the second term of the power series.

4. A performance analysis method based on hard-isolated passive optical fiber network as claimed in claim 3, characterized in that: Before calculating the bit error rate and achievable information rate of each channel in the passive optical fiber network according to the approximate analytical solution, the method further includes: The normalized variance of each of the channels is calculated using the following formula: Among them, NSD i is the normalized variance of the ith channel, A′ i (t,z) is the actual measured result representing the variation of the optical signal in the i-th channel over time and space; Determining whether each of the simulation models is accurate according to the normalized variance of each of the channels; When it is determined that the simulation models are all accurate, the bit error rate and the achievable information rate of each channel in the passive optical fiber network are calculated according to the approximate analytical solution.

5. A performance analysis method based on hard-isolated passive optical fiber network as claimed in claim 4, characterized in that: Calculating the bit error rate of each channel in the passive optical fiber network according to the approximate analytical solution includes: Obtaining a preset noise residual coefficient, a preset noise spectral density, and a bandwidth of each channel; Calculating the signal-to-noise ratio of each channel according to the approximate analytical solution, the noise residual coefficient, the noise spectral density, and the bandwidth; Based on a preset M-QAM modulation scheme, and according to the signal-to-noise ratio, calculating a signal-to-noise ratio per bit; According to the signal-to-noise ratio per bit, the bit error rate of each channel is calculated by the following formula: Among them, BER i,M-QAM is the bit error rate of the i-th channel, M is the modulation coefficient of the M-QAM modulation scheme, SNR b is the signal-to-noise ratio per bit.

6. A performance analysis method based on hard-isolated passive optical fiber network as claimed in claim 6, characterized in that: The calculating the signal-to-noise ratio of each channel according to the approximate analytical solution, the noise residual coefficient, the noise spectral density, and the bandwidth includes: The signal-to-noise ratio of each channel is calculated according to the approximate analytical solution, the noise residual coefficient, the noise spectral density, and the bandwidth by the following formula: Among them, SNR i is the signal-to-noise ratio of the ith channel, n0 is the noise spectral density, T is the preset observation period length, B i is the bandwidth of the ith channel, and α is the noise spectral density.

7. A performance analysis method based on hard-isolated passive optical fiber network as claimed in claim 6, characterized in that: The step of calculating the achievable information rate of each channel in the passive optical fiber network according to the approximate analytical solution comprises: According to the bandwidth and signal-to-noise ratio of each channel, the achievable information rate of each channel is calculated according to the following formula: C i =B i log2(1+SNR i ); Among them, C i is the achievable information rate of the ith channel, B i is the bandwidth of the ith channel, SNR i is the signal-to-noise ratio of the ith channel.

8. A performance analysis device based on a hard-isolated passive optical fiber network, characterized in that: include: The data acquisition module includes: obtaining the group velocity dispersion parameter, the preset attenuation coefficient, and the preset nonlinear coefficient of each isolated channel in the passive optical fiber network to be analyzed; A model building module, used for inputting the optical signal into each of the channels, and modeling the process of the optical signal propagating in each channel according to the group velocity dispersion parameter, attenuation coefficient, and nonlinear coefficient of the channel to obtain a simulation model of each channel; A model solving module, used for converting the optical signal into a power series based on the nonlinear coefficient, and solving the simulation model to obtain an approximate analytical solution of each simulation model; wherein the approximate analytical solution is a function that characterizes the variation of the optical signal in the channel with time and space; A data calculation module, used for calculating the bit error rate and achievable information rate of each channel in the passive optical fiber network according to the approximate analytical solution; A performance analysis module is used to generate a performance analysis result of the passive optical fiber network according to the bit error rate and the achievable information rate.

9. A terminal device, characterized in that: It comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and 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 one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium includes a stored computer program, wherein when the computer program is executed, the device where the storage medium is located is controlled to execute a performance analysis method based on a hard-isolated passive optical fiber network as described in any one of claims 1 to 7.

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