A method, device and storage medium for jointly estimating signal-to-noise ratio and mode coupling of mode division multiplexing optical fiber communication

Abstract

The application discloses a kind of mode division multiplexing optical fiber communication signal-to-noise ratio and mode coupling joint estimation method, device and storage medium, mode division multiplexing optical fiber communication signal-to-noise ratio and mode coupling joint estimation method includes obtaining the multiple groups of communication signals of mode division multiplexing optical fiber communication, obtains the spectral correlation function of communication signal, according to spectral correlation function determines cyclic correlation function, according to cyclic correlation function determines signal-to-noise ratio and optical fiber length, according to the signal-to-noise ratio corresponding to each group of communication signals respectively and the optical fiber length, determine power coupling coefficient value etc.Step.The application can monitor signal-to-noise ratio and mode coupling simultaneously, and this kind of joint monitoring capacity can more comprehensively evaluate communication quality, and does not need complex data acquisition or synthetic process, to simplify the implementation and operation of monitoring system, can realize real-time monitoring, guarantee the reliability and timely detection of communication system.The application is widely applied in optical communication technical field.

Description

Technical Field

[0001] This invention relates to the field of optical communication technology, and in particular to a method, apparatus and storage medium for joint estimation of signal-to-noise ratio and mode coupling in mode-division multiplexing optical fiber communication. Background Technology

[0002] With the explosive growth in demand for the Internet and data communication, optical communication systems have become the backbone of modern communication networks due to their advantages such as high bandwidth, low loss, and resistance to electromagnetic interference. Especially in high-speed coherent optical communication systems, ultra-high-speed data transmission can be achieved through the use of advanced modulation formats and digital signal processing technologies.

[0003] Coherent detection technology in coherent optical communication systems offers higher signal detection sensitivity and enables precise measurement of the amplitude and phase of optical signals, which is crucial for achieving high-speed transmission and improving signal quality. Mode multiplexing (MDM) technology was developed to address the bandwidth limitations of single-mode fiber (SMF) networks. MDM technology provides additional information transmission channels by using multiple orthogonal modes.

[0004] In coherent transmission systems, transmitted optical signals are susceptible to dynamic optical impairments such as amplified spontaneous emission (ASE), dispersion (CD), and polarization mode dispersion (PMD). In MDM systems, mode coupling effects (MC) make monitoring more complex than in single-mode fiber (SMF). Therefore, in mode-division multiplexing fiber communication using coherent transmission, it is difficult to monitor the signal-to-noise ratio and mode coupling parameters. Summary of the Invention

[0005] To address the technical problem of difficulty in monitoring the signal-to-noise ratio and mode coupling parameters of mode-division multiplexing optical fiber communication using coherent transmission, the present invention aims to provide a method, apparatus, and storage medium for joint estimation of signal-to-noise ratio and mode coupling in mode-division multiplexing optical fiber communication.

[0006] On one hand, embodiments of the present invention include a joint estimation method for signal-to-noise ratio (SNR) and mode coupling in mode-division multiplexing optical fiber communication, wherein the joint estimation method for SNR and mode coupling in mode-division multiplexing optical fiber communication includes the following steps:

[0007] Acquire multiple sets of communication signals from modulus-division multiplexing optical fiber communication;

[0008] For any set of the communication signals, obtain the spectral correlation function of the communication signals, determine the cyclic correlation function based on the spectral correlation function, and determine the signal-to-noise ratio and fiber length based on the cyclic correlation function;

[0009] The power coupling coefficient value is determined based on the signal-to-noise ratio and fiber length corresponding to each group of communication signals.

[0010] Furthermore, the acquisition of multiple sets of communication signals in the mode-division multiplexing optical fiber communication includes:

[0011] Signals from modulus-division multiplexing optical fiber communication are received by a coherent receiver under various channel environment conditions.

[0012] The signals received under each of the aforementioned channel environment conditions form a set of communication signals.

[0013] Further, obtaining the spectral correlation function of the communication signal includes:

[0014] For any set of communication signals, multiple discrete digital signal components corresponding to the communication signals are obtained; each discrete digital signal component has a corresponding polarization multiplexing dimension and mode multiplexing dimension.

[0015] For any group of the communication signals, each discrete digital signal component is truncated according to the same time length, and multiple cyclic periodic diagrams are determined based on the truncated signals.

[0016] For any set of the communication signals, the spectral correlation function in matrix form is determined using the corresponding plurality of cyclic periodic diagrams as elements in the matrix.

[0017] Further, determining the cyclic correlation function based on the spectral correlation function includes:

[0018] Perform inverse Fourier transform on each element of the spectral correlation function;

[0019] The cyclic correlation function in matrix form is determined by using the inverse Fourier transform of the elements in the spectral correlation function as the elements in the matrix.

[0020] Further, determining the signal-to-noise ratio and fiber length based on the cyclic correlation function includes:

[0021] Determine the maximum correlation strength based on the cyclic correlation function;

[0022] Obtain the upper sideband average power and lower sideband average power of each discrete digital signal component;

[0023] The normalized correlation coefficient is determined based on the maximum correlation strength, the average power of the upper sideband, and the average power of the lower sideband.

[0024] The signal-to-noise ratio is determined based on the normalized correlation coefficient.

[0025] Further, the normalized correlation coefficient is determined based on the maximum correlation strength, the average power of the upper sideband, and the average power of the lower sideband, according to the following formula:

[0026]

[0027] Where δ is the normalized correlation coefficient, R sum P1 represents the maximum correlation strength, P2 represents the average power of the upper sideband, and P2 represents the average power of the lower sideband.

[0028] The signal-to-noise ratio is determined based on the normalized correlation coefficient using the following formula:

[0029]

[0030] Wherein, SNR is the signal-to-noise ratio.

[0031] Further, determining the signal-to-noise ratio and fiber length based on the cyclic correlation function includes:

[0032] The length of the optical fiber was determined using the CAF-4CDE algorithm.

[0033] Further, determining the power coupling coefficient value based on the signal-to-noise ratio and fiber length corresponding to each group of communication signals includes:

[0034] Perform a quadratic or linear fit based on the signal-to-noise ratio and the fiber length;

[0035] The coefficient values ​​determined by the fitting are used as the power coupling coefficient values.

[0036] On the other hand, embodiments of the present invention also include a computer device, including a memory and a processor, the memory for storing at least one program, and the processor for loading at least one program to execute the signal-to-noise ratio and mode coupling joint estimation method for mode-division multiplexing optical fiber communication in the embodiments.

[0037] On the other hand, embodiments of the present invention also include a computer-readable storage medium storing a processor-executable program, which, when executed by a processor, is used to perform the signal-to-noise ratio and mode coupling joint estimation method for mode-division multiplexing optical fiber communication in the embodiments.

[0038] The beneficial effects of the present invention are as follows: the joint estimation method of signal-to-noise ratio and mode coupling in the mode division multiplexing optical fiber communication in the embodiments can simultaneously monitor the signal-to-noise ratio (SNR) and mode coupling (MC). This joint monitoring capability can more comprehensively evaluate the communication quality, and does not require a complex data acquisition or synthesis process, thereby simplifying the implementation and operation of the monitoring system, enabling real-time monitoring, and ensuring the reliability and timely detection of the communication system. Attached Figure Description

[0039] Figure 1 This is a schematic diagram of a mode-division multiplexing fiber optic communication system in which the signal-to-noise ratio and mode coupling joint estimation method can be applied in the embodiment.

[0040] Figure 2 This is a schematic diagram illustrating the steps of the joint estimation method for signal-to-noise ratio and mode coupling in the mode-division multiplexing optical fiber communication embodiment;

[0041] Figure 3 This is a flowchart illustrating the joint estimation method of signal-to-noise ratio and mode coupling in mode-division multiplexing optical fiber communication in the embodiment.

[0042] Figure 4 A schematic diagram illustrating the SNR estimation performance under different channel environment conditions obtained for numerical simulation verification.

[0043] Figure 5 A schematic diagram showing the estimated curves of the power coupling coefficient h for different fiber lengths obtained for numerical simulation verification. Detailed Implementation

[0044] Terminology Explanation:

[0045] 1. Coherent Optical Communication: Coherent optical communication is an advanced optical communication technology that utilizes a laser as a light source and employs coherent detection techniques to improve signal reception sensitivity and transmission quality. In a coherent optical communication system, the signal at the transmitting end modulates the optical carrier of the laser, while the receiving end uses a local laser synchronized with the transmitting end to perform coherent mixing, thereby detecting the amplitude and phase information of the transmitted signal. This technology significantly improves signal transmission distance and system capacity.

[0046] 2. Chromatic Dispersion (CD): Chromatic dispersion refers to the pulse broadening phenomenon caused by the different speeds of light at different wavelengths in optical fibers. Dispersion distorts the time-domain waveform of the signal, affecting signal integrity and thus reducing the performance of the communication system. Dispersion includes material dispersion and waveguide dispersion, and is one of the key impairment factors that need to be considered and compensated for in long-distance optical fiber communication systems.

[0047] 3. Nyquist System: A Nyquist system is a signal sampling system that satisfies the Nyquist sampling theorem. According to the Nyquist sampling theorem, to avoid aliasing, the sampling frequency of the signal should be at least twice the highest frequency of the signal. In fiber optic communication, a Nyquist system typically refers to a digital signal processing system that meets this condition, capable of accurately reconstructing the original analog signal from the sampled data.

[0048] 4. Timing Error Estimation: Timing error estimation refers to the process of estimating the deviation between the sampling clock of the received signal and the actual clock of the signal in a digital communication system. Accurate timing error estimation is crucial for synchronous demodulation of signals, ensuring that the signal is sampled at the optimal time, thereby improving the accuracy of signal demodulation and the overall performance of the system.

[0049] 5. DSP Technology (Digital Signal Processing Technology): DSP technology refers to the technology of processing signals using digital circuits or computers. In fiber optic communication systems, DSP technology is widely used in key aspects such as signal modulation, demodulation, equalization, dispersion compensation, and timing recovery. Through DSP technology, efficient signal processing can be achieved, system performance can be optimized, and transmission quality can be improved.

[0050] 6. Signal-to-Noise Ratio (SNR): The signal-to-noise ratio is a measure of signal strength relative to background noise.

[0051] 7. Mode Coupling (MC): Mode coupling refers to the signal exchange between different modes due to reasons such as fiber bending.

[0052] 8. Optical Performance Monitoring (OPM): Used for real-time monitoring of signal quality in optical fiber communication systems.

[0053] 9. Polarization Mode Dispersion (PMD): Pulse broadening in optical fibers caused by changes in polarization state.

[0054] 10. Mode Division Multiplexing (MDM): Also known as mode multiplexing, it is a method to increase transmission capacity in optical fiber communication. It increases transmission bandwidth by simultaneously transmitting multiple spatial modes in the optical fiber. Traditional single-mode optical fiber communication uses only one spatial mode in the fiber, while mode division multiplexing technology precisely controls the optical signal of each mode, allowing them to be transmitted independently in the optical fiber without interfering with each other, thereby significantly improving the transmission capacity of the optical fiber.

[0055] 11. Few-mode fiber (FMF): A type of optical fiber capable of transmitting a limited number of modes.

[0056] 12. Single-mode fiber (SMF): A fiber that can only transmit a single mode.

[0057] 13. Amplified Spontaneous Emission (ASE): A noise source in fiber optic amplifiers.

[0058] 14. Deep Learning (DL): A machine learning technique used to process large amounts of data.

[0059] 15. Machine Learning (ML): An artificial intelligence technique used to learn patterns from data.

[0060] Current optical performance monitoring (OPM) techniques primarily rely on statistical features extracted from received signals. These methods are generally suitable for direct detection, but they are unsuitable for coherent communication systems due to their wider application scenarios and greater monitoring challenges. Deep learning (DL)-based OPM techniques require large datasets to achieve good performance, but obtaining diverse datasets is both difficult and time-consuming. The shortcomings of current technologies can be summarized as follows:

[0061] 1. Difficulty in obtaining datasets: Deep learning (DL) requires a large amount of datasets, and obtaining these datasets is very difficult and time-consuming.

[0062] 2. Monitoring methods are not applicable: Existing machine learning-based OPM technology is not applicable to coherent communication systems because these systems have a wider range of applications and are more difficult to monitor.

[0063] 3. Mode Coupling Effect: The mode coupling effect (MC) in the MDM system makes the monitoring process more complicated and related technologies difficult to monitor accurately.

[0064] In other words, for coherent communication systems that employ mode multiplexing (MDM), it is necessary to address the challenges faced by integrated sensing and communication in weakly coupled conditions within MDM systems, particularly the accurate monitoring of signal-to-noise ratio (SNR) and mode coupling (MC).

[0065] Based on the above principles, this embodiment provides a joint estimation method for signal-to-noise ratio (SNR) and mode coupling in mode-division multiplexing (MDF) optical fiber communication. This joint estimation method for SNR and mode coupling in MDF optical fiber communication can be used to... Figure 1 The mode-division multiplexing fiber optic communication system shown performs joint estimation of signal-to-noise ratio and mode coupling. (Refer to...) Figure 1 This mode-division multiplexing fiber optic communication system uses coherent optical communication and mode-division multiplexing technology for communication.

[0066] In this embodiment, refer to Figure 2The joint estimation method for signal-to-noise ratio and mode coupling in mode-division multiplexing optical fiber communication includes the following steps:

[0067] S1. Acquire multiple sets of communication signals from the mode-division multiplexing optical fiber communication;

[0068] S2. For any set of communication signals, obtain the spectral correlation function of the communication signals, determine the cyclic correlation function based on the spectral correlation function, and determine the signal-to-noise ratio and fiber length based on the cyclic correlation function;

[0069] S3. Determine the power coupling coefficient value based on the signal-to-noise ratio and fiber length of each group of communication signals.

[0070] In this embodiment, steps S1-S3 can be executed by a DSP. DSP technology plays a core role in coherent optical communication systems, enabling it to perform necessary processing on received optical signals, including symbol rate estimation, carrier frequency offset correction, dispersion compensation, and adaptive signal equalization.

[0071] In this embodiment, a specific execution flow of steps S1-S3 is as follows: Figure 3 As shown. (Refer to...) Figure 3 Steps S1-S3 specifically employ two techniques: "SNR estimation" and "mode coupling estimation." The principle of SNR estimation is that in a coherent optical communication system, each polarization and mode can be modeled using a stochastic process. The SNR can be estimated through an extended cyclic matrix and a cyclic autocorrelation function (CAF). The principle of mode coupling estimation is that the fiber optic deployment environment can be estimated by analyzing radiation loss and the power exchange of mode coupling. The details of these two techniques include:

[0072] A. SNR estimation

[0073] In coherent optical communication systems, each polarization and mode can be modeled using a stochastic process. In this embodiment, the cyclic frequency characteristics of the signal are first analyzed using an extended cyclic matrix. This method allows the calculation of the cyclic autocorrelation function (CAF) of the signal, and subsequently, the signal-to-noise ratio (SNR). The specific steps are as follows:

[0074] 1. Signal Model: First, the signal is modeled, taking into account the effects of polarization-dependent loss (PDL) and mode-dependent loss (MDL).

[0075] 2. Cyclic Matrix: An extended cyclic matrix is ​​constructed to describe the cyclic frequency characteristics of the signal.

[0076] 3. Cyclic Autocorrelation Function (CAF): The CAF is obtained by performing an inverse Fourier transform on the signal. It can be used to estimate the time delay and phase change of the signal.

[0077] 4. SNR Estimation: A new normalized correlation coefficient δ is proposed to correct the SNR estimator, thereby obtaining a more accurate SNR estimate.

[0078] B. Mode Coupling Estimation

[0079] In MDM systems, mode coupling (MC) is a significant influencing factor, leading to power exchange between different signal modes. This embodiment proposes a method to estimate mode coupling, with the following specific steps:

[0080] 1. Power Coupling Model: A model was established to describe the power exchange between random coupling modes caused by factors such as fiber bending.

[0081] 2. Distributed inter-mode crosstalk (DMC) and inter-mode interference (IMI): DMC and IMI are considered as additional noise that can affect system performance.

[0082] 3. Mode Coupling Estimation: A method is proposed to estimate the fiber optic laying environment by estimating the system SNR and performing quadratic or linear fitting.

[0083] In this embodiment, Figure 1 The few-mode optical fibers in the field, experimental environment or during the laying process are subject to the effects of vibration and other factors.

[0084] During steps S1-S3, an arbitrary waveform generator can generate a communication signal, which is transmitted along a few-mode fiber to a coherent receiver for sensing and monitoring of the fiber optic installation environment. During step S1, the DSP reads the received communication signal from the coherent receiver.

[0085] In this embodiment, during step S1, the DSP can read the communication signals received by the coherent receiver at different time periods. The communication signals read by the DSP in each time period form a set of communication signals. Because Figure 1 The few-mode fiber in the channel is subject to the influence of vibration and other factors. The vibration intensity and bending degree of the few-mode fiber are generally different. Therefore, at different times, the few-mode fiber generally faces different vibration intensities and bending degrees. A set of communication signals read by the DSP at a certain time period is transmitted under the channel environment conditions of the few-mode fiber at that time period.

[0086] For the multiple sets of communication signals obtained in step S1, each set of communication signals is processed in step S2. Since the principle of performing step S2 on each set of communication signals is the same, the process of performing step S2 on any one set of communication signals will be used as an example for explanation.

[0087] In this embodiment, when performing the step S2 of obtaining the spectrum correlation function of the communication signal, the following steps can be specifically performed:

[0088] S201. Obtain multiple discrete digital signal components corresponding to the communication signal;

[0089] S202. Extract each discrete digital signal component according to the same time period, and determine multiple cyclic periodic diagrams based on the extracted signals;

[0090] S203. Using the corresponding multiple cyclic periodograms as elements in the matrix, determine the matrix-form spectral correlation function.

[0091] In step S201, for Figure 1 The mode-division multiplexing optical fiber communication system shown can decompose a set of communication signals received from two orthogonal polarization direction dimensions, namely the first polarization direction and the second polarization direction, or it can decompose it from two mode multiplexing dimensions, namely +I and -I, thereby obtaining multiple discrete digital signal components. For example, for a set of communication signals, the discrete digital signal component with the first polarization direction and the +I component is denoted as x, the discrete digital signal component with the first polarization direction and the -I component is denoted as x1, the discrete digital signal component with the second polarization direction and the +I component is denoted as y, and the discrete digital signal component with the second polarization direction and the -I component is denoted as y1, thereby obtaining four discrete digital signal components: x, x1, y, and y1.

[0092] In step S202, taking discrete digital signal components x and y as examples, preprocessing such as matched filtering and resampling is performed on x and y through digital signal processing (DSP). Then, the preprocessed x and y are truncated according to the same time length W. By using the resampled and matched-filtered data, MDM channel effects such as CD, PMD, and MC can be resisted. The signal truncated from x is X. W The signal extracted from y is Y. W Next, set the cycle frequency α, and then calculate the cycle period diagram according to the following formula:

[0093]

[0094] In formulas (1) and (2), X represents W The conjugate of f is the frequency variable.

[0095] Using formulas (1) and (2), the cyclic periodicity diagram P in the frequency domain can be obtained. xx P xy P yx and P yy wait.

[0096] Based on the principles of formulas (1) and (2), the four discrete digital signal components x, x1, y, and y1 can be processed in pairs to obtain other cyclic periodic diagrams. For example, to process the two discrete digital signal components x and x1, specifically, when applying formulas (1) and (2), the variable y in formulas (1) and (2) is replaced with x1 to obtain P. xx , and Isocyclic periodic diagram.

[0097] Based on the above principle, P can be obtained by executing step S202. xx P xy , P yx P yy , and A 16-cycle periodic chart.

[0098] The cyclic periodic graph obtained by performing step S202 can be regarded as an estimate of the spectrum correlation function (SCF) of the communication signal. In this embodiment, the cyclic periodic graph obtained by step S202 can be directly used as the spectrum correlation function (SCF).

[0099] Specifically, in this embodiment, it is assumed that Figure 1 The mode-division multiplexing fiber optic communication system shown is a weakly coupled MDM system. When PDL and MDL are ignored, its spectral correlation function (SCF) can be written as a 4×4 matrix, containing... and Elements such as...

[0100] Since the cyclic periodogram can be regarded as an estimate of the spectrum correlation function (SCF) of the communication signal, and the cyclic periodogram can be directly used as the SCF, in this embodiment, let and They are respectively equal to P xx P xy , P yx P yy , and Right now This allows us to determine the values ​​of each element in the spectrum correlation function (SCF).

[0101] In step S203, the specific form of the obtained spectral correlation function SCF is:

[0102]

[0103] The spectral correlation function SCF includes modeling results for effects such as polarization rotation, PMD, and MC.

[0104] After obtaining the spectral correlation function SCF(f) through steps S201-S203, the cyclic correlation function CAF can be further obtained. There are two feasible methods for calculating the cyclic correlation function CAF. The first method is to obtain it by performing an inverse Fourier transform on the spectral correlation function SCF(f). The second method is to directly calculate it in the time domain according to the definition of the cyclic correlation function CAF to obtain the CAF sequence. After the CAF sequence is generated, the delay amount n is determined based on the peak position of a specific formula. Subsequently, the first value of the delayed CAF sequence is used to construct the CAF matrix.

[0105] In this embodiment, the first method is used to obtain the cyclic correlation function (CAF). Specifically, in step S2, when determining the cyclic correlation function based on the spectral correlation function, the following steps can be performed:

[0106] S204. Perform inverse Fourier transform on each element of the spectral correlation function;

[0107] S205. Using the elements of the spectral correlation function after inverse Fourier transform as the elements of the matrix, determine the cyclic correlation function in matrix form.

[0108] In step S204, an inverse Fourier transform is performed on each element of SCF(f), for example, on the frequency domain. Perform an inverse Fourier transform to obtain the time-domain... For the frequency domain Perform an inverse Fourier transform to obtain the time-domain... ...for the frequency domain Perform an inverse Fourier transform to obtain the time-domain...

[0109] In step S205, for the inverse Fourier transform obtained Elements, keep ……right The relative positions of the elements in SCF(f) remain unchanged, thus forming a matrix-form cyclic correlation function CAF(τ):

[0110]

[0111] Where τ is a time variable.

[0112] In this embodiment, by using the matrix-form spectral correlation function SCF(f) and the cyclic correlation function CAF(τ), the spectral response H = e^(-t / t) corresponding to the dispersion effect can be obtained.jkf2 The study investigates the effects on individual SCFs and CAFs, as well as the effects of polarization on SCFs and CAFs.

[0113] After obtaining the Cyclic Correlation Function (CAF) through steps S204-S205, the signal-to-noise ratio (SNR) and fiber length can be further obtained. In this embodiment, when performing the step of determining the SNR and fiber length based on the Cyclic Correlation Function in step S2, the following steps can be specifically executed:

[0114] S206. Determine the maximum correlation strength based on the cyclic correlation function;

[0115] S207. Obtain the average power of the upper sideband and the average power of the lower sideband for each discrete digital signal component;

[0116] S208. Determine the normalized correlation coefficient based on the maximum correlation strength, the average power of the upper sideband, and the average power of the lower sideband;

[0117] S209. Determine the signal-to-noise ratio based on the normalized correlation coefficient;

[0118] S210. Use the CAF-4CDE algorithm to determine the fiber length.

[0119] In step S206, the nth element of the cyclic correlation function CAF(τ) is denoted as R. n (τ), R n (τ) is a function of time τ. In Figure 1 In the modulus-division multiplexing fiber optic communication system shown, when dispersion (CD) exists, it can be determined according to a specific time τ = τ CD Time R n The value of (τ) determines the maximum correlation strength R. sum .

[0120] Specifically, τ CD satisfy

[0121]

[0122] In determining τ CD The maximum correlation strength R can then be calculated using the following formula. sum :

[0123]

[0124] Since uncompensated or residual channel benefits significantly affect the accuracy of signal-to-noise ratio (SNR) estimation in actual fiber optic links, this embodiment proposes using a normalized correlation coefficient to correct the SNR estimate. Specifically, when the ASE noise is linear, in step S207, the upper sideband average power P1 and lower sideband average power P2 of all discrete digital signal components such as x, x1, y, and y1 are obtained. In step S208, the normalized correlation coefficient δ is calculated according to the following formula:

[0125]

[0126] In step S209, the signal-to-noise ratio (SNR) is calculated according to the following formula:

[0127]

[0128] In step S210, the CAF-4CDE algorithm is used to process the cyclic correlation function CAF(τ), which can be estimated. Figure 1 The fiber length L of the few-mode fiber in the modal division multiplexing optical fiber communication system shown is used to transmit this set of communication signals.

[0129] By executing step S2, the signal-to-noise ratio (SNR) and the corresponding fiber length L of any group of communication signals can be obtained. That is, each group of communication signals can obtain the corresponding SNR and fiber length L by executing step S2.

[0130] Due to radiation loss caused by random bending of the fiber optic cable laid in the field, the power exchange between randomly coupled modes can be described by power coupling. In sparse MDM systems, distributed mode crosstalk (DMC) and inter-mode interference (IMI) are likely two major impairments associated with multimode coupling crosstalk. The effects of IMI and DMC on sparse MDM systems can be considered as additive noise. When there is severe vibration in the external environment, the fiber bending is stronger, resulting in a larger power coupling coefficient and causing additional SNR impairment. Therefore, in step S3, the monitored power coupling coefficient value can be obtained by performing a quadratic or linear fitting on the fiber length.

[0131] Specifically, taking quadratic fitting as an example, the fitting formula can be expressed as:

[0132]

[0133] Since performing step S2 on each group of communication signals yields the corresponding signal-to-noise ratio (SNR) and fiber length L, and multiple groups of communication signals can be monitored and multiple SNRs and fiber lengths L can be obtained as the channel environment changes, the power coupling coefficient h can be obtained by fitting a formula. Specifically, this coefficient includes the quadratic term h caused by inter-mode interference (IMI).IMI and the coefficient h of the first term caused by distributed modal crosstalk (DMC) DMC The constant term SNR0 is obtained through fitting. In this fitting formula, the signal-to-noise ratio SNR can use a linear value instead of a logarithmic value.

[0134] The signal-to-noise ratio (SNR) and mode coupling (MC) joint estimation method for mode-division multiplexing optical fiber communication in this embodiment can simultaneously monitor both SNR and MC, providing an effective monitoring method for integrated sensing and communication systems. It employs the following novel technical methods:

[0135] 1. Applications of extended circular matrices:

[0136] Modeling the cyclic frequency characteristics of a signal using an extended cyclic matrix is ​​an innovative application of existing technology that improves the accuracy of signal processing.

[0137] 2. Innovative calculation of the cyclic autocorrelation function (CAF):

[0138] Using CAF to estimate the time delay and phase change of a signal is an innovation in signal processing technology, which improves the understanding of signal characteristics.

[0139] 3. The introduction of the normalized correlation coefficient δ:

[0140] We propose using the normalized correlation coefficient δ to correct the SNR estimate, which improves the accuracy of the SNR estimate.

[0141] 5. Mode-coupled power coupling model:

[0142] A model is established to describe the power exchange between random coupling modes caused by factors such as fiber bending, and the mode coupling effect is studied in depth.

[0143] 6. Resistance to random polarization effects: By using an extended cyclic matrix, it is possible to resist random polarization effects and improve the robustness of the system.

[0144] 7. Tolerance to polarization effects: It is specially designed to have high tolerance to dispersion (CD), polarization mode dispersion (PMD) and polarization mode dispersion (DGD) in optical fiber channels. This is especially important for practical optical communication systems, as these effects can seriously affect signal integrity and the accurate estimation of signal-to-noise ratio.

[0145] In summary, the joint estimation method for signal-to-noise ratio and mode coupling in mode-division multiplexing optical fiber communication in this embodiment can achieve the following effects:

[0146] 1. Joint monitoring capability:

[0147] The ability to monitor both signal-to-noise ratio (SNR) and mode coupling (MC) simultaneously enables the system to more comprehensively assess communication quality.

[0148] 2. Higher accuracy:

[0149] By using an extended cyclic matrix and a cyclic autocorrelation function (CAF), the signal-to-noise ratio (SNR) can be estimated more accurately, especially in the presence of polarization mode dispersion (PMD), dispersion (CD), and mode coupling effects.

[0150] 3. Greater robustness:

[0151] It has stronger resistance to random polarization effects, which means it can provide more stable performance when faced with various disturbances common in fiber optic communication systems.

[0152] 4. Simplified data requirements:

[0153] Compared to deep learning methods that require large datasets, the joint estimation method for signal-to-noise ratio and mode coupling in mode-division multiplexing fiber communication in this embodiment does not require complex data acquisition or synthesis processes, thus simplifying the implementation and operation of the monitoring system.

[0154] 5. Real-time monitoring capability:

[0155] The ability to perform real-time monitoring is crucial for ensuring the reliability of communication systems and for timely detection of problems.

[0156] Numerical simulations were performed to verify the joint estimation method of signal-to-noise ratio (SNR) and mode coupling in mode-division multiplexing optical fiber communication in this embodiment. The SNR estimation performance under different channel environment conditions is as follows: Figure 4 As shown, the estimated curves of the power coupling coefficient h for different fiber lengths are as follows: Figure 5 As shown. According to Figure 4 and Figure 5 It can be seen that the joint estimation method of signal-to-noise ratio and mode coupling in mode-division multiplexing optical fiber communication can jointly estimate the signal-to-noise ratio and the power coupling coefficient of mode coupling in the mode-division multiplexing optical fiber communication system.

[0157] A computer program can be written to execute the signal-to-noise ratio and mode coupling joint estimation method of mode-division multiplexing optical fiber communication in this embodiment. The computer program can be written into a computer device or storage medium. When the computer program is read out and run, the signal-to-noise ratio and mode coupling joint estimation method of mode-division multiplexing optical fiber communication in this embodiment can be executed, thereby achieving the same technical effect as the signal-to-noise ratio and mode coupling joint estimation method of mode-division multiplexing optical fiber communication in the embodiment.

[0158] It should be noted that, unless otherwise specified, when a feature is referred to as "fixed" or "connected" to another feature, it can be directly fixed or connected to the other feature, or indirectly fixed or connected to the other feature. Furthermore, the descriptions of "upper," "lower," "left," and "right" used in this disclosure are only relative to the relative positional relationships of the components of this disclosure in the accompanying drawings. The singular forms "a," "an," and "the" used in this disclosure are also intended to include the plural forms, unless the context clearly indicates otherwise. Moreover, unless otherwise defined, all technical and scientific terms used in this embodiment have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in this embodiment specification is only for describing particular embodiments and is not intended to limit the invention. The term "and / or" as used in this embodiment includes any combination of one or more of the associated listed items.

[0159] It should be understood that although the terms first, second, third, etc., may be used to describe various elements in this disclosure, these elements should not be limited to these terms. These terms are only used to distinguish elements of the same type from each other. For example, a first element may also be referred to as a second element without departing from the scope of this disclosure, and similarly, a second element may also be referred to as a first element. The use of any and all instances or exemplary language (“e.g.,” “such as,” etc.) provided in this embodiment is intended only to better illustrate embodiments of the invention and, unless otherwise required, does not impose a limitation on the scope of the invention.

[0160] It should be recognized that embodiments of the present invention can be implemented or carried out by computer hardware, a combination of hardware and software, or by computer instructions stored in a non-transitory computer-readable storage medium. The method can be implemented using standard programming techniques—including a non-transitory computer-readable storage medium configured with a computer program, wherein such a storage medium causes the computer to operate in a specific and predefined manner—according to the methods and drawings described in the specific embodiments. Each program can be implemented in a high-level procedural or object-oriented programming language to communicate with the computer system. However, if desired, the program can be implemented in assembly or machine language. In any case, the language can be a compiled or interpreted language. Furthermore, for this purpose, the program can run on a programmed application-specific integrated circuit (ASIC).

[0161] Furthermore, the procedures described in this embodiment can be performed in any suitable order unless otherwise indicated by this embodiment or clearly contradicted by the context. The procedures (or variations and / or combinations thereof) described in this embodiment can be executed under the control of one or more computer systems configured with executable instructions, and can be implemented by hardware or a combination thereof as code (e.g., executable instructions, one or more computer programs, or one or more applications) that commonly executes on one or more processors. A computer program includes multiple instructions executable by one or more processors.

[0162] Furthermore, the method can be implemented in any suitable type of computing platform, including but not limited to personal computers, minicomputers, mainframes, workstations, networked or distributed computing environments, standalone or integrated computer platforms, or in communication with charged particle tools or other imaging devices, etc. Aspects of the invention can be implemented as machine-readable code stored on a non-transitory storage medium or device, whether removable or integrated into a computing platform, such as a hard disk, optical read and / or write storage medium, RAM, ROM, etc., such that it is readable by a programmable computer, and when the storage medium or device is read by the computer, it can be used to configure and operate the computer to perform the processes described herein. Furthermore, the machine-readable code, or portions thereof, can be transmitted via wired or wireless networks. The invention of this embodiment includes these and other different types of non-transitory computer-readable storage media when such media comprises instructions or programs that implement the steps above in conjunction with a microprocessor or other data processor. When programmed according to the methods and techniques of the invention, the invention also includes the computer itself.

[0163] A computer program can be applied to input data to perform the functions of this embodiment, thereby transforming the input data to generate output data stored in non-volatile memory. The output information can also be applied to one or more output devices, such as a display. In a preferred embodiment of the invention, the transformed data represents physical and tangible objects, including a specific visual depiction of physical and tangible objects generated on the display.

[0164] The above are merely preferred embodiments of the present invention. The present invention is not limited to the above-described embodiments. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention, as long as they achieve the technical effects of the present invention by the same means, should be included within the scope of protection of the present invention. Within the scope of protection of the present invention, the technical solutions and / or implementation methods can have various modifications and variations.

Claims

1. A method for jointly estimating the signal-to-noise ratio and mode coupling in mode-division multiplexing optical fiber communication, characterized in that, The joint estimation method for signal-to-noise ratio and mode coupling in the mode-division multiplexing optical fiber communication includes: Acquire multiple sets of communication signals from modulus-division multiplexing optical fiber communication; For any set of the communication signals, obtain the spectral correlation function of the communication signals, determine the cyclic correlation function based on the spectral correlation function, and determine the signal-to-noise ratio and fiber length based on the cyclic correlation function; The power coupling coefficient value is determined based on the signal-to-noise ratio and fiber length corresponding to each group of communication signals. The step of obtaining the spectrum correlation function of the communication signal includes: For any set of communication signals, multiple discrete digital signal components corresponding to the communication signals are obtained; each discrete digital signal component has a corresponding polarization multiplexing dimension and mode multiplexing dimension. For any group of the communication signals, each discrete digital signal component is truncated according to the same time length, and multiple cyclic periodic diagrams are determined based on the truncated signals. For any set of the communication signals, the matrix-form spectrum correlation function is determined using the corresponding plurality of cyclic periodic diagrams as elements in the matrix. The step of determining the signal-to-noise ratio and fiber length based on the cyclic correlation function includes: Determine the maximum correlation strength based on the cyclic correlation function; Obtain the upper sideband average power and lower sideband average power of each discrete digital signal component; The normalized correlation coefficient is determined based on the maximum correlation strength, the average power of the upper sideband, and the average power of the lower sideband. The signal-to-noise ratio is determined based on the normalized correlation coefficient.

2. The joint estimation method for signal-to-noise ratio and mode coupling in mode-division multiplexing optical fiber communication according to claim 1, characterized in that, The acquisition of multiple sets of communication signals in the modulus-division multiplexing optical fiber communication includes: Signals from modulus-division multiplexing optical fiber communication are received by a coherent receiver under various channel environment conditions. The signals received under each of the aforementioned channel environment conditions form a set of communication signals.

3. The joint estimation method for signal-to-noise ratio and mode coupling in mode-division multiplexing optical fiber communication according to claim 1, characterized in that, The step of determining the cyclic correlation function based on the spectral correlation function includes: Perform inverse Fourier transform on each element of the spectral correlation function; The cyclic correlation function in matrix form is determined by using the inverse Fourier transform of the elements in the spectral correlation function as the elements in the matrix.

4. The joint estimation method for signal-to-noise ratio and mode coupling in mode-division multiplexing optical fiber communication according to claim 1, characterized in that: The normalized correlation coefficient is determined based on the maximum correlation strength, the average power of the upper sideband, and the average power of the lower sideband, according to the following formula: in, The normalized correlation coefficient is... This represents the maximum value of the correlation strength. The average power of the upper sideband, The lower sideband average power; The signal-to-noise ratio is determined based on the normalized correlation coefficient using the following formula: in, The signal-to-noise ratio is denoted as .

5. The joint estimation method for signal-to-noise ratio and mode coupling in mode-division multiplexing optical fiber communication according to claim 1, characterized in that, The step of determining the signal-to-noise ratio and fiber length based on the cyclic correlation function includes: The length of the optical fiber was determined using the CAF-4 CDE algorithm.

6. The joint estimation method for signal-to-noise ratio and mode coupling in mode-division multiplexing optical fiber communication according to any one of claims 1-5, characterized in that, The step of determining the power coupling coefficient value based on the signal-to-noise ratio and fiber length corresponding to each group of communication signals includes: Perform a quadratic or linear fit based on the signal-to-noise ratio and the fiber length; The coefficient values ​​determined by the fitting are used as the power coupling coefficient values.

7. A computer device, characterized in that, It includes a memory and a processor, the memory being used to store at least one program, and the processor being used to load at least one program to execute the signal-to-noise ratio and mode coupling joint estimation method for mode-division multiplexing optical fiber communication as described in any one of claims 1-6.

8. A computer-readable storage medium storing a processor-executable program, characterized in that, The processor-executable program, when executed by the processor, is used to perform the signal-to-noise ratio and mode coupling joint estimation method for mode-division multiplexing optical fiber communication as described in any one of claims 1-6.

Images