Signal interference suppression method combined with multi-level carrier coordination and dynamic optical path compensation

Abstract

The present application relates to the field of optical communication technology, and discloses a signal interference suppression method combined with multi-level carrier cooperation and dynamic optical path compensation, comprising the following steps: a transmitting unit transmits a cognitive probe carrier and a data carrier to an optical path; a receiving unit receives and processes the transmitted cognitive probe carrier to generate cognitive probe carrier data; a cooperative intelligent controller updates a dynamic optical path twin model based on the data, evaluates the model parameter confidence, and determines an adaptive cognitive probe carrier detection strategy accordingly, instructing the transmitting unit to reconfigure the transmission of the cognitive probe carrier; and the cooperative intelligent controller cooperates to adjust the transmission parameters of the data carrier and controls a dynamic optical path compensation unit to compensate the optical path based on the optical path twin model with high confidence. The present application can actively respond to dynamic interference in the optical path, significantly improving the signal transmission quality, reliability and environmental adaptability of the optical communication system.

Description

Technical Field

[0001] This invention relates to the field of optical communication technology, specifically to a signal interference suppression method that combines multi-level carrier coordination and dynamic optical path compensation. Background Technology

[0002] With the explosive growth of global data traffic, the requirements for bandwidth capacity, transmission distance, and flexibility of optical communication networks are becoming increasingly stringent. While optical fiber, as the primary medium for information transmission, offers advantages such as low loss and enormous bandwidth, it is not an ideal transmission channel. When optical signals are transmitted long distances in optical fibers, they are inevitably affected by the cumulative effects of various physical effects. These effects include linear impairments such as chromatic dispersion (CD) and polarization mode dispersion (PMD), as well as nonlinear effects that are more pronounced at higher optical power, such as self-phase modulation (SPM), cross-phase modulation (XPM), and four-wave mixing (FWM). These physical impairments lead to signal waveform distortion, pulse broadening, increased phase noise, and crosstalk between different wavelength channels, ultimately limiting the system's transmission performance and the achievable bit rate-distance product.

[0003] More complexly, the aforementioned optical path damage characteristics are not static but exhibit significant dynamism. Environmental factors such as temperature changes, mechanical vibrations, or stress applied to the optical cable, as well as the aging of optical devices in the network and dynamic reconfiguration of the optical path, can all cause the physical parameters of the fiber optic link to drift and fluctuate rapidly or slowly over time. For example, the PMD characteristic is particularly sensitive to environmental changes, and its instantaneous value may change on the order of milliseconds or even microseconds. This dynamic uncertainty poses a significant challenge to the stable and reliable operation of optical communication systems, making traditional static or slow adaptive compensation schemes inadequate.

[0004] Existing optical communication systems employ a range of techniques to monitor and compensate for these impairments. For example, optical performance monitoring (OPM) modules are deployed to acquire key link parameters such as optical signal-to-noise ratio (OSNR) and dispersion. At the receiver, digital signal processing (DSP) techniques are widely used to compensate for linear impairments, such as electronic dispersion compensation (EDC) and adaptive equalization of dispersion modulators (PMD) using digital filters. For nonlinear effects, researchers have also proposed compensation algorithms such as digital back-propagation (DBP).

[0005] However, current technologies still have many shortcomings in dealing with complex and dynamic optical network environments. Many existing optical performance monitoring methods often provide incomplete, unreal-time, or limited-precision optical path state information, making it difficult to accurately and dynamically capture the instantaneous changes of all key impairment parameters. This leads to compensation mechanisms often lagging behind actual optical path changes or being based on inaccurate channel models, thus affecting compensation effectiveness. When multiple impairments coexist and are intertwined, existing technologies struggle to effectively decouple them and perform targeted, precise compensation. Furthermore, there is often a lack of efficient coordination mechanisms and a unified, real-time understanding of optical path states between the transmitter's parameter configuration (such as modulation format, transmit power, precoding, etc.), the adjustment of physical compensation devices in the optical path (such as tunable dispersion compensators, dynamic gain equalizers, etc.), and the receiver's signal processing. This lack of coordination and fragmented, "lone wolf" approach to compensation can easily lead to the overall system performance falling into local optima, making it difficult to fully exploit the potential of fiber optic links. Especially when faced with rapidly changing optical path conditions, existing systems generally lack a comprehensive capability to proactively and intelligently adjust the detection strategy to focus on the damage parameters with the highest uncertainty, and on this basis, to perform global optimization and collaborative compensation.

[0006] Therefore, there is an urgent need for a technical solution that can perceive the dynamic optical path status in real time and accurately, and on this basis, perform multi-level, intelligent collaborative interference suppression to effectively improve the transmission performance, reliability and adaptability of optical communication systems in complex dynamic environments. Summary of the Invention

[0007] The present invention aims to solve the problems of insufficient ability to suppress complex dynamic interference, inaccurate perception of optical path status, and low degree of collaborative optimization of multiple components in existing optical communication systems.

[0008] To achieve the above objectives, the present invention provides the following technical solution: The first aspect of this invention provides a signal interference suppression method that combines multi-level carrier coordination and dynamic optical path compensation.

[0009] This method, by introducing an innovative collaborative mechanism, achieves proactive sensing, accurate modeling, adaptive adjustment, and collaborative suppression of signal interference in optical communication systems. Specifically: 1. Active optical path sensing and interference feature extraction based on cognitive probe carrier: This method first transmits specially designed cognitive probe carriers (CPCs) in the optical path. These CPCs are not traditional fixed pilots, but have a specific signal structure that makes them highly sensitive to impairments and interference factors in the fiber optic link, such as dispersion, polarization mode dispersion (PMD), nonlinear effects, and various crosstalk and noise. The CPCs are transmitted at low power to minimize the impact on data carriers (DCs).

[0010] At the receiving end or a designated monitoring point in the optical path, the transmitted CPCs signal is captured and analyzed to extract information such as signal distortion, attenuation, phase change, and intermodulation products, forming cognitive probe carrier data. These data reflect the instantaneous state of the optical path and the characteristics of any interference present.

[0011] 2. Construction, updating, and confidence assessment of the dynamic optical path twin model (OPT): This method further utilizes the acquired cognitive probe carrier data And optionally, historical performance feedback from the data carrier. To dynamically build and update an optical path twin (OPT) model, i.e. This OPT is a digital, real-time representation of the actual physical characteristics of the optical path and the signal propagation effects. The state vector of the OPT... This can include the dispersion coefficients of each segment of the optical path. PMD vector Nonlinear coefficients and characteristics of specific interference sources Estimation of parameters such as...

[0012] The OPT update process can be summarized as follows: ; in, This represents an update algorithm based on, for example, Kalman filtering, particle filtering, or machine learning, which incorporates new observation data into the existing model. This is the parameter set of the algorithm.

[0013] One of the key innovations is that this method applies different parameters in the OPT model. The estimation incorporates a confidence assessment mechanism. Parameters confidence level This reflects the reliability or accuracy of the current estimate of this parameter, and its evaluation can be expressed as: ; in, It is the variance of the parameter estimates. It is a measure of the quality of relevant CPC data. It is a confidence assessment function. Its parameters.

[0014] 3. Adaptive cognitive probe carrier detection strategy based on OPT confidence: Another core innovation of this method is that it does not employ a fixed CPC detection scheme, but rather evaluates the results based on the confidence level of the OPT model parameters. Dynamically adjust and optimize the CPC detection strategy for the next cycle. This can be represented as: ; in, It is the policy generation function. Specifically, it depends on the confidence level of certain parameters in OPT. Below the preset threshold If the parameters change rapidly, indicating that the model's understanding of these aspects is inaccurate or outdated, the system will instruct CPCs to adjust their emission parameters, for example: Increase the transmit power of CPCs that are more sensitive to detecting these low-confidence parameters (within permissible limits).

[0015] Modify the signal waveform or sequence of CPCs to enhance the detection capability for specific effects.

[0016] Adjust the spectral location or spatial deployment of CPCs (if supported) to more precisely focus on optical path segments or effects with high uncertainty.

[0017] Increase the frequency or density of detection for these parameters. This adaptive detection mechanism enables detection resources to be intelligently directed to where information is most needed, thereby efficiently improving the overall accuracy and timeliness of OPT and forming a closed loop of on-demand sensing.

[0018] 4. Multi-level cooperative interference suppression based on high-precision OPT: To obtain a high-confidence, high-precision OPT model Subsequently, the method performs multi-level coordinated adjustments through a Cooperative Intelligent Controller (CIC) to suppress signal interference on the data carrier: Data carrier transmission parameter optimization: CIC utilization Predict the transmission performance of the data carrier under current channel conditions, and optimize the transmission parameters of the data carrier accordingly. Examples of such parameters include modulation format, coding scheme, power allocation, digital predistortion coefficients, or precoding matrix, in order to maximize predefined performance metrics (such as expected transmission quality, rate, etc.).

[0019] ; Dynamic optical path compensation unit coordinated adjustment: Simultaneously, CIC according to... Accurate estimation of optical path damage (such as cumulative dispersion and transient PMD) is used to generate control signals. It is used to adjust the settings of dynamic compensation units (such as tunable dispersion compensators, polarization controllers, dynamic gain equalizers, etc.) in the optical path.

[0020] ; This coordinated adjustment between the transmitter (data carrier) and the transmission link (optical path compensation unit) is based on the same precise OPT model calibrated in real time by CPCs, ensuring the global optimality and consistency of each adjustment measure, thereby enabling more effective response to complex and time-varying interference.

[0021] Through iterative execution of the above steps, this method establishes a closed-loop adaptive interference suppression system encompassing perception, modeling, decision-making, and execution. Its innovation lies in: significantly improving the depth and accuracy of dynamic optical path environment perception by introducing a cognitive probe carrier with an adaptive detection strategy; providing a reliable basis for subsequent coordinated control by constructing and real-time calibrating an optical path twin model and introducing a confidence assessment mechanism; and ultimately achieving unprecedented deep coordination between data carrier optimization and dynamic optical path compensation, thereby more effectively suppressing complex dynamic interference that is difficult to handle with traditional methods.

[0022] A second aspect of the present invention provides a signal interference suppression system that combines multi-level carrier coordination and dynamic optical path compensation.

[0023] A transmitting unit is configured to transmit cognitive probe carriers and data carriers on demand in the optical path. Specifically, this transmitting unit can transmit cognitive probe carriers with specific initial parameters according to instructions from the cooperative intelligent controller, and reconfigure and transmit these cognitive probe carriers according to a subsequently determined adaptive cognitive probe carrier detection strategy. Simultaneously, this transmitting unit also configures and transmits data carriers according to instructions from the cooperative intelligent controller.

[0024] A receiving unit (or monitoring unit): configured to receive cognitive probe carriers after optical path transmission and process the received cognitive probe carriers to generate cognitive probe carrier data reflecting optical path characteristics and interference conditions. This receiving unit can also be used to monitor the performance of data carriers.

[0025] A collaborative intelligent controller: As the core control hub of the system, this controller is operatively connected to the transmitting unit and the receiving unit (or monitoring unit), and is configured to perform the following core functions: Receive and process cognitive probe carrier data from the receiving unit.

[0026] Based on the received cognitive probe carrier data and optional data carrier performance feedback, the optical twin model used to characterize the optical path properties is continuously updated and calibrated.

[0027] Confidence assessment is performed on the parameters or predictions in the optical twin model.

[0028] Based on the confidence assessment results of the optical twin model, the adaptive cognitive probe carrier detection strategy for subsequent cognitive probe carrier transmission is dynamically determined and adjusted, and the configuration instructions related to the strategy are sent to the transmission unit.

[0029] Based on the updated, higher-confidence optical twin model, decisions are made to generate instructions for coordinating the adjustment of data carrier transmission parameters and send them to the transmission unit, and / or to generate instructions for coordinating the adjustment of the settings of one or more dynamic optical path compensation units in the optical path and send them to the corresponding compensation units, so as to actively and accurately suppress signal interference to the data carrier.

[0030] Through the collaborative work of its various units, the system achieves closed-loop perception of the optical path state and adaptive collaborative suppression of interference, demonstrating the same innovative principles and technological advantages as at the methodological level.

[0031] A third aspect of the present invention provides a computer-readable storage medium.

[0032] The computer-readable storage medium stores computer-executable instructions that, when executed by one or more processors (e.g., a processor integrated in the aforementioned cooperative intelligent controller), enable the processor (or the system containing the processor) to perform all or part of the steps in the multi-level carrier coordination and dynamic optical path compensation combined signal interference suppression method described in the first aspect, particularly the core algorithm logic related to data processing, model updating, policy generation, and control decision-making. This allows the core intelligent control function of the present invention to be implemented and deployed in software.

[0033] This invention provides a signal interference suppression method combining multi-level carrier coordination and dynamic optical path compensation. It has the following beneficial effects: 1. This invention achieves deep, accurate, and efficient dynamic sensing of optical network states by introducing a cognitive probe carrier with an adaptively adjustable detection strategy and combining it with the evaluation of the confidence level of optical path twin model parameters. The system can intelligently focus detection resources on the most uncertain or drastically changing optical path characteristics, thereby obtaining the highest-value channel information with minimal overhead and significantly improving the real-time characterization capability of complex dynamic optical links.

[0034] 2. Based on a high-fidelity optical path twin model continuously calibrated by a cognitive probe carrier, this invention enables precise and coordinated optimization of data carrier transmission parameters and dynamic compensation unit settings in the optical path. This joint adjustment based on unified and accurate channel understanding ensures targeted optimization and compensation of the signal at each key node of generation and transmission, thereby more effectively combating various linear and nonlinear interferences and significantly enhancing the transmission robustness and spectral efficiency of the optical communication system.

[0035] 3. This invention constructs a complete closed-loop adaptive interference suppression framework, encompassing proactive sensing, precise modeling, intelligent decision-making, and collaborative execution. This end-to-end intelligent collaborative mechanism enables the system to proactively adapt to the ever-changing service demands and environmental interference in optical networks, without relying on prior knowledge of interference types. This provides crucial physical layer adaptive capabilities and performance guarantees for the future evolution of optical networks towards higher capacity and greater complexity. Attached Figure Description

[0036] Figure 1 This is a system architecture block diagram of the present invention; Figure 2 This is a schematic diagram of the method flow of the present invention.

[0037] Among them, 110 is the transmitting unit; 101 is the optical path; 120 is the receiving unit; 130 is the collaborative intelligent controller; and 140 is the dynamic optical path compensation unit. Detailed Implementation

[0038] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are primarily used to explain the invention and are not intended to limit the scope of protection of the invention.

[0039] Please see the appendix Figure 1 -Appendix Figure 2 This invention provides a signal interference suppression method that combines multi-level carrier coordination and dynamic optical path compensation, aiming to proactively and intelligently sense and suppress complex and dynamically changing signal interference in optical communication links.

[0040] like Figure 2 As shown, the method may include the following steps: S100, the transmitting unit transmits cognitive probe carriers according to the initial strategy.

[0041] During system initialization or stable operation, the transmitting unit 110 transmits one or more specially designed cognitive probe carriers into the optical path 101 according to a preset or determined initial cognitive probe carrier (CPC) transmission strategy by the collaborative intelligent controller 130. These cognitive probe carriers have predetermined signal characteristics, making them highly sensitive to specific physical damage (such as dispersion, polarization mode dispersion, and nonlinear effects) and interference sources (such as crosstalk and noise) in the optical path 101.

[0042] S200, The receiving unit captures and processes the transmitted cognitive probe carrier to generate data.

[0043] After transmission through optical path 101, receiving unit 120 (or a monitoring unit deployed midway through the optical path) captures the cognitive probe carrier. Receiving unit 120 processes the received cognitive probe carrier signal, such as through signal amplification, filtering, demodulation, and feature extraction, thereby generating cognitive probe carrier data. This data quantitatively reflects the changes in signal distortion, power attenuation, phase rotation, and spectral broadening experienced by the cognitive probe carrier during transmission through optical path 101, and contains the current state information of optical path 101.

[0044] The S300 and Collaborative Intelligent Controller use cognitive probe carrier data to update the optical path twin model.

[0045] The collaborative intelligent controller 130 receives cognitive probe carrier data from the receiving unit 120. The Optical Path Twin (OPT) module inside the Collaborative Intelligent Controller 130 uses this real-time data to update or calibrate the optical path twin model used to digitally characterize the properties of the optical path 101. The model It is a dynamic mathematical model whose state vector contains estimates of key optical path parameters, such as the cumulative dispersion of each link segment. Differential group delay Nonlinear coefficients and equivalent parameters of specific interference The model update process can be abstractly represented as: ; in, This represents the state of the OPT model at the previous time step. This refers to the cognitive probe carrier data acquired at the current moment. Optional historical performance feedback (such as bit error rate, signal-to-noise ratio, etc.) from the data carrier. It represents one or more model update algorithms, such as extended Kalman filter, unscented Kalman filter, or machine learning-based regression model.

[0046] The S400 and collaborative intelligent controller evaluate the confidence level of the optical twin model parameters.

[0047] The OPT model module within the Collaborative Intelligent Controller 130 further refines the updated optical path twin model. The confidence level or uncertainty of the estimated parameters in the model is evaluated. For example, for the first parameter in the model... Parameters Its confidence level The posterior variance can be estimated based on this parameter. CPC data quality metrics used to estimate this parameter The confidence level is calculated by comprehensively considering factors such as the historical stability of parameter changes. Confidence assessment helps determine the reliability of the current model's description of various aspects of the optical path state.

[0048] The S500 and collaborative intelligent controller determine the cognitive probe carrier detection strategy for the next cycle based on model confidence.

[0049] Based on optical twin model The confidence assessment results of the state and its parameters The adaptive CPC detection strategy module within the collaborative intelligent controller 130 determines the next cycle. Cognitive probe carrier detection strategy This decision aims to prioritize improving detection accuracy for low-confidence or rapidly changing optical path parameters by adjusting the CPC's emission parameters. For example, if the parameters... confidence level If the value falls below a certain threshold, the strategy module may decide to increase the detection... Increase the transmission power of a more sensitive CPC signal, change its waveform characteristics, or increase its detection frequency.

[0050] The S600 and the collaborative intelligent controller command transmission unit reconfigure the cognitive probe carrier according to the new strategy.

[0051] The collaborative intelligent controller 130 will use the newly determined adaptive cognitive probe carrier detection strategy This is translated into specific configuration instructions and sent to the transmitting unit 110. The transmitting unit 110 then reconfigures the transmission parameters of the cognitive probe carrier for the next round of optical path detection. This step ensures dynamic optimization of CPC detection behavior, enabling detection resources to be intelligently allocated to where they are most needed.

[0052] The S700 and the collaborative intelligent controller adjust the data carrier transmission and optical path compensation unit based on a high-confidence model.

[0053] With a newer and more confident optical twin model Based on this, the multi-level cooperative interference suppression module of the cooperative intelligent controller 130 makes decisions to suppress signal interference to the data carrier (DC) in the optical path 101. This includes two cooperative aspects: Firstly, the collaborative intelligent controller 130 is based on Predict the transmission performance of the data carrier under current optical path conditions, and optimize the transmission parameters of the data carrier accordingly. For example, adjusting the modulation format, forward error correction coding (FEC) scheme, transmit power spectrum or digital predistortion (DPD) coefficients, these instructions are also sent to the transmit unit 110.

[0054] Secondly, the collaborative intelligent controller 130 according to Accurate estimation of accumulated damage in the optical path (such as total dispersion and instantaneous PMD vector) generates control signals. It is used to adjust the settings of one or more dynamic optical path compensation units (LPUs) 140 (such as tunable dispersion compensation modules, polarization controllers, dynamic gain equalizers, etc.) coupled to the optical path 101.

[0055] S800, the system iteratively executes the above steps to form a closed-loop adaptive control.

[0056] The system continuously iterates through steps S100 to S700, forming a closed-loop adaptive control circuit. Through this continuous cycle of sensing, modeling, decision-making, and execution, the system can dynamically track changes in the optical path environment and optimize signal transmission and compensation strategies in real time, thereby effectively suppressing various dynamic interferences and ensuring the quality and reliability of data transmission.

[0057] In a specific embodiment of the present invention, in order to realize the transmission of the cognitive probe carrier in step S100 and the generation of cognitive probe carrier data in step S200, the system employs a series of carefully designed and regulated cognitive probe carriers. The design, configuration, transmission, reception, and processing of these cognitive probe carriers are described in detail below.

[0058] First, prior to transmitting the cognitive probe carrier, the method of the present invention includes a cognitive probe carrier characteristic selection and configuration step. This step is led by the cooperative intelligent controller 130 or executed according to its preset logic. The selection and configuration are based on a comprehensive consideration of historical optical path data, current detection requirements (e.g., which parameters in the optical path twin model have low confidence levels), and system operational objectives (e.g., whether to focus on rapid link change tracking or fine-grained nonlinear effect characterization).

[0059] The core design principle of cognitive probe carriers lies in their known signal structure and sensitivity to specific optical path effects. Unlike data carriers, the waveform, sequence, or spectral characteristics of cognitive probe carriers are predefined and known to the system. This allows the receiver to accurately quantify the various physical effects the signal experiences in the optical path by comparing the received distorted signal with an ideal reference signal.

[0060] The cognitive probe carriers in this embodiment of the invention may include, but are not limited to, one or more of the following types, and the collaborative intelligent controller 130 may dynamically select or combine them according to the detection requirements: 1. Broadband linear frequency modulated signals (Chirp Signals) or multi-tone comb spectrum signals: This type of cognitive probe carrier is particularly suitable for accurately measuring the cumulative dispersion (CD) of optical fibers.

[0061] For a linear frequency modulated signal, its instantaneous frequency changes linearly with time. ,in It is the starting frequency. It refers to the frequency modulation slope. Dispersion causes different frequency components to have different group delays. By analyzing the pulse broadening or phase distortion after demodulation of the received signal, the dispersion value can be accurately extracted.

[0062] For a multitone comb spectrum signal, it contains multiple discrete tones with known frequency intervals. By measuring the relative time difference of arrival of these tones at the receiver. or relative phase difference This allows us to calculate the dispersion slope and cumulative dispersion of the link.

[0063] 2. Specific polarization state or polarization scan signal: This type of cognitive probe carrier is used to detect and quantify polarization mode dispersion (PMD) effects.

[0064] It can transmit light with a specific input polarization state (SOP), such as linearly polarized light or circularly polarized light, and estimate the differential group delay (DGD) and main polarization state (PSP) by analyzing the polarization state change of the received signal, polarization dependent loss (PDL), or pulse splitting / broadening caused by PMD.

[0065] Another approach is to transmit cognitive probe carriers that rapidly switch or continuously scan between multiple predetermined polarization states. The receiver can then more comprehensively characterize PMD by analyzing the output signal characteristics corresponding to different input polarization states, for example, using Jones matrix eigenvalue analysis or Mueller matrix analysis.

[0066] 3. Multi-tone signals for nonlinear effect detection: By transmitting two or more tones with known frequencies and powers, nonlinear effects in optical fibers, such as four-wave mixing (FWM) and cross-phase modulation (XPM), can be detected.

[0067] For example, emit two tones and If a significant FWM effect exists, it will be observed in the spectrum of the received signal at a location [missing information]. and Newly generated frequency components at equal frequencies. The power of these newly generated components. Nonlinear coefficient of optical fiber Related to factors such as link length and main tone power, through measurement It can indirectly assess the intensity of nonlinear damage.

[0068] For XPM, a cognitive probe carrier can be used as a “victim” signal to observe how its phase or spectrum is affected by a neighboring strong data carrier (“attacker” signal).

[0069] 4. Low-power signals modulated by pseudo-random binary sequence (PRBS): Modulating a low-power optical carrier with a long-period PRBS sequence can be used to comprehensively evaluate various linear impairments in the optical path, such as attenuation, dispersion, and some noise characteristics. PRBS sequences have good autocorrelation characteristics, which facilitates synchronization and channel impulse response estimation at the receiver.

[0070] In configuring the transmission parameters of the cognitive probe carrier, the cooperative intelligent controller 130 not only determines which type of CPC to use, but also its specific transmission parameters. Key parameters include: Transmit power As mentioned earlier, it is typically much lower than the data carrier power, for example... ,in It is a safety margin, for example, 10 dB to 20 dB, and the specific value can be dynamically adjusted by CIC according to the current link loss, detection sensitivity requirements and interference tolerance for data services.

[0071] Signal bandwidth or chip rate / symbol rate: Selected based on the bandwidth characteristics of the effect to be detected. For example, detecting rapidly changing PMDs may require a higher time resolution CPC.

[0072] Spectrum location: The center frequency or band of the CPC can be placed within the data service channel (e.g., by very low power), the guard band, or a dedicated monitoring channel.

[0073] Transmission time slot or duty cycle: For time-division multiplexed CPC, the duration and repetition period of its transmission are controlled by CIC to balance the real-time performance of detection and system overhead.

[0074] Subsequently, during the execution of the aforementioned step S100, the transmitting unit 110 performs the actual transmission process of the cognitive probe carrier. Based on the detailed configuration instructions (including CPC type, waveform parameters, power, time-frequency resources, etc.) provided by the cooperative intelligent controller 130, the transmitting unit 110 utilizes its internal light source, modulator (such as a Mach-Zehnder modulator, phase modulator), and possibly an arbitrary waveform generator (AWG) to generate and precisely inject the selected cognitive probe carrier into the optical path 101.

[0075] During the aforementioned step S200, the receiving unit 120 (or a designated monitoring point) performs reception and preliminary processing of the transmitted cognitive probe carrier. This includes standard operations such as photoelectric conversion, amplification, and filtering. The key lies in the subsequent digital signal processing stage, the purpose of which is to process the received cognitive probe carrier, which may have been severely distorted. Extract useful information from it.

[0076] This process includes: 1. Precise synchronization: Utilizing the known structure of the cognitive probe carrier (such as the specific header of the PRBS sequence, the start phase of the chirp signal, and the frequency relationship of the multi-tone signal), the receiver performs time synchronization, carrier frequency offset correction, and phase recovery.

[0077] 2. Channel effect quantization: For amplitude information, measure the power of the received signal. The path attenuation is obtained by comparing it with the nominal transmit power (or the equivalent transmit power calibrated by other means).

[0078] For phase information, the phase changes experienced by the signal are extracted by comparing it with the locally recovered carrier or differential detection. It is used to analyze phase noise or nonlinear phase shift.

[0079] For dispersion, as mentioned earlier, cumulative dispersion is estimated by analyzing the group delay difference of different frequency components or the characteristics of the demodulated signal. .

[0080] For PMD, DGD values ​​are estimated using a polarization analyzer or digital signal processing algorithms (such as polarization demultiplexing based on constant modulus algorithm (CMA) or methods based on independent component analysis (ICA)). and principal polarization state .

[0081] For nonlinear effects, the power of intermodulation products such as FWM is detected by spectrum analysis. Alternatively, the nonlinear phase shift and amplitude noise introduced by XPM / SPM can be analyzed through constellation diagrams.

[0082] 3. Feature Parameter Extraction and Data Vector Construction: The quantified physical quantities or estimated channel parameters are organized into a feature vector, i.e., cognitive probe carrier data. .For example, It is the average received power. It is the estimated dispersion value. It is the DGD value. This includes FWM product power, etc. This data vector is then transmitted to the Cooperative Intelligent Controller 130 for subsequent optical path twin model updates (step S300).

[0083] Through the above-described design, adaptive configuration, transmission and reception processing of the cognitive probe carrier, this invention can obtain rich and targeted optical path state information, laying the foundation for subsequent modeling and cooperative interference suppression.

[0084] In these embodiments of the present invention, in order to update the optical path twin model described in step S300 and evaluate the confidence level of the model parameters described in step S400, the collaborative intelligent controller 130 integrates and runs a dynamic optical path twin model (OPT) module. The function of this module is to construct, maintain, and evaluate a digital model that can accurately characterize the physical properties and signal transmission effects of the optical path 101 in real time.

[0085] This optical twin model At its core is a mathematical expression designed to simulate the various linear and nonlinear effects encountered by optical signals propagating in actual fiber optic links. This model can be viewed as a time-varying... The evolving state vector contains estimates of several key parameters in the optical path. Specifically, the state vector... The following parameters may be included, but are not limited to: Cumulative dispersion : Represents the distance from the starting point to the position in the optical path. Location, at wavelength Below, at the time The total cumulative dispersion. In a simplified model, this can be the total cumulative dispersion of the entire link. .

[0086] Polarization mode dispersion (PMD) related parameters: for example, the differential group delay (DGD) values ​​of the link or its segments. and principal polarization state (PSP) direction vector For dynamic PMD, these parameters are time-varying.

[0087] Effective nonlinear coefficients : A parameter characterizing the intensity of nonlinear effects in a specific segment of an optical fiber, which may be affected by factors such as temperature and stress.

[0088] Link attenuation coefficient : Describes the power attenuation rate of an optical signal at a specific location and wavelength in an optical fiber.

[0089] Nonlinear interference (NLI) contribution factor : Used to quantify the impact of nonlinear noise generated in a specific link segment or frequency range on the signal.

[0090] Other specific interference source parameters: for example, crosstalk coupling strength at a specific location. Additional losses or reflections introduced by point damage (such as poor weld joints). .

[0091] The granularity of the model can be adjusted according to application requirements and computing resources. For example, it can be a lumped parameter model of the entire end-to-end link, or a more refined multi-span model, in which the fiber segments between each optical amplifier are modeled separately.

[0092] Initialization of the optical twin model, i.e. The establishment of a cognitive probe can be based on a variety of information sources. For example, it can be achieved using the factory specifications of optical fibers and devices, theoretical values ​​from network design databases, or by obtaining an initial estimate through a relatively comprehensive cognitive probe carrier scan.

[0093] During system operation, the iterative update of the optical path twin model (corresponding to step S300) is its core function. This update process utilizes cognitive probe carrier data obtained from the receiving unit 120. This is to continuously correct and calibrate the model parameters. (See the formula mentioned above.) As shown, the update function A variety of advanced estimation algorithms can be used.

[0094] A common approach is based on the Kalman filter and its variants, such as the extended Kalman filter (EKF) or the unscented Kalman filter (UKF), which is particularly suitable for handling state estimation problems of noisy dynamic systems.

[0095] Within this framework, the state evolution of the OPT model can be represented as: ; in, Discrete time The model state vector, It is the state transition matrix, describing how the model parameters change from... Evolving to Time (for slowly changing parameters) (Approaching unit array) This represents the effect of known external inputs or controls on the model (if any). It is the process noise vector, and its covariance matrix is , indicating the uncertainty of the model itself.

[0096] Cognitive probe carrier observation data The relationship between the model state and the observation equation can be expressed as: ; in, It is a (typically non-linear) observation function that takes the current model state. and the known CPC parameters of the transmission ( Mapped to the expected CPC measurement value, It is the observation noise vector, and its covariance matrix is The Kalman filter recursively provides the optimal estimate of the model state and its covariance matrix through two steps: prediction and update. .

[0097] For scenarios with highly nonlinearity or non-Gaussian noise, particle filters provide another efficient update mechanism. Particle filters approximate the posterior probability distribution of the model state using a set of weighted random samples (particles), enabling them to better handle complex systems.

[0098] In addition, machine learning methods, such as neural network-based regression models or Gaussian Process Regression (GPR), can also be used to achieve this. These models can learn from cognitive probe carrier data. (and optional historical model states) and data carrier feedback To the updated model parameters The complex mapping relationships between them. Training such models requires sufficient data, which can come from high-precision simulations, laboratory measurements, or historical operating records of actual deployed systems.

[0099] After the OPT model parameters are updated, step S400 is executed, which involves evaluating the confidence level of the model parameters. Quantify the system's response to the first element in the model. Parameters The confidence level of the current estimate. A high confidence level means that the parameter estimate is relatively accurate and reliable, while a low confidence level indicates that the parameter may have significant uncertainty and requires further exploration or calibration.

[0100] Confidence assessment can be based on one or more of the following factors: 1. Estimating variance or uncertainty interval: If a Kalman filter is used, its output state covariance matrix... diagonal elements The parameters are given directly. Estimated variance. Confidence level. Usually with Inversely proportional, for example ,in It is a scaling factor. For particle filters, uncertainty can be assessed by calculating the dispersion (such as variance or entropy) of the particle distribution corresponding to specific parameters.

[0101] For some machine learning models (such as Gaussian process regression or Bayesian neural networks), the predicted variance or confidence interval can be directly output.

[0102] 2. Cognitive probe carrier data quality If used for parameter estimation If the relevant CPC measurement data itself has a low signal-to-noise ratio, is subject to strong interference, or has sparse data points, then even if the estimation algorithm is advanced, the confidence level of the obtained parameters should be reduced accordingly.

[0103] 3. Residuals between model predictions and actual observations: Comparison based on the current OPT model Predicted CPC behavior versus actual received CPC data The difference between the parameters (residuals). A consistently large residual may mean that the model does not accurately describe the parameter, thus reducing its confidence level.

[0104] 4. Parameter change rate or stability: If a parameter has recently exhibited drastic or unpredictable fluctuations, the confidence level of its current estimate in the validity of the parameter in the near future may be reduced.

[0105] Taking all these factors into account, the collaborative intelligent controller 130 can calculate a normalized confidence score for each key parameter in the OPT model. This confidence vector... This will serve as an important basis for subsequent adaptive cognitive probe carrier detection strategy adjustments (step S500) and multi-level collaborative interference suppression decisions (step S700).

[0106] Through this dynamically constructed, continuously updated, and confidence-aware OPT model, the present invention provides a solid and reliable foundation of optical path state information for subsequent intelligent decision-making.

[0107] In a specific embodiment of the present invention, in order to determine the cognitive probe carrier detection strategy described in step S500 and reconfigure the transmission of the cognitive probe carrier based on the new strategy as described in step S600, the cooperative intelligent controller 130 includes an adaptive cognitive probe carrier (CPC) detection strategy module. The core function of this module is to intelligently adjust the transmission parameters of the cognitive probe carrier in the next cycle based on the confidence evaluation results of the state and parameters of the optical path twin model (OPT), so as to achieve more efficient and accurate perception of the optical path state.

[0108] The formulation of this adaptive detection strategy first relies on the analysis of current detection requirements. The collaborative intelligent controller 130 reviews the model parameters provided by the OPT module. and their corresponding confidence levels When the confidence level of one or more parameters falls below a preset threshold... If these parameters show rapid changes in the near future, or if they have a significant impact on the expected data carrier performance but the current estimate is uncertain, the system considers that there is a high demand for probing these parameters.

[0109] After analyzing the detection requirements, the detection strategy module translates these requirements into adjustment instructions for the specific transmission parameters of the cognitive probe carrier. Its objective function can be expressed as maximizing the utility of information acquisition in the next detection cycle. At the same time, constrain detection overhead (Extra power consumption, time and frequency resources occupied, etc.) are within acceptable limits.

[0110] ; in, The CPC launch strategy represents the next detection cycle; it is a collection of various CPC types and their launch parameters. Utility function. This is usually related to the degree to which it is expected to reduce the uncertainty of the OPT model, for example, the degree to which it is expected to improve low-confidence parameters. The magnitude or reduction of its estimated variance The degree of correlation.

[0111] The specific adaptive adjustment mechanism can be reflected in the following aspects, and the Collaborative Intelligent Controller 130 can be flexibly combined and used according to the actual situation: 1. Dynamic selection or ratio adjustment of cognitive probe carrier types: If the OPT model shows a low confidence level in the current dispersion estimate, the detection strategy module will instruct the transmission unit 110 to increase the number or frequency of transmissions of dispersion-sensitive CPC types (such as the aforementioned broadband linear frequency modulated signals or multi-tone comb signals). Conversely, if the dispersion estimate is already very stable and has a high confidence level, the transmission of such CPCs can be appropriately reduced, and resources can be allocated to other parameters with higher detection requirements.

[0112] 2. Adaptive adjustment of cognitive probe carrier transmit power: For CPCs associated with low-confidence OPT parameters, the system can moderately increase its transmit power. This is done to improve the signal-to-noise ratio (SNR) at the receiver, thereby improving the measurement data extracted from these CPCs. This results in greater precision, thereby improving the accuracy of the estimation of relevant OPT parameters. The upper limit of power adjustment is limited by the threshold of interference with the data carrier and fiber nonlinear effects.

[0113] 3. Optimization of the carrier signal structure or waveform parameters of the cognitive probe: For example, if more precise detection of the second-order effects of PMD is required, the detection strategy module may instruct the transmission of a CPC with a specific pulse width or polarization modulation rate. If it is necessary to detect nonlinear crosstalk in a specific frequency band, the tone frequency of the CPC can be tuned to be closer to or more sensitive to the region of crosstalk.

[0114] 4. Adjustment of the spectral position or time slot allocation of the cognitive probe carrier: If the OPT model indicates significant performance fluctuations for a specific wavelength channel, more CPC resources (such as denser time slots or wider detector subbands) can be allocated to the vicinity of that wavelength channel. In time-division multiplexing schemes, for rapidly changing optical path parameters, the CPC's transmit frequency can be increased (and the frequency decreased). (to improve temporal resolution).

[0115] 5. Focusing adjustment of the detection area or range (for systems supporting spatial resolution detection): If the system has the ability to inject or monitor CPC at different locations in the optical path (e.g., by selecting different optical segments through a reconfigurable optical add-drop multiplexer ROADM), and the OPT model can distinguish the parameter characteristics of different optical segments, then CPC detection resources can be preferentially directed to specific optical segments with high parameter uncertainty.

[0116] Decision-making logic can be based on rule engines, optimization algorithms, or reinforcement learning agents.

[0117] For example, rule-based decision-making might include the following rules: "IF" AND THEN increase by ANDselect CPC waveform optimal for DGD estimation”.

[0118] Optimization algorithm-based methods attempt to solve the aforementioned utility maximization problem.

[0119] Based on reinforcement learning, the agent (the policy module of the cooperative intelligent controller 130) can learn the optimal CPC adjustment policy through interaction with the optical path environment (simulated by the OPT model or directly through feedback from the actual optical path). Its goal is to maximize the long-term cumulative detection utility or system performance.

[0120] After determining the new detection strategy Subsequently, the collaborative intelligent controller 130 converts it into specific control commands and sends these commands to the transmitting unit 110 in step S600. The transmitting unit 110 then reconfigures its internal signal generator, modulator, and power controller according to these commands in order to transmit a cognitive probe carrier with new parameters in the next detection cycle.

[0121] This adaptive detection mechanism ensures that the cognitive probe carrier is no longer blindly or statically emitted, but intelligently guided based on real-time feedback of the optical path state and the model's confidence level. It guarantees that detection resources are efficiently used to address the most critical cognitive uncertainties, thereby obtaining the most crucial and timely information about the optical path state with minimal system overhead, providing high-quality input for subsequent interference suppression.

[0122] In a specific embodiment of the present invention, when the collaborative intelligent controller 130 obtains an updated optical path twin model with higher confidence through the aforementioned steps S100 to S600, Then, a multi-level cooperative interference suppression process is initiated. This process aims to actively and cooperatively adjust the transmission parameters of the data carrier (DC) and the settings of the dynamic optical path compensation unit (LPU) in the optical path using precise optical path state information, so as to minimize signal interference and ensure data transmission quality.

[0123] This cooperative interference suppression process is executed by a multi-level cooperative interference suppression module within the cooperative intelligent controller 130, and its decision-making is based on the current high-confidence optical path twin model. This module optimizes and adjusts at two levels simultaneously: data carrier transmitter optimization and dynamic compensation during optical path transmission.

[0124] First, regarding the collaborative optimization of data carrier transmission parameters, the collaborative intelligent controller 130 utilizes an optical path twin model. The expected transmission performance under different data carrier configurations is predicted. Specifically, for a set of candidate data carrier transmission parameters... The system can be based on Estimate the expected signal quality metrics after transmission, such as bit error rate (BER), signal-to-noise ratio (SNR), Q factor, or mutual information (MI).

[0125] This prediction process can be represented as... ,in It is a performance prediction function, which can be based on analytical or semi-analytical calculations of a physical model (e.g., transmission simulations considering the cumulative effects of dispersion, PMD, and nonlinear effects), or on a machine learning model trained on historical data (e.g., given an input of...). and The characteristics, the output is (The neural network).

[0126] Subsequently, the goal of the collaborative intelligent controller 130 is to select an optimal set of data carrier transmission parameters. This allows the predefined utility function to be used under the current optical path conditions. Maximize. This utility function typically considers factors such as transmission quality, data rate, and power consumption. ; Limited by ,in It is the set of allowed parameters.

[0127] Optimizable data carrier transmission parameters include: 1. Modulation Format: For example, choose the most suitable format from BPSK, QPSK, 8QAM, 16QAM, 64QAM, etc. Higher-order modulation formats can achieve higher spectral efficiency and data rate, but are more sensitive to channel impairments.

[0128] 2. Forward Error Correction (FEC) Coding Scheme and Code Rate (FEC_Rate): Select FEC codes with different error correction capabilities and overheads, such as different code rates for LDPC codes, Turbo codes, or polar codes. Stronger FEC can tolerate worse channel conditions, but will sacrifice some net data rate.

[0129] 3. Transmit power spectral density or total power (Power): Under the premise of meeting the total power constraint and avoiding excessive nonlinear effects, optimize the power allocation of each data subcarrier, or adjust the total transmit power to obtain the best signal-to-noise ratio at the receiver.

[0130] 4. Digital predistortion (DPD) or pre-compensation coefficients: If the transmitter has digital signal processing capabilities, it can be based on... Predicted linear impairments (such as dispersion) and nonlinear impairments are pre-distorted at the transmitter to ensure the signal recovers to a more ideal state after passing through the actual optical path. For example, digital dispersion pre-compensation can be performed for dispersion; and nonlinear pre-distortion based on Volterra series or other models can be performed for certain nonlinear effects.

[0131] 5. Multiple-input multiple-output (MIMO) precoding matrix (for spatial multiplexing or polarization multiplexing systems): Optimize the precoding matrix to decouple the channel or maximize the transmission performance of a specific mode.

[0132] The collaborative intelligent controller 130 will calculate the optimized data carrier transmission parameters. The configuration instructions are converted into configuration commands and sent to the transmitting unit 110, which then adjusts its internal modulator, encoder, power amplifier, and digital signal processor accordingly.

[0133] Secondly, regarding the coordinated adjustment of the dynamic optical path compensation unit, the coordinated intelligent controller 130 is also based on the optical path twin model. This generates control signals to adjust the settings of one or more Dynamic Optical Path Compensation Units (LPUs) 140 deployed in the optical path 101. These compensation units are designed to cancel or mitigate signal impairments accumulated in the optical path in real time.

[0134] Adjustable dynamic optical path compensation unit types include: 1. Tunable Dispersion Compensator (TDC): For example, a TDC based on a fiber Bragg grating (FBG) or a virtual imaging phased array (VIPA). The cooperative intelligent controller 130 operates according to the OPT model. Medium cumulative dispersion The precise estimate is used to calculate the amount of dispersion compensation that the TDC needs to provide. And instruct TDC to adjust to that value.

[0135] 2. Polarization Controller (PC) and PMD Compensator: For dynamically changing PMD, a high-speed electro-optic polarization controller can be used to adjust the polarization state of the signal to align with the instantaneous low PMD axis of the link or to cooperate with polarization demultiplexing at the receiver. More advanced PMD compensators (PMDCs) may include multiple adjustable delay lines and polarization rotators, whose configuration parameters are optimized by the cooperative intelligent controller 130 based on DGD and PSP information estimated by the OPT model.

[0136] 3. Dynamic Gain Equalizer (DGE) or Adjustable Optical Attenuator (VOA) Array: Used to compensate for power imbalances between different wavelength channels in a wavelength division multiplexing (WDM) system caused by gain flatness issues in the erbium-doped fiber amplifier (EDFA) or other wavelength-related losses in the link. The Cooperative Intelligent Controller 130 adjusts the attenuation of each channel in the DGE based on the attenuation estimate of each channel using the OPT model.

[0137] For the One dynamic optical path compensation unit Its optimal setting Determined by the collaborative intelligent controller 130, with the aim of maximizing the compensation effect or minimizing specific damage indicators: ; in, It is a control algorithm or mapping function for the compensation unit, which transforms the current optical path model state and the target damage type of the compensation unit (e.g., total dispersion for TDC, polarization distortion for PC) into specific control voltages, tuning parameters, etc.

[0138] The synergy between this data carrier transmitter optimization and the adjustment of the physical compensation unit in the optical path is reflected in the fact that they are both based on the same high-confidence optical path twin model calibrated in real time by the cognitive probe carrier. This ensures that all adjustments are made based on a unified and accurate understanding of the optical path state, avoiding conflicts or suboptimal results that may result from local optimization, thereby achieving global and deep suppression of signal interference.

[0139] Through the synergistic effect of these two levels, the present invention can more effectively address the complex, dynamic, and often multi-factor-intertwined signal impairments in optical communication systems, thereby significantly improving the system's transmission performance, reliability, and adaptability to environmental changes.

[0140] In a specific embodiment of the present invention, the core intelligent functions involved in the aforementioned steps S300 to S700, such as cognitive probe carrier data processing, optical path twin model construction and updating, parameter confidence assessment, adaptive detection strategy generation, and multi-level cooperative interference suppression decision-making, are all executed by the Cooperative Intelligent Controller (CIC) 130. The physical implementation of the Cooperative Intelligent Controller 130 can take various forms, such as a centralized processing unit, or a logically centralized but physically possibly partially distributed control system.

[0141] The present invention also provides a computer device, including: a processor and a memory, the memory storing a computer program executable by the processor, the computer program performing the method described above when executed by the processor.

[0142] The present invention also provides a storage medium storing a computer program, which is executed by a processor to perform the method described above.

[0143] The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0144] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A signal interference suppression method combining multi-level carrier coordination and dynamic optical path compensation, characterized in that, Includes the following steps: A cognitive probe carrier is transmitted in the optical path; the cognitive probe carrier includes a predefined pseudo-random sequence or a specific waveform that is sensitive to a specific type of optical path damage or interference, and the cognitive probe carrier is transmitted at a power level significantly lower than that of the data carrier. Generate cognitive probe carrier data based on the cognitive probe carrier transmitted through the optical path; Based on the cognitive probe carrier data, update the optical path twin model used to characterize the optical path characteristics; Based on the confidence assessment of the optical path twin model, an adaptive cognitive probe carrier detection strategy for subsequent transmission of the cognitive probe carrier is determined. According to the adaptive cognitive probe carrier detection strategy, the transmission parameters of the cognitive probe carrier are reconfigured; as well as Based on the updated optical twin model, the transmission parameters of the data carrier and the settings of the dynamic optical path compensation unit are adjusted in a coordinated manner to suppress signal interference of the data carrier.

2. The signal interference suppression method combining multi-level carrier coordination and dynamic optical path compensation according to claim 1, characterized in that, The steps for updating the optical path twin model also include: The optical path twin model is updated based on the performance feedback data of the data carrier transmitted through the optical path.

3. The signal interference suppression method combining multi-level carrier coordination and dynamic optical path compensation according to claim 1, characterized in that, The steps for determining the adaptive cognitive probe carrier detection strategy include: Evaluate the confidence level of multiple parameters or predictions within the optical twin model; Identify at least one target parameter or prediction with a confidence level below a predetermined threshold or exhibiting high uncertainty.

4. The signal interference suppression method combining multi-level carrier coordination and dynamic optical path compensation according to claim 3, characterized in that, The step of reconfiguring the transmission parameters of the cognitive probe carrier according to the adaptive cognitive probe carrier detection strategy includes modifying at least one of the following: The transmit power of one or more of the cognitive probe carriers; Signal waveform characteristics of one or more of the cognitive probe carriers; Spectrum allocation of one or more of the cognitive probe carriers; or The detection density of the cognitive probe carrier, To enhance the detection of optical path characteristics associated with at least one identified target parameter or prediction.

5. The signal interference suppression method combining multi-level carrier coordination and dynamic optical path compensation according to claim 1, characterized in that, The steps for collaboratively adjusting the transmission parameters of the data carrier include: Using the updated optical path twin model, predict the expected transmission performance of the data carrier under a set of candidate transmission parameters; and Select a subset of the candidate transmission parameters that can optimize the predefined performance metrics of the data carrier. The candidate transmission parameters include at least one of the following: modulation format, symbol rate, forward error correction scheme, subcarrier power allocation, digital predistortion coefficients, or precoding matrix.

6. The signal interference suppression method combining multi-level carrier coordination and dynamic optical path compensation according to claim 1, characterized in that, The steps for collaboratively adjusting the settings of the dynamic optical path compensation unit include: Based on parameter estimates derived from the updated optical twin model, control settings for one or more of the dynamic optical path compensation units are calculated. The dynamic optical path compensation units include at least one of a tunable dispersion compensator, a polarization controller, a dynamic gain equalizer, or a reconfigurable optical add-drop multiplexer to counteract specific optical path impairments identified by the optical twin model.

7. The signal interference suppression method combining multi-level carrier coordination and dynamic optical path compensation according to claim 1, characterized in that, The optical path twin model includes a state vector, which represents an estimate of at least one of the following within the optical path: The dispersion coefficient of a specific optical path segment, the polarization mode dispersion vector of a specific optical path segment, the nonlinear coefficient of a specific optical path segment, the characteristics of environmental interference sources, or the optical attenuation or gain distribution of a specific optical path segment.

8. A signal interference suppression system combining multi-level carrier coordination and dynamic optical path compensation, used to perform the method as described in any one of claims 1-7, characterized in that, include: The transmitting unit is used to transmit cognitive probe carriers and data carriers in the optical path, and to reconfigure the transmission parameters of the cognitive probe carriers according to instructions from the collaborative intelligent controller; A receiving unit is configured to receive the cognitive probe carrier transmitted through the optical path and generate cognitive probe carrier data based on the received cognitive probe carrier. The collaborative intelligent controller is used for: Receive the cognitive probe carrier data from the receiving unit; Based on the cognitive probe carrier data, update the optical path twin model used to characterize the optical path characteristics; Evaluate the confidence level of the optical twin model; Based on the confidence level of the assessment, an adaptive cognitive probe carrier detection strategy for subsequent transmission of the cognitive probe carrier is determined, and instructions for reconfiguring the cognitive probe carrier transmission parameters are provided to the transmission unit accordingly. as well as Based on the updated optical twin model, instructions for coordinating the adjustment of the data carrier transmission parameters are generated for the transmitting unit, and / or instructions for coordinating the adjustment of the dynamic optical path compensation unit settings are generated for the dynamic optical path compensation unit, so as to suppress signal interference of the data carrier.

9. A computer-readable storage medium having computer-executable instructions stored thereon, characterized in that, When executed by a processor, the computer-executable instructions cause the processor to perform the method as described in any one of claims 1-7.

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