Nonlinear compensation method and device

Abstract

The present application relates to the field of signal processing, and provides a nonlinear compensation method and device, used for more accurately calculating perturbation-based compensation (PBC) expansion coefficients by means of hard value assistance data. In addition, a target PBC expansion coefficient is determined in an offline phase, so that the algorithm complexity of nonlinear compensation can be reduced. The method comprises: performing online PBC nonlinear compensation on a target signal on the basis of a target PBC expansion coefficient, wherein the target PBC expansion coefficient is an optimized expansion coefficient determined in an offline phase by means of at least one set of soft value data and at least one set of corresponding hard value assistance data.

Description

A nonlinear compensation method and apparatus

[0001] This application claims priority to Chinese Patent Application No. 202311370800.8, filed on October 20, 2023, entitled “A Nonlinear Compensation Method and Apparatus”, the entire contents of which are incorporated herein by reference. Technical Field

[0002] This application relates to the field of signal processing, and more particularly to a nonlinear compensation method and apparatus. Background Technology

[0003] To mitigate nonlinear effects in optical fibers, various nonlinear compensation techniques assisted by digital signal processors (DSPs) have been extensively studied, such as digital back propagation (DBP), Volterra series filtering, and perturbation-based compensation (PBC). However, these methods all encounter challenges in balancing power consumption complexity and performance at the application level, making it difficult to achieve significant gains in nonlinear compensation with limited chip resources. Therefore, a low-power, high-performance nonlinear compensation algorithm is still needed.

[0004] For example, in existing nonlinear compensation algorithms, the signal A after impulse generation... x,y (z=0,k) is transmitted through the channel and processed by oDSP to obtain the receiving soft symbol A. x,y (z=L,k) is then transmitted to the soft core or central processing unit (CPU) for online perturbation-based compensation (PBC) coefficient calculation, yielding the PBC expansion coefficients W(m,n). Simultaneously, the receiving soft-value symbol A is... x,y (z=L,k) is transmitted to the online PBC correction module, which works with the PBC expansion coefficients W(m,n) output by the soft core or CPU module to complete the online correction. Finally, the output result is sent to the decision completion module, and after the decision, it is sent to the forward error correction (FEC) module to complete the entire signal transmission process.

[0005] Existing nonlinear compensation algorithms only utilize the soft-value symbols processed by the receiving end oDSP for PBC coefficient calculation. However, the inherent bit error rate (BER) of the soft-value symbols prevents the optimization of PBC coefficients from being calculated solely from them, resulting in insufficient nonlinear compensation gain in the final PBC correction. Furthermore, existing nonlinear compensation algorithms employ a purely online computation framework, leading to high algorithmic complexity.

[0006] Summary of the Invention

[0007] This application provides a nonlinear compensation method and apparatus that can more accurately calculate the PBC expansion coefficients using hard-valued auxiliary data; at the same time, the target PBC expansion coefficients are determined in the offline stage, reducing the algorithmic complexity of nonlinear compensation.

[0008] In a first aspect, embodiments of this application provide a method for signal synchronization, the method comprising: performing digital signal processing on a target signal; and performing online PBC nonlinear compensation on the target signal based on the perturbation theory-based compensation PBC expansion coefficients, wherein the target PBC expansion coefficients are optimized expansion coefficients determined in the offline stage through at least one set of soft value data and at least one set of corresponding hard value auxiliary data.

[0009] In this possible implementation, the target PBC expansion coefficients are optimized expansion coefficients determined based on soft-value data and corresponding hard-value auxiliary data. The target PBC expansion coefficients determined by combining hard-value auxiliary data are more accurate, improving the accuracy and efficiency of nonlinear compensation. Furthermore, these target PBC expansion coefficients are determined by the nonlinear compensation device in the offline stage, eliminating the need for online calculations. This reduces the computational load in the online stage, lowers the complexity of online PBC nonlinear compensation, and improves the efficiency of online PBC nonlinear compensation.

[0010] In one possible implementation, after digital signal processing of the target signal and before performing online PBC nonlinear compensation on the target signal based on the target's perturbation-based compensation PBC expansion coefficients, the method further includes: acquiring at least one set of first soft-value data and corresponding first hard-value auxiliary data, wherein the first hard-value auxiliary data is a hard-value data auxiliary sequence corresponding to the first soft-value data; calculating at least one set of PBC coefficients corresponding to at least one set of first soft-value data and corresponding first hard-value auxiliary data; and determining the target PBC expansion coefficients based on at least one set of PBC coefficients.

[0011] In one possible implementation, the above calculation of at least one set of PBC coefficients corresponding to at least one set of first soft value data and corresponding first hard value auxiliary data includes: calculating at least one set of PBC coefficients corresponding to at least one set of first soft value data and corresponding first hard value auxiliary data through an optimization algorithm; or calculating at least one set of PBC coefficients corresponding to at least one set of first soft value data and corresponding first hard value auxiliary data through a least mean square algorithm; or calculating at least one set of PBC coefficients corresponding to at least one set of first soft value data and corresponding first hard value auxiliary data through a least mean square algorithm, wherein the first hard value auxiliary data is approximate hard value auxiliary data.

[0012] In one possible implementation, determining the target PBC expansion coefficient based on at least one set of PBC coefficients includes: determining the target PBC expansion coefficient based on at least one set of PBC coefficients using a feature extraction algorithm, wherein the target PBC expansion coefficient is the optimized PBC expansion coefficient.

[0013] In one possible implementation, after performing online PBC nonlinear compensation on the target signal based on the target's perturbation-based compensated PBC expansion coefficients, the method further includes: making a decision on the online PBC nonlinear compensation target signal; and, if the decision is passed, calculating the bit error rate of the online PBC nonlinear compensation target signal.

[0014] Secondly, embodiments of this application provide a nonlinear compensation device, which includes: a receiving-end optical digital signal processor (oDSP) module and an online PBC correction module, wherein: the oDSP module is used to perform digital signal processing on the target signal; the online PBC correction module is used to perform online PBC nonlinear compensation on the target signal according to the target's perturbation-based compensation PBC expansion coefficients, and the target PBC expansion coefficients are optimized expansion coefficients determined in the offline stage through at least one set of soft value data and at least one set of corresponding hard value auxiliary data.

[0015] In one possible implementation, the nonlinear compensation device further includes a hard value input module and a soft core or CPU module, wherein: the hard value input module is used to acquire at least one set of first soft value data and corresponding first hard value auxiliary data, the first hard value auxiliary data being a hard value data auxiliary sequence corresponding to the first soft value data; the soft core or CPU module is used to calculate at least one set of PBC coefficients corresponding to at least one set of first soft value data and corresponding first hard value auxiliary data; the soft core or CPU module is also used to determine the target PBC expansion coefficients based on at least one set of PBC coefficients.

[0016] In one possible implementation, the soft core or CPU module is further configured to: calculate at least one set of PBC coefficients corresponding to at least one set of first soft value data and corresponding first hard value auxiliary data using an optimization algorithm; or calculate at least one set of PBC coefficients corresponding to at least one set of first soft value data and corresponding first hard value auxiliary data using a least mean square algorithm; or calculate at least one set of PBC coefficients corresponding to at least one set of first soft value data and corresponding first hard value auxiliary data using a least mean square algorithm, wherein the first hard value auxiliary data is approximate hard value auxiliary data.

[0017] In one possible implementation, the soft core or CPU module is further used to: determine the target PBC expansion coefficients based on at least one set of PBC coefficients using a feature extraction algorithm, wherein the target PBC expansion coefficients are the optimized PBC expansion coefficients.

[0018] In one possible implementation, the soft core or CPU module is also used to: make a decision on the target signal after online PBC nonlinear compensation; and, if the decision is passed, calculate the bit error rate of the target signal after online PBC nonlinear compensation.

[0019] Thirdly, embodiments of this application provide a nonlinear compensation device, which includes: a digital signal processing module for performing digital signal processing on a target signal; and a PBC nonlinear compensation module for performing online PBC nonlinear compensation on the target signal based on the perturbation theory-based compensation PBC expansion coefficients, wherein the target PBC expansion coefficients are optimized expansion coefficients determined in the offline stage through at least one set of soft value data and at least one set of corresponding hard value auxiliary data.

[0020] In one possible implementation, the nonlinear compensation device further includes: an acquisition module, configured to acquire at least one set of first soft value data and corresponding first hard value auxiliary data, wherein the first hard value auxiliary data is a hard value data auxiliary sequence corresponding to the first soft value data; a first calculation module, configured to calculate at least one set of PBC coefficients corresponding to at least one set of first soft value data and corresponding first hard value auxiliary data; and a determination module, configured to determine the target PBC expansion coefficients based on at least one set of PBC coefficients.

[0021] In one possible implementation, the first calculation module is specifically used to: calculate at least one set of PBC coefficients corresponding to at least one set of first soft value data and corresponding first hard value auxiliary data through an optimization algorithm; or calculate at least one set of PBC coefficients corresponding to at least one set of first soft value data and corresponding first hard value auxiliary data through a least mean square algorithm; or calculate at least one set of PBC coefficients corresponding to at least one set of first soft value data and corresponding first hard value auxiliary data through a least mean square algorithm, wherein the first hard value auxiliary data is approximate hard value auxiliary data.

[0022] In one possible implementation, the determining module is specifically used to: determine the target PBC expansion coefficients based on at least one set of PBC coefficients using a feature extraction algorithm, wherein the target PBC expansion coefficients are the optimized PBC expansion coefficients.

[0023] In one possible implementation, the nonlinear compensation device further includes: a decision module for making a decision on the target signal after online PBC nonlinear compensation; and a second calculation module for calculating the bit error rate of the target signal after online PBC nonlinear compensation if the decision is passed.

[0024] Fourthly, embodiments of this application provide a nonlinear compensation device, including a processor and a memory. The processor is coupled to the memory; the memory stores computer instructions, which are loaded and executed by the processor to enable the nonlinear compensation device to implement any of the methods provided in the first aspect.

[0025] Fifthly, embodiments of this application provide a chip, the chip comprising: a processor and an interface circuit; the interface circuit being configured to receive code instructions and transmit them to the processor; and the processor being configured to execute the code instructions to perform any of the methods provided in the first aspect.

[0026] In a sixth aspect, embodiments of this application provide a computer-readable storage medium storing at least one computer program instruction that is loaded and executed by a processor to implement any of the methods provided in the first aspect above.

[0027] In a seventh aspect, embodiments of this application provide a computer program product, including computer execution instructions, which, when executed on a computer, cause the computer to perform any of the methods provided in the first aspect.

[0028] The technical effects of any of the implementation methods in aspects two through seven can be found in the technical effects of the corresponding implementation methods in aspect one, and will not be repeated here. Attached Figure Description

[0029] Figure 1 is a schematic diagram of a scenario for a nonlinear compensation method;

[0030] Figure 2 is a schematic diagram of a scenario for a nonlinear compensation method provided in an embodiment of this application;

[0031] Figure 3 is a schematic diagram of the architecture of a nonlinear compensation device provided in an embodiment of this application;

[0032] Figure 4a is an offline scenario diagram of a nonlinear compensation method provided in an embodiment of this application;

[0033] Figure 4b is a schematic diagram of an online scenario for a nonlinear compensation method provided in an embodiment of this application;

[0034] Figure 5 is a flowchart illustrating a nonlinear compensation method provided in an embodiment of this application;

[0035] Figure 6 is a schematic diagram of another nonlinear compensation method provided in the embodiments of this application;

[0036] Figure 7 is a schematic diagram of another nonlinear compensation method provided in the embodiments of this application;

[0037] Figure 8 is a schematic diagram of another nonlinear compensation method provided in the embodiments of this application;

[0038] Figure 9 is a schematic diagram of a nonlinear compensation device provided in an embodiment of this application;

[0039] Figure 10 is a schematic diagram of another nonlinear compensation device provided in an embodiment of this application. Detailed Implementation

[0040] In the description of this application, unless otherwise stated, " / " indicates that the objects before and after are in an "or" relationship. For example, A / B can mean A or B. "And / or" in this application is merely a description of the relationship between the related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist simultaneously, and B exists alone. A and B can be singular or plural.

[0041] In the description of this application, unless otherwise stated, "multiple" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0042] Furthermore, to facilitate a clear description of the technical solutions in the embodiments of this application, the terms "first" and "second" are used in the embodiments of this application to distinguish identical or similar items with substantially the same function and effect. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and the terms "first" and "second" are not necessarily different.

[0043] In the embodiments of this application, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design that is described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a specific manner to facilitate understanding.

[0044] It is understood that the term "embodiment" used throughout the specification means that a specific feature, structure, or characteristic related to an embodiment is included in at least one embodiment of this application. Therefore, various embodiments throughout the specification do not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. It is understood that in the various embodiments of this application, the sequence number of each process does not imply the order of execution; the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0045] It is understood that some optional features in the embodiments of this application can be implemented independently in certain scenarios without relying on other features, such as the current solution on which they are based, to solve the corresponding technical problems and achieve the corresponding effects. Alternatively, they can be combined with other features as needed in certain scenarios. Correspondingly, the apparatus given in the embodiments of this application can also implement these features or functions, which will not be elaborated here.

[0046] In this application, unless otherwise specified, the same or similar parts between the various embodiments can be referred to each other. Unless otherwise specified or logically conflicting, the terminology and / or descriptions between different embodiments are consistent and can be mutually referenced. Different embodiments can be combined to form new embodiments based on their inherent logical relationships. The following embodiments of this application do not constitute a limitation on the scope of protection of this application.

[0047] Gilder's Law states that backbone network bandwidth will double every six months for the next 25 years, a growth rate three times faster than the computing power growth predicted by Moore's Law. The increasing bandwidth demand of communication networks continues to follow Gilder's Law, with the demand for single-fiber capacity increasing daily, from 100Gb / s, 200Gb / s to 400Gb / s and 800Gb / s. With single-fiber capacity increasing exponentially while maintaining long transmission distances, the nonlinear effects of optical fibers become a core issue in long-distance, high-speed transmission scenarios.

[0048] To mitigate nonlinear effects in optical fibers, various nonlinear compensation techniques assisted by digital signal processors (DSPs) have been extensively studied, such as digital back propagation (DBP), Volterra series filtering, and perturbation-based compensation (PBC). However, these methods all encounter challenges in balancing power consumption complexity and performance at the application level, making it difficult to achieve significant gains in nonlinear compensation with limited chip resources. Therefore, a low-power, high-performance nonlinear compensation algorithm is still needed.

[0049] For compensation based on perturbation theory, assume the transmitting signal is A. x,y (z=0,t)={A x,y (z=0,k)} k＝1,…,M The signal processed by the receiving optical digital signal processor (oDSP) is A. x,y (z=L,t)={A x,y (z=L,k)} k＝1,…,M Where M represents the number of coincidences. First, assume that the PBC-corrected signal after transmission over L kilometers can be expressed as: Where W(m,n) are the PBC expansion coefficients to be determined, and T x,y (m,n) are PBC expansion terms, which can be represented as: The subscripts x and y represent x-polarization and y-polarization, respectively. Since in practice, the transmitting signal A... x,y (z=0,t)={A x,y (z=0,k)} k＝1，...，M Since the signal is unknown, the receiving signal A is required. x,y (z=L,t)={A x,y (z=L,k)} k＝1,…,M Signals after the verdict To replace the transmitting signal, therefore in practical use It can be represented as:

[0050] The PBC weights W(m, n) can be obtained by applying the least squares (LS) method or the mean square least squares (LMS) method. Therefore, the online PBC correction in practical applications can be expressed as:

[0051] As shown in Figure 1, for example, in existing nonlinear compensation algorithms, the signal A_(x,y) (z=0,k) after impulse generation is transmitted through the channel and processed by oDSP to obtain the receiving soft-value symbol A. x,y (z=L,k) is then transmitted to the soft core or central processing unit (CPU) for online PBC coefficient calculation, yielding the PBC expansion coefficients W(m,n). Simultaneously, the receiving soft-value symbol A is... x,y (z=L,k) is transmitted to the online PBC correction module, which works with the PBC expansion coefficients W(m,n) output by the soft core or CPU module to complete the online correction. Finally, the output result is sent to the decision completion module, and after the decision, it is sent to the forward error correction (FEC) module to complete the entire signal transmission process.

[0052] Existing nonlinear compensation algorithms only utilize the soft-value symbols processed by the receiving end oDSP and employ LSM or LS to calculate the PBC coefficients. However, the inherent bit error rate (BER) of the soft-value symbols ultimately prevents the optimization of the PBC coefficients from being calculated solely based on them, resulting in insufficient nonlinear compensation gain in the final PBC correction. Furthermore, existing nonlinear compensation algorithms employ a purely online computation framework, leading to high algorithmic complexity.

[0053] Based on this, as shown in Figure 2, this application provides a nonlinear compensation method, which includes: performing digital signal processing on the target signal; performing online PBC nonlinear compensation on the target signal according to the target's perturbation theory-based compensation PBC expansion coefficients, wherein the target PBC expansion coefficients are the optimized expansion coefficients determined in the offline stage through at least one set of soft value data and at least one set of corresponding hard value auxiliary data.

[0054] The nonlinear compensation method provided in this application embodiment can be applied to the nonlinear compensation device shown in Figure 3. This nonlinear compensation device includes a receiving-end optical digital signal processor (oDSP) module, a hard-value input module, a soft-core or CPU module, and an online PBC correction module, as well as corresponding interfaces, including a soft-core or CPU hard-value input interface, a soft-core or CPU soft-value input interface, a soft-core or CPU output interface, an online PBC correction module soft-value input interface, and an online PBC correction module PBC coefficient input interface, wherein:

[0055] The oDSP module is used to output soft-value data after digital signal processing.

[0056] The hard value input module is used to provide hard value auxiliary data, providing hard value sequences to assist calculations for the soft core or CPU module.

[0057] The soft core or CPU module is used to calculate the PBC coefficients offline using an optimization algorithm based on optical fiber transmission physics theory, and extract features to obtain the optimized PBC coefficients. The soft core or CPU module can acquire hard value data through the soft core or CPU hard value input interface, acquire soft value data through the soft core or CPU soft value input interface, and output the optimized PBC coefficients through the soft core or CPU output interface.

[0058] The online PBC correction module is used to receive PBC coefficients and perform nonlinear compensation. The online PBC correction module can obtain soft value data through its soft value input interface, and it can also obtain the optimized PBC coefficients through its PBC coefficient input interface.

[0059] Specifically, as shown in Figure 4a, during the offline phase, the soft core or CPU module receives the soft core signal processed by the receiving end oDSP and the corresponding hard input. It then uses an optimization algorithm based on optical fiber transmission physics to calculate a set of PBC expansion coefficients W(m,n). After at least one calculation, at least one set of PBC expansion coefficients is obtained. Aligning and extracting features yields the optimized PBC expansion coefficients. It is also provided to the online PBC correction module.

[0060] As shown in Figure 4b, in the online phase, the optical signal is transmitted through optical fiber and linearly compensated by the receiving end oDSP before being input to the online PBC correction module. This is combined with the optimized PBC expansion coefficients input in the offline phase. The signal is corrected to achieve nonlinear compensation, then output to the decision module, and finally input to the FEC module to complete the online optical communication process.

[0061] It is understood that in the embodiments of this application, the executing entity may perform some or all of the steps in the embodiments of this application. These steps or operations are merely examples, and the embodiments of this application may also perform other operations or variations thereof. Furthermore, the various steps may be executed in different orders as presented in the embodiments of this application, and it is not necessarily necessary to execute all the operations in the embodiments of this application.

[0062] It should be noted that the message names between devices or the names of parameters in the messages in the embodiments of this application are just examples. In specific implementations, other names may also be used. This application does not specifically limit this.

[0063] As shown in Figure 5, a nonlinear compensation method provided in an embodiment of this application is included, which comprises the following steps:

[0064] In the offline phase, the nonlinear compensation method provided in this application embodiment can perform the following steps:

[0065] 501. Perform oDSP processing on the target signal.

[0066] After acquiring the target signal, the nonlinear compensation device performs oDSP processing on the received target signal.

[0067] In this embodiment, the nonlinear compensation device can transmit the target signal processed by the oDSP to the online PBC correction module. For example, the pulse signal generated by the pulse is shaped and then transmitted to the oDSP module through an optical fiber. The oDSP module determines the first soft value data after processing by a matched filter, clock domain crossing (CDC), equalization, and phase recovery.

[0068] 502. Obtain the first soft value data and the first hard value auxiliary data.

[0069] In the offline phase (i.e., the offline side), the nonlinear compensation device can acquire first soft value data and first hard value auxiliary data. The first soft value data is soft value data processed by oDSP, and the first hard value auxiliary data is a hard value data auxiliary sequence corresponding to the first soft value data.

[0070] [Corrected according to Rule 91 05.07.2024] Specifically, as shown in Figure 6, the nonlinear compensation device can process the first soft value data A after it has been processed by the oDSP module. x,y (z=L,k) is input to the soft core or CPU module through the soft value input interface of the soft core or CPU, while the first hard value auxiliary data A provided by the hard value input module is also input. x,y (z = L, k) is input to the soft core or CPU module through the soft value input interface.

[0071] In this embodiment of the application, the nonlinear compensation device may acquire a set of first soft value data and first hard value auxiliary data, or multiple sets of first soft value data and first hard value auxiliary data. Subsequently, the same processing is performed on at least one set of first soft value data and first hard value auxiliary data, and the specific processing is not limited.

[0072] In this embodiment of the application, the first hard value data may be approximate Tx data obtained after processing by the oDSP module, FEC feedback data, and external light sensor channel input data. In addition, it may be other data, such as Tx data directly obtained by the transmitter.

[0073] In this embodiment, the soft-value data and hard-value auxiliary data can be transmitted to the nonlinear compensation device as signals, i.e., they can be understood as soft-value signals and hard-value auxiliary signals. For example, after pulse shaping, the pulse signal generated by the pulse is transmitted to the oDSP module through optical fiber. The oDSP module determines the first soft-value data after processing by a matched filter, clock domain crossing (CDC), equalization, and phase recovery.

[0074] 503. Calculate the PBC coefficient.

[0075] The nonlinear compensation device uses an optimization algorithm based on the physical theory of optical fiber transmission to calculate offline the perturbation-based compensation (PBC) coefficients corresponding to each set of transmitted data (i.e., the first soft value data and the first hard value auxiliary data).

[0076] In this embodiment, the optimization algorithm includes a loss function based on the characteristics of soft-decision FEC and NLSE; or a loss function designed by a physical information neural network (PINN). In addition, it can also be other optimization algorithms, such as a weighted least squares weight function designed based on channel transmission distortion and constellation spacing. The specific algorithm is not limited here.

[0077] In one possible implementation, the nonlinear compensation method can be applied to high-speed scenarios with speeds above 1000km and 80GBaud or higher, and WDM dual-polarization scenarios, where the transmitting signal can adopt QAM modulation format.

[0078] As shown in Figure 6, the nonlinear compensation device can first convert at least one set of soft-value symbols A after oDSP processing. x,y (z = L, k) and its corresponding originating symbol A x,y (z=0,k) In conjunction with the optimization module in the offline PBC optimization of the soft core or CPU, a series (or a set) of corresponding PBC expansion coefficients W(m,n) are calculated using the optimization algorithm.

[0079] In another possible implementation, the nonlinear compensation method can be applied to high-speed scenarios with speeds above 1000km and 80GBaud or higher, and WDM dual-polarization scenarios, where the transmitting signal can adopt the QAM modulation format.

[0080] As shown in Figure 7, the nonlinear compensation device can first convert at least one set of soft-value symbols A after oDSP processing. x,y (z = L, k) and its corresponding originating symbol A x,y(z=0,k) is input into the soft core or CPU module, and then at least one set of PBC expansion coefficients W(m,n) is calculated using the traditional least mean square (LMS) algorithm.

[0081] In another possible implementation, the nonlinear compensation method can be applied to high-speed scenarios with speeds above 1000km and 80GBaud or higher, and WDM dual-polarization scenarios, where the transmitting signal can adopt the QAM modulation format.

[0082] As shown in Figure 8, the nonlinear compensation device can first convert at least one set of soft-value symbols A after oDSP processing. x,y (z=L,k) and its approximate starting sign A x,y (z = 0, k) is input into the soft core or CPU module, and the approximate starting symbol A x,y (z=0,k) are hard-valued symbols with bit error rate, and then at least one set of PBC expansion coefficients W(m,n) are calculated using the traditional LMS algorithm.

[0083] In this embodiment, the PBC coefficient can be calculated in various ways. For example, as shown in Figure 6, the PBC coefficient can be calculated using hard value assistance, optimization modules, and a hierarchical architecture for nonlinear compensation. Alternatively, as shown in Figure 7, the PBC coefficient can be calculated using hard value assistance and a hierarchical architecture for nonlinear compensation. Or, as shown in Figure 8, the PBC coefficient can be calculated using approximate hard value assistance and a hierarchical architecture for nonlinear compensation. No specific method is limited here.

[0084] 504. Determine the expansion coefficients after optimization.

[0085] The nonlinear compensation device determines the target PBC expansion coefficients using a feature extraction algorithm based on at least one set of PBC coefficients. The target PBC expansion coefficient is the optimized PBC expansion coefficient.

[0086] Specifically, after determining at least one set of PBC coefficients, the nonlinear compensation device can extract features from them using a feature extraction algorithm, thereby determining the optimized target expansion coefficients.

[0087] In this embodiment of the application, after determining the optimized expansion coefficient, the nonlinear compensation device can transmit the target expansion coefficient to the online PBC correction module through the soft core or CPU output interface.

[0088] In this embodiment of the application, steps 503 and 504 in the offline stage can be understood as being executed by the offline module, or as the target PBC expansion coefficients determined in advance.

[0089] During the online phase, the nonlinear compensation method provided in this application embodiment can perform the following steps:

[0090] 505. Perform online PBC nonlinear compensation based on the expansion coefficients after optimization.

[0091] The nonlinear compensation device is based on the optimized expansion coefficient. Online PBC nonlinear compensation is performed on the target signal. This online PBC nonlinear compensation can be understood as PBC correction.

[0092] In one possible implementation, the nonlinear compensation device of this application embodiment may further perform the following steps when performing online PBC nonlinear compensation:

[0093] 506. Make a decision on the target signal and calculate the bit error rate.

[0094] After completing online PBC nonlinear compensation, the nonlinear compensation device then makes a decision on the target signal after online PBC nonlinear compensation.

[0095] After the decision is made, the nonlinear compensation device can also calculate the bit error rate (BER) of the target signal after nonlinear compensation by the online PBC.

[0096] In this embodiment, the target PBC expansion coefficient is an optimized expansion coefficient determined based on soft value data and corresponding hard value auxiliary data. The target PBC expansion coefficient determined by combining hard value auxiliary data is more accurate, improving the accuracy and efficiency of nonlinear compensation. Furthermore, this target PBC expansion coefficient is determined by the nonlinear compensation device in the offline stage, eliminating the need for online calculations, reducing the computational load in the online stage, lowering the complexity of online PBC nonlinear compensation, and improving the efficiency of online PBC nonlinear compensation.

[0097] This application provides a nonlinear compensation device 900. In this embodiment, the nonlinear compensation device 900 can be divided into functional modules according to the above method example. For example, each function can be divided into its own functional modules, or two or more functions can be integrated into one processing module. The integrated modules can be implemented in hardware or as software functional modules. It should be noted that the module division in this embodiment is illustrative and only represents one logical functional division; other division methods may be used in actual implementation.

[0098] With each functional module divided according to its corresponding function, Figure 9 shows a possible structural schematic diagram of the nonlinear compensation device 900 involved in the above embodiments. As shown in Figure 9, the nonlinear compensation device 900 includes:

[0099] The digital signal processing module 901 is used to perform digital signal processing on the target signal; for example, in step 501, the received target signal is processed by oDSP.

[0100] The acquisition module 902 is used to acquire at least one set of first soft value data and corresponding first hard value auxiliary data, wherein the first hard value auxiliary data is the hard value data auxiliary sequence corresponding to the first soft value data; for example, step 502, acquiring the first soft value data and the first hard value auxiliary data.

[0101] The first calculation module 903 is used to calculate at least one set of PBC coefficients corresponding to the at least one set of first soft value data and the corresponding first hard value auxiliary data; for example, step 503, calculating the PBC coefficients.

[0102] In one possible implementation, the first calculation module 903 is specifically used to: calculate at least one set of PBC coefficients corresponding to the at least one set of first soft value data and the corresponding first hard value auxiliary data through an optimization algorithm; or

[0103] Calculate at least one set of PBC coefficients corresponding to the at least one set of first soft value data and the corresponding first hard value auxiliary data using the least mean square algorithm; or

[0104] The least mean square algorithm is used to calculate at least one set of PBC coefficients corresponding to the at least one set of first soft value data and the corresponding first hard value auxiliary data, wherein the first hard value auxiliary data is approximate hard value auxiliary data. For example, step 502: Calculate the PBC coefficients.

[0105] The determination module 904 is used to determine the target PBC expansion coefficients based on the at least one set of PBC coefficients. For example, step 504 determines the optimized expansion coefficients.

[0106] In one possible implementation, the determining module 904 is specifically used to: determine the target PBC expansion coefficients based on the at least one set of PBC coefficients using a feature extraction algorithm, wherein the target PBC expansion coefficients are the optimized PBC expansion coefficients. For example, step 503: determining the optimized expansion coefficients.

[0107] The PBC nonlinear compensation module 905 is used to perform online PBC nonlinear compensation on the target signal based on the target's perturbation-based compensation PBC expansion coefficients. The target PBC expansion coefficients are optimized expansion coefficients determined offline using at least one set of soft-value data and at least one set of corresponding hard-value auxiliary data. For example, step 505 involves performing online PBC nonlinear compensation based on the optimized expansion coefficients.

[0108] The decision module 906 is used to make a decision on the target signal after online PBC nonlinear compensation; for example, step 506, making a decision on the target signal and calculating the bit error rate.

[0109] The second calculation module 907 is used to calculate the bit error rate of the target signal after online PBC nonlinear compensation, if the decision is passed. For example, in step 506, a decision is made on the target signal and the bit error rate is calculated.

[0110] Each module of the aforementioned nonlinear compensation device can also be used to perform other actions in the above method embodiments. All relevant content of each step involved in the above method embodiments can be referenced from the functional description of the corresponding functional module, and will not be repeated here.

[0111] Figure 10 is a schematic diagram of a nonlinear compensation device provided in an embodiment of this application. The nonlinear compensation device 1000 may include one or more central processing units (CPUs) 1001 and a memory 1005, in which one or more application programs or data are stored.

[0112] The memory 1005 can be volatile or persistent storage. The program stored in the memory 1005 can include one or more modules, each module including a series of instruction operations on the nonlinear compensation device. Furthermore, the central processing unit 1001 can be configured to communicate with the memory 1005 and execute the series of instruction operations in the memory 1005 on the nonlinear compensation device 1000.

[0113] The central processing unit 1001 executes the computer program in the memory 1005, enabling the nonlinear compensation device 1000 to perform: digital signal processing on the target signal; and online PBC nonlinear compensation on the target signal based on the target perturbation theory-based compensation PBC expansion coefficients, where the target PBC expansion coefficients are the optimized expansion coefficients determined offline using at least one set of soft-value data and at least one set of corresponding hard-value auxiliary data. For specific implementation details, please refer to steps 501-506 in the embodiment shown in Figure 5, which will not be repeated here.

[0114] The nonlinear compensation device 1000 may also include one or more power supplies 1002, one or more wired or wireless network interfaces 1003, one or more input / output interfaces 1004, and / or one or more operating systems, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™, etc.

[0115] The nonlinear compensation device 1000 can perform the operations performed by the nonlinear compensation device in the embodiment shown in Figure 5 above, and the specifics will not be repeated here.

[0116] This application also provides a computer program product containing instructions. The computer program product may be a software or program product containing instructions, capable of running on a plug-in result multiplexing device or stored on any usable medium. When the computer program product runs on the plug-in result multiplexing device, it causes the nonlinear compensation device to perform the nonlinear compensation method executed in the embodiment shown in FIG5 above.

[0117] This application also provides a computer-readable storage medium. The computer-readable storage medium can be any available medium that a cache server can store, or a data storage device such as a data center containing one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state drive). The computer-readable storage medium includes instructions that direct the nonlinear compensation device to perform the nonlinear compensation method executed in the embodiment shown in FIG5.

[0118] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented using software programs, implementation can be, in whole or in part, in the form of a computer program product. This computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes (or functions) of the embodiments of this application are implemented. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device containing one or more servers, data centers, etc., that can be integrated with the medium. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state disks (SSDs)). In embodiments of this application, the computer may include the aforementioned devices.

[0119] Although this application has been described herein in conjunction with various embodiments, those skilled in the art, by reviewing the accompanying drawings, the disclosure, and the appended claims, will understand and implement other variations of the disclosed embodiments in carrying out the claimed application. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude multiple instances. A single processor or other unit can implement several functions listed in the claims. While different dependent claims may recite certain measures, this does not mean that these measures cannot be combined to produce good results.

Claims

1. A nonlinear compensation method, characterized in that, The method includes: Perform digital signal processing on the target signal; Online PBC nonlinear compensation is performed on the target signal based on the target perturbation theory-based compensation PBC expansion coefficients. The target PBC expansion coefficients are the optimized expansion coefficients determined in the offline stage through at least one set of soft value data and at least one set of corresponding hard value auxiliary data.

2. The method according to claim 1, characterized in that, Before performing online PBC nonlinear compensation on the target signal based on the target's perturbation-based compensated PBC expansion coefficients, and after performing digital signal processing on the target signal, the method further includes: Obtain at least one set of first soft value data and corresponding first hard value auxiliary data, wherein the first hard value auxiliary data is the hard value data auxiliary sequence corresponding to the first soft value data; Calculate at least one set of PBC coefficients corresponding to the at least one set of first soft value data and the corresponding first hard value auxiliary data; The target PBC expansion coefficient is determined based on the at least one set of PBC coefficients.

3. The method according to claim 2, characterized in that, The calculation of at least one set of PBC coefficients corresponding to the at least one set of first soft value data and the corresponding first hard value auxiliary data includes: The at least one set of PBC coefficients corresponding to the at least one set of first soft value data and the corresponding first hard value auxiliary data are calculated using an optimization algorithm; or Calculate at least one set of PBC coefficients corresponding to the at least one set of first soft value data and the corresponding first hard value auxiliary data using the least mean square algorithm; or The least one set of PBC coefficients corresponding to the at least one set of first soft value data and the corresponding first hard value auxiliary data are calculated by the least mean square algorithm, wherein the first hard value auxiliary data is approximate hard value auxiliary data.

4. The method according to claim 3, characterized in that, Determining the target PBC expansion coefficient based on the at least one set of PBC coefficients includes: The target PBC expansion coefficient is determined by a feature extraction algorithm based on the at least one set of PBC coefficients, and the target PBC expansion coefficient is the optimized PBC expansion coefficient.

5. The method according to any one of claims 1-4, characterized in that, After performing online PBC nonlinear compensation on the target signal based on the target's perturbation-based compensated PBC expansion coefficients, the method further includes: Make a decision on the target signal after online PBC nonlinear compensation; Calculate the bit error rate of the target signal after online PBC nonlinear compensation, assuming the decision is passed.

6. A nonlinear compensation device, characterized in that, The nonlinear compensation device is used to implement the method described in any one of claims 1-5, and the nonlinear compensation device comprises: a receiving-end optical digital signal processor (oDSP) module and an online PBC correction module, wherein: The oDSP module is used to perform digital signal processing on the target signal; The online PBC correction module is used to perform online PBC nonlinear compensation on the target signal based on the target's perturbation theory-based compensation PBC expansion coefficients. The target PBC expansion coefficients are the optimized expansion coefficients determined in the offline stage through at least one set of soft value data and at least one set of corresponding hard value auxiliary data.

7. The nonlinear compensation device according to claim 6, characterized in that, The nonlinear compensation device further includes a hard-value input module and a soft-core or CPU module, wherein: The hard value input module is used to acquire at least one set of first soft value data and corresponding first hard value auxiliary data, wherein the first hard value auxiliary data is a hard value data auxiliary sequence corresponding to the first soft value data; A soft core or CPU module is used to calculate at least one set of PBC coefficients corresponding to the at least one set of first soft value data and the corresponding first hard value auxiliary data. The soft core or CPU module is also used to determine the target PBC expansion coefficients based on the at least one set of PBC coefficients.

8. The nonlinear compensation device according to claim 7, characterized in that, The soft core or CPU module is also used for: The at least one set of PBC coefficients corresponding to the at least one set of first soft value data and the corresponding first hard value auxiliary data are calculated using an optimization algorithm; or The least one set of PBC coefficients corresponding to the at least one set of first soft value data and the corresponding first hard value auxiliary data are calculated using the least mean square algorithm. or The least one set of PBC coefficients corresponding to the at least one set of first soft value data and the corresponding first hard value auxiliary data are calculated by the least mean square algorithm, wherein the first hard value auxiliary data is approximate hard value auxiliary data.

9. The nonlinear compensation device according to claim 8, characterized in that, The soft core or CPU module is also used for: The target PBC expansion coefficient is determined by a feature extraction algorithm based on the at least one set of PBC coefficients, and the target PBC expansion coefficient is the optimized PBC expansion coefficient.

10. The nonlinear compensation device according to any one of claims 6-9, characterized in that, The soft core or CPU module is also used for: Make a decision on the target signal after online PBC nonlinear compensation; Calculate the bit error rate of the target signal after online PBC nonlinear compensation, assuming the decision is passed.

11. A nonlinear compensation device, characterized in that, The nonlinear compensation device includes: The digital signal processing module is used to perform digital signal processing on the target signal. The PBC nonlinear compensation module is used to perform online PBC nonlinear compensation on the target signal based on the target perturbation theory-based compensation PBC expansion coefficients. The target PBC expansion coefficients are the optimized expansion coefficients determined in the offline stage through at least one set of soft value data and at least one set of corresponding hard value auxiliary data.

12. The nonlinear compensation device according to claim 11, characterized in that, The nonlinear compensation device further includes: The acquisition module is used to acquire at least one set of first soft value data and corresponding first hard value auxiliary data, wherein the first hard value auxiliary data is a hard value data auxiliary sequence corresponding to the first soft value data; The first calculation module is used to calculate at least one set of PBC coefficients corresponding to the at least one set of first soft value data and the corresponding first hard value auxiliary data. A determination module is used to determine the target PBC expansion coefficients based on the at least one set of PBC coefficients.

13. The nonlinear compensation device according to claim 12, characterized in that, The first calculation module is specifically used for: The at least one set of PBC coefficients corresponding to the at least one set of first soft value data and the corresponding first hard value auxiliary data are calculated using an optimization algorithm; or The least one set of PBC coefficients corresponding to the at least one set of first soft value data and the corresponding first hard value auxiliary data are calculated using the least mean square algorithm. or The least one set of PBC coefficients corresponding to the at least one set of first soft value data and the corresponding first hard value auxiliary data are calculated by the least mean square algorithm, wherein the first hard value auxiliary data is approximate hard value auxiliary data.

14. The nonlinear compensation device according to claim 13, characterized in that, The determining module is specifically used for: The target PBC expansion coefficient is determined by a feature extraction algorithm based on the at least one set of PBC coefficients, and the target PBC expansion coefficient is the optimized PBC expansion coefficient.

15. The nonlinear compensation device according to any one of claims 11-14, characterized in that, The nonlinear compensation device further includes: The decision module is used to make decisions on the target signal after online PBC nonlinear compensation. The second calculation module is used to calculate the bit error rate of the target signal after online PBC nonlinear compensation, provided that the decision is passed.

16. A nonlinear compensation device, characterized in that, The nonlinear compensation device includes a processor and a memory; the processor is coupled to the memory; the memory is used to store computer instructions, which are loaded and executed by the processor to enable the nonlinear compensation device to implement the method as described in any one of claims 1-5.

17. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one computer program instruction, which is loaded and executed by a processor to implement the method as described in any one of claims 1-5.

18. A computer program product, characterized in that, The computer program product includes computer-executable instructions, which, when executed on a computer, enable the computer to implement the method as described in any one of claims 1-5.

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