Signal phase recovery method and device based on maximum posterior probability symbol decision error, equipment and medium

Abstract

The invention belongs to the field of optical communication, and relates to a signal phase recovery method, device and equipment based on a maximum posterior probability symbol decision error and a medium, and the method comprises the following steps: carrying out symbol decision on a received signal based on the maximum posterior probability, and calculating the symbol decision error; constructing an error function according to the symbol judgment error, and calculating the gradient of the error function relative to a phase noise value; updating the phase estimation value along the gradient descending direction; and performing phase noise recovery of the symbol according to the phase estimation value to obtain an output signal after phase recovery. Compared with the prior art, the method has the advantages that the error gradient descent algorithm is combined with the high-performance phase recovery to meet the compensation requirement for serious linear random phase noise generated by the laser linewidth in coherent transmission; meanwhile, the phase noise estimation precision is improved based on a symbol decision error form, the use of a time window is effectively avoided, and the calculation complexity is reduced.

Description

Technical Field

[0001] The present invention relates to a phase recovery method in the field of high-speed coherent optical fiber communication, and in particular to a signal phase recovery method, device, equipment and medium based on maximum a posteriori probability symbol decision error. Background Art

[0002] In the 5G era and the upcoming 6G, with the rise of bandwidth-consuming services such as the Internet of Things, virtual reality, and augmented reality, fiber-optic communication networks, as the backbone networks that carry data traffic, need to further improve their transmission rate and capacity. Coherent transmission technology has successfully achieved a breakthrough in the rate of fiber-optic communication by using more degrees of freedom through coherent beat frequency technology. However, in coherent transmission systems, the random phase noise caused by the laser linewidth severely restricts the effective transmission of high-order modulated signals, becoming a bottleneck factor restricting the further development of coherent optical communication rates.

[0003] In order to solve the signal damage caused by phase noise, researchers have proposed blind phase search (BPS) and phase recovery algorithms based on Viterbi-Viterbi decoding (VV) to estimate the phase noise received during signal transmission. Although these traditional phase algorithms can effectively compensate for random phase noise to a certain extent, there is still room for improvement in computational complexity and phase noise tracking performance. Especially when using probabilistically shaped signals to further increase the optical transmission rate, these traditional phase recovery algorithms will produce additional performance degradation due to the non-uniform probability distribution of the transmitted signal. Therefore, how to achieve a low-complexity, high-performance, robust signal phase recovery method for probabilistically shaped signals is a key task to increase the rate of coherent optical communications. Summary of the invention

[0004] The purpose of the present invention is to provide a low-complexity signal phase recovery method, device, equipment and medium based on maximum a posteriori probability symbol decision error for an optical probability shaping signal.

[0005] The purpose of the present invention can be achieved by the following technical solutions:

[0006] According to the first aspect of the present invention, a low-complexity signal phase recovery method based on maximum a posteriori probability symbol decision error for optical probability shaping signals is provided. This method not only improves the accuracy of tracking phase noise by analyzing symbol errors, but also its implementation mechanism without time windows can greatly reduce the computational complexity and delay required for signal phase recovery, further improving the tolerance of high-order modulation format transmission signals to large linewidth lasers. In order to reduce the impact of the non-uniform probability distribution of probability shaping on the performance of the phase estimation algorithm, the maximum a posteriori probability decision is introduced to reduce the problems caused by the prior probability of the transmission symbol. By constructing a suitable error function for the received symbol decision error and performing gradient descent, high-precision symbol phase noise tracking is achieved. This low-complexity signal phase recovery method based on maximum a posteriori probability symbol decision error can be applied to a variety of coherent optical communication transmission scenarios, especially in high-speed coherent data center optical interconnection systems using large linewidth lasers.

[0007] Specifically, the method includes four parts: "signal judgment", "error gradient calculation", "phase noise estimation" and "phase noise recovery":

[0008] Signal decision: Perform symbol decision on the received signal based on the maximum a posteriori probability and calculate the symbol decision error;

[0009] Error gradient calculation: constructing an error function according to the symbol decision error, and taking into account the relationship between the symbol decision error and the phase noise, calculating the gradient of the error function with respect to the phase noise value;

[0010] Phase noise estimation: The phase estimate is updated along the gradient descent direction using the minimum mean square error method;

[0011] Phase noise recovery: The phase noise of the symbol is recovered according to the phase estimation value to obtain the output signal after phase recovery.

[0012] The present invention also provides a signal phase recovery device based on maximum a posteriori probability symbol decision error, which is used to implement the above method, and the device includes:

[0013] Signal decision module: performs symbol decision on the received signal based on the maximum a posteriori probability and calculates the symbol decision error;

[0014] Error gradient calculation module: construct an error function according to the symbol decision error, and calculate the gradient of the error function with respect to the phase noise value;

[0015] Phase noise estimation module: updates the phase estimation value along the direction of gradient descent;

[0016] Phase noise recovery module: recovers the phase noise of the symbol according to the phase estimation value to obtain the output signal after phase recovery.

[0017] The present invention also provides an electronic device, comprising a memory, a processor, and a program stored in the memory, wherein the processor implements the above method when executing the program.

[0018] The present invention also provides a storage medium on which a program is stored, and when the program is executed, the method as described above is implemented.

[0019] Compared with the prior art, the present invention has the following beneficial effects:

[0020] (1) The present invention utilizes a high-performance error gradient descent algorithm and maximum a posteriori probability judgment to achieve high-performance phase recovery suitable for optical probability shaping signals, effectively solving the damage to signal quality caused by random phase noise caused by line width.

[0021] (2) The present invention includes a feedback update structure. The gradient update factor has a great influence on the system performance and is related to the laser line width, signal modulation format and signal-to-noise ratio used in the actual application system. High-precision phase tracking can be achieved by setting the gradient update factor.

[0022] (3) The present invention has strong flexibility and universality, and can be applied to a variety of coherent transmission optical communication scenarios, including long-distance backbone network transmission, short-distance access network transmission, and high-speed coherent data center optical interconnection scenarios.

[0023] (4) The present invention has extremely low computational complexity and extremely high channel robustness. By using a signal error-based phase tracking method without the need for a time window, the computational complexity and delay required for high-performance carrier recovery are effectively reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 is a flow chart of the method of the present invention;

[0025] Figure 2 It is the principle diagram of the present invention;

[0026] Figure 3 The diagram is a schematic diagram of maximum a posteriori probability decision of the present invention applied to a probability shaping signal. DETAILED DESCRIPTION

[0027] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0028] like Figure 1As shown, this embodiment provides a signal phase recovery method based on maximum a posteriori probability symbol decision error, the method comprising the following steps:

[0029] S1, such as Figure 2 As shown, symbol decision is performed on the received signal based on the maximum a posteriori probability and the symbol decision error is calculated.

[0030] Before using the phase recovery method, it is necessary to use a pilot signal to estimate the transmission system noise variance in advance and to know the prior probability vector of the transmitted symbol.

[0031] S11, perform symbol decision on the received signal based on the maximum a posteriori probability:

[0032] Considering that most of the noise in the optical fiber transmission channel conforms to the Gaussian white noise model, the receiving end will determine the possibility measure of the received signal belonging to each standard constellation point based on the constellation point position of the received signal, the prior probability of the transmitted signal, and the total noise variance during the transmission process, and complete the symbol decision:

[0033] d x =argmin(|y x -x i | 2 -N0ln(p x (x i )))

[0034] Among them, x i is the possible standard constellation point in the QAM constellation set, p x is the prior probability vector of the transmitted symbol, N0 is the noise variance during the transmission process, d x is the maximum a posteriori probability for determining the target constellation point, y x To receive the signal.

[0035] By introducing a penalty term including the sign prior probability ln(p x (x i ), the maximum a posteriori probability-based decision can effectively expand the decision area of ​​the constellation points with higher initial probability, thereby reducing the possibility of erroneous decision of the probability shaping signal.

[0036] S12, calculate the symbol decision error:

[0037] e x =d x -y x

[0038] Among them, e x is the symbol decision error.

[0039] like Figure 3As shown, the MAP decision boundary is the decision boundary obtained by using the maximum a posteriori probability of the present invention to perform symbol decision, and the ML decision boundary is the decision boundary of the prior art. Figure 3 It can be seen that the MAP decision boundary introduces a penalty term based on the initial symbol probability compared to the ML decision boundary, which makes the decision range of constellation points with greater probability larger and reduces the possibility of erroneous decision of the probability shaping signal.

[0040] S2, such as Figure 2 As shown, an error function is constructed according to the symbol decision error, and the gradient of the error function with respect to the phase noise value is calculated.

[0041] S21, considering that phase noise mainly affects the amplitude of the error signal, the square of the symbol decision error modulus is used as the error function:

[0042] f x =|e x | 2

[0043] Among them, f x is the error function.

[0044] S22, considering that the error function is related to the phase estimation value, the phase estimation value can be updated by the gradient descent algorithm, and the method for calculating the gradient of the error function with respect to the phase noise value is:

[0045]

[0046] Among them, g x is the gradient of the error function with respect to the phase noise value, θ x is the phase estimate, It means taking the imaginary part of a complex number, * is the conjugate operation, and j is the imaginary unit.

[0047] S3, such as Figure 2 As shown, the phase estimate is updated along the gradient descent direction based on the minimum mean square error algorithm:

[0048]

[0049] Among them, μ is the gradient update factor that balances the update speed and update accuracy, and its size is related to the laser line width used, the transmission signal modulation format, and the signal-to-noise ratio of the received signal.

[0050] S4, such as Figure 2 As shown, the phase noise of the symbol is recovered according to the phase estimation value to obtain an output signal after phase recovery.

[0051] According to the updated phase estimate, the received signal is rotated accordingly to compensate for the phase noise, and the output signal after phase recovery is obtained:

[0052] y xo =y x ×exp(-jθ x+1 )

[0053] Among them, y xo is the output signal, y x is the received signal, θ x+∫ is the updated phase estimate.

[0054] The present invention combines the error gradient descent algorithm with high-performance phase recovery to meet the compensation requirements for severe linear random phase noise generated by the laser line width in coherent transmission. At the same time, based on the form of symbol decision error, while improving the accuracy of phase noise estimation, it effectively avoids the use of time windows and has a good performance in reducing computational complexity. In terms of application scenarios, the present invention can be applied to various scenarios such as polarization multiplexing long-distance single-carrier coherent optical transmission and mode multiplexing short-distance coherent transmission systems, providing a low-complexity and reliable solution for ultra-high-speed data center optical interconnection.

[0055] This embodiment further provides a signal phase recovery device based on maximum a posteriori probability symbol decision error, which is used to implement the above method. The device includes:

[0056] Signal decision module: performs symbol decision on the received signal based on the maximum a posteriori probability and calculates the symbol decision error;

[0057] Error gradient calculation module: construct an error function according to the symbol decision error, and calculate the gradient of the error function with respect to the phase noise value;

[0058] Phase noise estimation module: updates the phase estimation value along the direction of gradient descent;

[0059] Phase noise recovery module: recovers the phase noise of the symbol according to the phase estimation value to obtain the output signal after phase recovery.

[0060] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the described module can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0061] The electronic device of the present invention includes a central processing unit (CPU), which can perform various appropriate actions and processes according to computer program instructions stored in a read-only memory (ROM) or loaded from a storage unit into a random access memory (RAM). In the RAM, various programs and data required for device operation can also be stored. The CPU, ROM and RAM are connected to each other via a bus. An input / output (I / O) interface is also connected to the bus.

[0062] Multiple components in the device are connected to the I / O interface, including: input units, such as keyboards, mice, etc.; output units, such as various types of displays, speakers, etc.; storage units, such as disks, optical disks, etc.; and communication units, such as network cards, modems, wireless communication transceivers, etc. The communication unit allows the device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunication networks.

[0063] The processing unit performs the various methods and processes described above, such as methods S1 to S4. For example, in some embodiments, methods S1 to S4 may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit. In some embodiments, part or all of the computer program may be loaded and / or installed on the device via a ROM and / or a communication unit. When the computer program is loaded into the RAM and executed by the CPU, one or more steps of methods S1 to S4 described above may be performed. Alternatively, in other embodiments, the CPU may be configured to execute methods S1 to S4 in any other appropriate manner (e.g., by means of firmware).

[0064] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0065] The program code for implementing the method of the present invention can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code can be executed entirely on the machine, partially on the machine, partially on the machine as a stand-alone software package and partially on a remote machine, or entirely on a remote machine or server.

[0066] In the context of the present invention, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0067] The preferred specific embodiments of the present invention are described in detail above. It should be understood that a person skilled in the art can make many modifications and changes based on the concept of the present invention without creative work. Therefore, any technical solution that can be obtained by a person skilled in the art through logical analysis, reasoning, or limited experiments based on the concept of the present invention on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. A signal phase recovery method based on maximum a posteriori probability symbol decision error, characterized in that: The method comprises the following steps: Perform symbol decision on the received signal based on maximum a posteriori probability and calculate the symbol decision error; Constructing an error function according to the symbol decision error, and calculating the gradient of the error function with respect to the phase noise value; Update the phase estimate along the direction of gradient descent; The phase noise of the symbol is restored according to the phase estimation value to obtain an output signal after phase recovery.

2. The signal phase recovery method based on maximum a posteriori probability symbol decision error according to claim 1, characterized in that: The symbol decision of the received signal based on the maximum a posteriori probability is specifically as follows: According to the constellation point position of the received signal, the prior probability of the transmitted signal and the total noise variance during the transmission process, the possibility measure of the received signal belonging to each standard constellation point is determined, and the symbol decision is completed: d x =argmin(|y x -x i | 2 -N0ln(p x (x i ))) Among them, x i is the possible standard constellation point in the QAM constellation set, p x is the prior probability vector of the transmitted symbol, N0 is the noise variance during the transmission process, d x is the maximum a posteriori probability for determining the target constellation point, y x To receive the signal.

3. The signal phase recovery method based on maximum a posteriori probability symbol decision error according to claim 2, characterized in that: The method for calculating the symbol decision error is: e x =d x -y x Among them, e x is the symbol decision error.

4. The signal phase recovery method based on maximum a posteriori probability symbol decision error according to claim 1, characterized in that: The error function is expressed as: f x =|e x | 2 Among them, f x is the error function.

5. The signal phase recovery method based on maximum a posteriori probability symbol decision error according to claim 4, characterized in that: The method for calculating the gradient of the error function with respect to the phase noise value is: Among them, g x is the gradient of the error function with respect to the phase noise value, θ x is the phase estimate, It means taking the imaginary part of a complex number, * is the conjugate operation, and j is the imaginary unit.

6. The signal phase recovery method based on maximum a posteriori probability symbol decision error according to claim 5, characterized in that: The updating of the phase estimate along the direction of gradient descent is implemented based on the minimum mean square error algorithm: Among them, μ is the gradient update factor that balances the update speed and update accuracy, and its size is related to the laser line width used, the transmission signal modulation format, and the signal-to-noise ratio of the received signal.

7. The signal phase recovery method based on maximum a posteriori probability symbol decision error according to claim 1, characterized in that: The phase noise recovery of the symbol according to the phase estimation value is specifically as follows: According to the updated phase estimate, the received signal is rotated accordingly to compensate for the phase noise, and the output signal after phase recovery is obtained: y xo =y x ×exp(-jθ x+1 ) Among them, y xo is the output signal, y x is the received signal, θ x+1 is the updated phase estimate.

8. A signal phase recovery device based on maximum a posteriori probability symbol decision error, characterized in that: For implementing the method according to any one of claims 1 to 7, the device comprises: Signal decision module: performs symbol decision on the received signal based on the maximum a posteriori probability and calculates the symbol decision error; Error gradient calculation module: construct an error function according to the symbol decision error, and calculate the gradient of the error function with respect to the phase noise value; Phase noise estimation module: updates the phase estimation value along the direction of gradient descent; Phase noise recovery module: recovers the phase noise of the symbol according to the phase estimation value to obtain the output signal after phase recovery.

9. An electronic device comprising a memory, a processor, and a program stored in the memory, characterized in that: When the processor executes the program, the method according to any one of claims 1 to 7 is implemented.

10. A storage medium having a program stored thereon, characterized in that: When the program is executed, the method according to any one of claims 1 to 7 is implemented.

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