Signal phase retrieval method, apparatus, equipment, and medium based on maximum a posteriori probability symbol decision error.

Abstract

This invention belongs to the field of optical communication and relates to a signal phase recovery method, apparatus, device, and medium based on maximum a posteriori probability symbol decision error. The method includes the following steps: performing symbol decision on the received signal based on maximum a posteriori probability and calculating the symbol decision error; constructing an error function based on the symbol decision error and calculating the gradient of the error function with respect to the phase noise value; updating the phase estimate along the gradient descent direction; and performing phase noise recovery of the symbol based on the phase estimate to obtain the phase-recovered output signal. Compared with existing technologies, this 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 laser linewidth in coherent transmission. Simultaneously, the symbol decision error-based approach improves the accuracy of phase noise estimation while effectively avoiding the use of time windows, thus reducing computational complexity.

Description

Technical Field

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

[0002] With the advent of 5G and 6G, and the continuous rise of bandwidth-intensive services such as the Internet of Things (IoT), virtual reality (VR), and augmented reality (AR), fiber optic communication networks, as the backbone networks carrying data traffic, need to further improve their transmission rates and capacities. Coherent transmission technology, through coherent beat frequency technology, has successfully achieved a breakthrough in fiber optic communication speed by utilizing more degrees of freedom. However, in coherent transmission systems, random phase noise caused by the laser linewidth severely restricts the effective transmission of high-order modulation signals, becoming a bottleneck factor limiting the further development of coherent optical communication speeds.

[0003] To address signal degradation caused by phase noise, researchers have proposed blind phase search (BPS) and phase retrieval algorithms based on Viterbi-Viterbi decoding (VV) to estimate the phase noise received during signal transmission. While these traditional phase algorithms can effectively compensate for random phase noise to some extent, there is still room for improvement in computational complexity and phase noise tracking performance. Especially when using probabilistically shaped signals to further increase optical transmission rates, these traditional phase retrieval algorithms suffer additional performance degradation due to the non-uniform probability distribution of the transmitted signal. Therefore, developing a low-complexity, high-performance signal phase retrieval method robust to probabilistically shaped signals is a key task for improving coherent optical communication rates. Summary of the Invention

[0004] The purpose of this invention is to provide a low-complexity signal phase recovery method, apparatus, device, and medium based on maximum a posteriori probability symbol decision error for optically probabilistically shaped signals.

[0005] The objective of this invention can be achieved through the following technical solutions:

[0006] According to a first aspect of the present invention, a low-complexity signal phase recovery method based on maximum a posteriori (MAP) symbol decision error is provided for optically probabilistically shaped signals. This method not only improves the accuracy of tracking phase noise by analyzing symbol errors, but also significantly reduces the computational complexity and latency required for signal phase recovery by eliminating the need for a time window implementation mechanism, further enhancing the tolerance of high-order modulation format transmission signals to large-linewidth lasers. To reduce the impact of the non-uniform probability distribution of probabilistic shaping on the performance of the phase estimation algorithm, MAP decision is introduced to mitigate the problems caused by the prior probability of transmitted symbols. 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 MAP symbol decision error can be applied to various coherent optical communication transmission scenarios, especially in high-speed coherent data center optical interconnect systems using large-linewidth lasers.

[0007] Specifically, the method comprises four parts: "signal decision", "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: Construct an error function based on the symbol decision error, and considering the relationship between the symbol decision error and the phase noise, calculate 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 based on the phase estimate to obtain the phase-recovered output signal.

[0012] The present invention also provides a signal phase recovery device based on maximum a posteriori probability symbol decision error, for implementing the method described above, the device comprising:

[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: Constructs an error function based on the symbol decision error, and calculates the gradient of the error function with respect to the phase noise value;

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

[0016] Phase noise recovery module: Performs symbolic phase noise recovery based on the phase estimate to obtain the phase-recovered output signal.

[0017] The present invention also provides an electronic device, including a memory, a processor, and a program stored in the memory, wherein the processor executes the program to implement the method described above.

[0018] The present invention also provides a storage medium having a program stored thereon, which, when executed, implements the method described above.

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

[0020] (1) This invention utilizes a high-performance error gradient descent algorithm and maximum a posteriori probability decision to achieve high-performance phase recovery applicable to optical probability-shaped signals, effectively solving the damage to signal quality caused by random phase noise due to linewidth.

[0021] (2) The present invention includes a feedback update structure. The gradient update factor has a significant impact on the system performance and is related to the laser linewidth, 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 versatility 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 phase tracking method based on signal error without time window, it effectively reduces the computational complexity and latency required for high-performance carrier recovery. Attached Figure Description

[0024] Figure 1 This is a flowchart of the method of the present invention;

[0025] Figure 2 This is a schematic diagram of the present invention;

[0026] Figure 3 This is a schematic diagram illustrating the application of the present invention to the maximum a posteriori probability decision of probabilistically shaped signals. Detailed Implementation

[0027] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. These embodiments are based on the technical solution of the present invention and provide detailed implementation methods and specific operating procedures. However, the scope of protection 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, which includes the following steps:

[0029] S1, 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 this phase recovery method, it is necessary to estimate the noise variance of the transmission system in advance using pilot signals and to know the prior probability vector of the transmitted symbols.

[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 fiber optic transmission channel conforms to the Gaussian white noise model, the receiver determines the probability 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 transmission, and then completes the symbol decision:

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

[0034] Where, x i For the possible standard constellation points in the QAM constellation set, p x Let N0 be the prior probability vector of the transmitted symbol, and d be the noise variance during transmission. x To determine the target constellation point with the maximum a posteriori probability, y x To receive signals.

[0035] By introducing a penalty term ln(p) that includes the symbolic prior probability. x (x i The decision based on the maximum a posteriori probability can effectively expand the decision region of constellation points with higher initial probabilities, thereby reducing the possibility of incorrect decisions in probability-shaping signals.

[0036] S12, Calculate the symbol decision error:

[0037] e x =d x -y x

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

[0039] like Figure 3As shown, the MAP decision boundary is the decision boundary obtained by using the maximum a posteriori probability of this invention for sign decision, while 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 higher probabilities larger and reduces the possibility of incorrect decision-making for probabilistically shaped signals.

[0040] S2, as Figure 2 As shown, an error function is constructed based on 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 magnitude of the sign decision error is used as the error function:

[0042] f x =|e x | 2

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

[0044] S22, considering that the error function is related to the phase estimate, the phase estimate can be updated using the gradient descent algorithm. The method for calculating the gradient of the error function with respect to the phase noise value is as follows:

[0045]

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

[0047] S3, 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] Where μ is the gradient update factor that balances update speed and update accuracy, and its magnitude is related to the linewidth of the laser used, the modulation format of the transmitted signal, and the signal-to-noise ratio of the received signal.

[0050] S4, as Figure 2 As shown, the phase noise of the symbol is recovered based on the phase estimate to obtain the phase-recovered output signal.

[0051] Based on the updated phase estimate, the received signal is phase-rotated accordingly to compensate for phase noise, resulting in a phase-recovered output signal.

[0052] y xo =y x ×exp(-jθ x+1 )

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

[0054] This invention combines an error gradient descent algorithm with high-performance phase retrieval to meet the compensation requirements for severe linear random phase noise caused by laser linewidth in coherent transmission. Simultaneously, by using a sign-decision error approach, it improves the accuracy of phase noise estimation while effectively avoiding the use of time windows, demonstrating significant performance in reducing computational complexity. In terms of application scenarios, this invention can be applied to various scenarios such as polarization-multiplexed long-distance single-carrier coherent optical transmission and mode-multiplexed short-distance coherent transmission systems, providing a low-complexity and reliable solution for ultra-high-speed data center optical interconnects.

[0055] This embodiment also provides a signal phase recovery device based on maximum a posteriori probability symbol decision error, used to implement the method described above. 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: Constructs an error function based on the symbol decision error, and calculates the gradient of the error function with respect to the phase noise value;

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

[0059] Phase noise recovery module: Performs symbolic phase noise recovery based on the phase estimate to obtain the phase-recovered output signal.

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

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

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

[0063] The processing unit executes 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 computer software programs 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 ROM and / or a communication unit. When the computer program is loaded into 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 by any other suitable means (e.g., by means of firmware).

[0064] The functions described above in this document can be performed, at least in part, by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: Field Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), Application Standard Products (ASSPs), System-on-Chip (SoCs), Complex Programmable Logic Devices (CPLDs), and so on.

[0065] The program code used to implement the methods of the present invention can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

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

[0067] The preferred embodiments of the present invention have been described in detail above. It should be understood that those skilled in the art can make numerous modifications and variations based on the concept of the present invention without creative effort. Therefore, all technical solutions that can be obtained by those skilled in the art based on the concept of the present invention through logical analysis, reasoning, or limited experimentation on the basis of existing technology should be within the scope of protection defined by the claims.

Claims

1. A signal phase recovery method based on maximum a posteriori probability symbol decision error, characterized in that, The method includes the following steps: Perform symbol decision on the received signal based on the maximum a posteriori probability and calculate the symbol decision error; An error function is constructed based on the symbol decision error, and the gradient of the error function with respect to the phase noise value is calculated. Update the phase estimate along the direction of gradient descent; Phase noise recovery of the symbol is performed based on the phase estimate to obtain the phase-recovered output signal; Specifically, the symbol decision based on the maximum a posteriori probability of the received signal is as follows: Based on the constellation point position of the received signal, the prior probability of the transmitted signal, and the total noise variance during transmission, the probability measure of the received signal belonging to each standard constellation point is determined, and the symbol decision is completed: in, For the possible standard constellation points of the QAM constellation, For the prior probability vector of the transmitted symbol, This refers to the noise variance during transmission. To determine the target constellation point based on the maximum a posteriori probability, To receive signals; The method for calculating the symbol decision error is as follows: in, For sign decision error; The error function is expressed as: in, It is the error function; The method for calculating the gradient of the error function with respect to the phase noise value is as follows: in, Let the gradient of the error function with respect to the phase noise value be denoted as . This is the phase estimate. This indicates taking the imaginary part of the complex number. This is a conjugate operation, where j is the imaginary unit; The phase estimate is updated along the gradient descent direction based on the minimum mean square error algorithm: in, The gradient update factor, which balances update speed and update accuracy, is related to the linewidth of the laser used, the modulation format of the transmitted signal, and the signal-to-noise ratio of the received signal. The specific steps for recovering the phase noise of the symbol based on the phase estimate are as follows: Based on the updated phase estimate, the received signal is phase-rotated accordingly to compensate for phase noise, resulting in a phase-recovered output signal. in, For output signal, In order to receive signals, This is the updated phase estimate.

2. A signal phase recovery device based on maximum a posteriori probability symbol decision error, characterized in that, For implementing the method as claimed in claim 1, the apparatus 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: Constructs an error function based on the symbol decision error, and calculates the gradient of the error function with respect to the phase noise value; Phase noise estimation module: Updates the phase estimate along the direction of gradient descent; Phase noise recovery module: Performs symbolic phase noise recovery based on the phase estimate to obtain the phase-recovered output signal.

3. An electronic device, comprising a memory, a processor, and a program stored in the memory, characterized in that, When the processor executes the program, it implements the method as described in claim 1.

4. A storage medium having a program stored thereon, characterized in that, When the program is executed, it implements the method as described in claim 1.

Images