Integrated optical signal receiver and method

Abstract

The invention discloses an integrated optical signal receiver and method, particularly relates to the field of optical communication, is used for solving the problem of video stream transmission quality reduction caused by optical signal distortion in a low optical signal-to-noise ratio environment, and aims to eliminate cross coupling caused by a birefringence effect by constructing a dynamic polarization state transfer matrix. Generating a clear independent polarization state electric signal sequence; time-frequency domain processing is carried out on the electric signal sequences, linear phase noise caused by laser linewidth and phase deviation caused by an optical fiber nonlinear effect are compensated, and signals with higher phase accuracy are output; then, the signals are mapped to a four-dimensional constellation space, a clustering method is used for generating an adaptive judgment boundary, symbol judgment is optimized, and the anti-noise capability is improved; through cooperative work of an inner loop and an outer loop, system parameters are dynamically adjusted according to signal characteristics, and processing stability and adaptability are enhanced. And finally, performing soft decision decoding on the signal, improving the decoding accuracy in combination with phase residual correction, and outputting a reconstructed video data stream.

Description

Technical Field

[0001] The present invention relates to the field of optical communications, and more particularly to an integrated optical signal receiver and method. Background Art

[0002] In an integrated optical signal receiver for high-speed wireless video transmission, the optical communication module needs to process polarization-multiplexed high-order modulated signals to achieve ultra-high-definition video streaming. However, in a low optical signal-to-noise ratio environment, the spontaneous radiation noise of the optical amplifier and the phase noise caused by the nonlinear effects of the fiber (such as the Kerr effect and the birefringence effect) are coupled with each other, resulting in asymmetric distortion of the signal in the joint polarization and phase space. The nonlinear phase rotation caused by the limited laser linewidth and the fiber Kerr effect causes the constellation points to drift randomly around the origin, destroying the certainty of the symbol phase information; the polarization state crosstalk caused by the birefringence effect causes the cross-coupling of the two orthogonal polarization state electric field components, further blurring the clustering boundaries of the constellation points. The traditional carrier recovery algorithm is based on the phase-locked loop tracking mechanism based on the assumption of signal phase stationarity. Under low optical signal-to-noise ratio conditions, the phase jump rate dominated by the noise exceeds the loop bandwidth and loses lock, causing the constellation diagram to have irreversible cluster center dispersion and decision area overlap in both polarization and phase dimensions, causing a nonlinear and steep increase in the bit error rate, which seriously restricts the effective transmission distance and marginal performance reliability of the high-order modulation system.

[0003] In order to solve the above problems, a technical solution is now provided. Summary of the invention

[0004] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides an integrated optical signal receiver and method, which eliminates the cross-coupling caused by the birefringence effect by constructing a dynamic polarization state transfer matrix, generates a clear independent polarization state electrical signal sequence, and lays the foundation for subsequent processing; then these electrical signal sequences are processed in the time and frequency domain to compensate for the linear phase noise caused by the laser linewidth and the phase offset caused by the nonlinear effect of the optical fiber, and output signals with higher phase accuracy; then these signals are mapped to a four-dimensional constellation space, and a clustering method is used to generate an adaptive decision boundary to further optimize symbol decisions and improve noise resistance; through the collaborative work of the inner and outer loops, the system parameters are dynamically adjusted according to the signal characteristics to enhance the processing stability and adaptability; finally, soft decision decoding is performed on the signal, and the decoding accuracy is improved in combination with phase residual correction, and the reconstructed video data stream is output to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions: An integrated optical signal receiving method comprises the steps of: S1: Separate the two orthogonal polarization state signals transmitted by optical fiber, construct a dynamic polarization state transfer matrix, and output an independent polarization state electrical signal sequence; S2: Perform time-frequency domain processing on the independent polarization state electrical signal sequence and output a phase compensation signal; S3: Map the phase compensation signal to the four-dimensional constellation space, perform constellation point clustering, generate the decision boundary and update the equalizer tap coefficient, and output the equalized signal; S4: Under the coordination mechanism of the outer and inner loops, the outer loop updates the polarization state transfer matrix according to the cluster center dispersion, and the inner loop adjusts the phase compensation step size based on the symbol error rate; S5: Perform confidence propagation-assisted soft decision decoding on the equalized signal, construct a phase residual model to correct the symbol confidence, and output the reconstructed video data stream.

[0006] In a preferred embodiment, step S1 includes the following contents: First, a complex electric field component of an X polarization state and a complex electric field component of a Y polarization state are received from optical fiber transmission, and the complex electric field component of the X polarization state and the complex electric field component of the Y polarization state are converted into a discrete signal sequence of an X polarization state and a discrete signal sequence of a Y polarization state by analog-to-digital conversion; Then, in view of the birefringence effect in the optical fiber, a polarization state transfer matrix is ​​introduced to describe the dynamic cross-coupling relationship between the X polarization state and the Y polarization state. The polarization state transfer matrix is ​​a two-dimensional complex matrix and is initialized according to the prior measurement data. Then, the Kalman filter algorithm is applied to dynamically estimate the polarization state transfer matrix. By defining the state vector, the state transfer model and the observation model, the estimated value of the polarization state transfer matrix is ​​iteratively optimized using the prediction step and the update step. Finally, the inverse matrix of the estimated polarization state transfer matrix is ​​used to decouple the discrete signal sequence of the X polarization state and the discrete signal sequence of the Y polarization state, and output independent polarization state electrical signal sequences, including independent electrical signal sequences of the X polarization state and independent electrical signal sequences of the Y polarization state.

[0007] In a preferred embodiment, step S2 includes the following contents: The phase drift rate is estimated by adaptive filtering technology, and the phase components of the X polarization state independent electrical signal sequence and the Y polarization state independent electrical signal sequence are reversely adjusted using the estimated phase drift rate to generate the X polarization state signal and the Y polarization state signal after time domain compensation.

[0008] In a preferred embodiment, step S2 further includes the following contents: The X polarization state signal and the Y polarization state signal after time domain compensation are converted into frequency domain signals through fast Fourier transform, and the nonlinear phase offset is estimated by split-step Fourier method. The phase of the frequency domain signal is reversely adjusted by using the estimated nonlinear phase offset to generate the X polarization state signal and the Y polarization state signal after frequency domain compensation. The X polarization state signal and the Y polarization state signal after frequency domain compensation are then converted back into the time domain through inverse fast Fourier transform to obtain the X polarization state signal and the Y polarization state signal after phase compensation. The X polarization state signal and the Y polarization state signal after phase compensation are integrated into a two-dimensional complex signal sequence.

[0009] In a preferred embodiment, step S3 includes the following contents: The X polarization state phase compensation complex signal and the Y polarization state phase compensation complex signal are decomposed into the X polarization state real part, the X polarization state imaginary part, the Y polarization state real part and the Y polarization state imaginary part, and the X polarization state real part, the X polarization state imaginary part, the Y polarization state real part and the Y polarization state imaginary part of each sampling point are combined into a four-dimensional vector to form a signal point set in the four-dimensional constellation space; then, the fuzzy C-means algorithm is applied to cluster the signal point set in the four-dimensional constellation space, the number of clusters is set according to the modulation format order, the cluster center is initialized, the membership degree of each signal point to the cluster center is calculated and the cluster center is updated, and it is iterated until the cluster center converges to generate a cluster center set.

[0010] In a preferred embodiment, step S3 further includes the following contents: Then, based on the cluster center set, the median hyperplane between each pair of cluster centers is calculated to form a decision boundary set; then, the equalizer tap coefficients are defined, and the equalizer tap coefficients are updated by the minimum mean square error objective function and the gradient descent method. The optimization is iteratively performed until the objective function converges to obtain the optimal equalizer tap coefficients; then, the four-dimensional signal points are equalized using the optimal equalizer tap coefficients to generate an equalized four-dimensional signal, and the equalized four-dimensional signal is converted into an equalized signal containing an X-polarization state complex signal and a Y-polarization state complex signal.

[0011] In a preferred embodiment, step S4 includes the following contents: The dispersion of the cluster center set is calculated in the outer loop, and the polarization state transfer matrix is ​​updated by using the gradient descent method by comparing the dispersion with the preset threshold. Then, a hard decision is made on the equalized signal in the inner loop, and the symbol error rate is calculated. The symbol error rate is compared with the preset threshold, and the time domain phase compensation step and the frequency domain phase compensation step are adjusted by using an adaptive strategy. Subsequently, the polarization state transfer matrix updated by the outer loop is fed back to step S1 for the next round of polarization state decoupling processing; the time domain phase compensation step and the frequency domain phase compensation step adjusted by the inner loop are fed back to step S2 for the next round of phase noise compensation processing.

[0012] In a preferred embodiment, step S5 includes the following contents: Perform confidence propagation-assisted soft decision decoding on the equalized signal to generate a soft information set of the symbol; construct a phase residual model based on the equalized signal and the soft information set of the symbol, calculate the actual phase, estimated phase and phase residual, and optimize the parameters of the phase residual model; use the phase residual model to adjust the soft information set of the symbol, calculate the phase residual prediction value and correct the soft information of the symbol; make a hard decision on the corrected soft information set of the symbol to generate an estimated symbol sequence, convert it into a binary bit stream and perform decoding and deinterleaving operations to output a reconstructed video data stream.

[0013] An integrated optical signal receiver, comprising: a polarization decoupling unit, a phase compensation unit, a constellation equalization unit, a loop optimization unit and a soft decision decoding unit; Polarization decoupling unit: Separates the two orthogonal polarization state signals transmitted by optical fiber, constructs a dynamic polarization state transfer matrix using the Kalman filter algorithm, eliminates the cross coupling caused by the birefringence effect, and outputs an independent polarization state electrical signal sequence; Phase compensation unit: performs time-frequency domain processing on the independent polarization state electrical signal sequence, compensates for the linear phase noise caused by the laser line width and the nonlinear phase shift caused by the fiber Kerr effect, and outputs a phase compensation signal; Constellation equalization unit: maps the phase compensation signal to the four-dimensional constellation space, uses the fuzzy C-means algorithm to cluster the constellation points, generates an adaptive decision boundary, updates the equalizer tap coefficients, and outputs the equalized signal; Loop optimization unit: updates the polarization state transfer matrix according to the cluster center dispersion and adjusts the phase compensation step size based on the symbol error rate; Soft decision decoding unit: performs confidence propagation-assisted soft decision decoding on the equalized signal, constructs a phase residual model to correct symbol confidence, and outputs a reconstructed video data stream.

[0014] The technical effects and advantages of an integrated optical signal receiver and method of the present invention are as follows: By constructing a dynamic polarization state transfer matrix, the cross-coupling caused by the birefringence effect is eliminated, and a clear independent polarization state electrical signal sequence is generated, laying the foundation for subsequent processing; then these electrical signal sequences are processed in the time and frequency domain to compensate for the linear phase noise caused by the laser linewidth and the phase offset caused by the nonlinear effect of the optical fiber, and output signals with higher phase accuracy; then these signals are mapped to the four-dimensional constellation space, and the clustering method is used to generate adaptive decision boundaries to further optimize symbol decisions and improve noise resistance; through the collaborative work of the inner and outer loops, the system parameters are dynamically adjusted according to the signal characteristics to enhance processing stability and adaptability; finally, soft decision decoding is performed on the signal, and the decoding accuracy is improved in combination with phase residual correction, and the reconstructed video data stream is output. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 The figure is a flow chart of an integrated optical signal receiving method of the present invention.

[0016] Figure 2 The figure is a schematic diagram of the structure of an integrated optical signal receiver of the present invention. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] Embodiment 1: Figure 1 The present invention provides an integrated optical signal receiving method, comprising: S1: Separate the two orthogonal polarization state signals transmitted by optical fiber, use the Kalman filter algorithm to construct a dynamic polarization state transfer matrix, and output an independent polarization state electrical signal sequence.

[0019] S2: Perform time-frequency domain processing on the independent polarization state electrical signal sequence and output a phase compensation signal.

[0020] S3: Map the phase compensation signal to the four-dimensional constellation space, perform constellation point clustering, generate decision boundaries and update the equalizer tap coefficients, and output the equalized signal.

[0021] S4: Under the coordination mechanism of the outer and inner loops, the outer loop updates the polarization state transfer matrix according to the cluster center dispersion, and the inner loop adjusts the phase compensation step size based on the symbol error rate.

[0022] S5: Perform confidence propagation-assisted soft decision decoding on the equalized signal, construct a phase residual model to correct the symbol confidence, and output the reconstructed video data stream.

[0023] In high-speed wireless video transmission chips, optical communication modules need to process polarization multiplexed high-order modulation signals to achieve the transmission of ultra-high-definition video streams. According to the background of the invention, in a low optical signal-to-noise ratio environment, the spontaneous radiation noise of the optical amplifier and the phase noise caused by the nonlinear effect of the optical fiber are coupled with each other, resulting in asymmetric distortion of the signal in the joint space of polarization and phase. Specifically, the birefringence effect in the optical fiber transmission link causes the cross-coupling of the electric field components of the two orthogonal polarization states, which makes the clustering boundaries of the constellation points of the received signal blurred, seriously affecting the accuracy of symbol judgment. To solve this problem, the processing scheme proposes step S1, which separates the two orthogonal polarization state signals of the optical fiber transmission, and uses the Kalman filter algorithm to construct a dynamic polarization state transfer matrix to eliminate the cross-coupling caused by the birefringence effect and output an independent polarization state electrical signal sequence.

[0024] Step S1 includes the following contents: S1. Signal reception and preprocessing: First, two orthogonal polarization state signals are received from the optical fiber transmission link. The two signals are represented as the complex electric field component of the X polarization state and the complex electric field component of the Y polarization state, respectively. The two orthogonal polarization state signals are subjected to analog-to-digital conversion processing to convert them from continuous-time signals to discrete-time sequences. Specifically, through sampling operations, the complex electric field component of the X polarization state is converted into a discrete signal sequence of the X polarization state, and the complex electric field component of the Y polarization state is converted into a discrete signal sequence of the Y polarization state, and each sampling point is identified by a time index. After completing the analog-to-digital conversion, the discrete signal sequence of the X polarization state and the discrete signal sequence of the Y polarization state become the basic input for subsequent digital signal processing. The purpose of converting the continuous-time signal into a discrete-time sequence is to realize the digital representation of the signal, which facilitates the application of precise signal processing techniques in the digital domain, such as filtering and matrix operations.

[0025] S2. Polarization state transfer matrix modeling: In the polarization state transfer matrix modeling stage, the polarization state transfer matrix is ​​used to describe the dynamic relationship between the X polarization state and the Y polarization state caused by the birefringence effect in optical fiber transmission. The polarization state transfer matrix is ​​a two-dimensional complex matrix with 2 rows and columns, which is used to represent the coupling characteristics between the X polarization state and the Y polarization state. Specifically, each element of the polarization state transfer matrix is ​​a complex number, which represents the coupling amplitude and phase changes from the X polarization state to the X polarization state, the X polarization state to the Y polarization state, the Y polarization state to the X polarization state, and the Y polarization state to the Y polarization state. In the initial stage, the polarization state transfer matrix can be set to a unit matrix, that is, assuming that there is no coupling between the X polarization state and the Y polarization state, or the matrix elements can be initialized and assigned according to the prior measurement data to be closer to the actual coupling state.

[0026] The purpose of introducing the polarization state transfer matrix is ​​to quantitatively describe the polarization state cross coupling caused by the optical fiber birefringence effect through a mathematical model, and to provide a theoretical basis for signal decoupling. This modeling method can transform complex physical phenomena into a computable matrix form, which is convenient for the application of digital signal processing technology. At the same time, the flexibility of initialization enables the model to adapt to different optical fiber transmission conditions and improve the adaptability and accuracy of processing.

[0027] S3. Kalman filter algorithm application: In the application stage of the Kalman filter algorithm, in order to dynamically estimate the polarization state transfer matrix, the four complex elements of the polarization state transfer matrix are first flattened into a column vector, called the state vector. Next, the state transfer model is defined, assuming that the state vector is only affected by process noise between adjacent time indexes, and the process noise has zero mean and obeys Gaussian distribution characteristics. Then, the observation model is defined, indicating that the received discrete signal sequence of the X polarization state and the discrete signal sequence of the Y polarization state are the results of the polarization state transfer matrix acting on the independent polarization state signal of the transmitter, and then superimposed with observation noise, where the observation noise also has zero mean and obeys Gaussian distribution characteristics. Since the independent polarization state signal of the transmitter is unknown, the initial estimate can be obtained by iterative calculation of the decoupling result of the previous time index or the reference signal. Based on the above model, the Kalman filter algorithm dynamically updates the estimated value of the state vector by combining the state transfer model and the observation model, thereby realizing real-time optimization of the polarization state transfer matrix.

[0028] The purpose of using the Kalman filter algorithm is to utilize its state estimation capability in time-varying systems and dynamically track the changes in the polarization state transfer matrix by fusing the estimation results of the previous time index and the observation data of the current time index.

[0029] S4. Kalman filter iteration: In the iterative stage of Kalman filtering, the estimation process of the polarization state transfer matrix is ​​divided into two parts: the prediction step and the update step. In the prediction step, the estimated value of the state vector of the previous time index is used to predict the state vector value of the current time index, and the covariance matrix of the prediction error is calculated. The covariance matrix represents the degree of uncertainty of the prediction result. In the update step, the Kalman gain is calculated based on the discrete signal sequence of the X polarization state and the discrete signal sequence of the Y polarization state received at the current time index. The Kalman gain is a weight factor used to balance the influence of the prediction result and the observation data. When calculating the Kalman gain, it depends on the observation matrix, which is determined by the linearization approximation of the independent polarization state signal at the transmitter. Subsequently, the estimated value of the state vector is updated using the Kalman gain, and the error covariance matrix is ​​updated. By repeatedly executing the prediction step and the update step, the estimated value of the polarization state transfer matrix gradually approaches the true value.

[0030] The purpose of the Kalman filter iteration process is to gradually reduce the error of the polarization state transfer matrix estimate through cyclic optimization, ensuring that the estimation result can accurately reflect the dynamic coupling characteristics in optical fiber transmission. This iterative method can effectively deal with time-varying noise and nonlinear effects, improve the stability and accuracy of the decoupling process, and provide high-quality matrix parameters for subsequent steps.

[0031] S5. Polarization decoupling: In the polarization state decoupling stage, the polarization state transfer matrix obtained by Kalman filtering iteration estimation is used to decouple the received discrete signal sequence of the X polarization state and the discrete signal sequence of the Y polarization state. Specifically, by calculating the inverse matrix of the polarization state transfer matrix, the coupling effect of the received signal is reversely eliminated, and the independent electrical signal sequence of the X polarization state and the independent electrical signal sequence of the Y polarization state are converted. The decoupling process is based on the principle of linear algebra, that is, through the inverse matrix operation, the received signal is restored to an independent polarization state signal that is not affected by cross-coupling at the transmitting end. After the decoupling is completed, an independent polarization state electrical signal sequence is output, including an independent electrical signal sequence of the X polarization state and an independent electrical signal sequence of the Y polarization state.

[0032] The purpose of performing polarization decoupling is to eliminate the cross coupling between the X polarization state and the Y polarization state caused by the fiber birefringence effect, so that subsequent signal processing can operate on each independent polarization state separately. This decoupling method not only improves the efficiency of signal processing, but also significantly enhances the accuracy of symbol decision, laying the foundation for the reliability of ultra-high-definition video streaming transmission.

[0033] In a low optical signal-to-noise ratio environment, the spontaneous radiation noise of the optical amplifier and the phase noise caused by the nonlinear effect of the optical fiber are coupled with each other, resulting in asymmetric distortion of the signal in the joint space of polarization and phase. The limited linewidth of the laser causes linear phase drift, and the fiber Kerr effect causes nonlinear phase rotation, causing the constellation points to drift randomly around the origin, destroying the certainty of the symbol phase information. Step S1 has eliminated the cross-coupling caused by the birefringence effect through the Kalman filter algorithm, and outputted a sequence of independent polarization state electrical signals. Based on the output of step S1, step S2 performs time-frequency domain processing on the linear and nonlinear phase noise, and outputs a phase compensation signal.

[0034] Step S2 includes the following contents: S2.1, time domain linear phase noise compensation: In the time domain linear phase noise compensation stage, the linear phase noise caused by the laser line width is processed. Linear phase noise manifests itself as a linear drift of the signal phase over time, which directly affects the accuracy of the symbol phase information. First, the phase components of the independent electrical signal sequence of the X polarization state and the independent electrical signal sequence of the Y polarization state are extracted to obtain the phase component of each signal sequence. The phase component consists of three parts: initial phase, phase drift rate and residual noise. The phase drift rate is caused by the laser line width and is the main source of linear phase drift. Next, the phase drift rate is estimated by adaptive filtering technology. The specific method is to analyze the relationship between the phase component and the time index and calculate the average rate at which the phase changes over time. Then, the phase components of the independent electrical signal sequence of the X polarization state and the independent electrical signal sequence of the Y polarization state are adjusted in reverse using the estimated phase drift rate to generate the X polarization state signal after time domain compensation and the Y polarization state signal after time domain compensation.

[0035] For example, the process is as follows: Sequence of independent electrical signals for X polarization state and Y polarization states of the independent electrical signal sequence Perform time domain processing separately.

[0036] For each polarization signal , calculate its phase .

[0037] Assume the phase noise model is ,in: represents the initial phase; represents the phase drift rate, caused by the laser line width; represents the residual noise.

[0038] Estimating Phase Drift Rate Using Wiener Filter , and compensate the phase drift by the following formula: ; in is the estimated phase drift rate.

[0039] Output: signal after time domain compensation and .

[0040] The purpose of time domain linear phase noise compensation is to restore the phase stability of the independent electrical signal sequence in the X polarization state and the independent electrical signal sequence in the Y polarization state by estimating and offsetting the phase drift rate.

[0041] S2.2, frequency domain nonlinear phase offset cancellation: In the frequency domain nonlinear phase offset cancellation stage, the nonlinear phase offset caused by the fiber Kerr effect is compensated. The nonlinear phase offset is closely related to the instantaneous power of the signal, resulting in nonlinear rotation of the signal phase. First, the X polarization state signal and the Y polarization state signal after time domain compensation are converted to the frequency domain by fast Fourier transform, and the frequency domain X polarization state signal and the frequency domain Y polarization state signal are obtained respectively. Then, the split-step Fourier method is used to simulate the reverse process of optical fiber transmission to estimate the nonlinear phase offset. Specifically, the magnitude of the nonlinear phase offset is determined by the fiber nonlinear coefficient, the effective fiber length and the square of the signal power spectrum, and the phase rotation amount of each frequency component is obtained by numerical calculation. Then, the estimated nonlinear phase offset is used to reversely adjust the phase of the frequency domain X polarization state signal and the frequency domain Y polarization state signal to generate the frequency domain compensated X polarization state signal and the frequency domain compensated Y polarization state signal. Finally, the frequency domain compensated X polarization state signal and the frequency domain compensated Y polarization state signal are converted back to the time domain by inverse fast Fourier transform, and the phase compensated X polarization state signal and the phase compensated Y polarization state signal are obtained respectively.

[0042] For example, the process is as follows: The time-domain compensated signal Convert to frequency domain: ; in Represents the frequency index.

[0043] Estimating nonlinear phase offset , the split-step Fourier method (SSFM) is used to simulate the reverse process of optical fiber transmission, and the calculation formula is: ; in: represents the fiber nonlinear coefficient; Indicates the effective optical fiber length; Represents the signal power spectrum.

[0044] To cancel nonlinear phase shift: ; Convert the frequency-domain compensated signal back to the time domain: ; Output: Phase compensation signal and .

[0045] The purpose of frequency domain nonlinear phase offset cancellation is to accurately eliminate the nonlinear phase rotation effect by simulating the reverse process of optical fiber transmission. As an efficient numerical method, the split-step Fourier method can accurately estimate the nonlinear phase offset and improve the compensation accuracy. Combined with the time domain compensation processing method, the phase noise problem can be fully solved.

[0046] S2.3, Phase compensation signal integration: In the phase compensation signal integration stage, the phase compensated X polarization state signal and the phase compensated Y polarization state signal are integrated into a composite signal sequence. Specifically, the phase compensated X polarization state signal and the phase compensated Y polarization state signal are aligned according to the time index and combined into a two-dimensional complex signal sequence, where each time index corresponds to a vector containing X polarization state and Y polarization state signal components. The integrated two-dimensional complex signal sequence facilitates the unified processing and management of subsequent steps.

[0047] The purpose of integrating the phase-compensated X-polarization state signal and the phase-compensated Y-polarization state signal is to provide a standardized input format for the subsequent constellation space mapping and clustering algorithms, ensure the uniformity and efficiency of signal processing, reduce data redundancy, and improve processing efficiency.

[0048] Step S2 receives the independent polarization state electrical signal sequence output by step S1, compensates the linear phase noise caused by the laser line width through the time domain Wiener filter, and then offsets the nonlinear phase shift caused by the fiber Kerr effect through the frequency domain split step Fourier method, and finally outputs the phase compensation signal. This process effectively alleviates the constellation point drift problem caused by phase noise in a low optical signal-to-noise ratio environment, provides a stable signal input for the four-dimensional constellation space mapping and fuzzy C-means clustering in step S3, and ensures the phase information accuracy of ultra-high-definition video stream transmission.

[0049] Step S3 receives the phase compensation signal outputted from step S2, and outputs an equalized signal by mapping to a four-dimensional constellation space, clustering constellation points, generating a decision boundary, and updating equalizer coefficients.

[0050] Step S3 includes the following contents: S3.1, signal mapping to four-dimensional constellation space: At the stage of signal mapping to the four-dimensional constellation space, a phase compensation signal from step S2 is received, the phase compensation signal is composed of a phase compensation complex signal of an X polarization state and a phase compensation complex signal of a Y polarization state, and each signal is identified by a sampling point index. First, the phase compensation complex signal of the X polarization state is decomposed into the real part of the X polarization state and the imaginary part of the X polarization state, and the phase compensation complex signal of the Y polarization state is decomposed into the real part of the Y polarization state and the imaginary part of the Y polarization state. Then, for each sampling point, the real part of the X polarization state, the imaginary part of the X polarization state, the real part of the Y polarization state, and the imaginary part of the Y polarization state are combined into a four-dimensional vector, which represents a signal point in the four-dimensional constellation space. The set of four-dimensional vectors corresponding to all sampling points constitutes a signal point set in the four-dimensional constellation space.

[0051] The four-dimensional constellation space refers to a mathematical representation method used to describe the signal distribution state of polarization-multiplexed high-order modulated signals after processing at the receiving end. A four-dimensional vector corresponds to a point in the four-dimensional Euclidean space. The set of four-dimensional vectors corresponding to all sampling points forms the constellation diagram of the signal, and the four-dimensional space where this constellation diagram is located is the four-dimensional constellation space. The four-dimensional constellation space can fully characterize the amplitude and phase information of the polarization-multiplexed signal, which is convenient for subsequent clustering analysis, decision boundary generation and equalization processing, thereby supporting accurate demodulation and data recovery of high-order modulation formats.

[0052] S3.2, fuzzy C-means algorithm for constellation point clustering: In the stage of clustering constellation points by the fuzzy C-means algorithm, clustering is performed on the signal point set in the four-dimensional constellation space to determine the cluster center of the constellation point. First, the number of clusters is set to the order of the modulation format. For example, for M-order modulation, the number of clusters is set to M. Next, the cluster center is initialized, which can be set by randomly selecting signal points or according to the prior knowledge of the modulation format. Then, the membership degree of each signal point belonging to each cluster is calculated, and the membership degree represents the degree of similarity between the signal point and the cluster center. Next, the cluster center is updated. The specific method is to perform weighted summation on all signal points, and the weight during weighting is the square of the membership degree. Finally, the weighted sum is normalized to obtain a new cluster center. Repeat the process of membership calculation and cluster center update until the change of the cluster center is less than the preset threshold or reaches the predetermined maximum number of iterations, and finally obtain the cluster center set.

[0053] The purpose of using the fuzzy C-means algorithm for constellation point clustering is to effectively deal with the fuzzy boundary of constellation points in a low optical signal-to-noise ratio environment. The fuzzy C-means algorithm improves the robustness and accuracy of clustering by assigning multiple cluster memberships to each signal point, providing reliable cluster center support for the subsequent generation of decision boundaries.

[0054] S3.3, Generate adaptive decision boundary: In the stage of generating adaptive decision boundaries, the cluster center set is used to generate the decision boundary for subsequent symbol decisions. First, for any two different cluster centers in the four-dimensional constellation space, the median hyperplane between the two is calculated as the decision boundary. The specific calculation method is: the difference vector of the two cluster centers is used as the normal vector of the median hyperplane, and the median hyperplane passes through the midpoint position of the two cluster centers. The set of median hyperplanes corresponding to all cluster center pairs constitutes a decision boundary set, which divides the four-dimensional constellation space into multiple regions, each region corresponds to a cluster center, and is used to determine the symbol category to which the signal point belongs.

[0055] The purpose of generating an adaptive decision boundary is to adaptively adjust the decision area according to the actual cluster distribution of the received signal to adapt to the influence of signal distortion and noise. Compared with a fixed decision boundary, an adaptive decision boundary can significantly improve the accuracy of symbol decision and reduce the bit error rate, thereby providing technical support for the reliable operation of high-order modulation systems in complex transmission environments.

[0056] S3.4, update the equalizer tap coefficients: In the stage of updating the tap coefficients of the equalizer, the tap coefficients of the equalizer are optimized based on the cluster center set and the decision boundary set to improve the signal quality. First, the tap coefficients of the equalizer are defined as a vector, and the equalized signal is the inner product of the tap coefficients and the four-dimensional signal point. Then, the objective function is set to the minimum mean square error, specifically the expected value of the square of the Euclidean distance between the equalized signal and the cluster center to which it belongs, where the cluster center is determined by the nearest neighbor principle, that is, the cluster center closest to the equalized signal is selected. Next, the tap coefficients are updated using the gradient descent method. The specific method is: calculate the gradient of the objective function to the tap coefficients, the gradient is determined by the expected value of the product of the difference between the equalized signal and the cluster center to which it belongs and the four-dimensional signal point, and then update the tap coefficients in the opposite direction of the gradient with a preset step size. Repeat the update process until the objective function converges, and finally obtain the optimal tap coefficients of the equalizer.

[0057] The purpose of updating the tap coefficients of the equalizer is to reduce the influence of signal distortion and noise on the distribution of constellation points by optimizing the equalizer parameters, thereby improving the clustering density and decision accuracy of the signal. As an efficient optimization method, the gradient descent method can quickly converge to the optimal solution, ensure the real-time and effectiveness of the equalization process, and provide a high-quality foundation for subsequent signal output.

[0058] The four-dimensional signal point is equalized using the optimal equalizer tap coefficient to generate an equalized four-dimensional signal. The specific method is to calculate the inner product of the tap coefficient and the four-dimensional signal point to obtain the equalized four-dimensional signal. Then, the equalized four-dimensional signal is converted into a two-dimensional complex signal. The specific conversion method is: the first two components of the equalized four-dimensional signal are used as the real part and imaginary part of the X polarization state, and the last two components are used as the real part and imaginary part of the Y polarization state, respectively, and they are combined into a complex signal of the X polarization state and a complex signal of the Y polarization state, respectively, to finally form an equalized signal.

[0059] Step S3 receives the phase compensation signal outputted by step S2, maps it to the four-dimensional constellation space, clusters the constellation points using the fuzzy C-means algorithm, generates an adaptive decision boundary, and updates the tap coefficients of the equalizer, and finally outputs an equalized signal. This process effectively solves the problem of fuzzy constellation point boundaries in low optical signal-to-noise ratio environments, improves the decision accuracy of the signal, and provides high-quality equalized signals for the outer and inner loop coordination mechanisms of step S4, ensuring the reliability of ultra-high-definition video stream transmission.

[0060] Step S4 includes the following contents: Step S4 receives the equalized signal and cluster center set outputted by step S3, and dynamically updates the polarization state transfer matrix and phase compensation step length through the coordination mechanism of the outer and inner loops. The specific processing technology logic is as follows: Outer loop: Update the polarization state transfer matrix based on the cluster center dispersion.

[0061] In the outer loop stage, the polarization state transfer matrix is ​​adjusted according to the dispersion of the cluster center set to reduce the dispersion of the constellation point distribution. First, the cluster center dispersion is calculated. The specific method is: calculate the average Euclidean distance from all cluster centers to their geometric centers, where the geometric center is the average value of all cluster center vectors. Then, a dispersion threshold is set. When the calculated cluster center dispersion is greater than the dispersion threshold, the update of the polarization state transfer matrix is ​​triggered. The gradient descent method is used to update the polarization state transfer matrix. Specifically, by calculating the gradient of the cluster center dispersion to the polarization state transfer matrix, the polarization state transfer matrix is ​​adjusted in the opposite direction of the gradient at a preset learning rate. The learning rate is used as a small positive number to control the adjustment step.

[0062] The purpose of updating the polarization state transfer matrix based on the cluster center dispersion is to improve the accuracy of symbol decision by reducing the dispersion of constellation point distribution. The gradient descent method adaptively adjusts the polarization state transfer matrix according to the change of dispersion, which can maintain the best polarization state decoupling effect in a dynamic environment, thereby enhancing the system's robustness and adaptability to complex channel environments.

[0063] Inner loop: adjusts the phase compensation step size based on the symbol error rate.

[0064] In the inner loop stage, the phase compensation step size is adjusted based on the symbol error rate to optimize the phase noise compensation effect. First, a hard decision is made on the equalized signal to generate an estimated symbol sequence. Then, the current symbol error rate is calculated by calculating the ratio of the number of erroneous symbols to the total number of symbols. Next, a symbol error rate threshold is set. When the calculated symbol error rate is greater than the symbol error rate threshold, the phase compensation step size is adjusted. An adaptive strategy is used to adjust the time domain phase compensation step size and the frequency domain phase compensation step size. Specifically, the current time domain phase compensation step size and the frequency domain phase compensation step size are multiplied by an adjustment factor greater than 1, respectively. The adjustment factor is proportional to the symbol error rate, and the adjustment coefficient is used to control the sensitivity of the adjustment. The purpose of adjusting the phase compensation step size based on the symbol error rate is to optimize the compensation effect of phase noise and reduce the symbol error rate by adaptively adjusting the time domain phase compensation step size and the frequency domain phase compensation step size. The adaptive strategy dynamically adjusts the phase compensation step size according to the actual signal quality to ensure that the best compensation effect can be obtained in different noise environments, thereby improving the stability of the system and signal processing performance.

[0065] Loop coordination mechanism: In the loop coordination mechanism stage, the outer loop and the inner loop work together to dynamically optimize the polarization state transfer matrix and the phase compensation step. Specifically, the outer loop updates the polarization state transfer matrix at fixed time intervals, and the inner loop adjusts the time domain phase compensation step and the frequency domain phase compensation step in each symbol period. Then, the polarization state transfer matrix updated by the outer loop is fed back to step S1 for the next round of polarization state decoupling processing; the time domain phase compensation step and the frequency domain phase compensation step adjusted by the inner loop are fed back to step S2 for the next round of phase noise compensation processing.

[0066] Step S5 includes the following contents: Step S5 receives the equalized signal transmitted by step S4, and needs to use soft decision decoding assisted by belief propagation to construct a phase residual model to correct the symbol confidence and output the reconstructed video data stream. The specific processing technology logic of step S5 is described in detail below.

[0067] S5.1, Belief Propagation Assisted Soft Decision Decoding: In the soft decision decoding stage assisted by belief propagation, soft decision decoding is performed on the equalized signal to generate soft information for each symbol. First, the equalized signal is input into the belief propagation decoder. The belief propagation decoder iteratively calculates the marginal posterior probability of each symbol through a message passing mechanism based on the prior distribution of the signal and the channel model. The specific calculation process is as follows: in the factor graph, the variable nodes exchange messages with the check nodes, and the symbol confidence is gradually updated until the message converges or the preset maximum number of iterations is reached. Then, the log-likelihood ratio of each symbol is calculated as soft information. The log-likelihood ratio represents the logarithm of the ratio of the probability that the symbol is 1 to the probability that the symbol is 0.

[0068] The purpose of using soft decision decoding assisted by belief propagation is to improve the accuracy of decoding through iterative optimization. Especially in low optical signal-to-noise ratio environments, it can effectively deal with noise interference, improve the reliability of symbol confidence, and provide high-quality soft information support for the subsequent construction of phase residual models.

[0069] S5.2, build phase residual model: In the phase residual model construction stage, the phase error is quantified based on the equalized signal and the soft information set. First, the actual phase of each symbol is calculated based on the equalized signal. The specific method is: take the inverse tangent of the ratio of the imaginary part to the real part of the complex value of the equalized signal to obtain the actual phase angle. Next, the estimated phase is inferred based on the soft information set, and the ideal constellation point phase of the symbol is determined by the maximum a posteriori probability. Then, the phase residual is calculated, that is, the difference between the actual phase and the estimated phase. After that, the phase residual model is constructed, and the phase residual is expressed as a linear combination of the eigenvectors plus the model error. The eigenvectors include the amplitude of the symbol and the phase influence of the adjacent symbols. Finally, the model parameters are solved by optimization. The specific method is: minimize the sum of the squares of the difference between the phase residual and the model prediction value to obtain the optimal model parameters.

[0070] The purpose of building a phase residual model is to quantify the impact of phase noise on symbol confidence. By fitting the phase residual with a linear model, the phase error can be accurately estimated, providing a basis for correcting the symbol confidence.

[0071] S5.3, Corrected Sign Confidence: In the stage of correcting symbol confidence, the phase residual model is used to adjust the soft information set to improve the accuracy of symbol confidence. First, the phase residual prediction value is calculated for each symbol. The specific method is: the inner product of the eigenvector and the model parameter is used as the prediction value. Then, the soft information set is adjusted. The specific method is: the original soft information plus the negative value of the phase residual prediction value is multiplied by an adjustment factor. The adjustment factor is used to control the amplitude of the correction, and its value is pre-set according to system requirements.

[0072] The purpose of correcting symbol confidence is to improve the accuracy of the soft information set by offsetting the impact of phase error on confidence. The adjusted soft information can more realistically reflect the probability distribution of symbols, thereby improving the accuracy of subsequent hard decisions, ensuring the reconstruction quality of video data streams, and providing support for high-quality video transmission.

[0073] S5.4, output the reconstructed video data stream: In the stage of outputting the reconstructed video data stream, a hard decision is made on the modified soft information set to generate an estimated symbol sequence. The specific method is: when the soft information is greater than 0, the decision symbol is 1, otherwise the decision is 0. Then, the hard decision result is converted into a binary bit stream. Next, the binary bit stream is decoded and deinterleaved to restore the original video data stream. The decoding process is carried out according to the preset coding rules.

[0074] Embodiment 2: Figure 2 The present invention provides an integrated optical signal receiver, comprising: a polarization decoupling unit, a phase compensation unit, a constellation equalization unit, a loop optimization unit and a soft decision decoding unit; Polarization decoupling unit: Separates the two orthogonal polarization state signals transmitted by optical fiber, constructs a dynamic polarization state transfer matrix using the Kalman filter algorithm, eliminates the cross coupling caused by the birefringence effect, and outputs an independent polarization state electrical signal sequence; Phase compensation unit: performs time-frequency domain processing on the independent polarization state electrical signal sequence, compensates for the linear phase noise caused by the laser line width and the nonlinear phase shift caused by the fiber Kerr effect, and outputs a phase compensation signal; Constellation equalization unit: maps the phase compensation signal to the four-dimensional constellation space, uses the fuzzy C-means algorithm to cluster the constellation points, generates an adaptive decision boundary, updates the equalizer tap coefficients, and outputs the equalized signal; Loop optimization unit: updates the polarization state transfer matrix according to the cluster center dispersion and adjusts the phase compensation step size based on the symbol error rate; Soft decision decoding unit: performs confidence propagation-assisted soft decision decoding on the equalized signal, constructs a phase residual model to correct symbol confidence, and outputs a reconstructed video data stream.

[0075] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0076] It should be noted that the system of the present invention can be deployed on the device itself to realize embedded applications, and can also be run on a PC or other terminal with a user interface, thereby meeting a variety of hardware environments and usage requirements.

[0077] The above description is only by way of illustration of certain exemplary embodiments of the present invention. It is undoubted that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

[0078] It should be noted that, in this article, if there are relational terms such as first and second, etc., they are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "including a..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0079] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. An integrated optical signal receiving method, characterized in that: Includes steps: S1: Separate the two orthogonal polarization state signals transmitted by optical fiber, construct a dynamic polarization state transfer matrix, and output an independent polarization state electrical signal sequence; S2: Perform time-frequency domain processing on the independent polarization state electrical signal sequence and output a phase compensation signal; S3: Map the phase compensation signal to the four-dimensional constellation space, perform constellation point clustering, generate the decision boundary and update the equalizer tap coefficient, and output the equalized signal; S4: Under the coordination mechanism of the outer and inner loops, the outer loop updates the polarization state transfer matrix according to the cluster center dispersion, and the inner loop adjusts the phase compensation step size based on the symbol error rate; S5: Perform confidence propagation-assisted soft decision decoding on the equalized signal, construct a phase residual model to correct the symbol confidence, and output the reconstructed video data stream.

2. The integrated optical signal receiving method according to claim 1, characterized in that: Step S1 includes the following contents: receiving a complex electric field component of an X polarization state and a complex electric field component of a Y polarization state from optical fiber transmission, and converting the complex electric field component of the X polarization state and the complex electric field component of the Y polarization state into a discrete signal sequence of an X polarization state and a discrete signal sequence of a Y polarization state through analog-to-digital conversion; Aiming at the birefringence effect in optical fiber, a polarization state transfer matrix is ​​introduced to describe the dynamic cross-coupling relationship between the X polarization state and the Y polarization state. The polarization state transfer matrix is ​​a two-dimensional complex matrix and is initialized according to the prior measurement data. The Kalman filter algorithm is used to dynamically estimate the polarization state transfer matrix. By defining the state vector, state transfer model and observation model, the estimated value of the polarization state transfer matrix is ​​iteratively optimized using the prediction step and the update step. The inverse matrix of the estimated polarization state transfer matrix is ​​used to decouple the discrete signal sequence of the X polarization state and the discrete signal sequence of the Y polarization state, and an independent polarization state electrical signal sequence is output, including an independent electrical signal sequence of the X polarization state and an independent electrical signal sequence of the Y polarization state.

3. The integrated optical signal receiving method according to claim 2, characterized in that: Step S2 includes the following contents: The phase drift rate is estimated by adaptive filtering technology, and the phase components of the X polarization state independent electrical signal sequence and the Y polarization state independent electrical signal sequence are reversely adjusted using the estimated phase drift rate to generate the X polarization state signal and the Y polarization state signal after time domain compensation.

4. The integrated optical signal receiving method according to claim 3, characterized in that: Step S2 also includes the following contents: The X polarization state signal and the Y polarization state signal after time domain compensation are converted into frequency domain signals through fast Fourier transform, and the nonlinear phase offset is estimated by split-step Fourier method. The phase of the frequency domain signal is reversely adjusted by using the estimated nonlinear phase offset to generate the X polarization state signal and the Y polarization state signal after frequency domain compensation. The X polarization state signal and the Y polarization state signal after frequency domain compensation are then converted back into the time domain through inverse fast Fourier transform to obtain the X polarization state signal and the Y polarization state signal after phase compensation. The X polarization state signal and the Y polarization state signal after phase compensation are integrated into a two-dimensional complex signal sequence.

5. The integrated optical signal receiving method according to claim 4, characterized in that: Step S3 includes the following contents: The X polarization state phase compensation complex signal and the Y polarization state phase compensation complex signal are decomposed into the X polarization state real part, the X polarization state imaginary part, the Y polarization state real part and the Y polarization state imaginary part, and the X polarization state real part, the X polarization state imaginary part, the Y polarization state real part and the Y polarization state imaginary part of each sampling point are combined into a four-dimensional vector to form a signal point set in the four-dimensional constellation space; then, the fuzzy C-means algorithm is applied to cluster the signal point set in the four-dimensional constellation space, the number of clusters is set according to the modulation format order, the cluster center is initialized, the membership degree of each signal point to the cluster center is calculated and the cluster center is updated, and it is iterated until the cluster center converges to generate a cluster center set.

6. The integrated optical signal receiving method according to claim 5, characterized in that: Step S3 also Includes the following: Then, based on the cluster center set, the median hyperplane between each pair of cluster centers is calculated to form a decision boundary set; then, the equalizer tap coefficients are defined, and the equalizer tap coefficients are updated by the minimum mean square error objective function and the gradient descent method. The optimization is iteratively performed until the objective function converges to obtain the optimal equalizer tap coefficients; then, the four-dimensional signal points are equalized using the optimal equalizer tap coefficients to generate an equalized four-dimensional signal, and the equalized four-dimensional signal is converted into an equalized signal containing an X-polarization state complex signal and a Y-polarization state complex signal.

7. The integrated optical signal receiving method according to claim 6, characterized in that: Step S4 includes the following contents: The dispersion of the cluster center set is calculated in the outer loop, and the polarization state transfer matrix is ​​updated by using the gradient descent method by comparing the dispersion with the preset threshold. Then, the equalized signal is hard-determined in the inner loop, the symbol error rate is calculated, and the time domain phase compensation step and the frequency domain phase compensation step are adjusted by using an adaptive strategy by comparing the symbol error rate with the preset threshold. Then, the polarization state transfer matrix updated by the outer loop is fed back to step S1 for the next round of polarization state decoupling processing. The time domain phase compensation step size and frequency domain phase compensation step size adjusted by the inner loop are fed back to step S2 for the next round of phase noise compensation processing.

8. The integrated optical signal receiving method according to claim 7, characterized in that: Step S5 includes the following contents: Perform confidence propagation-assisted soft decision decoding on the equalized signal to generate a soft information set of the symbol; construct a phase residual model based on the equalized signal and the soft information set of the symbol, calculate the actual phase, estimated phase and phase residual, and optimize the parameters of the phase residual model; use the phase residual model to adjust the soft information set of the symbol, calculate the phase residual prediction value and correct the soft information of the symbol; make a hard decision on the corrected soft information set of the symbol to generate an estimated symbol sequence, convert it into a binary bit stream and perform decoding and deinterleaving operations to output a reconstructed video data stream.

9. An integrated optical signal receiver, used to implement an integrated optical signal receiving method according to any one of claims 1 to 8, characterized in that: include: Polarization decoupling unit, phase compensation unit, constellation equalization unit, loop optimization unit and soft decision decoding unit; Polarization decoupling unit: Separates the two orthogonal polarization state signals transmitted by optical fiber, constructs a dynamic polarization state transfer matrix using the Kalman filter algorithm, eliminates the cross coupling caused by the birefringence effect, and outputs an independent polarization state electrical signal sequence; Phase compensation unit: performs time-frequency domain processing on the independent polarization state electrical signal sequence, compensates for the linear phase noise caused by the laser line width and the nonlinear phase shift caused by the fiber Kerr effect, and outputs a phase compensation signal; Constellation equalization unit: maps the phase compensation signal to the four-dimensional constellation space, uses the fuzzy C-means algorithm to cluster the constellation points, generates an adaptive decision boundary, updates the equalizer tap coefficients, and outputs the equalized signal; Loop optimization unit: updates the polarization state transfer matrix according to the cluster center dispersion and adjusts the phase compensation step size based on the symbol error rate; Soft decision decoding unit: performs confidence propagation-assisted soft decision decoding on the equalized signal, constructs a phase residual model to correct symbol confidence, and outputs a reconstructed video data stream.

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