A detection method and system for efficiently realizing polarization recovery

Abstract

The application discloses a kind of detection method and system for efficiently realizing polarization recovery, the method comprises: the symbol mapping of sending data is carried out, and is carried out series-parallel conversion, obtains the first branch data of multiple parallel branches;Using offset selection algorithm, the first branch data of multiple parallel branches is handled, and pseudo training sequence is obtained;The series-parallel conversion of pseudo training sequence is carried out, and the second branch data of multiple parallel branches is obtained;The second branch data of multiple parallel branches is handled, and training data is obtained;Using stokes vector method, polarization compensation is solved according to training data, and polarization recovery is realized.The system comprises: sending data conversion module, sending data processing module, receiving data conversion module, receiving data processing module and polarization recovery module.Implementation of the method, can be directly according to the transmission signal of transceiver construction training data, without special training data, in the case of transmission effective data, improve the spectral efficiency of transmission.

Description

Technical Field

[0001] This invention relates to the field of optical communication and signal processing, and in particular to a detection method and system for efficiently realizing polarization restoration. Background Technology

[0002] In optical communication systems, the optical signal transmitted by the transmitting end is in a specific polarization state. However, due to external interference, the optical signal received by the receiving end of the optical communication system changes over time, affecting the normal optical communication process. Therefore, a polarization restoration system needs to be set up at the receiving end.

[0003] Detection techniques in optical fiber communication are broadly categorized into direct detection and coherent detection. Compared to traditional intensity-modulated direct detection systems, coherent detection technology possesses the ability to recover full-dimensional information. However, coherent receivers are complex, particularly the expensive narrow-linewidth lasers and high-power digital signal processing, hindering their application in short-distance optical communication. Stokes vector direct detection (SV-DD), employing low-cost distributed feedback (DFB) lasers and direct detection technology, can recover three-dimensional information, offering an attractive solution for low-cost and high-capacity transmission. However, current methods for polarization recovery using Stokes vector direct detection employ special training data to solve for the parameters of the rotation matrix. Since the training sequence cannot transmit effective data, the spectral efficiency of the transmission decreases. Summary of the Invention

[0004] To address the aforementioned technical problems, the present invention aims to provide a detection method and system for efficiently achieving polarization recovery. This method directly utilizes transmitted data to construct training data and solve for polarization recovery, thereby improving the spectral efficiency of transmission while transmitting effective data.

[0005] The first technical solution adopted in this invention is: a detection method for efficiently realizing polarization restoration, comprising the following steps:

[0006] The transmitted data is symbol-mapped and then converted from serial to parallel to obtain the first branch data of the multiple parallel branches;

[0007] The offset selection algorithm is used to process the data of the first branch of multiple parallel branches to obtain a pseudo-training sequence;

[0008] The pseudo-training sequence is converted from serial to parallel to obtain the second branch data of multiple parallel branches;

[0009] The data from the second branch of the multiple parallel branches are processed to obtain training data;

[0010] Polarization recovery is achieved by solving the polarization compensation problem based on the training data using the Stokes vector method.

[0011] In this scheme, training data is directly constructed from the original transmitted data. The rotation matrix is ​​solved using the Stokes vector method to obtain the offset compensation amount, thereby realizing polarization restoration. This method not only ensures effective data transmission but also improves the spectral efficiency of transmission. Moreover, the method is simple and efficient.

[0012] Furthermore, the step of processing the first branch data of multiple parallel branches using the offset selection algorithm to obtain the pseudo-training sequence specifically includes:

[0013] Multiple offsets are performed on a portion of the target group data in the first branch data of multiple parallel branches;

[0014] Calculate the average value of the target group after each offset;

[0015] Compare the distance between the average value of the target group after the offset and the ideal training data, and select the group corresponding to the smallest distance;

[0016] The offset of the target group corresponding to the minimum distance is taken as the optimal offset, and each group of the first branch data of the multi-parallel branch is offset according to the optimal offset to obtain the pseudo-training sequence.

[0017] Furthermore, the step of repeatedly shifting a portion of the target group data of the first branch data of the multi-parallel branches specifically includes:

[0018] Set the offset vector and select the target group in the first branch data of the multi-parallel branch;

[0019] The data in the target group is offset multiple times according to the offset vector.

[0020] Furthermore, the offset vector is specifically represented as follows:

[0021]

[0022] in, Represents the offset vector. This represents the j-th offset value. Z represents the number of offset values ​​in the offset vector.

[0023] Furthermore, the partial data of the target group is offset multiple times according to the offset vector, specifically as follows:

[0024]

[0025] in, This represents the offset of the nth point in the i-th target group. The point after, This represents the nth point in the i-th target group. This represents the j-th offset value, where i = 1, 2, ... M, where M represents the M groups of data from the first branch of a multi-parallel branch, n=1, 2, ... , N, where N represents the number of points in the target group.

[0026] Furthermore, the formula for calculating the average value of the target group after each offset is specifically expressed as follows:

[0027]

[0028] in, This represents the average value of the i-th target group after the offset. This represents the offset of the nth point in the i-th target group. The point after, This represents the summation of N points in the i-th target group. This represents the k-th point in the i-th target group.

[0029] Furthermore, the step of using the Stokes vector method to solve for polarization compensation based on training data to achieve polarization restoration specifically includes:

[0030] Construct the Jones matrix using the training data;

[0031] Construct Stokes vectors from the Jones matrix;

[0032] Solving for the rotation matrix parameters using Stokes vectors;

[0033] Polarization compensation is solved based on the rotation matrix parameters, thereby achieving polarization restoration.

[0034] The second technical solution adopted in this invention is: a detection system for efficiently realizing polarization restoration, comprising:

[0035] The data conversion module performs symbol mapping on the transmitted data and performs serial-to-parallel conversion to obtain the first branch data of multiple parallel branches;

[0036] The data processing module uses an offset selection algorithm to process the data of the first branch of multiple parallel branches to obtain a pseudo-training sequence. Specifically, this includes: offsetting a portion of the target group data in the first branch data of the multiple parallel branches multiple times; calculating the average value of the target group after each offset; comparing the distance between the average value of the target group after offset and the ideal training data, and selecting the group corresponding to the minimum distance; taking the offset of the target group corresponding to the minimum distance as the optimal offset, and offsetting each group of the first branch data of the multiple parallel branches according to the optimal offset to obtain the pseudo-training sequence.

[0037] The receiving data conversion module converts the pseudo-training sequence into a serial-to-parallel conversion to obtain the second branch data of the multiple parallel branches;

[0038] The receiving data processing module processes the second branch data of the multiple parallel branches to obtain training data;

[0039] The polarization recovery module uses the Stokes vector method to solve for polarization compensation based on training data, thereby achieving polarization recovery.

[0040] The beneficial effects of the detection method and system for efficient polarization restoration provided by this invention are as follows: This invention directly uses the transmitted raw data for data conversion processing to construct training data, and then uses the training data to solve the rotation matrix based on the Stokes vector method to obtain polarization compensation, thus achieving polarization restoration. Compared with the traditional method of solving polarization compensation using a special training sequence based on the Stokes vector method, this scheme can transmit effective data and improve spectral efficiency while achieving polarization restoration in a simple and efficient manner. Attached Figure Description

[0041] Figure 1 This is a flowchart of the steps of a detection method for efficiently realizing polarization restoration according to the present invention;

[0042] Figure 2 This is a structural block diagram of a detection system for efficiently realizing polarization recovery according to the present invention. Detailed Implementation

[0043] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments. The step numbers in the following embodiments are only for ease of explanation and do not limit the order of the steps. The execution order of each step in the embodiments can be adapted according to the understanding of those skilled in the art.

[0044] like Figure 1 As shown, this invention provides a detection method for efficiently achieving polarization restoration, the method comprising the following steps:

[0045] S101. The transmitted data is symbol-mapped and serial-to-parallel converted to obtain the first branch data of the multiple parallel branches.

[0046] In this embodiment, the transmitted data generated in the SV-DD transceiver is first symbol-mapped. This can be done based on three variables: phase, polarization state, and data symbol, to obtain a baseband symbol stream in the X / Y polarization state; alternatively, a pre-set mapping mode for the transmitted data can be used for symbol mapping. After obtaining the mapped symbols, serial-to-parallel conversion is performed to obtain the first branch data of the multiple parallel branches.

[0047] S102. The offset selection algorithm is used to process the data of the first branch of the multi-parallel branch to obtain the pseudo-training sequence.

[0048] Specifically, first, set the offset vector. Based on the offset vector, a portion of the data in the target group of the first branch data of the multi-parallel branches is offset multiple times, specifically as follows:

[0049]

[0050] in, This represents the offset of the nth point in the i-th target group. The point after, This represents the nth point in the i-th target group. This represents the j-th offset value.

[0051] After the offset, the formula for calculating the average value of the target group data after each offset is as follows:

[0052]

[0053] in, This represents the average value of the i-th target group after the offset. This represents the offset of the nth point in the i-th target group. The point after, This represents the summation of N points in the i-th target group. This represents the k-th point in the i-th group.

[0054] After obtaining the average value of each offset target group, compare its distance with the ideal training data, where the distance is the absolute value of the difference between the two, and select the target group corresponding to the minimum distance. Further obtain the offset added to the target group corresponding to the minimum distance, and take the offset as the optimal offset. Based on this, offset the first branch data of the multiple parallel paths according to the optimal offset to obtain the pseudo training sequence.

[0055] Specifically, the offset vector takes three offset values, namely The first branch data of multiple parallel branches contains three groups, namely , , Choose any one of the groups as the target group, and offset the first 3 points in the target group respectively, as follows:

[0056]

[0057]

[0058]

[0059] After shifting the target group three times, the average value of the target group after the three shifts is calculated and compared with the ideal data. Here, we take 0 as an example, which is represented as follows:

[0060]

[0061]

[0062]

[0063] Choosing the one with the longest distance, as shown below:

[0064]

[0065] After selecting the minimum distance, it is used as the optimal offset. All groups in the data of the first branch of the multi-parallel branches are offset according to the optimal offset to obtain pseudo-training data.

[0066] S103. Convert the pseudo-training sequence from serial to parallel to obtain the second branch data of the multi-parallel branch.

[0067] At the receiver of the SV-DD transceiver, the received training sequence is converted from serial to parallel to obtain the second branch data of the multiple parallel branches. During this process, since the first branch data of the multiple parallel branches in the transmitter has been offset, it is necessary to de-offset the data received by the receiver. This can be achieved by using forward error correction coding introduced in the fiber optic transmission system to correct the generated errors; alternatively, the offset value corresponding to the offset vector added to each packet of the first branch data of the multiple parallel branches can be transmitted via pilot signals.

[0068] S104. Process the second branch data of the multiple parallel branches to obtain training data.

[0069] In this embodiment, after obtaining the second branch data of the multiple parallel branches, it is transmitted to a functional module to identify, select, and extract the second branch data of the multiple parallel branches to obtain estimated grouped data. Then, the training data is obtained by averaging the estimated group data, specifically:

[0070]

[0071] in, Let N represent the mean of the i-th estimated group of data, and let N represent the number of points in the estimated group of data. Let n represent the nth point in the i-th estimated group.

[0072] S105. Use the Stokes vector method to solve the polarization compensation based on the training data to achieve polarization restoration.

[0073] After obtaining the training data, a Jones matrix is ​​constructed using the training data. In this embodiment, three sets of Jones matrices are constructed. ];[ 2];[ 3], the corresponding three-dimensional Stokes vector is:

[0074]

[0075] Where Re() and Im() represent taking the real part and imaginary part, respectively. This indicates taking the conjugate.

[0076] After passing through the optical fiber channel, due to the polarization change in the fiber, the received Stokes vector (SV) can be expressed as:

[0077]

[0078] The superscripts T and R represent the transmitted and received data, respectively. The above equation can be understood as a transmission channel model with only polarization rotation. It can be seen that if the rotation matrix parameters are obtained and inverted, the transmitted SV can be recovered, i.e.

[0079]

[0080] Since the constructed Stokes vector is a 3D vector, it is necessary to send the SV vector three times to estimate the 9 parameters in the rotation matrix, obtain the estimated rotation matrix, and perform polarization compensation based on the estimated rotation matrix to recover the original Stokes vector.

[0081] like Figure 2 As shown, a detection system for efficiently achieving polarization recovery includes:

[0082] The data conversion module performs symbol mapping on the transmitted data and performs serial-to-parallel conversion to obtain the first branch data of multiple parallel branches;

[0083] The data processing module uses an offset selection algorithm to process the data of the first branch of multiple parallel branches to obtain a pseudo-training sequence. Specifically, this includes: offsetting a portion of the target group data in the first branch data of the multiple parallel branches multiple times; calculating the average value of the target group after each offset; comparing the distance between the average value of the target group after offset and the ideal training data, and selecting the group corresponding to the minimum distance; taking the offset of the target group corresponding to the minimum distance as the optimal offset, and offsetting each group of the first branch data of the multiple parallel branches according to the optimal offset to obtain the pseudo-training sequence.

[0084] The receiving data conversion module converts the pseudo-training sequence into a serial-to-parallel conversion to obtain the second branch data of the multiple parallel branches;

[0085] The receiving data processing module processes the second branch data of the multiple parallel branches to obtain training data;

[0086] The polarization recovery module uses the Stokes vector method to solve for polarization compensation based on training data, thereby achieving polarization recovery.

[0087] The content of the above method embodiments is applicable to this system embodiment. The specific functions implemented in this system embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0088] The above is a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the embodiments described. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of this application.

Claims

1. A detection method for efficiently implementing polarization recovery, characterized in that, Includes the following steps: The transmitted data is symbol-mapped and then converted from serial to parallel to obtain the first branch data of the multiple parallel branches; The offset selection algorithm is used to process the first branch data of the multi-parallel branch to obtain a pseudo-training sequence; specifically, the target group data in the first branch data of the multi-parallel branch is offset multiple times. Calculate the average value of the target group after each offset; compare the distance between the average value of the target group after each offset and the ideal training data, and select the group corresponding to the minimum distance; take the offset of the target group corresponding to the minimum distance as the optimal offset, and offset each group of the data of the first branch of the multi-parallel branch according to the optimal offset to obtain the pseudo-training sequence. The pseudo-training sequence is converted from serial to parallel to obtain the second branch data of multiple parallel branches; The data from the second branch of the multiple parallel branches are processed to obtain training data; Polarization recovery is achieved by solving the polarization compensation problem based on the training data using the Stokes vector method.

2. The detection method of claim 1, wherein, The step of shifting a portion of the target group data in the first branch data of the multi-parallel branches multiple times specifically includes: Set the offset vector and select the target group in the first branch data of the multi-parallel branch; The data in the target group is offset multiple times according to the offset vector.

3. The detection method of claim 2, wherein the polarization recovery is efficiently realized. The offset vector is specifically represented as follows: wherein represents an offset vector, represents the j th offset value, , represents the number of offset values in the offset vector.

4. The detection method of claim 2, wherein the polarization recovery is efficiently realized. The process of offsetting a portion of the target group's data multiple times according to the offset vector is specifically expressed as follows: in, Indicates the first i The first target group in the n offset of points The point after, Indicates the first i The first target group in the n One point, Indicates the first j One offset value, i =1, 2, M, where M represents the M packets of data from the first branch of a multi-parallel branch. n =1, 2, , N, where N represents the number of points in the target group.

5. The method of claim 1, wherein the polarization recovery is efficiently achieved. The formula for calculating the average value of the target group after each offset is specifically expressed as follows: in, Indicates the offset after the first i The average value of each target group, Indicates the first i The first target group in the n offset of points The point after, Indicates the first i Summing the N points in a target group, Indicates the first i The first target group in the k One point.

6. The detection method of claim 1, wherein the polarization recovery is efficiently realized. The step of using the Stokes vector method to solve for polarization compensation based on training data to achieve polarization restoration includes: Construct the Jones matrix using the training data; Construct Stokes vectors from the Jones matrix; Solving for the rotation matrix parameters using Stokes vectors; Polarization compensation is solved based on the rotation matrix parameters, thereby achieving polarization restoration.

7. A detection system for efficiently achieving polarization restoration, comprising: The data conversion module performs symbol mapping on the transmitted data and performs serial-to-parallel conversion to obtain the first branch data of multiple parallel branches; The data processing module uses an offset selection algorithm to process the data of the first branch of the multi-parallel branches to obtain a pseudo-training sequence; specifically, it performs multiple offsets on a portion of the target group data in the first branch of the multi-parallel branches. Calculate the average value of the target group after each offset; compare the distance between the average value of the target group after each offset and the ideal training data, and select the group corresponding to the minimum distance; take the offset of the target group corresponding to the minimum distance as the optimal offset, and offset each group of the data of the first branch of the multi-parallel branch according to the optimal offset to obtain the pseudo-training sequence. The receiving data conversion module converts the pseudo-training sequence into a serial-to-parallel conversion to obtain the second branch data of the multiple parallel branches; The receiving data processing module processes the second branch data of the multiple parallel branches to obtain training data; The polarization recovery module uses the Stokes vector method to solve for polarization compensation based on training data, thereby achieving polarization recovery.

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