Soft decision decoding method and system embedded with carrier phase recovery

Abstract

The application provides a soft decision decoding method and system embedded with carrier phase recovery, comprising: calculating symbol log-likelihood ratio and soft symbol expectation according to a received signal; dynamically tracking time-varying phase noise by using Kalman filtering, and performing phase correction on the received signal based on the soft symbol expectation; optimizing the log-likelihood ratio through the phase-corrected signal; generating extrinsic information by using a Chase-2 decoder, and updating the log-likelihood ratio and the soft symbol expectation by using the extrinsic information to form a closed-loop iteration; and outputting a decoding result until a convergence condition is met. The application can significantly improve the system robustness in a high dynamic phase noise scene, reduce the bit error rate through the cooperative optimization of phase recovery and decoding, and achieve fast convergence with low complexity, is compatible with high-order modulation and various FEC encodings, and can adapt to existing coherent optical communication systems without increasing hardware costs.

Description

Technical Field

[0001] This invention relates to the field of optical communication technology, and in particular to a soft-decision decoding method and system with embedded carrier phase recovery. Background Technology

[0002] With the rapid development of high-speed optical communication, the robustness requirements of phase retrieval algorithms in communication systems are increasing. Phase noise, mainly caused by laser frequency drift and fiber nonlinear effects, can lead to the rotation of the received signal constellation points, severely reducing the system's bit error rate performance. Especially in scenarios such as coherent optical communication and high-order modulation, the dynamic time-varying characteristics of phase noise pose a serious challenge to traditional phase retrieval algorithms.

[0003] In existing technologies, blind phase search (BPS) algorithms achieve blind phase recovery by traversing candidate phase hypotheses, but their computational complexity increases exponentially with the improvement of phase resolution, making them unsuitable for scenarios with high real-time requirements. While phase recovery methods based on maximum likelihood estimation (MLE) can reduce complexity through analytical solutions, they rely on known or precisely estimated transmitted symbols and cannot be directly applied to blind phase recovery scenarios. Furthermore, traditional phase tracking algorithms (such as phase-locked loops) have limited tracking capabilities for rapidly changing phases and are prone to loss of lock in high-noise environments. On the other hand, modern communication systems widely employ forward error correction (FEC) coding techniques to approximate channel capacity. Soft-input soft-output (SISO) decoders (such as LDPC and Turbo codes) achieve near-Shannon limit performance by iteratively updating the log-likelihood ratio (LLR). However, in scenarios where phase noise is not effectively compensated, the accuracy of the LLR is severely compromised, leading to a significant decrease in decoding performance. Existing schemes typically separate phase recovery from decoding, resulting in phase estimation errors that cannot be corrected during iteration, forming an error propagation loop.

[0004] Based on the aforementioned technical shortcomings, the academic community has proposed a partial joint phase retrieval and decoding scheme, such as iterative phase estimation based on the expectation-maximization (EM) algorithm. These methods alternately optimize phase and symbol estimation, but still have the following limitations in dynamic phase tracking: First, they have high computational complexity; the EM algorithm requires multiple iterations to calculate the posterior symbol probability, making it difficult to meet low latency requirements. Second, they rely on a fixed phase model, assuming that phase changes follow a static statistical model, which cannot effectively track sudden phase jumps. Third, they lack sufficient utilization of extrinsic information, failing to fully leverage the structured constraints of FEC codes to optimize phase estimation, resulting in limited iterative gain. Summary of the Invention

[0005] In view of this, embodiments of the present invention provide a soft-decision decoding method and system with embedded carrier phase recovery to eliminate or improve one or more defects existing in the prior art.

[0006] On one hand, the present invention provides a soft-decision decoding method with embedded carrier phase recovery, the method comprising the following steps:

[0007] In one iteration:

[0008] If it is the first iteration, the log-likelihood ratio of the symbol is calculated based on the received signal, and the soft symbol expectation is calculated using the log-likelihood ratio; if it is not the first iteration, the soft symbol expectation is calculated using the log-likelihood ratio obtained in the previous iteration.

[0009] Based on Kalman filtering, time-varying phase noise is estimated according to the received signal and the soft symbol expectation, and the received signal is corrected using the time-varying phase noise to obtain a phase-corrected signal;

[0010] The log-likelihood ratio residual is calculated based on the phase correction signal, and the log-likelihood ratio is updated using the log-likelihood ratio residual to obtain the updated log-likelihood ratio.

[0011] Based on the updated log-likelihood ratio, candidate codewords are generated using a preset algorithm; the Euclidean distance between each candidate codeword and the phase correction signal is calculated, and the optimal codeword is selected based on the Euclidean distance; extrinsic information is generated based on the difference between the optimal codeword and the updated log-likelihood ratio, and the updated log-likelihood ratio is corrected using the extrinsic information to obtain the log-likelihood ratio for this iteration.

[0012] Perform multiple rounds of iteration; when the change in the log-likelihood ratio is less than a preset threshold or reaches a preset maximum number of iterations, stop the iteration and output the optimal codeword as the decoding result.

[0013] In some embodiments of the present invention, if it is the first iteration, calculating the log-likelihood ratio of the symbols based on the received signal includes:

[0014] The modulation symbols are mapped to a predetermined number of bits, and the log-likelihood ratio for each bit position is calculated using the following formula:

[0015] ;

[0016] Using the Max-Log approximation of the formula for calculating the log-likelihood ratio, we obtain:

[0017] ;

[0018] in, express Time of the first Log-likelihood ratio of bits; and These represent the constellations in the diagram. and One symbol point; Indicates the first A set of constellation points with 0 bits; Indicates the first A set of constellation points with each bit set to 1; Indicates the received signal; This represents the variance of channel noise.

[0019] In some embodiments of the present invention, the soft sign expectation is calculated based on the log-likelihood ratio obtained from the received signal or from the previous iteration, including:

[0020] The posterior probability of the sign is calculated for each constellation point using the following formula:

[0021] ;

[0022] The soft symbol expectation is generated by weighted summation of each constellation point and the posterior probability of the symbol, calculated as follows:

[0023] ;

[0024] in, Indicates receiving signal Below, symbol The posterior probability; Indicates the first A constellation point; Represents the set of all constellation points; Indicates the received signal; Indicates the channel noise variance; This indicates a soft symbol expectation.

[0025] In some embodiments of the present invention, based on Kalman filtering, time-varying phase noise is estimated according to the received signal and the soft symbol expectation, and the received signal is corrected using the time-varying phase noise to obtain a phase-corrected signal, including:

[0026] The phase noise is modeled as a Wiener process, and the state equation and observation equation are constructed as follows:

[0027] ;

[0028] ;

[0029] Perform Kalman filter iteration based on the state equation and the observation equation;

[0030] The phase state and error covariance are predicted using the following formula:

[0031] ;

[0032] ;

[0033] The Kalman gain is calculated based on the error covariance, using the following formula:

[0034] ;

[0035] The predicted phase state is corrected using the observed values, and the error covariance is updated. The calculation formula is as follows:

[0036] ;

[0037] ;

[0038] The received signal is corrected using the corrected phase state to obtain a phase-corrected signal, calculated as follows:

[0039] ;

[0040] in, express Phase noise at any given moment; The state noise is represented by a mean of 0 and a variance of . Gaussian distribution; Indicates the phase observation value; Indicates the received signal; The conjugate of the expectation of soft symbols; The observed noise is represented by a mean of 0 and a variance of 1. Gaussian distribution; Indicates Kalman gain; This represents the predicted phase noise value; Represents the error covariance matrix; This indicates a phase correction signal.

[0041] In some embodiments of the present invention, calculating the log-likelihood ratio residual based on the phase correction signal, and updating the log-likelihood ratio using the log-likelihood ratio residual to obtain the updated log-likelihood ratio includes:

[0042] The log-likelihood ratio residual is calculated based on the phase correction signal, using the following formula:

[0043] ;

[0044] The log-likelihood ratio is updated using the log-likelihood ratio residuals, and the update rule satisfies the following formula:

[0045] ;

[0046] in, Represents the log-likelihood ratio to the residuals; Indicates the channel noise variance; and These represent the constellations in the diagram. and One symbol point; Indicates the first A set of constellation points with 0 bits; Indicates the first A set of constellation points with each bit set to 1; Indicates the phase correction signal; Indicates the first The output log-likelihood ratio after the next iteration; Indicates the first The input log-likelihood ratio before the next iteration.

[0047] In some embodiments of the present invention, candidate codewords are generated by decoding using a preset algorithm based on the updated log-likelihood ratio, including:

[0048] Using the Chase-2 decoding algorithm, the updated log-likelihood ratio of each symbol is sorted by absolute value. Based on the sorting, the first preset number of lowest confidence bit positions are selected as candidate codewords.

[0049] In some embodiments of the present invention, the Euclidean distance between each candidate codeword and the phase correction signal is calculated using the following formula:

[0050] ;

[0051] in, Indicates candidate codewords With phase correction signal The Euclidean distance; Indicates the number of symbols; This represents the modulation mapping function.

[0052] In some embodiments of the present invention, extrinsic information is generated based on the difference between the optimal codeword and the updated log-likelihood ratio, calculated as follows:

[0053] ;

[0054] in, Indicates the first One bit of external information; Indicates the optimal codeword; This indicates the update log-likelihood ratio.

[0055] In some embodiments of the present invention, the method further includes:

[0056] A damping factor is introduced into the external information to suppress overshoot; the formula for calculating the external information after the damping factor correction is as follows:

[0057] ;

[0058] in, This indicates the corrected external information; This indicates the external information before the correction; This represents the damping factor.

[0059] On the other hand, the present invention also provides a soft-decision decoding system with embedded carrier phase recovery, the system comprising:

[0060] The soft information calculation module is used to calculate symbol soft information based on the received signal and generate soft symbol expectations;

[0061] The Kalman filter phase tracking module is used to dynamically estimate the time-varying phase noise based on the soft symbol expectation generated by the soft information calculation module, correct the received signal based on the time-varying phase noise, and output a phase correction signal.

[0062] The soft information update module is used to update the log-likelihood ratio of the symbol based on the phase correction signal output by the Kalman filter phase tracking module to obtain the updated log-likelihood ratio.

[0063] The soft-input soft-output decoder module is used to perform a preset algorithm decoding based on the updated log-likelihood ratio obtained by the soft information update module, generate extrinsic information, and feed the extrinsic information back to the soft information calculation module to optimize the symbol soft information and form a closed loop; when the iteration stops, the final decoding result is output.

[0064] This invention provides a soft-decision decoding method and system with embedded carrier phase recovery, comprising: calculating the symbol log-likelihood ratio and soft symbol expectation based on the received signal; dynamically tracking time-varying phase noise using Kalman filtering and performing phase correction on the received signal based on the soft symbol expectation; optimizing the log-likelihood ratio using the phase correction signal; generating extrinsic information using a Chase-2 decoder and updating the log-likelihood ratio and soft symbol expectation using the extrinsic information to form a closed-loop iteration; and outputting the decoding result after the convergence condition is met. This invention can significantly improve the system robustness in high dynamic phase noise scenarios, reduce the bit error rate through the synergistic optimization of phase recovery and decoding; achieve fast convergence with low complexity, be compatible with high-order modulation and various FEC codes, and adapt to existing coherent optical communication systems without increasing hardware costs.

[0065] Additional advantages, objects, and features of the invention will be set forth in part in the description which follows, and will also become apparent in part to those skilled in the art upon studying the description, or may be learned by practice of the invention. The objects and other advantages of the invention can be realized and obtained by means of the structures specifically pointed out in the description and drawings.

[0066] Those skilled in the art will understand that the objectives and advantages achievable with the present invention are not limited to those specifically described above, and that the above and other objectives achievable with the present invention will become clearer from the following detailed description. Attached Figure Description

[0067] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, are not intended to limit the scope of the invention. In the drawings:

[0068] Figure 1 This is a schematic diagram illustrating the steps of a soft-decision decoding method with embedded carrier phase recovery in one embodiment of the present invention.

[0069] Figure 2 This is a flowchart of a soft-decision decoding system with embedded carrier phase recovery in one embodiment of the present invention.

[0070] Figure 3 This is a flowchart of a soft information calculation method in one embodiment of the present invention.

[0071] Figure 4 This is a flowchart of a Kalman filter phase tracking method in one embodiment of the present invention.

[0072] Figure 5 This is a flowchart of a soft information update method in one embodiment of the present invention.

[0073] Figure 6 This is a flowchart of a soft-input soft-output decoding method in one embodiment of the present invention.

[0074] Figure 7 This is a communication system block diagram of a soft-decision decoding method and system based on embedded carrier phase recovery in one embodiment of the present invention. Detailed Implementation

[0075] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the embodiments and accompanying drawings. Here, the illustrative embodiments and descriptions of this invention are used to explain the invention, but are not intended to limit the invention.

[0076] It should also be noted that, in order to avoid obscuring the invention with unnecessary details, only the structures and / or processing steps closely related to the solution according to the invention are shown in the accompanying drawings, while other details that are not closely related to the invention are omitted.

[0077] It should be emphasized that the term "including / comprises" as used herein refers to the presence of a feature, element, step, or component, but does not exclude the presence or addition of one or more other features, elements, steps, or components.

[0078] It should also be noted that, unless otherwise specified, the term "connection" in this article can refer not only to a direct connection, but also to an indirect connection involving an intermediary.

[0079] In the following description, embodiments of the invention will be illustrated with reference to the accompanying drawings. In the drawings, the same reference numerals represent the same or similar parts, or the same or similar steps.

[0080] It should be emphasized here that the step markers mentioned below are not a limitation on the order of the steps, but should be understood as meaning that the steps can be executed in the order mentioned in the embodiments, or in a different order than in the embodiments, or several steps can be executed simultaneously.

[0081] To address the problems of high computational complexity, the need for multiple iterations of the expectation-maximization algorithm to calculate the posterior symbol probability, difficulty in meeting low latency requirements, reliance on a fixed phase model to effectively track sudden phase transitions, insufficient utilization of extrinsic information, and failure to fully leverage the structured constraints of FEC codes for phase estimation, resulting in limited iterative gain, this invention provides a soft-decision decoding method embedded with carrier phase recovery. Figure 1 As shown, the method includes the following steps S101~S105:

[0082] Step S101: If it is the first iteration, calculate the log-likelihood ratio of the symbol based on the received signal, and use the log-likelihood ratio to calculate the soft symbol expectation; if it is not the first iteration, use the log-likelihood ratio obtained in the previous iteration to calculate the soft symbol expectation.

[0083] Step S102: Based on Kalman filtering, estimate the time-varying phase noise according to the received signal and soft symbol expectation, and use the time-varying phase noise to correct the received signal to obtain the phase-corrected signal.

[0084] Step S103: Calculate the log-likelihood ratio residual based on the phase correction signal, update the log-likelihood ratio using the log-likelihood ratio residual, and obtain the updated log-likelihood ratio.

[0085] Step S104: Based on the updated log-likelihood ratio, decode using a preset algorithm to generate candidate codewords; calculate the Euclidean distance between each candidate codeword and the phase correction signal, and select the optimal codeword based on the Euclidean distance; generate extrinsic information based on the difference between the optimal codeword and the updated log-likelihood ratio, and use the extrinsic information to correct the updated log-likelihood ratio to obtain the log-likelihood ratio for this iteration.

[0086] Step S105: Perform multiple iterations of steps S101 to S104. When the change in the log-likelihood ratio is less than a preset threshold or the preset maximum number of iterations is reached, stop the iteration and output the optimal codeword as the decoding result.

[0087] like Figure 2 As shown, the transmitting end generates the received signal required by the present invention, and the receiving end includes the soft decision decoding method steps of embedded carrier phase recovery provided by the present invention.

[0088] Specifically, at the transmitting end, the original binary information sequence is sequentially processed through channel coding, symbol mapping, and modulation to generate a complex symbol sequence, which is then transmitted to the receiving end via the channel. At the receiving end, after coherent demodulation of the received signal, the steps of a soft-decision decoding method with embedded carrier phase recovery are executed.

[0089] In step S101, the log-likelihood ratio and soft symbol expectation of the symbol are calculated based on the received signal to provide probabilistic input for subsequent steps.

[0090] like Figure 3 As shown, first determine whether it is the first iteration. If it is the first iteration, calculate the log-likelihood ratio of the symbol based on the received signal, and then use the calculated log-likelihood ratio to calculate the soft symbol expectation. If it is not the first iteration, directly use the log-likelihood ratio obtained in the previous iteration to calculate the soft symbol expectation.

[0091] In some embodiments, if it is the first iteration, the log-likelihood ratio of the symbols is calculated first. Assume the modulation symbols are mapped to... Each bit position (e.g., 4 bits for 16 QAM) Calculate its log-likelihood ratio, as shown in formula (1):

[0092] ; (1)

[0093] in, express Time of the first Log-likelihood ratio of bits; and These represent the constellations in the diagram. and One symbol point; Indicates the first A set of constellation points with 0 bits; Indicates the first A set of constellation points with each bit set to 1; Indicates the received signal; This represents the channel noise variance, which can be obtained through channel estimation or preset values.

[0094] In some embodiments, to simplify the calculation, the Max-Log approximation can be used to reduce the complexity. Therefore, the formula for calculating the log-likelihood ratio, i.e., formula (1), can be adjusted to formula (2):

[0095] ; (2)

[0096] It should be noted that in the formulas of this invention, all identical physical quantities have the same meaning. The physical quantities in formula (2) have been explained above, so they will not be repeated here. Also, if physical quantities are repeated below, they will not be repeated here either.

[0097] For each constellation point The posterior probability of the symbol is calculated as shown in formula (3):

[0098] ; (3)

[0099] in, Indicates receiving signal Below, symbol The posterior probability; Indicates the first A constellation point; Represents the set of all constellation points; Indicates the received signal; This represents the variance of channel noise.

[0100] Finally, the soft sign expectation is generated by weighted summation of the posterior probability of each constellation point and the sign, as shown in formula (4):

[0101] ; (4)

[0102] in, It represents the soft symbol expectation, which reflects the "statistical average" value of the symbol and is used to drive Kalman filter phase tracking.

[0103] In step S102, based on the received signal and soft symbol expectation, time-varying phase noise is dynamically estimated and compensated to provide a phase-corrected signal for subsequent steps.

[0104] like Figure 4 As shown, assuming the phase noise follows a first-order Markov process (Wiener process), the state equation and observation equation are constructed. The state equation is shown in equation (5), and the observation equation is shown in equation (6):

[0105] ; (5)

[0106] ; (6)

[0107] in, express Phase noise at any given moment; The state noise is represented by a mean of 0 and a variance of . Gaussian distribution; Indicates the phase observation value; Indicates the received signal; The conjugate of the expectation of soft symbols; The observed noise is represented by a mean of 0 and a variance of 1. The Gaussian distribution.

[0108] Based on the state equation and the observation equation, Kalman filtering iteration is performed. First, phase state prediction is performed, as shown in equation (7):

[0109] ; (7)

[0110] And predict the error covariance, as shown in formula (8):

[0111] ; (8)

[0112] in, This represents the predicted phase noise value; Represents the error covariance matrix; This represents the state noise covariance.

[0113] The Kalman gain is calculated based on the error covariance, as shown in formula (9):

[0114] ; (9)

[0115] in, Indicates Kalman gain; This represents the observation noise covariance.

[0116] Then, the phase state is updated, that is, using the observed values. For the predicted phase state Make corrections as shown in formula (10):

[0117] ; (10)

[0118] And update the error covariance, as shown in formula (11):

[0119] ; (11)

[0120] in, This represents the Kalman gain.

[0121] Finally, the received signal is corrected based on the corrected phase state to obtain the phase-corrected signal, as shown in formula (12):

[0122] ;(12)

[0123] in, This indicates a phase correction signal.

[0124] In some embodiments, the observation noise covariance is dynamically adjusted based on the confidence level of the soft symbol expectation, as shown in Equation (13):

[0125] ; (13)

[0126] in, Represents the observation noise covariance; Indicates the phase correction signal Below, symbol The posterior probability.

[0127] In step S103, the log-likelihood ratio residual is calculated based on the phase correction signal, and the log-likelihood ratio is updated using the log-likelihood ratio residual.

[0128] like Figure 5 As shown, the log-likelihood ratio residual is first calculated based on the phase correction signal, as shown in formula (14):

[0129] ;(14)

[0130] in, It represents the log-likelihood ratio to the residual.

[0131] Then, the log-likelihood ratio calculated in step S101 is updated based on the log-likelihood ratio residual, and the update rule is shown in formula (15):

[0132] ; (15)

[0133] in, Indicates the first The output log-likelihood ratio after the next iteration is the updated log-likelihood ratio as defined above. Indicates the first The input log-likelihood ratio before the next iteration.

[0134] In some embodiments, the noise variance is updated based on the phase-corrected signal residual to improve the robustness of the log-likelihood ratio calculation to changes in channel noise, as shown in Equation (16):

[0135] ; (16)

[0136] in, Indicates the channel noise variance; Indicates the number of symbols.

[0137] In step S104, iterative decoding is performed based on the updated log-likelihood ratio to generate extrinsic information, which is used to update the symbol soft information. The extrinsic information is a correction value generated by the decoder based on the probability ratio difference between the optimal codeword and the updated log-likelihood ratio of the input; the soft information refers to the symbol's log-likelihood ratio and soft symbol expectation.

[0138] like Figure 6 As shown, the Chase-2 decoding algorithm is first used to sort the updated log-likelihood ratios of each symbol by absolute value, as shown in formula (17):

[0139] ; (17)

[0140] in, This represents the updated log-likelihood ratio obtained in step S103.

[0141] Select the first one according to the sorting. The lowest confidence bit positions. This indicates the number of candidate codewords. Preferably, the number of candidate codewords is 4 or 8.

[0142] Then for each bit combination that needs to be flipped (total) (multiple possibilities) to generate a set of candidate codewords .

[0143] Based on the candidate codeword set, for each candidate codeword, calculate its Euclidean distance to the phase correction signal, as shown in formula (18):

[0144] ; (18)

[0145] in, Indicates candidate codewords With phase correction signal The Euclidean distance; This represents the modulation mapping function.

[0146] The optimal codeword is selected based on the Euclidean distance, as shown in formula (19):

[0147] ; (19)

[0148] in, Represents the optimal codeword

[0149] By comparing the difference between the optimal codeword and the updated log-likelihood ratio, extrinsic information is generated, as shown in formula (20):

[0150] ; (20)

[0151] in, Indicates the first One bit of external information; This indicates the update log-likelihood ratio.

[0152] In some embodiments, a damping factor is introduced to suppress overshoot in order to smooth the external information. The formula for calculating the external information after introducing the damping factor is shown in formula (21):

[0153] ; (twenty one)

[0154] in, This indicates the corrected external information; This indicates the external information before the correction; This represents the damping factor, and preferably, the damping factor ranges from 0.5 to 0.8.

[0155] Finally, the log-likelihood ratio is updated using external information, as shown in formula (22):

[0156] ; (twenty two)

[0157] in, This represents the log-likelihood ratio after correction using external information. It is the log-likelihood ratio for this iteration and will be used in step S101 for the next iteration.

[0158] In step S105, steps S101 to S104 are repeated until the change in the log-likelihood ratio is less than a preset threshold or the preset maximum number of iterations is reached, at which point the iteration stops.

[0159] After stopping the iteration, the optimal codeword obtained in step S104 is output as the decoding result.

[0160] Corresponding to the soft-decision decoding method embedded with carrier phase recovery, the present invention also provides a soft-decision decoding system embedded with carrier phase recovery, the system comprising:

[0161] The soft information calculation module is used to calculate symbol soft information based on the received signal and generate soft symbol expectations.

[0162] The Kalman filter phase tracking module is used to dynamically estimate the time-varying phase noise based on the soft symbol expectation generated by the soft information calculation module, correct the received signal based on the time-varying phase noise, and output a phase correction signal.

[0163] The soft information update module is used to update the log-likelihood ratio of the symbols based on the phase correction signal output by the Kalman filter phase tracking module, and obtain the updated log-likelihood ratio.

[0164] The soft-input soft-output decoder module is used to perform a preset algorithm for decoding based on the updated log-likelihood ratio obtained from the soft information update module, generate extrinsic information, and feed the extrinsic information back to the soft information calculation module to optimize the symbol soft information and form a closed loop; when the iteration stops, the final decoding result is output.

[0165] The present invention will be further described below with reference to a specific embodiment.

[0166] like Figure 7 The diagram shown is a block diagram of a communication system implemented based on the soft decision decoding method and system with embedded carrier phase recovery provided by the present invention. The communication system specifically includes a transmitter, a transmission channel, and a receiver.

[0167] At the transmitting end, the original binary information sequence is channel-coded and symbol-mapped to generate a complex symbol sequence, which is then fed into an arbitrary waveform generator for digital-to-analog conversion. The radio frequency signal generated by the arbitrary waveform generator is modulated onto an optical carrier by a modulator to generate an optical signal, which is then sent into a 20km single-mode fiber channel. At the receiving end, a coherent optical receiver is used to detect the optical signal, a continuous-wave laser with the same frequency as the light source at the transmitting end is used as the local oscillator, and a 50G Sa / s digital oscilloscope is used to perform analog-to-digital conversion. Then, based on an adaptive equalizer and the soft-decision decoding system with embedded carrier phase recovery provided by this invention, offline digital signal processing is used to recover the original information.

[0168] Corresponding to the above method, the present invention also provides an electronic device including a computer device, the computer device including a processor and a memory, the memory storing computer instructions, the processor executing the computer instructions stored in the memory, and when the computer instructions are executed by the processor, the electronic device performs the steps of the method as described above.

[0169] This invention also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the aforementioned edge computing server deployment method. The computer-readable storage medium can be a tangible storage medium, such as random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, floppy disks, hard disks, removable storage disks, CD-ROMs, or any other form of storage medium known in the art.

[0170] Those skilled in the art will understand that the exemplary components, systems, and methods described in conjunction with the embodiments disclosed herein can be implemented in hardware, software, or a combination of both. Whether implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this invention. When implemented in hardware, it can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this invention are programs or code segments used to perform the desired tasks. The programs or code segments can be stored in a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried in a carrier wave.

[0171] It should be clarified that the present invention is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present invention is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of the present invention.

[0172] In this invention, features described and / or illustrated for one embodiment may be used in the same or similar manner in one or more other embodiments, and / or combined with or in place of features of other embodiments.

[0173] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, various modifications and variations of the embodiments of the present invention are possible. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A soft-decision decoding method with embedded carrier phase recovery, characterized in that, The method includes the following steps: In one iteration: If it is the first iteration, the log-likelihood ratio of the symbol is calculated based on the received signal, and the soft symbol expectation is calculated using the log-likelihood ratio; if it is not the first iteration, the soft symbol expectation is calculated using the log-likelihood ratio obtained in the previous iteration. Based on Kalman filtering, time-varying phase noise is estimated according to the received signal and the soft symbol expectation, and the received signal is corrected using the time-varying phase noise to obtain a phase-corrected signal; The log-likelihood ratio residual is calculated based on the phase correction signal, and the log-likelihood ratio is updated using the log-likelihood ratio residual to obtain the updated log-likelihood ratio. Based on the updated log-likelihood ratio, candidate codewords are generated using a preset algorithm; the Euclidean distance between each candidate codeword and the phase correction signal is calculated, and the optimal codeword is selected based on the Euclidean distance; extrinsic information is generated based on the optimal codeword and the updated log-likelihood ratio, calculated as follows: ； in, Indicates the first One bit of external information; Indicates the optimal codeword; This indicates the update log-likelihood ratio; The updated log-likelihood ratio is corrected using the external information to obtain the log-likelihood ratio for this iteration; Perform multiple rounds of iteration; stop iterating when the change in the log-likelihood ratio is less than a preset threshold or reaches a preset maximum number of iterations, and output the optimal codeword as the decoding result.

2. The soft-decision decoding method with embedded carrier phase recovery according to claim 1, characterized in that, If it is the first iteration, the log-likelihood ratio of the symbols is calculated based on the received signal, including: The modulation symbols are mapped to a predetermined number of bits, and the log-likelihood ratio for each bit position is calculated using the following formula: ； Using the Max-Log approximation of the formula for calculating the log-likelihood ratio, we obtain: ； in, express Time of the first Log-likelihood ratio of bits; and These represent the constellations in the diagram. and One symbol point; Indicates the first A set of constellation points with 0 bits; Indicates the first A set of constellation points with each bit set to 1; Indicates the received signal; This represents the variance of channel noise.

3. The soft-decision decoding method with embedded carrier phase recovery according to claim 1, characterized in that, The soft symbol expectation is calculated based on the log-likelihood ratio obtained from the received signal or the previous iteration, including: The posterior probability of the sign is calculated for each constellation point using the following formula: ； The soft symbol expectation is generated by weighted summation of each constellation point and the posterior probability of the symbol, calculated as follows: ； in, Indicates receiving signal Below, symbol The posterior probability; Indicates the first A constellation point; Represents the set of all constellation points; Indicates the received signal; Indicates the channel noise variance; This indicates a soft symbol expectation.

4. The soft-decision decoding method with embedded carrier phase recovery according to claim 1, characterized in that, Based on Kalman filtering, time-varying phase noise is estimated according to the received signal and the soft symbol expectation, and the received signal is corrected using the time-varying phase noise to obtain a phase-corrected signal, including: The phase noise is modeled as a Wiener process, and the state equation and observation equation are constructed as follows: ； ； Perform Kalman filter iteration based on the state equation and the observation equation; The phase state and error covariance are predicted using the following formula: ； ； The Kalman gain is calculated based on the error covariance, using the following formula: ； The predicted phase state is corrected using the observed values, and the error covariance is updated. The calculation formula is as follows: ； ； The received signal is corrected using the corrected phase state to obtain a phase-corrected signal, calculated as follows: ； in, express Phase noise at any given moment; The state noise is represented by a mean of 0 and a variance of . Gaussian distribution; Indicates the phase observation value; Indicates the received signal; The conjugate of the expectation of soft symbols; The observed noise is represented by a mean of 0 and a variance of 1. Gaussian distribution; Indicates Kalman gain; This represents the predicted phase noise value; Represents the error covariance matrix; This indicates a phase correction signal.

5. The soft-decision decoding method with embedded carrier phase recovery according to claim 1, characterized in that, The log-likelihood ratio residual is calculated based on the phase correction signal, and the log-likelihood ratio is updated using the log-likelihood ratio residual to obtain the updated log-likelihood ratio, including: The log-likelihood ratio residual is calculated based on the phase correction signal, using the following formula: ； The log-likelihood ratio is updated using the log-likelihood ratio residuals, and the update rule satisfies the following formula: ； in, Represents the log-likelihood ratio to the residuals; Indicates the channel noise variance; and These represent the constellations in the diagram. and One symbol point; Indicates the first A set of constellation points with 0 bits; Indicates the first A set of constellation points with each bit set to 1; Indicates the phase correction signal; Indicates the first The output log-likelihood ratio after the next iteration; Indicates the first The input log-likelihood ratio before the next iteration.

6. The soft-decision decoding method with embedded carrier phase recovery according to claim 1, characterized in that, Based on the updated log-likelihood ratio, candidate codewords are generated using a preset algorithm, including: Using the Chase-2 decoding algorithm, the updated log-likelihood ratio of each symbol is sorted by absolute value. Based on the sorting, the first preset number of lowest confidence bit positions are selected as candidate codewords.

7. The soft-decision decoding method with embedded carrier phase recovery according to claim 1, characterized in that, The Euclidean distance between each candidate codeword and the phase correction signal is calculated using the following formula: ； in, Indicates candidate codewords With phase correction signal The Euclidean distance; Indicates the number of symbols; This represents the modulation mapping function.

8. The soft-decision decoding method with embedded carrier phase recovery according to claim 1, characterized in that, The method further includes: A damping factor is introduced into the external information to suppress overshoot; the formula for calculating the external information after the damping factor correction is as follows: ； in, This indicates the corrected external information; This indicates the external information before the correction; This represents the damping factor.

9. A soft-decision decoding system with embedded carrier phase recovery, characterized in that, The system performs the steps of implementing the method as described in any one of claims 1 to 8, the system comprising: The soft information calculation module is used to calculate symbol soft information based on the received signal and generate soft symbol expectations; The Kalman filter phase tracking module is used to dynamically estimate the time-varying phase noise based on the soft symbol expectation generated by the soft information calculation module, correct the received signal based on the time-varying phase noise, and output a phase correction signal. The soft information update module is used to update the log-likelihood ratio of the symbol based on the phase correction signal output by the Kalman filter phase tracking module to obtain the updated log-likelihood ratio. The soft-input soft-output decoder module is used to perform a preset algorithm decoding based on the updated log-likelihood ratio obtained by the soft information update module, generate extrinsic information, and feed the extrinsic information back to the soft information calculation module to optimize the symbol soft information and form a closed loop; when the iteration stops, the final decoding result is output.

Images