Soft output symbol judgment method and device, equipment, storage medium and program product

Abstract

The invention relates to a soft output symbol judgment method and device, equipment, a storage medium and a program product. The method comprises the following steps: receiving a noise signal sequence; the noise signal sequence is a noisy symbol sequence obtained through optical fiber transmission after coding modulation is carried out on an original bit sequence by a sending end; for each piece of data in the noise signal sequence, obtaining each selectable value of the data, the occurrence probability of each selectable value in the original bit sequence, and noise variance information in the noise signal sequence; based on the data, each selectable value of the data, the occurrence probability corresponding to each selectable value and the noise variance information, determining the conditional probability of receiving the data when the real value of the data is each selectable value; and restoring the original bit sequence according to the conditional probability of each datum in the noise signal sequence. By adopting the method, the accuracy of a symbol judgment result can be improved.

Description

Technical Field

[0001] This application relates to the field of optical fiber communication technology, and in particular to a soft output symbol decision method, apparatus, computer equipment, computer-readable storage medium, and computer program product. Background Technology

[0002] With the rapid rise of new services (such as computing networks, large-scale artificial intelligence models, industrial private networks, and high-value services like financial transactions), optical fiber communication needs to continuously evolve towards higher reliability. To effectively address this demand, coherent optical communication technology assisted by probabilistic shaping has become the mainstream solution in the industry. This technology changes the transmission probability of signal points with different amplitudes to obtain non-uniformly distributed modulated signals with strong noise tolerance, thereby improving the system's transmission capacity.

[0003] Probabilistic shaping techniques can effectively improve system transmission reliability, but the non-uniform distribution of signal points also presents new challenges to backend digital signal processing algorithms. Taking the BCJR soft-decision algorithm (Bahl-Cocke-Jelinek-Raviv Algorithm, a symbol decision method) as an example, its good symbol decision performance relies on the premise that the signal is uniformly distributed and the transmission probability of each signal point is the same. However, for current optical communication systems based on probabilistic shaping, the distribution of signal points no longer satisfies an equal probability distribution. Therefore, the prerequisite of the BCJR algorithm is no longer met, resulting in degraded symbol decision performance and affecting the accuracy of the symbol decision results. Summary of the Invention

[0004] Therefore, it is necessary to provide a soft-output symbol decision method, apparatus, computer equipment, computer-readable storage medium, and computer program product that can improve the accuracy of symbol decision results in response to the above-mentioned technical problems.

[0005] Firstly, this application provides a soft output symbol determination method, including:

[0006] Receive a noise signal sequence; the noise signal sequence is a noisy symbol sequence obtained by encoding and modulating the original bit sequence at the transmitting end and transmitting it through optical fiber;

[0007] For each data point in the noise signal sequence, obtain each selectable value of the data, the probability of occurrence of each selectable value in the original bit sequence, and the noise variance information in the noise signal sequence;

[0008] determine a conditional probability of receiving the data with a real value of each of the optional values of the data based on the data, the optional values of the data, the occurrence probability of each of the optional values, and the noise variance information;

[0009] restore the original bit sequence according to the conditional probability of each data in the noise signal sequence.

[0010] In one of the embodiments, the determining of the conditional probability of receiving the data with a real value of each of the optional values of the data based on the data, the optional values of the data, the occurrence probability of each of the optional values, and the noise variance information comprises:

[0011] inputting the data, the optional values of the data, the occurrence probability of each of the optional values, and the noise variance information into a pre-constructed conditional probability calculation model to obtain the conditional probability of receiving the data with a real value of each of the optional values of the data.

[0012] In one of the embodiments, the conditional probability calculation model is constructed with any data in the noise signal sequence, any optional value of the data, the occurrence probability of the optional value, and the noise variance information as input variables, and the conditional probability of receiving the data with a real value of the optional value as an output variable.

[0013] The occurrence probability of each of the optional values in the original bit sequence corresponds to a modulation format and an entropy value of the transmitted signal, and when the transmitted signal is unchanged, the occurrence probability of each of the optional values is unchanged.

[0014] In one of the embodiments, the occurrence probability of each of the optional values is determined by the following method:

[0015] For each optional value, obtain a number of occurrences of the optional value in the original bit sequence.

[0016] According to the number of occurrences and a sequence length of the original bit sequence, calculate the occurrence probability of the optional value in the original bit sequence.

[0017] In one of the embodiments, the conditional probability of each data comprises a conditional probability of each bit of each data, and the restoring of the original bit sequence according to the conditional probability of each data in the noise signal sequence comprises:

[0018] For each bit of each data, determine a value of the bit according to the conditional probability of the value of the bit.

[0019] determining the value of each data according to the value of each bit of the data;

[0020] restoring the original bit sequence according to the value of each data.

[0021] In one embodiment, determining the value of the bit according to the conditional probability of the value of the bit comprises:

[0022] if the conditional probability of the value of the bit is greater than a threshold, determining the value of the bit as 0;

[0023] if the conditional probability of the value of the bit is less than or equal to the threshold, determining the value of the bit as 1.

[0024] In a second aspect, the present application further provides a soft output symbol decision device, comprising:

[0025] a sequence receiving module configured to receive a noise signal sequence; the noise signal sequence is a noise-affected symbol sequence obtained by transmitting an original bit sequence through an optical fiber after encoding and modulation of the original bit sequence by a sending end;

[0026] a data obtaining module configured to, for each data in the noise signal sequence, obtain each selectable value of the data, occurrence probability of the each selectable value in the original bit sequence, and noise variance information in the noise signal sequence;

[0027] a probability calculating module configured to determine, based on the data, each selectable value of the data, occurrence probability of the each selectable value, and the noise variance information, conditional probability of receiving the data when the true value of the data is each selectable value;

[0028] a signal restoring module configured to restore the original bit sequence according to the conditional probability of each data in the noise signal sequence.

[0029] In a third aspect, the present application further provides a computer device, comprising a memory and a processor, the memory stores a computer program, and the processor implements the following steps when executing the computer program:

[0030] receiving a noise signal sequence; the noise signal sequence is a noise-affected symbol sequence obtained by transmitting an original bit sequence through an optical fiber after encoding and modulation of the original bit sequence by a sending end;

[0031] for each data in the noise signal sequence, obtaining each selectable value of the data, occurrence probability of the each selectable value in the original bit sequence, and noise variance information in the noise signal sequence;

[0032] determine a conditional probability of receiving the data with a real value being each of the optional values of the data based on the data, the optional values of the data, the occurrence probabilities of the optional values of the data, and the noise variance information;

[0033] restore the original bit sequence according to the conditional probability of each data in the noise signal sequence.

[0034] In a fourth aspect, the present application further provides a computer readable storage medium, which has a computer program stored thereon, and the computer program is executed by a processor to implement the following steps:

[0035] receive a noise signal sequence; the noise signal sequence is a noise symbol sequence obtained by transmitting an original bit sequence through an optical fiber after encoding and modulating the original bit sequence by a sending end;

[0036] for each data in the noise signal sequence, obtain each optional value of the data, an occurrence probability of each optional value of the data in the original bit sequence, and noise variance information in the noise signal sequence;

[0037] determine a conditional probability of receiving the data with a real value being each of the optional values of the data based on the data, the optional values of the data, the occurrence probabilities of the optional values of the data, and the noise variance information;

[0038] restore the original bit sequence according to the conditional probability of each data in the noise signal sequence.

[0039] In a fifth aspect, the present application further provides a computer program product, which comprises a computer program, and the computer program is executed by a processor to implement the following steps:

[0040] receive a noise signal sequence; the noise signal sequence is a noise symbol sequence obtained by transmitting an original bit sequence through an optical fiber after encoding and modulating the original bit sequence by a sending end;

[0041] for each data in the noise signal sequence, obtain each optional value of the data, an occurrence probability of each optional value of the data in the original bit sequence, and noise variance information in the noise signal sequence;

[0042] determine a conditional probability of receiving the data with a real value being each of the optional values of the data based on the data, the optional values of the data, the occurrence probabilities of the optional values of the data, and the noise variance information;

[0043] restore the original bit sequence according to the conditional probability of each data in the noise signal sequence.

[0044] The aforementioned soft-output symbol decision method, apparatus, computer equipment, computer-readable storage medium, and computer program product involve the transmitting end encoding and modulating the original bit sequence and transmitting it to the receiving end via optical fiber. For the received noise signal sequence, the receiving end, for each data point in the noise signal sequence, acquires the selectable values ​​of that data, the probability of each selectable value appearing in the original bit sequence, and the noise variance information in the noise signal sequence. Based on this data, the selectable values ​​of that data, the probability of each selectable value appearing, and the noise variance information, it determines the conditional probability of receiving the data when the true value is each selectable value. Based on the conditional probability of each data point in the noise signal sequence, it reconstructs the original bit sequence. This scheme proposes a probability-assisted BCJR soft-output symbol decision method. This method, by introducing the prior probability information of the selectable values ​​of each data point in the noise signal sequence, corrects and optimizes the criterion of traditional symbol decision algorithms such as BCJR. By adding probability-related correction terms during the decision process, it effectively improves the estimation accuracy of the true probability density function of the signal by traditional algorithms, significantly enhancing the system performance. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1 This is a schematic diagram illustrating the principle of probabilistic shaping technology.

[0047] Figure 2 This is a diagram illustrating the application environment of the soft output symbol decision method in one embodiment;

[0048] Figure 3 This is a flowchart illustrating a soft output symbol decision method in one embodiment;

[0049] Figure 4 This is a flowchart illustrating the soft output symbol decision method in another embodiment;

[0050] Figure 5 This is a schematic diagram illustrating the workflow of the soft output symbol decision method in one embodiment;

[0051] Figure 6 This is a comparison chart of the probability density functions estimated by this application and conventional methods in one embodiment;

[0052] Figure 7This is a performance comparison diagram between the present application and a conventional solution in an optical back-to-back system in one embodiment;

[0053] Figure 8 This is a structural block diagram of a soft output symbol decision device in one embodiment;

[0054] Figure 9 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0055] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application. It should be noted that the terms "comprising" and "having," and any variations thereof, as used in this application, are intended to cover non-exclusive inclusion. The term "multiple" as used in this application refers to two or more.

[0056] To facilitate understanding of this application, the technical background of this application will be further explained below.

[0057] refer to Figure 1 The schematic diagram of probability shaping technology shown illustrates that for a traditional 16-QAM (16-ary Quadrature Amplitude Modulation, a digital baseband signal modulation format) signal, the probabilities of its 16 constellation points are consistently 1 / 16 (e.g., ...). Figure 1 (As shown in Figure (a)). To improve its performance, probabilistic shaping technology lowers the probability of the four corner constellation points and increases the probability of the four center constellation points, thereby improving the overall noise immunity of the signal.

[0058] Taking the BCJR soft-decision algorithm as an example, the prerequisite for this algorithm to achieve good symbol decision performance is that the signal is uniformly distributed, and the transmission probability of each signal point is the same, i.e. Figure 1 The situation is shown in Figure (a). However, in current optical communication systems based on probabilistic shaping, the distribution of signal points no longer satisfies an equiprobable distribution, such as... Figure 1 As shown in Figure (b), the prerequisites of the BCJR algorithm are no longer met, resulting in a deterioration in its symbol decision performance.

[0059] To address the aforementioned issues, this application proposes a soft-output symbol decision method based on prior probability assistance. This method utilizes the prior transmission probability of a reference symbol (selectable value) to appropriately modify the distance decision criterion between the received symbol and the standard reference symbol. While ensuring soft information output, it uses the symbol transmission probability as prior information to guide symbol decision-making, improving the accuracy of symbol decision-making and enhancing the system's transmission performance. In implementation, this scheme only modifies the distance criterion between symbols, achieving dynamic and accurate tracking of the optimal symbol-level criterion, thus enabling communication systems to move further towards highly reliable communication.

[0060] The soft output symbol decision method provided in this application embodiment can be applied to, for example, Figure 2 The application environment shown includes an OTN transmitter 101 and an OTN receiver 102. OTN stands for Optical Transport Network. The OTN transmitter encodes and modulates the original bit sequence and transmits the encoded and modulated sequence into the optical fiber. Due to impaired noise in the optical fiber, the encoded and modulated sequence becomes a noise signal sequence after transmission, which is received by the OTN receiver. The OTN receiver then processes the noise signal sequence to eliminate the impaired noise during optical fiber transmission and restore the original bit sequence. Specifically, the OTN receiver processes the noise signal sequence as follows: for each data point in the noise signal sequence, it obtains each selectable value of that data, the probability of each selectable value appearing in the original bit sequence, and the noise variance information in the noise signal sequence. Based on this data, each selectable value of that data, the probability of each selectable value appearing, and the noise variance information, it determines the conditional probability of receiving the data when the true value is each selectable value. Based on the conditional probability of each data point in the noise signal sequence, it restores the original bit sequence.

[0061] refer to Figure 3 This is a flowchart illustrating the soft output symbol decision method provided in this application embodiment, demonstrating the application of this method... Figure 1 Taking the OTN receiver 102 as an example, the following steps are included:

[0062] Step S310: Receive a noise signal sequence; the noise signal sequence is a noisy symbol sequence obtained by encoding and modulating the original bit sequence at the transmitting end and transmitting it through optical fiber.

[0063] In computer communication, a bit is the smallest unit of information, representing two states: 0 or 1. For example, when a message is sent, it is converted into binary code consisting of 0s and 1s (such as 01110100), where each 0 or 1 is a bit.

[0064] A raw bit sequence is a sequence of symbols consisting of 0s or 1s. For example, {00 10 11 01} is a raw bit sequence, where each data element represents a piece of information.

[0065] For example, taking the PAM4 signal as an example, the signal has four values: {0, 1, 2, 3}. Therefore, it needs to be represented by log2(4) = 2 bits. Generally, bit 00 represents the symbol 0, which can be written as 00→0. Similarly, there are 01→1, 10→2, and 11→3.

[0066] Let the original bit sequence be {11 01 00 01 01 10 11}. After PAM4 encoding, it becomes {3 1 0 1 1 23}. After transmission through optical fiber, it becomes a noisy symbol sequence {3.1 1.1 0.1 1.1 1.1 2.1 3.1}, where the noise level is 0.1 for each bit. The sequence received by the OTN receiver is this noisy symbol sequence, denoted as the noise signal sequence. The OTN receiver processes the noise signal sequence further, determining whether each bit of each data element in the noise signal sequence is 0 or 1, thus restoring it to the original bit sequence.

[0067] Step S320: For each data point in the noise signal sequence, obtain the selectable values ​​of the data, the probability of each selectable value appearing in the original bit sequence, and the noise variance information in the noise signal sequence.

[0068] Among these, noise variance is a quantity that varies with the channel. The formula for calculating noise variance is: σ 2 =sum(YX) 2 / N, where X is the transmitted signal and Y is the received signal, and the two sequences have the same length, N.

[0069] Specifically, after receiving a noisy signal sequence, the OTN receiver can first perform clock synchronization, channel equalization, and carrier recovery on it to minimize the damage that occurred during fiber optic transmission. Then, symbol decision is performed on the processed signal sequence.

[0070] It is understandable that for probabilistically shaped signals, the optimal symbol decision criterion is affected by both noise and symbol probability. Therefore, these two factors need to be considered when making symbol decisions. The decision criterion of the existing BCJR decision algorithm (see formula (1)) includes three input variables: the received signal y, the reference symbol, and the symbol y. The noise variance information σ does not include input variables related to the symbol probability. Therefore, when the BCJR criterion based on formula (1) is directly applied to the probabilistic shaping system, it cannot reflect the guiding role of the symbol probability, thus limiting the performance of the BCJR decision algorithm based on formula (1).

[0071] (1)

[0072] in, The decision criteria represent the selectable values. The conditional probability that the received signal is y when the i-th bit is valued as k. Selectable values: It represents all possible values ​​that each data point in the received signal y can take.

[0073] In this case, the selectable values ​​for each data point in the received noise signal sequence are all the same. Therefore, the selectable value for each data point can also be called a standard reference symbol, and the probability of each selectable value appearing in the original bit sequence can also be understood as the prior transmission probability.

[0074] For a system, once the modulation format is determined, the selectable values ​​are also fixed. For example, the NRZ signal only has two possible values, therefore... ∈{0,1}; for PAM4 signals, There are four possible values. ∈{0, 1, 2, 3}; for QPSK signals, there are also four possible values. ∈{1+1i, 1-1i, -1+1i, -1-1i}, etc. n∈(1,M), where M is the modulation order. For PAM4 signals, M=4.

[0075] Since the symbol decision effect of the BCJR decision algorithm using formula (1) will be affected, in this step, when determining the decision algorithm used for symbol decision, the prior transmission probability of the selectable value of the received signal is introduced. This allows for appropriate adjustments to the decision data regarding the distance between the received symbol and the standard selectable value.

[0076] Step S330: Based on the data, each selectable value of the data, the occurrence probability of each selectable value, and the noise variance information, determine the conditional probability of receiving the data when the true value of the data is each selectable value.

[0077] Specifically, a pre-constructed conditional probability calculation model, incorporating the prior transmission probability of each selectable value, can be used to calculate the conditional probability of receiving each data point when its true value is any of the selectable values. For each data point, the data, its selectable values, the probability of occurrence of each selectable value, and noise variance information can be input into the conditional probability calculation model to obtain the conditional probability of receiving the data when its true value is any of the selectable values.

[0078] Step S340: Reconstruct the original bit sequence based on the conditional probability of each data point in the noise signal sequence.

[0079] Specifically, the conditional probability of receiving a data point in a noise signal sequence when its true value is any of the selectable values ​​is the probability of that data point being any of the selectable values. Therefore, the higher the conditional probability of a data point with respect to a selectable value, the higher the probability that the data point's true value is that selectable value. Thus, the true value of each data point can be determined based on this conditional probability.

[0080] In the aforementioned soft-output symbol decision method, the transmitting end encodes and modulates the original bit sequence and transmits it to the receiving end via optical fiber. For the received noise signal sequence, the receiving end, for each data point in the noise signal sequence, obtains the selectable values ​​of that data, the probability of each selectable value appearing in the original bit sequence, and the noise variance information in the noise signal sequence. Based on this data, its selectable values, the probability of each selectable value appearing, and the noise variance information, it determines the conditional probability of receiving the data when its true value is each selectable value. Based on the conditional probability of each data point in the noise signal sequence, the original bit sequence is reconstructed. This scheme proposes a probability-assisted BCJR soft-output symbol decision method. This method introduces the prior probability information of the selectable values ​​of each data point in the noise signal sequence to modify and optimize the criterion of traditional symbol decision algorithms such as BCJR. By adding probability-related correction terms during the decision process, it effectively improves the estimation accuracy of the true probability density function of the signal by traditional algorithms, significantly enhancing the system performance.

[0081] In an exemplary embodiment, step S330 determines the conditional probability of receiving the data when the true value of the data is each selectable value based on the data, each selectable value of the data, the occurrence probability of each selectable value, and the noise variance information. This includes: inputting the data, each selectable value of the data, the occurrence probability of each selectable value, and the noise variance information into a pre-built conditional probability calculation model to obtain the conditional probability of receiving the data when the true value of the data is each selectable value.

[0082] The conditional probability calculation model is as follows:

[0083] (2)

[0084] in, This indicates that the actual values ​​of the data y in the noise signal sequence can be selected. The conditional probability of receiving the data y at that time. Indicates selectable values The probability of occurrence in the original bit sequence. This indicates that the i-th bit is the value of k, where k is either 0 or 1. This represents the noise variance information, where n ranges from (1 to M), and M is the signal modulation order.

[0085] As shown in formula (2), the conditional probability calculation model is constructed with any data in the noise signal sequence, any selectable value of the data, the probability of occurrence of the selectable value and the noise variance information as input variables, and the conditional probability of receiving the data when the actual value of the data is the selectable value as the output variable.

[0086] The probability of each selectable value appearing in the original bit sequence corresponds to the modulation format and entropy value of the transmitted signal. When the transmitted signal remains unchanged, the probability of each selectable value remains unchanged.

[0087] For example, still using the PAM4 signal, for the four selectable values ​​{0, 1, 2, 3}, it is agreed that bit 00 represents the symbol 0, which can be written as 00→0. Similarly, there are 01→1, 10→2, and 11→3. Therefore:

[0088] Selectable values ​​for each data point in the noise signal sequence The values ​​are: 0, 1, 2, 3. For example, The set of symbols whose first bit is 1 is represented by symbol 2 (corresponding to 10) and symbol 3 (corresponding to 11); similarly, This represents the set of symbols whose second bit is 0, corresponding to symbol 0 and symbol 2.

[0089] The probability of each selectable value appearing There are 4 possible outcomes, namely , , , Each of these represents the probability of occurrence of a given set of values. For example, This represents the sum of the probabilities of the occurrence of symbols 2 and 3 (i.e., 10 and 11) when the first bit is 1.

[0090] Conditional probability There are 4 possible outcomes, namely , , , . This represents the probability of receiving data y when its first bit is 0, given a data y in a noisy signal sequence.

[0091] It should be noted that the correction term introduced in formula (2) It corresponds one-to-one with the modulation format and entropy value of the signal. When the signal transmitted in the system remains unchanged, The prior probability of each selectable value remains unchanged; that is, once the signal transmitted in the system is determined, the probability of each selectable value is constant. It is definite and unchanging. Its calculation formula is shown in formula (3) below.

[0092] In this application, the modulation format is 16-QAM as an example. It should be noted that other modulation formats can also be used in practical applications.

[0093] Entropy is a statistical concept related to the probability of a signal. It is calculated based on the symbols transmitted at the OTN end, and its calculation steps and methods are as follows:

[0094] 1. Once the modulation format is given, all constellation points of the signal are determined. Assuming the modulation order is R, with values ​​of 1, 2, ..., R, then a total of R constellation points will be generated.

[0095] 2. Suppose a sequence of length N is sent. N1 symbols have a value of 1, N2 symbols have a value of 2, and so on. The symbols take values ​​of R. Therefore, the probabilities of symbols 1, 2, ..., R can be expressed as: Where i∈{1,2,…,R}.

[0096] 3. At this point, the entropy value of the signal is... .

[0097] In this embodiment, starting from the theoretical analysis of the BCJR algorithm, the probability of selectable values ​​is introduced as prior information. The formula is derived and the optimal criterion of BCJR in the probability shaping system is obtained, namely formula (2). Based on formula (2), the improved BCJR algorithm can more accurately estimate the actual probability density distribution of the signal, thereby realizing the dynamic tracking of the optimal criterion.

[0098] In an exemplary embodiment, the probability of occurrence of each selectable value is determined by: for each selectable value, obtaining the number of times the selectable value appears in the original bit sequence; and calculating the probability of occurrence of the selectable value in the original bit sequence based on the number of occurrences and the sequence length of the original bit sequence.

[0099] In practice, for each selectable value, the probability of its occurrence in the original bit sequence can be calculated by dividing the number of times that selectable value appears in the original bit sequence by the length of the original bit sequence. This process can be represented by formula (3):

[0100] (3)

[0101] Where N represents the length of the original bit sequence sent by the sender. Indicates selectable values The number of times it appears in the original bit sequence of length N.

[0102] For example, suppose the original bit sequence is: {11 01 00 01 01 10 11}, for the selectable values... The probability of its occurrence in the original bit sequence =4 / 7, meaning the probability of a symbol with the first bit being 0 (01 and 00) appearing in the original bit sequence is 4 / 7.

[0103] In this embodiment, the probability of occurrence of each selectable value of the noise signal sequence in the original bit sequence is calculated so that when determining the symbol decision data, the occurrence probability can be introduced as prior information, thereby improving the accuracy of subsequent symbol decision.

[0104] In an exemplary embodiment, the conditional probability corresponding to each data includes the conditional probability of each bit of each data; reconstructing the original bit sequence based on the conditional probability of each data in the noise signal sequence includes: for each bit of each data, determining the value of the bit based on the conditional probability of the bit value; determining the value of each data based on the values ​​of each bit of each data; and reconstructing the original bit sequence based on the value of each data.

[0105] Specifically, the original bit sequence of each data point is represented by at least one bit. When performing symbol decision to reconstruct the original bit sequence, a decision needs to be made for each bit. Therefore, the decision data calculated using formula (2) Let $\frac{i}{k}$ be the conditional probability of each bit corresponding to each data point in the noise signal, i.e., the conditional probability when the $i$-th bit is $k$. For each bit of each data point, its value is determined to be either 0 or 1 based on the conditional probability of that bit value. Then, based on the values ​​of each bit of each data point, the value of each data point is determined, and further, based on the values ​​of each data point, the original bit sequence is obtained, thus realizing the reconstruction of the original bit sequence.

[0106] For example, for a noisy signal sequence {3.1 1.1 0.1 1.1 1.1 2.1 3.1}, for each data point 3.1, 1.1, ..., 3.1, the value of each bit needs to be determined. Taking the first data point 3.1 as an example, the conditional probability of each bit of the true value of data 3.1 is calculated. Based on the conditional probability, the value of each bit is determined. For example, if the value of the first bit is determined to be 1 and the value of the second bit is determined to be 1, then the true value of data 3.1 can be determined to be 11. This operation is performed for each data point in the noisy signal sequence to restore the original bit sequence {11 01 00 01 01 10 11}.

[0107] Further, in an exemplary embodiment, determining the value of the bit based on the conditional probability of the bit's value includes: if the conditional probability of the bit's value is greater than a threshold, determining the value of the bit to be 0; if the conditional probability of the bit's value is less than or equal to the threshold, determining the value of the bit to be 1.

[0108] For example, taking the noise signal sequence {3.1 1.1 0.1 1.1 1.1 2.1 3.1} as an example, for the first data point 3.1, the conditional probability of the first bit of the calculated true value of data 3.1 is compared with a threshold. If it is greater than the threshold, the value of the first bit is determined to be 1; otherwise, the value is 0. Similarly, the conditional probability of the second bit of the true value of data 3.1 is also compared with the threshold to determine the value of the second bit, thus obtaining the true value of data 3.1.

[0109] In this embodiment, each data point in the noise signal sequence is restored sequentially at the bit level, thereby improving the accuracy of the restored original bit sequence.

[0110] refer to Figure 4 The following is a flowchart illustrating a soft output symbol decision method according to another embodiment, including the following steps:

[0111] Step S410: Receive a noise signal sequence; the noise signal sequence is a noisy symbol sequence obtained by encoding and modulating the original bit sequence at the transmitting end and transmitting it through optical fiber.

[0112] Step S420: For each data point in the noise signal sequence, obtain the selectable values ​​of the data, the probability of each selectable value appearing in the original bit sequence, and the noise variance information in the noise signal sequence.

[0113] Step S430: Input the data, each selectable value of the data, the occurrence probability of each selectable value, and the noise variance information into the pre-constructed conditional probability calculation model to obtain the conditional probability of receiving the data when the true value of the data is each selectable value; the conditional probability corresponding to each data includes the conditional probability of each bit of each data.

[0114] Step S440: For each bit of each data, if the conditional probability of the bit value is greater than the threshold, determine that the bit value is 0; if the conditional probability of the bit value is less than or equal to the threshold, determine that the bit value is 1.

[0115] Step S450: Determine the value of each data based on the value of each bit of each data.

[0116] Step S460: Reconstruct the original bit sequence based on the value of each data point.

[0117] This method modifies and optimizes the criterion of the traditional BCJR decision algorithm by introducing prior information on the probability of the reference symbol (selectable value). By adding a probability-related correction term during the decision process, it effectively improves the estimation accuracy of the true probability density function of the signal by the traditional BCJR algorithm and significantly enhances the performance of the system.

[0118] The algorithm proposed in this application can be integrated into the digital signal processing algorithm chip of the OTN receiver to enhance system performance. A schematic diagram is shown below. Figure 5 As shown. Specific details are as follows:

[0119] After encoding and modulating the original bit data B, the OTN transmitter sends symbol data D into the optical fiber. Due to damage in the optical fiber, symbol data D becomes symbol data D1 after transmission through the optical fiber, which is received by the OTN receiving equipment at the OTN receiving side. Subsequently, the OTN's digital signal processing algorithm chip processes symbol data D1 sequentially, including clock synchronization, channel equalization, and carrier recovery algorithms, in order to try to eliminate the damage during optical fiber transmission. Finally, the carrier recovery algorithm outputs the processed symbol data D2, where symbol data D2 is an approximate estimate of symbol data D, and the relationship between the two can be qualitatively expressed as formula (4).

[0120] D2 = D + noise (4)

[0121] Here, noise is damage noise.

[0122] After that, the symbol data D2 will be processed by the BCJR algorithm. Assume that the estimated sequence obtained by making a decision on the D2 sequence using the traditional scheme based on Equation (1) is B1, and the corresponding bit error rate is BER1. The estimated sequence obtained by making a decision on the D2 sequence using the probability-assisted BCJR algorithm based on Equation (2) in this application is B2, and the corresponding bit error rate is BER2. Since the traditional scheme does not consider the auxiliary effect of the correction term during the decision-making process, BER2 < BER1. The probability-assisted BCJR algorithm based on Equation (2) in this application has better performance than the traditional scheme in terms of the decision-making scheme because it considers the guiding effect of the correction term, has higher decision-making accuracy. Compared with the traditional scheme, this scheme can achieve better system performance at the same signal-to-noise ratio, that is, lower bit error rate, and can also effectively extend the farthest transmission distance of the system, enabling the optical network to continuously evolve towards high performance and long distance.

[0123] Embodiment 1: This scheme also provides an embodiment in a probability shaping 16-QAM system. Figure 6 It shows the estimation of the probability density function of the in-phase branch symbols of 16-QAM by the traditional BCJR algorithm and the probability-assisted BCJR algorithm proposed in this scheme in probability shaping 16-QAM. It can be seen that the probability density function estimated by the traditional BCJR algorithm represented by the black dashed line has a large deviation from the true probability density function of the signal represented by the blue bars, while the result estimated by the probability-assisted BCJR algorithm proposed in this scheme represented by the red solid line is closer to the true distribution of the actual signal, proving that this scheme can estimate the true characteristics of the signal well, and thus can effectively improve the decision-making accuracy of symbols and improve the system performance.

[0124] Embodiment 2: To present the actual effect of the present invention, this application also carried out algorithm verification work based on a single-carrier 64 GBaud probability shaping 16-QAM optical transmission system, and the results are as Figure 7 shown. It can be seen that since the scheme proposed in this application can better estimate the probability density function of the signal. Therefore, to achieve a system bit error rate lower than the set bit error rate threshold of 1E-3, the scheme of this application shown by the red line only requires an optical signal-to-noise ratio of 14.5 dB. Compared with the optical signal-to-noise ratio of 15.5 dB required by the traditional scheme, it can bring an optical signal-to-noise ratio advantage of 1 dB, fully proving that the scheme proposed in this application can effectively improve the symbol decision-making accuracy and improve the system performance.

[0125] This application proposes a probability-assisted BCJR soft-output symbol decision method. This method modifies and optimizes the criterion of the traditional BCJR decision algorithm by introducing prior information about the probability of a reference symbol (selectable value). By adding probability-related correction terms during the decision process, it effectively improves the estimation accuracy of the true probability density function of the signal by the traditional BCJR algorithm, significantly enhancing the system performance.

[0126] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0127] Based on the same inventive concept, this application also provides a soft output symbol decision apparatus for implementing the soft output symbol decision method described above. The solution provided by this apparatus is similar to the implementation described in the above method; therefore, the specific limitations in one or more soft output symbol decision apparatus embodiments provided below can be found in the limitations of the soft output symbol decision method described above, and will not be repeated here.

[0128] In one exemplary embodiment, such as Figure 8 As shown, a soft-output symbol decision device is provided, comprising:

[0129] The sequence receiving module 810 is used to receive a noise signal sequence; the noise signal sequence is a noisy symbol sequence obtained by encoding and modulating the original bit sequence at the transmitting end and transmitting it through optical fiber.

[0130] The data acquisition module 820 is used to acquire, for each data in the noise signal sequence, each selectable value of the data, the probability of occurrence of each selectable value in the original bit sequence, and the noise variance information in the noise signal sequence.

[0131] The probability calculation module 830 is used to determine the conditional probability of receiving the data when the true value of the data is each selectable value, based on the data, each selectable value of the data, the occurrence probability of each selectable value, and noise variance information.

[0132] The signal restoration module 840 is used to restore the original bit sequence based on the conditional probability of each data in the noise signal sequence.

[0133] In one embodiment, the probability calculation module 830 is further configured to input the data, each selectable value of the data, the occurrence probability of each selectable value, and noise variance information into a pre-built conditional probability calculation model to obtain the conditional probability of receiving the data when the true value of the data is each selectable value.

[0134] In one embodiment, the conditional probability calculation model is constructed using any data in the noise signal sequence, any selectable value of the data, the probability of occurrence of the selectable value, and noise variance information as input variables, and the conditional probability of receiving the data when the actual value of the data is the selectable value as the output variable; wherein, the probability of occurrence of each selectable value in the original bit sequence corresponds to the modulation format and entropy value of the transmitted signal, and the probability of occurrence of each selectable value remains unchanged when the transmitted signal remains unchanged.

[0135] In one embodiment, the data acquisition module 820 is further configured to acquire, for each selectable value, the number of times the selectable value appears in the original bit sequence; and calculate the probability of the selectable value appearing in the original bit sequence based on the number of occurrences and the sequence length of the original bit sequence.

[0136] In one embodiment, the conditional probability corresponding to each data includes the conditional probability of each bit of each data; the signal restoration module 840 is further configured to determine the value of each bit of each data based on the conditional probability of the bit value; determine the value of each data based on the values ​​of each bit of each data; and restore the original bit sequence based on the value of each data.

[0137] In one embodiment, the signal restoration module 840 is further configured to determine the value of the bit as 0 if the conditional probability of the bit value is greater than a threshold, and to determine the value of the bit value as 1 if the conditional probability of the bit value is less than or equal to the threshold.

[0138] Each module in the aforementioned soft output symbol decision device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0139] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 9As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores data involved in executing the soft-output symbol decision method. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network connection. When the computer program is executed by the processor, it implements a soft-output symbol decision method.

[0140] Those skilled in the art will understand that Figure 9 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0141] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0142] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0143] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0144] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0145] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0146] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A soft-output symbol decision method, characterized in that, The method includes: Receive a noise signal sequence; the noise signal sequence is a noisy symbol sequence obtained by encoding and modulating the original bit sequence at the transmitting end and transmitting it through optical fiber; For each data point in the noise signal sequence, obtain each selectable value of the data, the probability of occurrence of each selectable value in the original bit sequence, and the noise variance information in the noise signal sequence; Based on the data, the selectable values ​​of the data, the probability of occurrence of each selectable value, and the noise variance information, the conditional probability of receiving the data when the true value of the data is each selectable value is determined. The original bit sequence is reconstructed based on the conditional probability of each data point in the noise signal sequence.

2. The method according to claim 1, characterized in that, The determination of the conditional probability of receiving the data when the true value of the data is each selectable value, based on the data, each selectable value's corresponding occurrence probability, and the noise variance information, includes: The data, its selectable values, the probability of occurrence of each selectable value, and the noise variance information are input into a pre-built conditional probability calculation model to obtain the conditional probability of receiving the data when the true value of the data is each selectable value.

3. The method according to claim 2, characterized in that, The conditional probability calculation model is constructed by taking any data in the noise signal sequence, any selectable value of the data, the probability of occurrence of the selectable value, and noise variance information as input variables, and the conditional probability of receiving the data when the actual value of the data is the selectable value as the output variable. The probability of each selectable value appearing in the original bit sequence corresponds to the modulation format and entropy value of the transmitted signal. When the transmitted signal remains unchanged, the probability of each selectable value remains unchanged.

4. The method according to claim 1, characterized in that, The probability of occurrence of each selectable value is determined in the following way: For each selectable value, obtain the number of times that selectable value appears in the original bit sequence; The probability of the selectable value appearing in the original bit sequence is calculated based on the number of occurrences and the sequence length of the original bit sequence.

5. The method according to claim 1, characterized in that, The conditional probability corresponding to each data point includes the conditional probability of each bit of each data point; the step of reconstructing the original bit sequence based on the conditional probability of each data point in the noise signal sequence includes: For each bit of each data point, determine the value of that bit based on the conditional probability of that bit's value. The value of each data point is determined based on the value of each bit. Based on the value of each data point, the original bit sequence is reconstructed.

6. The method according to claim 5, characterized in that, Determining the value of a bit based on the conditional probability of that bit's value includes: If the conditional probability of the value of this bit is greater than the threshold, the value of this bit is determined to be 0; If the conditional probability of the value of this bit is less than or equal to the threshold, the value of this bit is determined to be 1.

7. A soft-output symbol decision device, characterized in that, The device includes: A sequence receiving module is used to receive a noise signal sequence; the noise signal sequence is a noisy symbol sequence obtained by encoding and modulating the original bit sequence at the transmitting end and transmitting it through optical fiber. The data acquisition module is used to acquire, for each data in the noise signal sequence, each selectable value of the data, the probability of occurrence of each selectable value in the original bit sequence, and the noise variance information in the noise signal sequence; The probability calculation module is used to determine the conditional probability of receiving the data when the true value of the data is each selectable value, based on the data, each selectable value of the data, the occurrence probability corresponding to each selectable value, and the noise variance information. The signal restoration module is used to restore the original bit sequence based on the conditional probability of each data in the noise signal sequence.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

Images