A method, apparatus, and equipment for optimizing irregular LDPC codes adapted to equalizers.

Abstract

This application relates to the field of communication technology, specifically to a method, apparatus, and device for optimizing irregular LDPC codes adapted to an equalizer. The method includes: selecting at least two check node degrees within the range of check node degree values ​​and pre-setting the edge ratio under each check node degree value to form an initial check node degree distribution; calculating the mutual information of variable nodes and the mutual information of check nodes respectively; designing an objective function; setting constraints and traversing to search for variable node degree distributions that meet the conditions; calculating the cost value of the cost function under all edge ratios; updating the edge ratios of the two check node degrees until the update cutoff condition is met, and repeating the calculation steps to obtain the cost value and the corresponding variable node degree distribution; outputting the variable node degree distribution and check node degree distribution with the minimum cost value. This application significantly expands the degree distribution design freedom by simultaneously setting multiple check node degrees and traversing to search for variable node degree distributions.

Description

Technical Field

[0001] This application relates to the field of communication technology, specifically to a method, apparatus, and device for optimizing irregular LDPC codes adapted to equalizers. Background Technology

[0002] LDPC (Low-Density Parity-Check) codes, as an efficient error-correction coding technique, have been widely deployed in various communication systems due to their ability to approximate Shannon channel capacity while maintaining low decoding complexity. In LDPC code design, degree distribution (i.e., the degree distribution of variable nodes and check nodes) is a core factor determining code set performance, directly affecting decoding convergence speed, bit error rate, and system throughput. Therefore, degree distribution optimization has long been considered one of the most crucial and challenging directions in LDPC code research, with existing work primarily focusing on improving performance by adjusting the degree distribution of variable nodes.

[0003] In related technologies, the design optimization of LDPC codes only independently optimizes the degree distribution of variable nodes under a single check node structure (such as a regular check node), completely ignoring the potential impact of irregular check nodes on the overall performance of the LDPC code set. This one-sided optimization strategy significantly compresses the degree distribution design freedom, causing the optimized code set to be unable to fully adapt to dynamic changes in the actual communication environment, thus leading to quantifiable performance losses. Given the urgent need for high-performance error correction capabilities in communication systems, how to design a joint optimization method for the degree distribution of LDPC code variable nodes and check nodes that is compatible with equalizers is a technical problem that urgently needs to be solved. Summary of the Invention

[0004] In related technologies, the design optimization of LDPC codes reduces the degree of freedom in optimization, which in turn leads to performance loss.

[0005] In a first aspect, embodiments of this application provide a non-regular LDPC code optimization method adapted to an equalizer, the non-regular LDPC code optimization method comprising: Initial settings are made for the range of variable node degree values, the range of check node degree values, and the external mutual information of the equalizer. At least two check node degrees are selected within the range of check node degree values, and the edge ratio under each check node degree value is preset to form an initial check node degree distribution. The variable node mutual information output by the variable node and the check node mutual information output by the two check node degrees under the current edge ratio are calculated based on the external mutual information of the equalizer. An objective function is designed, and constraints are set based on the variable node mutual information and check node mutual information, and the variable node degree distribution that meets the conditions is traversed and searched. The cost value of the cost function is calculated based on the variable node degree distribution, variable node mutual information, and check node mutual information. The edge ratio of the two check node degrees is updated until the update cutoff condition is met, and the variable node degree distribution calculation and cost value calculation steps are repeated to obtain the output cost value and corresponding variable node degree distribution under all edge ratios. The variable node degree distribution and check node degree distribution with the minimum cost value are output.

[0006] In conjunction with the first aspect, one implementation of updating the edge ratio of the degree of two verification nodes until the update cutoff condition is met includes: The ratio of the number of edges connected to two verifiable nodes under their respective degrees is updated according to a preset coefficient until the ratio of the number of edges of one verifiable node exceeds a preset threshold.

[0007] In conjunction with the first aspect, in one implementation, updating the ratio of the number of edges connected to the total number of edges for two verification nodes based on a preset coefficient includes: The degree of the selected first verification node is determined according to the formula. The percentage of edges and the degree of the second check node Update the percentage of edges:

[0008]

[0009] In the formula, The degree of the verification node is set to the degree of the first verification node. The proportion of the number of edges updated in real time to the total number of edges. The degree of the verification node is set to the degree of the first verification node. The proportion of edges before the update to the total number of edges. The degree of the verification node is set to the degree of the second verification node. The proportion of updated edges to the total number of edges. This is the preset step size parameter.

[0010] In conjunction with the first aspect, in one implementation, the step of calculating the variable node mutual information output by the variable node and the check node mutual information output by the two check nodes according to the external mutual information of the equalizer, respectively, includes: Calculate the output standard deviation of the equalizer based on the external mutual information in the mutual information of the equalizer; Calculate the mutual information of variable node outputs for all variable node degree values ​​based on the output standard deviation; The mutual information of two check nodes is calculated using the binary search method, taking into account the proportion of the degree of the two check nodes in the current number of edges.

[0011] In conjunction with the first aspect, in one implementation, the step of calculating the mutual information of the variable node outputs across all degree values ​​based on the output standard deviation includes: The variable node outputs mutual information when the i-th value is calculated according to the formula. :

[0012] In the formula, For the variable, the node degree. To presuppose prior mutual information, This represents the output standard deviation of the equalizer.

[0013] In conjunction with the first aspect, in one implementation, the step of calculating the mutual information of the check nodes using the bisection method as a percentage of the current number of edges includes: When calculating the i-th value using the formula, verify the mutual information of the nodes. :

[0014]

[0015] In the formula, This is the proportion of edges connected to a check node at the degree of the first check node out of the total number of edges. This represents the proportion of edges connected to a check node at the degree of the second check node out of the total number of edges. To pre-set mutual information, The degree of the first verification node. This is the degree of the second verification node.

[0016] In conjunction with the first aspect, in one implementation, the step of designing the objective function and setting constraints based on the mutual information of variable nodes and the mutual information of check nodes, and then traversing and searching for the degree distribution of variable nodes that meet the conditions, includes: Set the target function:

[0017] Set constraints according to the formula:

[0018]

[0019]

[0020] In the formula, This represents the proportion of edges connected to node i out of the total number of edges. The degree of the first verification node. The degree of the second verification node. To verify the mutual information between nodes, This is the critical distance value. This represents the maximum value of the variable node; The system iterates through the constraints to find the degree distribution of the variable nodes that meet the conditions.

[0021] In conjunction with the first aspect, in one implementation, the step of calculating the cost value of the cost function based on the variable node degree distribution, variable node mutual information, and verification node mutual information includes: The EXIT function for variable nodes is obtained by fitting the variable node degree distribution and variable node mutual information pairs. The EXIT function of the check nodes is obtained by fitting the mutual information pairs of the check nodes. Design a cost function based on the EXIT function of the variable node and the EXIT function of the check node, and calculate the cost value of the cost function.

[0022] Secondly, embodiments of this application provide a non-regular LDPC code optimization device adapted to an equalizer, the non-regular LDPC code optimization device comprising: The initialization unit is used to initially set the range of variable node degree values, the range of check node degree values, and the external mutual information of the equalizer. It selects at least two check node degrees within the range of check node degree values ​​and sets the edge ratio of each check node degree to form the initial check node degree distribution. The first calculation unit is used to calculate the variable node mutual information output by the variable node and the check node mutual information output by the two check nodes under the current edge ratio based on the external mutual information of the equalizer. The search unit is used to set the objective function, set constraints based on the variable node mutual information and check node mutual information, and traverse and search the variable node degree distribution. The second calculation unit is used to calculate the cost value of the cost function based on the variable node degree distribution, variable node mutual information, and check node mutual information. The update unit is used to update the edge ratio of the two check node degrees until the update cutoff condition is met, and calculate the cost value of the cost function output by the two check node degrees under all edge ratios and the corresponding variable node degree distribution. The filtering unit is used to output the variable node degree distribution and check node degree distribution corresponding to the edge ratio with the minimum cost value.

[0023] Thirdly, embodiments of this application provide an irregular LDPC code optimization device adapted to an equalizer. The irregular LDPC code optimization device includes a processor, a memory, and an irregular LDPC code optimization program stored in the memory and executable by the processor. When the irregular LDPC code optimization program is executed by the processor, it implements the steps of the irregular LDPC code optimization method as described in any of the preceding claims.

[0024] The beneficial effects of the technical solutions provided in this application include: This application significantly expands the degree distribution design freedom by simultaneously setting multiple check node degrees (e.g., selecting only the first and second check node degrees) and traversing the search variable node degree distribution. By combining the dynamic calculation of the cost function and the minimization of the cost value, it ensures that the optimized degree distribution and the equalizer parameters are accurately matched, thereby avoiding performance loss due to one-sided optimization and ultimately achieving a comprehensive improvement in the decoding performance of LDPC code sets in communication systems. Attached Figure Description

[0025] Figure 1 This is a flowchart illustrating the irregular LDPC code optimization method in the embodiments of this application; Figure 2 This is a schematic diagram of the hardware structure of the irregular LDPC code optimization device involved in the embodiments of this application. Detailed Implementation

[0026] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0027] In related technologies, the design optimization of LDPC codes reduces the degree of freedom in optimization, which in turn leads to performance loss.

[0028] In a first aspect, embodiments of this application provide a non-regular LDPC code optimization method adapted to an equalizer, the non-regular LDPC code optimization method comprising: Step S1: Initialize the variable node degree range, the check node degree range, and the external mutual information of the equalizer. Select at least two check node degrees within the check node degree range and set the edge ratio for each check node degree.

[0029] Step S1 above includes: Step S1a: Set the maximum variable node degree and the maximum check node degree Select two degrees under the maximum check node degree: the degree of the first check node. Second check node degree ,in In one embodiment, a maximum variable node degree is set. ≥2, maximum check node degree ≥2.

[0030] It should be noted that in the above embodiments of this application, only two degrees are selected: the degree of the first verification node. Second check node degree In practical applications, the more degrees of the check nodes selected, the higher the accuracy of subsequent calculations. The appropriate number of check node degrees can be determined based on computing power. The above embodiments consider the impact of irregular check nodes on the performance of the LDPC code set. Compared to optimization methods under regular check nodes, this invention increases the degree of freedom in degree distribution optimization.

[0031] Furthermore, based on the degree of the selected first verification node Second check node degree You can set the initial value. and .in, It is the degree of the check node. The proportion of edges connected to the verification node out of the total number of edges. . It is the degree of the check node. The proportion of edges connected to the verification node out of the total number of edges. . and satisfy Set step size Normally set and The initial values ​​are 0 and 1, respectively.

[0032] Understandably, in the initial settings and Take 0 and 1 respectively. Subsequent steps will be based on the step size. In the combination of the proportion of opposite sides and renew.

[0033] Step S1b: Initialize the external mutual information of the equalizer.

[0034] Specifically, the mutual information pairs input to the equalizer ( , ),in, It is the equalizer's prior mutual information, the first Value . It is the external mutual information of the equalizer, the first Value .

[0035] Step S2: Calculate the variable node mutual information output by the variable node and the check node mutual information output by the two check nodes according to the external mutual information of the equalizer.

[0036] Step S2a: Calculate the output standard deviation of the equalizer based on the external mutual information in the mutual information of the equalizer; Specifically, based on the external mutual information of the equalizer Calculate the output standard deviation of the equalizer , . No. one standard deviation Represented as:

[0037] in, yes The inverse function of .

[0038] Step S2b: Calculate the mutual information of variable node outputs for all degree values ​​based on the output standard deviation.

[0039] Specifically, step S2b includes: Step A: Set up prior mutual information , No. Value .

[0040] Step B: Calculate the degree of each variable node. Take 2 up to the maximum value The variable node outputs mutual information at that time. .

[0041] The variable node outputs mutual information when the i-th value is calculated according to the formula. :

[0042] In the formula, For the variable, the node degree. To presuppose prior mutual information, This represents the output standard deviation of the equalizer.

[0043] Step S2c: Calculate the mutual information of the check nodes under the degree values ​​of the two nodes using the bisection method.

[0044] The above step S2c includes: Step A: Set up mutual information , No. Value Calculate the degree of the first check node. Second check node degree Time verification node mutual information .

[0045] Solve the first problem using the formula. Mutual information among verification nodes :

[0046]

[0047] In the formula, This represents the proportion of edges connected to a check node at the degree of the first check node out of the total number of edges. This represents the proportion of edges connected to the second check node at its degree to the total number of edges. To pre-set mutual information, The degree of the first verification node. This is the degree of the second verification node.

[0048] Step S3: Set constraints based on the mutual information of variable nodes and the mutual information of check nodes, and traverse and search for the degree distribution of variable nodes that meet the conditions.

[0049] Step S3 above includes: Step S3a: Design the objective function ,in, It is the degree of the node with the degree as the variable. The proportion of the number of edges connected to a variable node out of the total number of edges.

[0050] Step S3b: Set constraints on the sum of the degree distributions of the variable nodes:

[0051]

[0052] In the formula, It is with degrees The proportion of the number of edges connected to a variable node out of the total number of edges.

[0053] Step S3c: Set constraints based on the mutual information of variable node outputs, the mutual information of check node inputs, and the degree distribution of variable nodes.

[0054]

[0055] In the formula, This represents the proportion of edges connected to the variable node out of the total number of edges. The degree of the first verification node. The degree of the second verification node. To verify the mutual information between nodes, This is the critical distance value. This represents the maximum value of the variable node.

[0056] Step S3d: Based on the above constraints, traverse and search for the degree distribution of variable nodes that meet the conditions. .

[0057] Step S4: Calculate the cost value of the cost function based on the variable node degree distribution, variable node mutual information, and verification node mutual information.

[0058] Step S4 specifically includes: Step S4a: Based on the distribution of variable node degree The EXIT function of the variable nodes is obtained by fitting the mutual information of the variable nodes calculated in step S2. .

[0059] Step S4b: Based on the mutual information of the verification nodes The fitting yielded the degree of the verification node. EXIT function for verification nodes .

[0060] Step S4c: Design the cost function based on the EXIT function of the variable node and the EXIT function of the check node. And calculate the cost value of the cost function (i.e. value).

[0061] Step S5: Update the edge ratio of the degree of the two verification nodes until the update cutoff condition is met, and repeat the variable node degree distribution calculation and cost value calculation steps to obtain the output cost value and the corresponding variable node degree distribution under all edge ratio combinations.

[0062] Step S5 includes: Step S5a: Update the ratio of the number of edges connected to the total number of edges for two check nodes according to the preset coefficient, until the ratio of the number of edges for one check node exceeds the preset threshold.

[0063] In one embodiment, the degree of the first verification node is determined according to the formula. percentage of the number of sides Second check node degree percentage of the number of sides Update:

[0064]

[0065] In the above formula, The degree of the verification node is set to the degree of the first verification node. The ratio of the number of edges updated each time to the total number of edges (i.e., the ratio of the number of edges to the degree) The proportion of edges connected to the verification node out of the total number of edges. The degree of the verification node is set to the degree of the second verification node. The ratio of the number of edges updated each time to the total number of edges (i.e., the ratio of the number of edges to the degree) The proportion of edges connected to the verification node out of the total number of edges. The preset step size parameter, step size This is the preset update parameter used in this step.

[0066] It is worth noting that, according to the preset step size The edge count ratio is updated as an update parameter. For example, the initial edge count ratio is the edge count ratio of the two verified nodes' degrees. and The values ​​are 0 and 1 respectively, assuming a step size. The value is 0.1. Therefore, the updated proportion of edges... and They are 0.1 and 0.9 respectively.

[0067] Furthermore, during the update process, if the updated degree of the first verification node... percentage of the number of sides A value greater than 1 indicates the degree of the first check node under the current value. Second check node degree The corresponding percentage of edges has been updated.

[0068] Step S5b: Calculate the cost value of the output cost function and the corresponding variable node degree distribution under the combination of the proportion of all edges when the degree values ​​of the two selected verification nodes are taken.

[0069] In some optional implementations, after completing steps S5a and S5b, when the degree of the first verification node is updated... percentage of the number of sides When >1, within the range of the degree of the verification node selected in step S1 (i.e., 2~ Reselect the degree of two check nodes. and and perform initialization settings. , Then repeat steps S2 to S5b until the first verification node. Second verification node The value range of the verifier node degree is traversed (i.e., 2~). ).

[0070] It should be noted that steps S1 to S5b above refer to the degree of the initially selected first verification node. Second check node degree The calculation of the degree of the first verification node during this process. Second check node degree The value remains unchanged. In the optional implementation described above, after calculating the initial degree values ​​of the two selected nodes, two new degree values ​​are selected, and the above steps are repeated to calculate the cost value until the degree values ​​of the two nodes traverse the initially set value range (i.e., 2~). ).

[0071] Step S6: Output the distribution of node degree corresponding to the percentage of edges with the minimum cost. and the corresponding degree distribution of verification nodes .

[0072] It is understandable that the above steps are to select the optimal degree distribution from all the above combinations based on the cost value (the values ​​of all verification nodes and the corresponding proportion of all edges).

[0073] In summary, this invention overcomes the limitations of existing technologies that only optimize the degree distribution of variable nodes under a single check node by jointly optimizing the degree distribution of variable nodes and check nodes. It fully considers the impact of irregular check nodes on the performance of LDPC code sets and significantly increases the degree of freedom in degree distribution optimization. On this basis, a novel cost function based on equalizer parameters is designed to accurately select the check node degree distribution and the corresponding variable node degree distribution that best fits the equalizer (i.e., minimizes the cost function). At the same time, by constructing an objective function, setting constraints, and introducing the optimal critical distance value verified by testing, it effectively ensures that the optimized degree distribution is highly adapted to the equalizer parameters, thereby achieving a comprehensive performance improvement in the communication system.

[0074] Secondly, this application provides an irregular LDPC code optimization device adapted to an equalizer, the irregular LDPC code optimization device comprising: an initialization unit, a first calculation unit, a search unit, a second calculation unit, an update unit, and a filtering unit; wherein, The system comprises the following components: an initialization unit, which sets the initial range of variable node degrees, the range of check node degrees, and the external mutual information of the equalizer; it selects at least two check node degrees within the check node degree range and sets the edge ratio of each check node degree to form an initial check node degree distribution; a first calculation unit, which calculates the variable node mutual information output by the variable node and the check node mutual information output by the two check node degrees under the current edge ratio, based on the external mutual information of the equalizer; a search unit, which sets the objective function, sets constraints based on the variable node mutual information and the check node mutual information, and iterates through the variable node degree distribution; a second calculation unit, which calculates the cost value of the cost function based on the variable node degree distribution, variable node mutual information, and check node mutual information; an update unit, which updates the edge ratio of the two check node degrees until the update cutoff condition is met, and calculates the cost value of the cost function output by the two check node degrees under all edge ratios and the corresponding variable node degree distribution; and a filtering unit, which outputs the variable node degree distribution and check node degree distribution corresponding to the edge ratio with the minimum cost value.

[0075] The functions of each module in the above-mentioned irregular LDPC code optimization device correspond to the steps in the above-mentioned irregular LDPC code optimization method embodiment, and their functions and implementation processes will not be described in detail here.

[0076] Thirdly, embodiments of this application provide a non-regular LDPC code optimization device, which can be a personal computer (PC), laptop computer, server, or other device with data processing capabilities.

[0077] Reference Figure 2, Figure 2 This is a schematic diagram of the hardware structure of the irregular LDPC code optimization device involved in the embodiments of this application. In this embodiment, the irregular LDPC code optimization device may include a processor, a memory, a communication interface, and a communication bus.

[0078] The communication bus can be of any type and is used to interconnect the processor, memory, and communication interface.

[0079] The communication interface includes input / output (I / O) interfaces, physical interfaces, and logical interfaces used for interconnecting devices within the irregular LDPC code optimization device, as well as interfaces used for interconnecting the irregular LDPC code optimization device with other devices (such as other computing devices or user equipment). Physical interfaces can be Ethernet interfaces, fiber optic interfaces, ATM interfaces, etc.; user equipment can be displays, keyboards, etc.

[0080] Memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical storage, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.

[0081] The processor can be a general-purpose processor, which can call the irregular LDPC code optimization program stored in memory and execute the irregular LDPC code optimization method provided in the embodiments of this application. For example, the general-purpose processor can be a central processing unit (CPU). The method executed when the irregular LDPC code optimization program is called can be referred to in the various embodiments of the irregular LDPC code optimization method of this application, and will not be repeated here.

[0082] Those skilled in the art will understand that Figure 2 The hardware structure shown does not constitute a limitation of this application and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0083] Fourthly, embodiments of this application also provide a computer-readable storage medium.

[0084] The present application stores a non-regular LDPC code optimization program on a computer-readable storage medium, wherein when the non-regular LDPC code optimization program is executed by a processor, it implements the steps of the non-regular LDPC code optimization method as described above.

[0085] The method implemented when the irregular LDPC code optimization procedure is executed can be referred to in various embodiments of the irregular LDPC code optimization method of this application, and will not be repeated here.

[0086] It should be noted that the sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0087] The terms "comprising" and "having," and any variations thereof, in the specification, claims, and accompanying drawings of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus. The terms "first," "second," and "third," etc., are used to distinguish different objects, etc., and do not indicate a sequence, nor do they limit "first," "second," and "third" to different types.

[0088] In the description of the embodiments of this application, terms such as "exemplary," "for example," or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "exemplary," "for example," or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary," "for example," or "for instance" is intended to present the relevant concepts in a concrete manner.

[0089] In the description of the embodiments of this application, unless otherwise stated, " / " means "or". For example, A / B can mean A or B. The "and / or" in the text is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist simultaneously, and B exists alone. In addition, in the description of the embodiments of this application, "multiple" means two or more.

[0090] In some processes described in the embodiments of this application, multiple operations or steps are included in a specific order. However, it should be understood that these operations or steps may not be executed in the order they appear in the embodiments of this application, or they may be executed in parallel. The sequence number of the operation is only used to distinguish different operations, and the sequence number itself does not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed sequentially or in parallel, and these operations or steps may be combined.

[0091] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device to execute the methods described in the various embodiments of this application.

[0092] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A method for optimizing irregular LDPC codes adapted to equalizers, characterized in that, The irregular LDPC code optimization method includes: Initial settings are made for the variable node degree range, the check node degree range, and the external mutual information of the equalizer. At least two check node degrees are selected within the check node degree range, and the proportion of edges under each check node degree value is preset to form the initial check node degree distribution. Calculate the variable node mutual information output by the variable node and the check node mutual information output by the two check nodes according to the current edge count ratio based on the external mutual information of the equalizer. Design the objective function and set constraints based on the mutual information of variable nodes and the mutual information of check nodes, and then traverse and search for the degree distribution of variable nodes that meet the conditions; The cost function is calculated based on the variable node degree distribution, variable node mutual information, and check node mutual information. Update the edge ratio of the degree of the two verification nodes until the update cutoff condition is met, and repeat the variable node degree distribution calculation and cost value calculation steps to obtain the output cost value and the corresponding variable node degree distribution under all edge ratios. Output the variable node degree distribution and the check node degree distribution that minimize the cost.

2. The irregular LDPC code optimization method as described in claim 1, characterized in that, The process of updating the edge ratio of the degree of the two verification nodes until the update cutoff condition is met includes: The ratio of the number of edges connected to two verifiable nodes under their respective degrees is updated according to a preset coefficient until the ratio of the number of edges of one verifiable node exceeds a preset threshold.

3. The irregular LDPC code optimization method as described in claim 2, characterized in that, The step of updating the ratio of the number of edges connected to the total number of edges for two verified nodes based on a preset coefficient includes: The degree of the selected first verification node is determined according to the formula. The percentage of edges and the degree of the second check node Update the percentage of edges: In the formula, The degree of the verification node is set to the degree of the first verification node. The proportion of updated edges to the total number of edges. The degree of the verification node is set to the degree of the first verification node. The proportion of edges before the update to the total number of edges. The degree of the verification node is set to the degree of the second verification node. The proportion of updated edges to the total number of edges. This is the preset step size parameter.

4. The irregular LDPC code optimization method as described in claim 1, characterized in that, The calculation of variable node mutual information output by variable nodes and check node mutual information output by two check nodes based on the external mutual information of the equalizer, respectively, includes: Calculate the output standard deviation of the equalizer based on the external mutual information in the mutual information of the equalizer; Calculate the mutual information of variable node outputs for all variable node degree values ​​based on the output standard deviation; The mutual information of two check nodes is calculated using the binary search method, taking into account the proportion of the degree of the two check nodes in the current number of edges.

5. The irregular LDPC code optimization method as described in claim 4, characterized in that, The step of calculating the mutual information of variable node outputs across all degree values ​​based on the output standard deviation includes: The variable node outputs mutual information when the i-th value is calculated according to the formula. : In the formula, For the variable, the node degree. To presuppose prior mutual information, This represents the output standard deviation of the equalizer.

6. The irregular LDPC code optimization method as described in claim 4, characterized in that, The method of calculating the mutual information of two check nodes using the bisection method with respect to the proportion of the current number of edges includes: When calculating the i-th value using the formula, verify the mutual information of the nodes. : In the formula, This is the proportion of edges connected to a check node at the degree of the first check node out of the total number of edges. This represents the proportion of edges connected to a check node at the degree of the second check node out of the total number of edges. To pre-set mutual information, The degree of the first verification node. This is the degree of the second verification node.

7. The irregular LDPC code optimization method as described in claim 1, characterized in that, The design objective function, based on the mutual information of variable nodes and the mutual information of verification nodes, sets constraints and then iterates to search for the degree distribution of variable nodes that meet the conditions, including: Set the target function: Set constraints according to the formula: In the formula, This represents the proportion of edges connected to node i out of the total number of edges. The degree of the first verification node. The degree of the second verification node. To verify the mutual information between nodes, This is the critical distance value. This represents the maximum value of the variable node; The system iterates through the constraints to find the degree distribution of the variable nodes that meet the conditions.

8. The irregular LDPC code optimization method as described in claim 1, characterized in that, The calculation of the cost function based on the variable node degree distribution, variable node mutual information, and check node mutual information includes: The EXIT function for variable nodes is obtained by fitting the variable node degree distribution and variable node mutual information pairs. The EXIT function of the check nodes is obtained by fitting the mutual information pairs of the check nodes. Design a cost function based on the EXIT function of the variable node and the EXIT function of the check node, and calculate the cost value of the cost function.

9. A non-regular LDPC code optimization device adapted to an equalizer, characterized in that, The irregular LDPC code optimization device includes: The initialization unit is used to initially set the range of variable node degree values, the range of check node degree values, and the external mutual information of the equalizer. It selects at least two check node degrees within the range of check node degree values ​​and sets the edge ratio of each check node degree to form the initial check node degree distribution. The first calculation unit is used to calculate the variable node mutual information output by the variable node and the check node mutual information output by the two check nodes according to the external mutual information of the equalizer. The search unit is used to set the objective function, set constraints based on the mutual information of variable nodes and the mutual information of check nodes, and traverse the degree distribution of search variable nodes. The second calculation unit is used to calculate the cost value of the cost function based on the variable node degree distribution, variable node mutual information, and verification node mutual information. The update unit is used to update the edge ratio of the degree of the two verification nodes until the update cutoff condition is met, and to calculate the cost value of the cost function output by the degree of the two verification nodes under all edge ratios and the corresponding variable node degree distribution. The filtering unit is used to output the variable node degree distribution and the check node degree distribution corresponding to the percentage of edges with the minimum cost.

10. A device for optimizing irregular LDPC codes adapted to an equalizer, characterized in that, The irregular LDPC code optimization device includes a processor, a memory, and an irregular LDPC code optimization program stored in the memory and executable by the processor, wherein when the irregular LDPC code optimization program is executed by the processor, it implements the steps of the irregular LDPC code optimization method as described in any one of claims 1 to 8.

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