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

Abstract

This application relates to the field of communication technology, specifically to an LDPC code optimization method adapted to an equalizer. The method first sets the degree range of variable nodes, the degree range of check nodes, and the external mutual information of the equalizer; then, it calculates the output mutual information of the variable nodes and check nodes based on the external mutual information of the equalizer; it designs an objective function and sets constraints, traversing and searching for variable node degree distributions that meet the conditions; it calculates the cost function value under different check node degrees; and finally outputs the variable node degree distribution with the minimum cost. This invention directly converts the external mutual information output by the equalizer into the output standard deviation, which serves as the prior input standard deviation of the LDPC code variable node external information. This realizes a new technical approach to optimizing LDPC code degree distribution based on equalizer parameters rather than channel state, providing an effective design basis for constructing an LDPC code parity-check matrix adapted to the equalizer, and solving the problem of "early closure or iterative divergence of the decoding channel" caused by traditional methods.

Description

Technical Field

[0001] This application relates to the field of communication technology, specifically to an LDPC code optimization method, apparatus, and device adapted to an equalizer. Background Technology

[0002] LDPC codes (Low-Density Parity-Check codes) have become a core error-correcting coding technique in modern communication systems due to their decoding performance, which approximates channel capacity, and their relatively low implementation complexity. Degree distribution has a significant impact on the performance of LDPC code sets, and degree distribution optimization is one of the most important research directions in LDPC code design.

[0003] In traditional designs, researchers have long focused on reducing the decoding threshold by optimizing the degree distribution. While this approach has achieved significant results in independent channel environments, the generated LDPC codes face a serious bottleneck when working in conjunction with an equalizer: the external mutual information output by the equalizer cannot effectively match the prior information input to the decoder, leading to premature closure of the decoding channel or divergence in the iteration process. This mismatch not only prevents the synergistic advantages of Turbo equalization from being realized but also significantly reduces the overall communication quality of the system. Therefore, how to construct an LDPC code degree distribution optimization method that adapts to the equalizer parameters has become a key technical challenge that urgently needs to be overcome. Summary of the Invention

[0004] In related technologies, the external mutual information output by the equalizer cannot be effectively matched with the prior information input to the decoder, leading to premature closure of the decoding channel or divergence in the iteration process.

[0005] In a first aspect, embodiments of this application provide an LDPC code optimization method adapted to an equalizer, the LDPC code optimization method comprising: Initialize the range of variable node degree values, the range of check node degree values, and the external mutual information of the equalizer. Calculate the variable node mutual information output by the variable nodes and the check node mutual information output by the check nodes based on the external mutual information of the equalizer. Design an objective function and set constraints based on the variable node mutual information and check node mutual information, then iterate through and search for variable node degree distributions that meet the conditions. Calculate the cost value of the cost function based on the variable node degree distribution, variable node mutual information, and check node mutual information. Update the check node degree values ​​until the range of check node degree values ​​is traversed, and repeat the variable node degree distribution calculation step and cost value calculation step to obtain the output cost value and corresponding variable node degree distribution for all check node degree values. Output the variable node degree distribution with the minimum cost value.

[0006] In conjunction with the first aspect, in one implementation, updating the degree value of the check node until the range of check node degree values ​​is traversed includes: The current value of the verification node degree is updated according to the preset coefficient until the verification node degree value is updated from the minimum value to the maximum value.

[0007] 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 check node based on the external mutual information of the equalizer 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 the check node is calculated based on the preset prior mutual information and output under the current check node value.

[0008] In conjunction with the first aspect, in one implementation, the step of calculating the variable node output mutual information based on the output standard deviation for all variable node degree values ​​includes: When calculating the i-th value according to the formula, the variable node outputs mutual information. :

[0009] In the formula, For the variable, the node degree. To presuppose prior mutual information, This represents the output standard deviation of the equalizer.

[0010] In conjunction with the first aspect, in one implementation, the step of calculating the check node mutual information output under all check node values ​​based on preset prior mutual information includes: The mutual information of the verification nodes is calculated using the formula. :

[0011] In the formula, To presuppose prior mutual information, To verify the degree of the node.

[0012] 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 meets the conditions, includes: Set the target function:

[0013] And set constraints according to the formula:

[0014]

[0015]

[0016] In the formula, For the variable, the node degree. For and degree is The proportion of edges connected to a variable node out of the total number of edges. To verify the mutual information between nodes, The mutual information output by the variable nodes. This is the critical distance value. This represents the maximum value of the node degree of the variable. The system iterates through the constraints to find the degree distribution of the variable nodes that meet the conditions.

[0017] In conjunction with the first aspect, in one implementation, the step of traversing and searching for the degree distribution of variable nodes that meet the constraints includes: The distribution of variable node degree is obtained by traversing and searching based on the constraints. :

[0018] In the formula, This represents the maximum value of the degree of the variable node. For the variable, the node degree. For and degree is The proportion of the number of edges connected to a variable node out of the total number of edges.

[0019] 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.

[0020] Secondly, this application provides an LDPC code optimization device adapted to an equalizer, the LDPC code optimization device comprising: an initialization unit, a first calculation unit, an optimization search unit, a second calculation unit, an update unit, and a filtering unit; wherein, The system comprises the following components: an initialization unit for setting the range of variable node degrees, the range of check node degrees, and the external mutual information of the equalizer; a first calculation unit for calculating the variable node mutual information output by the variable nodes and the check node mutual information output by the check nodes based on the external mutual information of the equalizer; an optimization search unit for designing the objective function and setting constraints based on the variable node mutual information and check node mutual information, then iterating through and searching for variable node degree distributions that meet the conditions; a second calculation unit for calculating 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 for updating the check node degree values ​​until the range of check node degree values ​​is traversed, and for repeatedly executing the variable node degree distribution calculation step and the cost value calculation step to obtain the output cost value and the corresponding variable node degree distribution for all check node degree values; and a filtering unit for outputting the variable node degree distribution and check node degree distribution with the minimum cost value.

[0021] Thirdly, embodiments of this application provide an LDPC code optimization device adapted to an equalizer. The LDPC code optimization device includes a processor, a memory, and an LDPC code optimization program stored in the memory and executable by the processor. When the LDPC code optimization program is executed by the processor, it implements the steps of the LDPC code optimization method as described in any of the preceding claims. The beneficial effects of the technical solutions provided in this application include: This application directly converts the external mutual information output by the equalizer into the output standard deviation as the prior input standard deviation for calculating the external information of LDPC code variable nodes, replacing the traditional channel variance. It explores a new approach that optimizes the degree distribution based on equalizer parameters, which is different from the traditional approach of optimizing LDPC code degree distribution based on channel state. This provides a design basis for constructing an LDPC code parity check matrix that adapts to the equalizer. Attached Figure Description

[0022] Figure 1 This is a flowchart illustrating an LDPC code optimization method in one embodiment of this application; Figure 2 This is a schematic diagram of the hardware structure of the LDPC code optimization device involved in the embodiments of this application. Detailed Implementation

[0023] 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.

[0024] In related technologies, the external mutual information output by the equalizer cannot be effectively matched with the prior information input to the decoder, leading to premature closure of the decoding channel or divergence in the iteration process.

[0025] Firstly, this application provides an LDPC code optimization method adapted to an equalizer, the LDPC code optimization method comprising: Step S1: Initialize the value range of variable node degree, the value range of check node degree, and the external mutual information of the equalizer.

[0026] Step S1 specifically includes: Step S1a: Initialize and set the maximum variable node degree. and the maximum check node degree

[0027] In one specific embodiment, the maximum variable node degree ≥2, maximum check node degree ≥2.

[0028] Step S1b, input mutual information pairs of the equalizer ( , ),in, It is the equalizer prior mutual information, where the first... Value . It is the external mutual information of the equalizer, where the first... Value .

[0029] Step S2: Calculate the variable node mutual information output by the variable node and the check node mutual information output by the check node based on the external mutual information of the equalizer.

[0030] Step S2 above includes: Step S2a: Calculate the output standard deviation of the equalizer based on the external mutual information of the equalizer.

[0031] Specifically, based on the external mutual information of the equalizer Calculate the output standard deviation of the equalizer , Where the i-th standard deviation The expression is:

[0032] In the formula, yes The inverse function of .

[0033] Step S2b: Calculate the mutual information of variable node outputs for all variable node degree values ​​based on the output standard deviation.

[0034] Specifically, step S2b includes: Step A: Set up prior mutual information , No. Value .

[0035] Step B: Calculate the degree of each variable node. Values ​​range from 2 to The variable node outputs mutual information at that time. Solve the first... Value The expression is:

[0036] In the formula, For the variable, the node degree. To presuppose prior mutual information, This represents the output standard deviation of the equalizer.

[0037] Step S2c: Calculate the mutual information of the check node output under the current check node value based on the preset prior mutual information.

[0038] Specifically, step S2c includes: Step A: Set up prior mutual information , No. Value .

[0039] Step B: Obtain the degree of the verification node Calculate the mutual information output by the verification node. , Verify node mutual information. The specific calculation formula is as follows:

[0040] In the formula, This refers to the prior mutual information preset in step A above. To verify the degree of the node.

[0041] Step S3: Design the objective function and set constraints based on the mutual information of variable nodes and the mutual information of verification nodes, and then traverse and search for the degree distribution of variable nodes that meet the conditions.

[0042] Specifically, step S3 includes: Step A: Design the objective function:

[0043] in, For and degree is The proportion of the number of edges connected to a variable node out of the total number of edges.

[0044] Step B: Set constraints according to the following formula:

[0045]

[0046]

[0047] In the formula, For the variable, the node degree. For and degree is The proportion of edges connected to a variable node out of the total number of edges. To verify the mutual information between nodes, The mutual information output by the variable nodes. This is the critical distance value. This represents the maximum value of the node degree.

[0048] Step C: Based on the constraints of the previous steps, traverse and search for the degree distribution of variable nodes that meet the conditions. .

[0049] 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.

[0050] Step S4 above includes: Step S4a: Obtain the EXIT function of the variable nodes by fitting the variable node degree distribution and variable node mutual information pair.

[0051] Specifically, based on the variable node degree distribution calculated in step S3 and the variable node output mutual information pair calculated in step S2b... The EXIT function of the variable node is obtained by fitting. .

[0052] Step S4b: Obtain the EXIT function of the verification node by fitting the mutual information pair of the verification nodes.

[0053] Specifically, based on the mutual information pairs of the verification nodes calculated in step S2c The fitting yielded a degree of The EXIT function of the verification node .

[0054] 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.

[0055] Specifically, the cost function is designed based on the EXIT function of the variable node and the EXIT function of the check node:

[0056] And calculate the cost function value (cost value).

[0057] Step S5: Update the value of the degree of the check node until the range of the degree value of the check node is traversed, and repeat the steps of calculating the degree distribution of the variable node and calculating the cost value to obtain the output cost value and the corresponding degree distribution of the variable node under all the degree values ​​of the check node.

[0058] Step S5 specifically includes: Step S5a: Update the current value of the check node degree according to the preset coefficient until the check node degree value is updated from the minimum value to the maximum value (i.e., traverse the range of check node degree values).

[0059] Step S5b: Repeat steps S2 to S4 for each degree value of the verification node to calculate the cost function value and the corresponding variable node degree distribution for each degree value of the verification node.

[0060] It is understandable that by designing a cost function, the degree of the verification node that best matches the equalizer (minimizes the cost function) and the corresponding variable node degree distribution can be further selected from all verification node degrees.

[0061] Step S6: Output the variable node degree distribution with the minimum cost and the corresponding check node degree distribution.

[0062] It is worth noting that the above steps are based on selecting the optimal degree distribution from all the values ​​of the above verification nodes according to the cost value.

[0063] In summary, this application directly converts the external mutual information output by the equalizer into the output standard deviation, which serves as the prior input standard deviation for calculating the external information of LDPC code variable nodes. This replaces the traditional channel variance, exploring a new approach that optimizes the degree distribution based on equalizer parameters—a different technical route from the traditional method of optimizing LDPC code degree distribution based on channel state. This provides a design basis for constructing an LDPC code parity-check matrix adapted to the equalizer. Furthermore, this application effectively ensures that the optimized degree distribution adapts as closely as possible to the equalizer parameters under the constraints of the current parity node degree and the parity-check matrix scale by carefully designing the objective function, setting constraints, and defining the optimal critical distance value after testing.

[0064] Secondly, this application provides an LDPC code optimization device adapted to an equalizer, the LDPC code optimization device comprising: an initialization unit, a first calculation unit, an optimization search unit, a second calculation unit, an update unit, and a filtering unit; wherein, The initialization unit is used to initially set the value range of variable node degrees, the value range of check node degrees, and the external mutual information of the equalizer; The first calculation unit calculates the mutual information of the variable nodes and the mutual information of the check nodes based on the external mutual information of the equalizer. The optimization search unit designs the objective function and sets constraints based on the mutual information of the variable nodes and the mutual information of the check nodes, then searches for the variable node degree distributions that meet the conditions. The second calculation unit calculates the cost value of the cost function based on the variable node degree distribution, the mutual information of the variable nodes, and the mutual information of the check nodes. The update unit updates the value of the check node degree until it traverses the range of check node degree values, and repeatedly executes the variable node degree distribution calculation step and the cost value calculation step to obtain the output cost value and the corresponding variable node degree distribution for all check node degree values. The filtering unit outputs the variable node degree distribution and the check node degree distribution with the minimum cost value.

[0065] The functions of each module in the LDPC code optimization device correspond to the steps in the LDPC code optimization method embodiment, and their functions and implementation processes will not be described in detail here.

[0066] Thirdly, embodiments of this application provide an LDPC code optimization device adapted to an equalizer. The LDPC code optimization device can be a personal computer (PC), laptop computer, server, or other device with data processing capabilities.

[0067] Reference Figure 2 , Figure 2This is a schematic diagram of the hardware structure of the LDPC code optimization device involved in the embodiments of this application. In the embodiments of this application, the LDPC code optimization device may include a processor, a memory, a communication interface, and a communication bus.

[0068] The communication bus can be of any type and is used to interconnect the processor, memory, and communication interface.

[0069] The communication interface includes input / output (I / O) interfaces, physical interfaces, and logical interfaces used for interconnecting devices within the LDPC code optimization device, as well as interfaces used for interconnecting the 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.

[0070] 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.

[0071] The processor can be a general-purpose processor, which can call the LDPC code optimization program stored in memory and execute the 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 LDPC code optimization program is called can be referred to in various embodiments of the LDPC code optimization method of this application, and will not be repeated here.

[0072] 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.

[0073] Fourthly, embodiments of this application also provide a computer-readable storage medium.

[0074] The present application provides a computer-readable storage medium storing an LDPC code optimization program, wherein when the LDPC code optimization program is executed by a processor, it implements the steps of the LDPC code optimization method described above.

[0075] The method implemented when the LDPC code optimization program is executed can be referred to in various embodiments of the LDPC code optimization method of this application, and will not be repeated here.

[0076] In summary, this invention directly converts the external mutual information output by the equalizer into the output standard deviation as the prior input standard deviation of the external information of the LDPC code variable nodes, replacing the traditional degree distribution optimization method based on channel variance. This effectively solves the technical bottlenecks such as premature closure of the decoding channel and divergence in the iteration process caused by the mutual information mismatch between the equalizer and decoder, providing a precise design basis for constructing an LDPC code parity check matrix adapted to the equalizer. At the same time, it ensures that the optimized degree distribution can adapt to the equalizer parameters to the greatest extent under the constraints of the degree of the parity check nodes and the scale of the parity check matrix, and selects the optimal solution that minimizes the cost function from all the degrees of the parity check nodes, thereby significantly improving the performance of the equalizer and decoder working together in the Turbo equalization communication system.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] 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. An LDPC code optimization method adapted to equalizers, characterized in that, The 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; Calculate the variable node mutual information output by the variable node and the check node mutual information output by the check node 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. The values ​​of the degree of the check nodes are updated until the range of the degree values ​​of the check nodes is traversed. The variable node degree distribution calculation step and the cost value calculation step are repeated to obtain the output cost value and the corresponding variable node degree distribution under all the values ​​of the check node degree. Output the variable node degree distribution with the minimum cost.

2. The LDPC code optimization method as described in claim 1, characterized in that, The process of updating the degree value of the verification node until the range of degree values ​​of the verification nodes is traversed includes: The current value of the verification node degree is updated according to the preset coefficient until the verification node degree value is updated from the minimum value to the maximum value.

3. The LDPC code optimization method as described in claim 1, characterized in that, The step of calculating the variable node mutual information output by the variable node and the check node mutual information output by the check node based on the external mutual information of the equalizer 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 the check node is calculated based on the preset prior mutual information and output under the current check node value.

4. The LDPC code optimization method as described in claim 3, characterized in that, The step of calculating the mutual information of variable node outputs based on the output standard deviation for all variable node degree values ​​includes: When calculating the i-th value according to the formula, the variable node outputs mutual information. : In the formula, For the variable, the node degree. To presuppose prior mutual information, This represents the output standard deviation of the equalizer.

5. The LDPC code optimization method as described in claim 3, characterized in that, The step of calculating the mutual information of the check node based on the preset prior mutual information and outputting the mutual information of the check node under the current value of the check node includes: The mutual information of the verification nodes is calculated using the formula. : In the formula, To presuppose prior mutual information, To verify the degree of the node.

6. The 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: And set constraints according to the formula: In the formula, For the variable, the node degree. For and degree is The proportion of edges connected to a variable node out of the total number of edges. To verify the mutual information between nodes, The mutual information output by the variable nodes. This is the critical distance value. This represents the maximum value of the node degree of the variable. The system iterates through the constraints to find the degree distribution of the variable nodes that meet the conditions.

7. The LDPC code optimization method as described in claim 6, characterized in that, The step of traversing and searching for the degree distribution of variable nodes that meet the constraints includes: The distribution of variable node degree is obtained by traversing and searching based on the constraints. : In the formula, This represents the maximum value of the degree of the variable node. For the variable, the node degree. For and degree is The proportion of the number of edges connected to a variable node out of the total number of edges.

8. The 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. An LDPC code optimization device adapted to an equalizer, characterized in that, The LDPC code optimization device includes: The initialization unit is used to initially set the value range of variable node degrees, the value range of check node degrees, and the external mutual information of the equalizer; 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 check node based on the external mutual information of the equalizer. The optimization search unit is used to design the objective function and set constraints based on the mutual information of variable nodes and the mutual information of check nodes, and then traverses and searches for the degree distribution of variable nodes that meet the conditions. 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 value of the degree of the check node until the range of the degree value of the check node is traversed, and is used to repeatedly execute the variable node degree distribution calculation step and the cost value calculation step to obtain the output cost value and the corresponding variable node degree distribution under all check node degree values. The filtering unit is used to output the variable node degree distribution and the check node degree distribution with the minimum cost.

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

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