Neighbor set-based multivariate LDPC code EMS decoding method and device

Abstract

The invention relates to the technical field of communication, in particular to a multivariate LDPC code EMS decoding method and device based on a neighbor set, and the method comprises the steps: calculating log-likelihood ratio (LLR) information according to a channel receiving value, and initializing a variable node and a check node; constructing a neighbor set based on the reliability sequence of check node input information and a Hamming distance relationship, and carrying out truncation processing on the input information; performing information updating on the check nodes according to the check node updating rule; updating the variable node information according to a variable node updating rule; calculating posterior probability information according to a variable node updating result to perform decoding judgment, and selecting to output a code word or reconstruct a neighbor set to update a check node according to the judgment whether the code word meets a decoding termination condition; based on the decoding method provided by the invention, good compromise between decoding performance and decoding complexity can be realized, the decoding complexity can be reduced, meanwhile, the performance of standard EMS decoding is almost kept, and the decoding method is suitable for multivariate LDPC code decoding in a high-order finite field.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and in particular to a method and device for EMS decoding of a multi-element LDPC code based on a neighboring set, which can realize an efficient channel decoding method and device in a satellite communication system, a wireless local area network and a cellular mobile communication system. Background Art

[0002] In recent years, NB-LDPC codes have attracted the attention of researchers due to their good performance and natural applicability to high-order modulation and multi-channels. The research on NB-LDPC codes was initiated by Davey and Mackay, who thought of The LDPC codes defined above are extended with the Sum-Product Algorithm (SPA) for q>2, usually called q-ary SPA (QSPA). NB-LDPC codes also have better performance in the medium and short code length fields because they can avoid the error floor problem.

[0003] Compared with binary LDPC codes, NB-LDPC codes can provide higher coding gain and stronger error correction capability. However, due to the high computational complexity of directly implementing the QSPA algorithm, NB-LDPC codes are difficult to apply in practice. In practical applications, in order to reduce decoding complexity and improve decoding efficiency, various improved decoding algorithms continue to emerge. Among them, the decoding algorithm based on extended minimum sum (EMS) has received widespread attention because of its good balance between performance and complexity.

[0004] Although the existing technology has proposed that the EMS algorithm has certain advantages in NB-LDPC decoding, it still faces the problem of excessive computation when processing large-scale data or in scenarios with high real-time requirements. This is mainly because in the decoding process, a large amount of information updates and iterative calculations need to be processed, resulting in excessive decoding time and excessive resource consumption. Summary of the invention

[0005] The object of the present invention is to provide a method and device for EMS decoding of multi-dimensional LDPC codes based on neighbor sets to solve the problems raised in the above background technology.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: A neighbor set-based EMS decoding method for multi-element LDPC codes, the method comprising: S1. Calculate the log-likelihood ratio information according to the channel reception value and initialize the variable nodes and check nodes; S2, replace the variable node information according to the check matrix H; S3, sort the reliability of the input information vector of the check node, and construct the proximity matrix P and the proximity set based on the reliability sorting and the Hamming distance relationship , truncate the input information according to the truncation rule; S4, according to the check node information update rule, update the check node information, calculate the check node external information and perform corresponding inverse permutation; S5. Update the variable node information according to the variable node information update rule; S6. Calculate the posterior probability information based on the updated variable node information to make a judgment, output the decoding result, and use the verification relationship to determine whether the decoding result is a codeword; if the decoding termination condition is met, the decoding ends and the decoding result is output, otherwise execute S2-S6 for the next iteration.

[0007] Preferably, S1 includes: S101. In finite fields In the channel information formula, the log-likelihood ratio information of the channel reception value y is calculated: ; in, Indicates the LLR information corresponding to the i-th symbol received by the channel; represents the jth bit of the i-th symbol received by the channel; Finite field symbol The jth bit of Among them, the finite field of multivariate LDPC code , then for a finite field element The corresponding binary bit sequence length p satisfies ; Preferably, in S101, the multivariate LDPC code applicable to the decoding method is -LDPC code; S102: Initialize the variable nodes and the check nodes according to the following rules: ; ; in, Represented by variable nodes To the verification node Vector information of , i.e. variable node information; Indicates that the check node To the variable node The vector information, i.e. the verification node information; Indicates that the channel receives the LLR information corresponding to the i-th symbol.

[0008] Preferably, S2 includes: According to the formula: ; Replace the variable node information; In step S4, according to the formula: , perform inverse permutation on the check node information; The multiplication in the above formula is finite field multiplication; the execution of the above steps is to optimize the information propagation in the EMS decoding process, improve the overall performance of the algorithm, reduce the bit error rate, and enhance the robustness of the decoding.

[0009] Preferably, S3 includes: S301, based on variable nodes To the verification node The input information vector , given the decoding parameters , use the sorting algorithm to obtain the largest information vector in turn The GF(q) field element corresponding to the components ; These index values Constructing a sorted index set ; S302, constructing a proximity matrix P based on the Hamming distance relationship: Get finite field elements The corresponding neighboring symbol vector , where the neighboring symbol vector Contains all finite field elements Finite field symbols whose Hamming distance is 1 in binary representation; vectors of neighboring symbols The set of all elements in is called the adjacent symbol set ; Set any finite field element , using a bit sequence of length p To indicate that , then the adjacent symbol set The following relations are satisfied: ; Among them, the adjacent symbol set Contains p elements, then the adjacent symbol set The corresponding neighboring symbol vector The size is , the size of the proximity matrix P is ; The proximity matrix P is obtained by all finite field elements The corresponding neighboring symbol vector Composition, that is ; Where the first column of the neighbor matrix P corresponds to the neighboring symbol set of element 0 , the second column corresponds to the set of neighboring symbols of element 1 , and so on, the qth column corresponds to the element The adjacent symbol set ; S303, according to the formula: , for the sorted index set and the corresponding neighboring symbol set in the neighboring matrix P Take the union and get the adjacent set ; Given the neighboring sets constructed by different check nodes The length is not necessarily fixed. To facilitate hardware implementation, the length is Truncation operation :If the neighboring set The length is greater than Only the previous elements, if the adjacent set The length is less than Then fill the tail with 0; S304: According to the neighboring set and truncation rules for variable nodes To the verification node The input information vector To truncate: ; in , Represents a neighboring set.

[0010] Preferably, the check node information update rule in S4 includes: S401, define two vectors respectively and is the forward iteration vector and the backward iteration vector; S402, forward iteration process: suppose , represents the initial forward iteration vector, let represents the degree of the i-th check node, that is, the number of non-zero values ​​in the i-th row of the check matrix H. , iterative calculation: ; S403, backward iteration process: suppose , represents the degree of the i-th check node. , iterative calculation: ; S404, external information extraction: for , calculate the output external information of the check node: ; in, represents the updated external information of the check node; the formula shows that the maximum component is selected as the update result from the obtained log-likelihood ratio information with the same sign.

[0011] Preferably, the variable node information update rule in S5 includes: S501, according to the formula: ; in, Represents a finite field element LLR information; is the first The index set of non-zero values ​​in the row; S502, As the variable node update result, the posterior probability information of the variable node is calculated according to the variable node update result: ; Then the posterior probability information The finite field symbol element corresponding to the maximum component in It is output as a decision codeword, that is, .

[0012] A multi-element LDPC code EMS decoding device based on neighboring sets, the device comprising: an initialization module, a construction and truncation module, a check node update module, a variable node update module and a decoding decision module; The initialization module is used to calculate the log-likelihood ratio information according to the channel reception value, initialize the variable node and the check node information, and set the current number of iterations. ; The construction and truncation module is used to sort the reliability of the input information vector of the check node, construct a neighboring set based on the reliability sorting and the Hamming distance relationship, and truncate the input information based on the truncation rule; The check node update module is used to update the check node information according to the check node update rule; The variable node updating module is used to update the information of the variable node according to the updating rule of the variable node; The decoding decision module is used to calculate the posterior probability information based on the updated variable node information, output the decoding result, and use the verification relationship to determine whether the decoding result is a codeword. If the decoding termination condition is met, the decoding ends and the decoding result is output, otherwise the next iteration is performed. .

[0013] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps in the above-mentioned neighbor set-based multi-element LDPC code EMS decoding method.

[0014] A computer device comprises a memory, a processor and a computer program stored in the memory and running on the processor. When the processor executes the program, the steps in the above-mentioned EMS decoding method of a multi-element LDPC code based on a neighboring set are implemented.

[0015] Compared with the prior art, the beneficial effects achieved by the present invention are: Aiming at the problem of high complexity of multi-element LDPC code decoding algorithm, the present invention proposes a low-complexity decoding scheme of multi-element LDPC code based on EMS. The multivariate LDPC code on the check node information vector is sorted , the cut-off length is Under the premise of , further reducing the need for sorting If in a finite field On, configure , which can achieve near-optimal decoding performance and reduce the complexity to that of the standard EMS decoding algorithm. times, effectively improving its practical application competitiveness; The present invention performs truncation processing on the check node information based on the decoding of EMS to ensure that the neighboring set The length is fixed at , which simplifies the logical structure of storing and processing data in hardware and improves the efficiency and stability of hardware implementation. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1 A flowchart of a method for EMS decoding of a multi-element LDPC code based on a neighboring set according to an embodiment of the present invention; Figure 2 A schematic diagram of constructing a proximity matrix P according to an embodiment of the present invention; Figure 3 A neighboring set of an embodiment of the present invention Schematic diagram of the construction; Figure 4 A performance comparison diagram of a decoding method according to an embodiment of the present invention and an EMS decoding method; Figure 5 A diagram comparing the complexity of a decoding method according to an embodiment of the present invention and an EMS decoding method; Figure 6 The block diagram of a neighbor set-based EMS decoding device for a multi-element LDPC code according to an embodiment of the present invention. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] See also Figure 1-Figure 6 , the present invention provides a technical solution: Embodiment 1: Embodiments of the present invention are directed to The code length is 160 symbols, the code rate is 0.5, and the check matrix is , check node degree and variable node degree The NB-LDPC codes with 6 and 3 are used to iteratively decode the multivariate LDPC using the extended minimum sum (EMS) algorithm based on the truncation rule, and the maximum number of decoding iterations is set. .

[0019] Reference Figure 1 , the implementation steps of the present invention are as follows: Step 1: In a finite field In the channel information formula, the log-likelihood ratio information corresponding to each symbol in the channel reception is calculated, and the size is : ; in Indicates the LLR information corresponding to the i-th symbol received by the channel; represents the jth bit of the i-th symbol received by the channel; Finite field symbol The jth bit of The corresponding binary bit sequence length p satisfies: , ; Preferably, in S101, the multivariate LDPC code applicable to the decoding method is -LDPC code; Then the channel information , as the reliability value vector corresponding to each bit symbol received by the channel, to initialize the check node information and variable node information, and set the number of iterations to ; ; ; in It is the variable node information, indicating that the variable node To the verification node Vector information of To verify the node information.

[0020] Step 2: According to the check matrix Input information to the verification node To perform a replacement: ; Perform inverse permutation on the variable node input information: ; The multiplication in the above formula is finite field multiplication.

[0021] Step 3: sort the reliability of the input information vector of the check node, and construct the proximity matrix P and the proximity set based on the reliability sorting and the Hamming distance relationship. , based on the truncation rule, the input information is fixed in length Truncation on : First, the input information vector is sorted. elements are sorted as a whole. Bubble sort algorithm or other algorithms can be used to obtain the largest elements in the information vector one by one. The GF(q) field element corresponding to the components , these index values Constructing a sorted index set ; Secondly, the proximity matrix is ​​constructed. The proximity matrix is ​​constructed based on the Hamming distance relationship between symbols. Above, any symbol It can be expressed as a length of The bit sequence ,in , the element The adjacent symbol set The following relations are satisfied: ; We can see that finite fields The size of the proximity matrix P constructed above is . Figure 2 Given the definition in Schematic diagram of the proximity matrix P constructed above.

[0022] Figure 3 Given a finite field The neighboring set The schematic diagram of the construction is shown in the figure, and the size relationship of the reliability is indicated in the figure, where Figure 3The numbers in brackets indicate the reliability ranking, and the smaller the number, the higher the reliability.

[0023] The number of sorting required in the embodiment , sorted in descending order by a certain check node information vector For example, find The corresponding adjacent symbol set , sort the index set and adjacent symbol sets Take the union to get the adjacent set : ; Given the neighboring sets constructed by different check nodes The length is not necessarily fixed. To facilitate hardware implementation, the length is Truncation operation :If the neighboring set The length is greater than Only the previous elements, if the adjacent set The length is less than Then fill the tail with 0. In the embodiment, the truncation length is set , Figure 3 It is also given that in a finite field A schematic diagram of performing a truncation operation on a proximity matrix P is shown, and the relationship between the reliability levels is indicated in the figure.

[0024] Third, the input information of the verification node is truncated. Update variable node To the verification node The input information vector Truncation is performed, and the truncation rules are as follows: ; in .

[0025] Step 4: Update the check nodes according to the check node update rules. Update the information and extract the external information: Define two vectors respectively and is the forward iteration vector and the backward iteration vector, and its calculation process is as follows: Forward iterative process: set up , represents the initial forward iteration vector, let represents the degree of the i-th check node, that is, the number of non-zero values ​​in the i-th row of the check matrix H. , iterative calculation: ; Backward iterative process: set up ,set up represents the degree of the i-th check node. , iterative calculation: ; External information extraction: for , calculate the output external information of the check node: ; in Represents the updated check node information. The formula shows that the log-likelihood ratio with the same sign is selected as the update result.

[0026] Step 5: Update variable node information according to variable node update rules :

[0027] in, Represents a finite field element LLR information; is the first The index set of non-zero values ​​in the row. The result of the above formula calculation As the variable node update result, the posterior probability information of the variable node is calculated according to the variable node update result: ; The posterior probability information The finite field symbol element corresponding to the maximum component in It is output as a decision codeword, that is, .

[0028] An exemplary embodiment is executed to simulate the decoding algorithm. Figure 4 , Figure 5 The performance comparison diagram and complexity comparison diagram of the decoding scheme of the present invention and the existing decoding scheme are given.

[0029] 1. Simulation conditions: The embodiment of the present invention is directed to The code length is 160 symbols and the code rate is , using the PEG algorithm to construct the check matrix , check node degree , variable node degree NB-LDPC code, using the extended minimum sum (EMS) algorithm based on the truncation rule to iteratively decode the multivariate LDPC, setting the sorting length , truncation length , maximum number of decoding iterations .

[0030] 2. Simulation content and results analysis: The simulation experiment of the present invention compares and analyzes the performance of the EMS algorithm of the present invention and the EMS decoding method, and the following results are obtained: Figure 4 As shown in the comparative simulation diagram, it can be seen that the EMS algorithm of the present invention has the same excellent error correction performance as the EMS decoding method under all signal-to-noise ratios (1, 1.5, 2, 2.5).

[0031] at the same time, Figure 5 The comparative simulation of algorithm complexity is shown. Taking the standard EMS algorithm as the benchmark, the total number of operations (ACS) of the decoding method of the present invention under an average of one frame of complete iterative decoding is used as an analysis indicator to measure the complexity. Under all signal-to-noise ratios, the complexity ratio is only As can be seen from the two figures, the EMS decoding method and device for multi-element LDPC codes based on neighbor sets of the present invention can greatly reduce the computational complexity while achieving near-optimal decoding performance, thereby effectively improving its practical application competitiveness.

[0032] Embodiment 2: Figure 6 FIG. 1 is a block diagram of a device for decoding EMS of a multi-element LDPC code based on a neighboring set according to an exemplary embodiment. Figure 6 The device 600 includes an initialization module 610, a construction and truncation module 620, a check node update module 630, a variable node update module 640 and a decoding decision module 650, wherein: Initialization module 610 is used to calculate the log likelihood ratio (LLR) information according to the channel acceptance value, initialize the variable node and the check node information, and set the current iteration number. ; A truncation module 620 is constructed to sort the check node input information vectors and construct a neighboring set based on the reliability sorting and the Hamming distance relationship. , and truncate the input information; A check node update module 630, configured to update information of the check nodes according to a check node update rule; The variable node update module 640 is used to update the information of the variable node according to the check node update rule; The decoding decision module 650 is used to calculate the posterior probability information according to the updated variable node information and make a decision, and output the decoding result, that is, , use the check relationship to determine whether it is a codeword. If the decoding termination condition is met, the decoding ends and the decoding result is output, otherwise the next round of iteration is performed ; The division of modules is illustrated by way of example. In practical applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0033] Embodiment 3: The computer-readable storage medium of this embodiment stores a computer program thereon, and when the program is executed by a processor, the steps in the neighboring set-based EMS decoding method of a multi-dimensional LDPC code in Embodiment 1 are implemented.

[0034] The computer-readable storage medium of this embodiment may be an internal storage unit of the terminal, such as a hard disk or memory of the terminal; the computer-readable storage medium of this embodiment may also be an external storage device of the terminal, such as a plug-in hard disk, a smart memory card, a secure digital card, a flash memory card, etc. equipped on the terminal; further, the computer-readable storage medium may also include both an internal storage unit of the terminal and an external storage device.

[0035] The computer-readable storage medium of this embodiment is used to store computer programs and other programs and data required by the terminal. The computer-readable storage medium can also be used to temporarily store data that has been output or is to be output.

[0036] Embodiment 4: The computer device of this embodiment includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps in the neighboring set-based multi-dimensional LDPC code EMS decoding method of Embodiment 1 are implemented.

[0037] In this embodiment, the processor may be a central processing unit, or other general-purpose processors, digital signal processors, application-specific integrated circuits, readily available programmable gate arrays or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The memory may include a read-only memory and a random access memory, and provide instructions and data to the processor. A portion of the memory may also include a non-volatile random access memory. For example, the memory may also store information about the device type.

[0038] Those skilled in the art will appreciate that the contents disclosed in the embodiments may be provided as methods, systems, or computer program products. Therefore, the present solution may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware. Moreover, the present solution may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) containing computer-usable program codes.

[0039] The present solution is described with reference to the method according to the embodiment of the present solution and the flowchart and / or block diagram of the computer program product. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions; these computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 one or more processes and / or methods Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0040] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 one or more processes and / or methods Figure 1 A function specified in one or more boxes.

[0041] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 one or more processes and / or methods Figure 1The steps for the functions specified in one or more boxes.

[0042] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing related hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0043] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A neighbor set based EMS decoding method for multi-element LDPC codes, characterized in that: The method comprises: S1. Calculate the log-likelihood ratio information according to the channel reception value and initialize the variable nodes and check nodes; S2, replace the variable node information according to the check matrix H; S3, sort the reliability of the input information vector of the check node, and construct the proximity matrix P and the proximity set based on the reliability sorting and the Hamming distance relationship , truncate the input information according to the truncation rule; S4, according to the check node information update rule, update the check node information, calculate the check node external information and perform corresponding inverse permutation; S5. Update the variable node information according to the variable node information update rule; S6. Calculate the posterior probability information based on the updated variable node information to make a judgment, output the decoding result, and use the verification relationship to determine whether the decoding result is a codeword; if the decoding termination condition is met, the decoding ends and the decoding result is output, otherwise execute S2-S6 for the next iteration.

2. The EMS decoding method for multi-element LDPC codes based on neighbor sets as claimed in claim 1, characterized in that: Said S1 comprises: S101. In finite fields In the channel information formula, the log-likelihood ratio information of the channel reception value y is calculated: ； in, Indicates the LLR information corresponding to the i-th symbol received by the channel; represents the jth bit of the i-th symbol received by the channel; Finite field symbol The jth bit of Among them, the finite field of multivariate LDPC code , then for a finite field element The corresponding binary bit sequence length p satisfies ; S102: Initialize the variable nodes and the check nodes according to the following rules: ； ； in, Represented by variable nodes To the verification node Vector information of , i.e. variable node information; Indicates that the check node To the variable node The vector information, i.e. the verification node information; Indicates that the channel receives the LLR information corresponding to the i-th symbol.

3. The EMS decoding method for multi-element LDPC codes based on neighbor sets as claimed in claim 2, characterized in that: The multivariate LDPC code in S101 is -LDPC code.

4. The EMS decoding method for multi-element LDPC codes based on neighbor sets as claimed in claim 1, characterized in that: The S3 includes: S301, based on variable nodes To the verification node The input information vector , given the decoding parameters , use the sorting algorithm to obtain the largest information vector in turn The GF(q) field element corresponding to the components ; These index values Constructing a sorted index set ; S302, constructing a proximity matrix P based on the Hamming distance relationship: Get finite field elements The corresponding neighboring symbol vector , where the neighboring symbol vector Contains all finite field elements Finite field symbols whose Hamming distance is 1 in binary representation; vectors of neighboring symbols The set of all elements in is called the adjacent symbol set ; Set any finite field element , using a bit sequence of length p To indicate that , then the adjacent symbol set The following relations are satisfied: ; Among them, the adjacent symbol set Contains p elements, then the adjacent symbol set The corresponding neighboring symbol vector The size is , the size of the proximity matrix P is ; The proximity matrix P is obtained by all finite field elements The corresponding neighboring symbol vector Composition, that is ; Where the first column of the neighbor matrix P corresponds to the neighboring symbol set of element 0 , the second column corresponds to the set of neighboring symbols of element 1 , and so on, the qth column corresponds to the element The adjacent symbol set ; S303, according to the formula: , for the sorted index set and the corresponding neighboring symbol set in the neighboring matrix P Take the union and get the adjacent set ; S304: According to the neighboring set and truncation rules for variable nodes To the verification node The input information vector To truncate: ; in , Represents a neighboring set.

5. The EMS decoding method for multi-element LDPC codes based on neighbor sets as claimed in claim 1, characterized in that: The check node information update rule in S4 includes: S401, define two vectors respectively and is the forward iteration vector and the backward iteration vector; S402, forward iteration process: suppose , represents the initial forward iteration vector, let represents the degree of the i-th check node, that is, the number of non-zero values ​​in the i-th row of the check matrix H. , iterative calculation: ； S403, backward iteration process: suppose , represents the degree of the i-th check node, for , iterative calculation: ； S404, external information extraction: for , calculate the output external information of the check node: ； in, represents the updated external information of the check node; the formula shows that the maximum component is selected as the update result from the obtained log-likelihood ratio information with the same sign.

6. The EMS decoding method for multi-element LDPC codes based on neighbor sets as claimed in claim 1, characterized in that: The variable node information update rules in S5 include: S501, according to the formula: ; in, Represents a finite field element LLR information; is the first The index set of non-zero values ​​in the row; S502, As the variable node update result, the posterior probability information of the variable node is calculated according to the variable node update result: ; Then the posterior probability information The finite field symbol element corresponding to the maximum component in It is output as a decision codeword, that is, .

7. A neighbor set-based EMS decoding device for multi-element LDPC codes, characterized in that: The device comprises: an initialization module, a construction and truncation module, a check node update module, a variable node update module and a decoding and judgment module; The initialization module is used to calculate the log-likelihood ratio information according to the channel reception value, initialize the variable node and the check node information, and set the current number of iterations. ; The construction and truncation module is used to sort the reliability of the input information vector of the check node, construct a neighboring set based on the reliability sorting and the Hamming distance relationship, and truncate the input information based on the truncation rule; The check node update module is used to update the check node information according to the check node update rule; The variable node updating module is used to update the information of the variable node according to the updating rule of the variable node; The decoding decision module is used to calculate the posterior probability information according to the updated variable node information, output the decoding result, and use the verification relationship to determine whether the decoding result is a codeword; if the decoding termination condition is met, the decoding ends and the decoding result is output, otherwise the next iteration is performed. .

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps in the EMS decoding method of a multi-element LDPC code based on a neighboring set as described in any one of claims 1 to 5 are implemented.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the program, the steps in the EMS decoding method of a multi-element LDPC code based on a neighbor set as described in any one of claims 1 to 5 are implemented.

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