A Method and Device for EMS Decoding of Multi - variable LDPC Codes Based on Proximity Sets

Abstract

The present invention relates to the field of communication technologies, and specifically to a method and apparatus for EMS decoding of a multi - ary LDPC code based on a neighboring set, including: calculating log - likelihood ratio (LLR) information according to the channel received value and initializing variable nodes and check nodes; constructing a neighboring set based on the reliability sorting of the input information of the check nodes and the Hamming distance relationship, and performing truncation processing on the input information; updating the information of the check nodes according to the check node update rule; updating the variable node information according to the variable node update rule; calculating the posterior probability information according to the variable node update result for decoding decision, and selecting an output codeword or reconstructing the neighboring set to update the check nodes according to whether the decoded codeword meets the decoding termination condition; based on the decoding method of the present invention, a good compromise can be achieved between decoding performance and decoding complexity, which can reduce the decoding complexity while almost maintaining the performance of the standard EMS decoding, and is applicable to the decoding of multi - ary LDPC codes in a high - order finite field.

Description

Technical Field

[0001] The present invention relates to the field of communication technologies, and specifically provides a method and device for EMS decoding of a multi - ary LDPC code based on a neighboring set, which can implement an efficient channel decoding method and device in satellite communication systems, wireless local area networks, and cellular mobile communication systems. Background Art

[0002] In recent years, multi - ary low - density parity - check (NB - LDPC) codes have attracted the attention of researchers due to their good performance and natural applicability to higher - order modulation and multi - ary channels. The research on NB - LDPC codes was initiated by Davey and Mackay, who thought of LDPC codes defined on and proposed an extension of the sum - product algorithm (SPA) for q>2, commonly known as the q - ary SPA (QSPA). NB - LDPC codes can also avoid the error - floor problem, so they have better performance in the field of medium and short code lengths.

[0003] Compared with binary LDPC codes, NB - LDPC codes can provide higher coding gain and stronger error - correction ability. However, due to the excessively high computational complexity of directly implementing the QSPA algorithm, it is difficult to apply NB - LDPC codes in practice. In practical applications, in order to reduce the decoding complexity and improve the decoding efficiency, various improved decoding algorithms have emerged. Among them, the decoding algorithm based on extended min - sum (EMS) has received wide 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, in scenarios where large - scale data is processed or high real - time requirements are imposed, it still faces the problem of excessive computational amount. This is mainly because during the decoding process, a large amount of information update and iterative calculation need to be processed, resulting in too long decoding time and excessive resource consumption. Summary of the Invention

[0005] The purpose of the present invention is to provide a method and device for EMS decoding of a multi - ary LDPC code based on a neighboring set to solve the problems raised in the above background art.

[0006] To solve the above - mentioned technical problems, the present invention provides the following technical solutions:

[0007] A method for EMS decoding of a multi - ary LDPC code based on a neighboring set, the method comprising:

[0008] S1. Calculate the log - likelihood ratio information according to the channel received value, and initialize the variable nodes and check nodes;

[0009] S2. Permute the variable node information according to the parity - check matrix H;

[0010] S3. Perform reliability sorting on the input information vector of the check node, and construct the proximity matrix P and the proximity set based on this reliability sorting and the Hamming distance relationship , and truncate the input information according to the truncation rule;

[0011] S4. According to the check node information update rule, update the information of the check node, calculate the extrinsic information of the check node, and perform the corresponding inverse permutation;

[0012] S5. According to the variable node information update rule, update the variable node information;

[0013] S6. Calculate the posterior probability information based on the updated variable node information for decision-making, output the decoding result, and use the parity-check relationship to determine whether the decoding result is a codeword; if the decoding termination condition is satisfied, the decoding ends and the decoding result is output, otherwise, execute S2 - S6 for the next iteration.

[0014] Preferably, S1 includes:

[0015] S101. In the finite field , use the channel information formula to calculate the log-likelihood ratio information for the channel received value y:

[0016] ;

[0017] where, represents the LLR information corresponding to the i-th symbol received by the channel; represents the j-th bit of the i-th symbol received by the channel; represents the j-th bit of the finite field symbol ;

[0018] where, for the finite field of the multi - variable LDPC code, for the finite field element , the length p of the corresponding binary bit sequence satisfies ;

[0019] Preferably, in S101, the multi - variable LDPC code applicable to the decoding method is -LDPC code;

[0020] S102. Initialize the variable nodes and the check nodes, and perform according to the following rules:

[0021] ;

[0022] ;

[0023] where, represents the variable node to the check node vector information, i.e., variable node information; represents the vector information from the check node to the variable node vector information, i.e., check quantity node information; represents the LLR information corresponding to the i-th symbol received by the channel.

[0024] Preferably, S2 includes:

[0025] According to the formula: ; permute the variable node information;

[0026] In step S4, according to the formula: , inverse permute the check node information;

[0027] 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 decoding.

[0028] Preferably, S3 includes:

[0029] S301. Based on the input information vector from the variable node to the check node , given the decoding parameter , use the sorting algorithm to sequentially obtain the GF(q)-domain elements corresponding to the largest components in the information vector ;

[0030] From these index values form a sorted index set ;

[0031] S302. Construct the proximity matrix P based on the Hamming distance relationship:

[0032] Obtain the proximity symbol vector corresponding to the finite field element , where the proximity symbol vector contains all finite field symbols with a Hamming distance of 1 from the finite field element in binary representation; the set of all elements in the proximity symbol vector is called the proximity symbol set ;

[0033] Set any finite field element , represented by a bit sequence of length p, where , then the proximity symbol set Satisfy the following relationship: ;

[0034] Wherein, the adjacent symbol set contains p elements, then the adjacent symbol vector corresponding to the adjacent symbol set has a size of , and the size of the adjacent matrix P is ;

[0035] The adjacent matrix P is obtained from all the adjacent symbol vectors corresponding to the finite field elements , that is ; wherein, the first column of the adjacent matrix P corresponds to the adjacent symbol set of the element 0, the second column corresponds to the adjacent symbol set of the element 1, and so on, and the q-th column corresponds to the adjacent symbol set of the element ;

[0036] S303. According to the formula: , take the union of the sorted index set and the corresponding adjacent symbol set in the adjacent matrix P to obtain the adjacent set ;

[0037] In view of the fact that the lengths of the adjacent sets constructed by different check nodes are not necessarily fixed, for the convenience of hardware implementation, perform a truncation operation of length : If the length of the adjacent set is greater than , only keep the first elements. If the length of the adjacent set is less than , pad with 0 at the end;

[0038] S304. According to the said adjacent set and the truncation rule, truncate the input information vector from the variable node to the check node : ; ;

[0039] Wherein , represents the adjacent set.

[0040] Preferably, the check node information update rule in S4 includes:

[0041] S401. Define two vectors and are the forward iteration vector and the backward iteration vector;

[0042] S402. Forward iteration process: Let represent the initial forward iteration vector, and let represent 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. For , perform iterative calculation:

[0043] ;

[0044] S403. Backward iteration process: Let , represent the degree of the i-th check node. For , perform iterative calculation:

[0045] ;

[0046] S404. Extracting extrinsic information: For , calculate the output extrinsic information of the check node:

[0047] ;

[0048] Among them, represents the updated extrinsic information of the check node; the formula indicates that the maximum component is selected as the update result from the log-likelihood ratio information with the same sign.

[0049] Preferably, the variable node information update rule in S5 includes:

[0050] S501. According to the formula: ;

[0051] Among them, represents the LLR information of the finite field element ; is the index set of non-zero values in the -th row of the check matrix H;

[0052] S502. Take as the variable node update result, and calculate the posterior probability information of the variable node according to the variable node update result: ;

[0053] Then take the finite field symbol element corresponding to the maximum component in the posterior probability information as the decoded codeword for output, that is .

[0054] A multi - ary LDPC code EMS decoding device based on a neighboring set, the device includes: an initialization module, a construction truncation module, a check node update module, a variable node update module, and a decoding decision module;

[0055] The initialization module is used to calculate log - likelihood ratio information according to the channel received value, initialize the variable node and check node information, and set the current iteration number ;

[0056] The construction truncation module is used to perform reliability sorting on the input information vector of the check node, construct a neighboring set based on this reliability sorting and Hamming distance relationship, and truncate the input information based on the truncation rule;

[0057] The check node update module is used to update the information of the check node according to the update rule of the check node;

[0058] The variable node update module is used to update the information of the variable node according to the update rule of the variable node;

[0059] The decoding decision module is used to calculate the posterior probability information for decision according to the updated variable node information, output the decoding result, and use the parity - check 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 。

[0060] A computer - readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the steps in the above - mentioned multi - ary LDPC code EMS decoding method based on a neighboring set are implemented.

[0061] A computer device, including a memory, a processor, and a computer program stored on the memory and running on the processor, and when the processor executes the program, the steps in the above - mentioned multi - ary LDPC code EMS decoding method based on a neighboring set are implemented.

[0062] Compared with the prior art, the beneficial effects achieved by the present invention are:

[0063] Aiming at the problem of high complexity of the multi - ary LDPC code decoding algorithm, the present invention proposes a low - complexity decoding scheme for multi - ary LDPC codes based on EMS. For multi - ary LDPC codes defined on , when the number of sorted check node information vectors is , and the truncation length is , not only is the number of basic update units significantly reduced , but also the sorting requirement is further reduced . If in the finite field , configured , it can achieve performance close to optimal decoding, and the complexity is reduced to about times that of the standard EMS decoding algorithm, effectively improving its competitiveness in practical applications;

[0064] Based on the EMS decoding of the present invention, the check node information is truncated to ensure that the length of the neighboring set is fixed at , which simplifies the logical structure for storing and processing data in the hardware, and improves the efficiency and stability of hardware implementation. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] The 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 to the present invention. In the drawings:

[0066] Figure 1 is a flowchart of a method for EMS decoding of a multi - ary LDPC code based on a neighboring set according to an embodiment of the present invention;

[0067] Figure 2 is a schematic diagram for constructing the neighboring matrix P according to an embodiment of the present invention;

[0068] Figure 3 is a schematic diagram for constructing the neighboring set according to an embodiment of the present invention;

[0069] Figure 4 is a performance comparison diagram between a decoding method according to an embodiment of the present invention and the EMS decoding method;

[0070] Figure 5 is a complexity comparison diagram between a decoding method according to an embodiment of the present invention and the EMS decoding method;

[0071] Figure 6 is a block diagram of a device for EMS decoding of a multi - ary LDPC code based on a neighboring set according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0072] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0073] Please refer to Figures 1-6 , the present invention provides the following technical solutions:

[0074] Embodiment 1: In the embodiment of the present invention, for the definition on Above, the code length is 160 symbols, the code rate is 0.5, and the parity-check matrix , the parity-check node degree and the variable node degree are 6 and 3 respectively for the NB-LDPC code. The extended min-sum (EMS) algorithm based on this truncation rule is used to perform iterative decoding on the multi-ary LDPC, and the maximum number of decoding iterations is set .

[0075] Refer to Figure 1 , and the implementation steps of the present invention are as follows:

[0076] Step 1, in the finite field , using the channel information formula, calculate the log-likelihood ratio information corresponding to each symbol in the channel reception, with the size of :

[0077] ;

[0078] where represents the LLR information corresponding to the i-th symbol received by the channel; represents the j-th bit of the i-th symbol received by the channel; represents the j-th bit of the finite field symbol ; for the binary bit sequence length p corresponding to the finite field element , it satisfies: , ;

[0079] Preferably, in S101, the multi-ary LDPC code applicable to the decoding method is -LDPC code;

[0080] Then, the channel information is used as the reliability value vector corresponding to each bit symbol received by the channel to initialize the parity-check node information and variable node information, and the number of iterations is set to ;

[0081] ;

[0082] ;

[0083] where is the variable node information, representing the vector information from the variable node to the parity-check node ; similarly is the parity-check node information.

[0084] Step 2, perform permutation on the parity-check node input information according to the parity-check matrix :

[0085] ;

[0086] Inverse permute the input information of the variable nodes:

[0087] ;

[0088] The multiplication in the above formula is multiplication in the finite field.

[0089] Step 3: Sort the input information vectors of the check nodes, and construct the proximity matrix P and the proximity set based on this reliability sorting and the Hamming distance relationship , and perform a fixed-length truncation on this input information :

[0090] First, sort the input information vector. It is not necessary to perform an overall sort on all elements of the vector. The bubble sort algorithm or other algorithms can be used to obtain the GF(q)-field elements corresponding to the largest components in the information vector one by one , and form a sorting index set from these index values ; ;

[0091] Second, construct the proximity matrix. The proximity matrix is constructed based on the Hamming distance relationship between symbols. In the finite field , any symbol can be represented as a bit sequence of length , where , and the proximity symbol set of the element satisfies the following relationship: ;

[0092] ;

[0093] It can be seen that the size of the proximity matrix P constructed on the finite field is . Figure 2 shows a schematic diagram of the proximity matrix P constructed on .

[0094] Figure 3 shows a schematic diagram of the construction of the proximity set in the finite field, and the magnitude relationship of the reliability is marked in the figure. Among them, the numbers in the brackets in Figure 3 represent the reliability sorting. The smaller the value, the higher the reliability.

[0095] The number of sorting required in the embodiment , taking the descending sorting index set after sorting a certain check node information vector as an example. Find in the adjacency matrix P the corresponding adjacency symbol set , and take the union of the sorting index set and the adjacency symbol set to obtain the adjacency set :

[0096] ;

[0097] Given that the lengths of the adjacency sets constructed by different check nodes are not necessarily fixed, for the convenience of hardware implementation, perform a truncation operation with a length of : If the length of the adjacency set is greater than , only keep the first elements. If the length of the adjacency set is less than , pad with 0 at the end. In the embodiment, set the truncation length , , Figure 3 also gives a schematic diagram of performing a truncation operation on the adjacency matrix P in the finite field , and indicates the magnitude relationship of the reliability in the figure.

[0098] Thirdly, truncate the input information of the check node. Update the input information vector of the variable node to the check node according to the latest adjacency set and perform truncation. The truncation rule is as follows:

[0099] ;

[0100] where .

[0101] Step 4, according to the check node update rule, update the information of the check node and extract its extrinsic information:

[0102] Define two vectors and as the forward iteration vector and the backward iteration vector respectively. Their calculation processes are as follows:

[0103] Forward iteration process:

[0104] Let , representing the initial forward iteration vector. Let ​​denotes the degree of the \(i\)-th check node, that is, the number of non-zero values in the \(i\)-th row of the parity-check matrix \(H\). For , iterative calculation:

[0105] ;

[0106] Backward iteration process:

[0107] Let , let denotes the degree of the \(i\)-th check node. For , iterative calculation:

[0108] ;

[0109] External information extraction:

[0110] For , calculate the output external information of the check node:

[0111] ;

[0112] where denotes the updated check node information. The formula indicates that the logarithm-likelihood information with the largest value among the logarithm-likelihood ratios with the same sign is selected as the update result.

[0113] Step 5, update the variable node information according to the variable node update rule :

[0114]

[0115] where denotes the LLR information of the finite field element ; is the index set of non-zero values in the -th row of the parity-check matrix \(H\). Take the result calculated by the above formula as the variable node update result, and calculate the posterior probability information of the variable node according to the variable node update result:

[0116] ;

[0117] Output the finite field symbol element corresponding to the largest component in the posterior probability information as the decoded codeword, that is .

[0118] Execute an exemplary embodiment 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.

[0119] 1. Simulation conditions:

[0120] In the embodiment of the present invention, for the NB-LDPC code defined on with a code length of 160 symbols and a code rate of , a parity-check matrix is constructed using the PEG algorithm , the check node degree is , the variable node degree is , the extended min-sum (EMS) algorithm based on this truncation rule is used to iteratively decode the multi-ary LDPC, the sorting length is set to , the truncation length is , and the maximum number of decoding iterations is .

[0121] 2. Simulation content and result analysis:

[0122] In the simulation experiment of the present invention, the EMS algorithm of the present invention is compared and analyzed with the EMS decoding method in terms of performance, and the comparative simulation diagram as shown in Figure 4 is obtained. It can be seen from the figure that the EMS algorithm of the present invention has the same excellent error correction performance as the EMS decoding method at all signal-to-noise ratios (1, 1.5, 2, 2.5).

[0123] Meanwhile, Figure 5 shows the comparative simulation of the algorithm complexity. Taking the standard EMS algorithm as the benchmark, the ratio of the total number of operations (ACS) of the decoding method of the present invention in an average complete iterative decoding of one frame is used as an analysis index to measure the complexity. At all signal-to-noise ratios, this complexity ratio is only about . It can be seen from the two figures that the EMS decoding method and device of the multi-ary LDPC code based on the neighboring set of the present invention can obtain near-optimal decoding performance while greatly reducing the computational complexity, effectively improving its practical application competitiveness.

[0124] Embodiment 2: Figure 6 is a block diagram of an EMS decoding device for a multi-ary LDPC code based on a neighboring set shown according to an exemplary embodiment. Referring to Figure 6 , the device 600 includes an initialization module 610, a construction truncation module 620, a check node update module 630, a variable node update module 640, and a decoding decision module 650, where:

[0125] The initialization module 610 is configured to calculate the log-likelihood ratio (LLR) information according to the channel received value, initialize the variable node and check node information, and set the current iteration number ;

[0126] Construct a truncation module 620 for sorting the check node input information vector, constructing a neighborhood set based on this reliability sorting and Hamming distance relationship, and truncating the input information; ;

[0127] A check node update module 630 for updating the information of the check node according to the check node update rule;

[0128] A variable node update module 640 for updating the information of the variable node according to the check node update rule;

[0129] A decoding decision module 650 for calculating the posterior probability information based on the updated variable node information for decision-making and outputting the decoding result, that is, judging whether it is a codeword by using the check relationship. If the decoding termination condition is satisfied, the decoding ends and the decoding result is output, otherwise the next round of iteration is performed; ;

[0130] The division of the modules is illustrated by way of example. In practical applications, the above functions can be allocated to different functional units and modules as needed, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other and do not limit the protection scope of the present application. The specific working processes of the units and modules in the above system can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0131] Embodiment 3: The computer-readable storage medium of this embodiment stores a computer program, and when the program is executed by a processor, it implements the steps in a method for EMS decoding of a multi-ary LDPC code based on a neighborhood set in Embodiment 1.

[0132] The computer-readable storage medium of this embodiment can be the internal storage unit of the terminal, such as the hard disk or memory of the terminal; the computer-readable storage medium of this embodiment can also be the external storage device of the terminal, such as the plug-in hard disk, smart memory card, secure digital card, flash card, etc. equipped on the terminal; further, the computer-readable storage medium can also include both the internal storage unit and the external storage device of the terminal.

[0133] 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 the data that has been output or will be output.

[0134] Embodiment 4: The computer device of this embodiment includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the steps in a method for EMS decoding of a multi-ary LDPC code based on a neighboring set in Embodiment 1.

[0135] In this embodiment, the processor can be a central processing unit, or it can also be other general-purpose processors, digital signal processors, application-specific integrated circuits, off-the-shelf programmable gate arrays, or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor, or the processor can also be any conventional processor, etc.; the memory can include a read-only memory and a random access memory, and provides instructions and data to the processor. A part of the memory can also include a non-volatile random access memory. For example, the memory can also store information about the device type.

[0136] Those skilled in the art should understand that the content disclosed in the embodiments can be provided as a method, a system, or a computer program product. Therefore, this solution can be implemented in the form of a hardware embodiment, a software embodiment, or a form combining software and hardware embodiments. Moreover, this solution can be implemented in the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories and optical memories, etc.) that contain computer-usable program codes.

[0137] This solution is described with reference to the flowcharts and / or block diagrams of the methods and computer program products according to the embodiments of this solution. It should be understood that each process and / or block in the flowchart and / or block diagram, and 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 the processors of general-purpose computers, special-purpose computers, embedded processors, or other programmable data processing devices to generate a machine, so that the instructions executed by the processors of the computer or other programmable data processing devices generate a device for implementing the functions specified in Figure 1 one process or multiple processes and / or block Figure 1 diagram or multiple blocks.

[0138] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device implements the functions in the process Figure 1One process or multiple processes and / or party schematic Figure 1 The functions specified in one box or multiple boxes.

[0139] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one process or multiple processes and / or party schematic. Figure 1 One process or multiple processes and / or party schematic Figure 1 The steps of the functions specified in one box or multiple boxes.

[0140] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM), etc.

[0141] Finally, it should be noted that the above are only the preferred embodiments of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within 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 The input information is truncated 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, based on the updated variable node information, the posterior probability information is calculated to make a judgment, the decoding result is output, and the verification relationship is used 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 S2-S6 is executed for the next iteration; Wherein, the S3 includes: S301, based on variable node V i To the check node C j The input information vector Given a decoding parameter t (1≤t≤q), use the sorting algorithm to sequentially obtain the GF(q) field elements corresponding to the largest t components in the information vector By these index values ​​s k Construct a sorted index set S t ={s0,s1,…,s t-1 }; S302, constructing a proximity matrix P based on the Hamming distance relationship: Get the neighboring symbol vector P corresponding to the finite field element i∈GF(q) i , where the neighboring symbol vector P i Contains all finite field symbols whose Hamming distance with the finite field element i∈GF(q) in binary representation is 1; the adjacent symbol vector P i The set of all elements in is called the adjacent symbol set Assume any finite field element i∈GF(q), use a bit sequence b of length p i =(b0,b1,…,b p-1 ), where p = log2q, then the neighboring 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 P i The size of is p×1, and the size of the neighbor matrix P is p×q; The proximity matrix P is obtained by the proximity symbol vector P corresponding to all finite field elements i∈GF(q) i composition, that is, P=[P0,P1,…,P q-1 ]; 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 for element 1 Similarly, the qth column corresponds to the adjacent symbol set of element q-1 S303, according to the formula: For the sorted index set S t 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 V i To the check node C j The input information vector To truncate: in Represents a neighboring set.

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 the finite field GF(q), the log-likelihood ratio information of the channel reception value y is calculated using the channel information formula: Among them, {LLR i (a)} a∈GF(q) 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; a (j) represents the jth bit of the finite field symbol a∈GF(q); Among them, the finite field GF(q) of the multivariate LDPC code = {0,1,…,q-1}, then for the finite field element a∈GF(q), the length of the binary bit sequence p corresponding to satisfies q = 2 p ; S102: Initialize the variable nodes and the check nodes according to the following rules: in, Represented by the variable node V i To the check node C j Vector information of , i.e. variable node information; Indicates that the check node C j To variable node V i vector information, i.e., the verification node information; {LLR i (a)} a∈GF(q) 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 GF(2 p )-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 check node information update rule in S4 includes: S401, define two vectors α respectively t =(α t (0),α t (1),…,α t (q-1)) and β t =(β t (0),β t (1),…,β t (q-1)) is the forward iteration vector and the backward iteration vector; S402, forward iteration process: Let α0 = (0, -∞, ..., -∞), representing the initial forward iteration vector, let d c 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. For 0≤t <d c -1, iterative calculation: S403, backward iteration process: suppose d c represents the degree of the i-th check node, for d c -1≥t>1, iterative calculation: S404, external information extraction: for 0≤t <d c -1, 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.

5. 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: Among them, {LLR i (a)} a∈GF(q) Represents the LLR information of the finite field element i∈GF(q); is the index set of non-zero values ​​in the i-th (0≤i≤q) row of the check matrix H; 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 a∈GF(q) corresponding to the maximum component in is output as the decision codeword, that is, 6. A multi-element LDPC code EMS decoding device using the neighbor set-based multi-element LDPC code EMS decoding method according to any one of claims 1 to 5, 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 iteration number Iter=0; 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 Iter=Iter+1 is performed.

7. 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.

8. 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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