WO2017113507A1 - Set decoding method and set decoder - Google Patents

Abstract

The present invention relates to a set decoding method and a set decoder, the method comprising, dividing, according to the difference in confidences of different symbols of a symbol information vector, the symbol information vector into a plurality of sets, and performing the calculation in the process of decoding in units of sets. The set decoding method and the set decoder provided by the present invention reduce the decoding complexity, and especially reduce the calculation complexity at a check node. Meanwhile, by means of reasonably dividing sets and using suitable set confidences, the present invention can provide a performance that is no lower than that of the existing decoding method, provide high-quality decoding results, improving the decoding efficiency.

Description

Set decoding method and set decoder Technical field

The present invention relates to the field of communications technologies, and in particular, to a set decoding method and a set decoder.

Background technique

The error control coding, also known as channel coding, can ensure the reliability of data transmission in the communication system. Low-density parity check code (LDPC code) is a kind of linear error correction code whose performance is close to the Shannon limit, and is widely used in transmission systems with high reliability requirements. In the past ten years, binary LDPC codes have attracted considerable attention and rapid development due to their outstanding performance. The multi-ary LDPC code can obtain greater performance gain than the binary LDPC code, but at the cost of extremely high computational complexity and storage memory, thus hindering the application and development of the multi-ary LDPC code in practice.

The binary graph of the decoding check matrix of the multi-ary LDPC code, that is, the Tanner graph, is composed of a variable node, a check node, and a side connecting the check node and the variable node. There are two main methods for decoding existing multi-ary LDPC codes: a confidence propagation (BP) based decoding algorithm and a large number logic (MLGD) based decoding algorithm. The BP-based decoding algorithm is the best multi-ary information propagation decoding algorithm with the best decoding performance, but its decoding complexity is also the largest. For the multi-ary LDPC code under the finite field GF(q)(2 ^r ), the BP-based decoding algorithm needs to store the confidence of all q domain elements of the symbol for each code character number in the decoding process. And propagate a confidence vector of length q. The computational complexity of the order of q ² is required at the update operation of each check node. The MLGD-based decoding algorithm stores and transmits only the most reliable domain elements of the code character number for each code character number in the decoding process. Only simple finite field addition and integer addition are performed in the decoding process, so the computational and storage complexity based on the BP decoding algorithm can be significantly reduced. However, the MLGD-based decoding method has a serious performance loss phenomenon, and as the column weight of the LDPC code check matrix decreases, the performance loss is more serious, and the error platform may even appear prematurely. Therefore, how to achieve balance between computational complexity, storage complexity and decoding performance is a difficult problem to be solved in multi-ary LDPC code decoding.

Summary of the invention

The technical problem to be solved by the present invention is how to reduce the computational complexity in the decoding process.

To this end, the present invention proposes a set decoding method, which comprises: dividing the symbol information vector into a plurality of sets according to different degrees of confidence of different symbols of the symbol information vector, and performing the decoding process in units of sets. Calculation in .

Preferably, the method comprises the steps of:

S1: receiving bit information, calculating a symbol information vector or directly receiving a symbol information vector;

S2: Initializing the symbol information vector into a bipartite graph, and transmitting the symbol information vector to a corresponding check node;

S3: dividing the symbol information vector into a plurality of sets according to different confidence levels of different symbols of the symbol information vector, performing check node update calculation on the check node in units of sets, and performing code simultaneously Word decision, if it is a legal codeword, terminate the decoding, and output the decoding result; otherwise, the verification node update calculation result is transmitted to the variable node;

S4: Perform a variable node update calculation, and pass the calculation result to the check node to perform the codeword decision again, and then iterate until the correct codeword is decoded or until the maximum number of iterations is reached.

Preferably, the method further comprises: dividing the symbol information vector with the confidence in the first preset range into a plurality of sets, each set containing one symbol; and the confidence level being in a second preset range The symbol information vector is divided into a plurality of sets, each set containing a plurality of symbols; wherein a confidence level of the first preset range is higher than a confidence level of the second preset range.

Preferably, the calculating in the decoding process in units of sets comprises: calculating a set containing a plurality of symbols in one symbol information vector and a set having the highest confidence in another symbol information vector.

Preferably, the calculating the set of the plurality of symbols in one symbol information vector and the set having the highest confidence in the other symbol information vector comprises:

The number of the set containing the plurality of symbols in one symbol information vector is summed with the symbol of the set having the highest confidence in the other symbol information vector.

Preferably, the calculating in the decoding process in units of sets comprises:

According to different sets of confidence, the set of multiple symbol information vectors is calculated according to a preset path.

Preferably, the method further comprises: using a plurality of symbols of the same set to use the same confidence in the calculation process.

Preferably, the confidence level of the set containing the plurality of symbols is a maximum value of the confidence of the plurality of symbols.

In another aspect, the present invention further provides a set decoder, comprising: a receiving unit, an initializing unit, a check node updating computing unit, and a variable node updating computing unit;

The receiving unit is configured to receive bit information, calculate a symbol information vector, or directly receive a symbol information vector;

The initialization unit is configured to initialize the symbol information vector into a bipartite graph, and transmit the symbol information vector to a corresponding check node;

The check node update calculation unit is configured to divide the symbol information vector into a plurality of sets according to different degrees of confidence of different symbols of the symbol information vector, and perform verification on the check node in units of sets. The node updates the calculation and simultaneously performs the codeword decision. If it is a legal codeword, the decoding is terminated, and the decoding result is output; otherwise, the verification node update calculation result is transmitted to the variable node;

The variable node update calculation unit is configured to perform a variable node update calculation, and pass the calculation result to the check node to perform a codeword decision again, and then iterate until the correct codeword is decoded or until the maximum number of iterations is reached.

Preferably, the check node update calculation unit is further configured to divide the symbol information vector with a confidence level in a first preset range into a plurality of sets, each set containing one symbol; and the confidence level is at a second preset The symbol information vector of the range is divided into multiple sets, each set containing a plurality of symbols; wherein the confidence of the first preset range is higher than the confidence of the second preset range

By adopting a set decoding method and a set decoder provided by the invention, the decoding complexity is reduced, and the computational complexity of the check node is especially reduced. At the same time, the present invention can provide performance of not less than the existing decoding method by reasonably dividing the set and adopting appropriate set confidence, providing high quality decoding results and improving decoding efficiency.

DRAWINGS

The features and advantages of the present invention are more clearly understood from the following description of the drawings.

FIG. 1 is a bipartite diagram showing an LDPC code check matrix of 5 rows and 10 columns;

2 is a schematic diagram showing a typical flow of an iterative decoding method based on a bipartite graph;

FIG. 3 is a schematic diagram showing a single-step calculation process of a check node in an EMS algorithm;

4 is a schematic flow chart of a set decoding method of the present invention;

FIG. 5 is a diagram showing an example of a method for dividing a set of the present invention;

FIG. 6 shows a possible example of calculating based on a path in the set decoding method of the present invention;

Fig. 7 is a diagram showing an example of a check node update calculation unit in the set decoder of the present invention.

detailed description

Embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

The set decoding method of the present invention can be applied to any probability decoder. The present invention describes a specific embodiment of the present invention by taking a multi-ary LDPC code as an example.

As shown in FIG. 1, it is a bipartite graph representation of a (10, 5) LDPC code check matrix, also known as a Tanner graph. The nodes in the Tanner graph are divided into variable node VN and check node CN. Each of the variable nodes VN corresponds to a column in the decoding matrix, representing one symbol in the codeword; each check node corresponds to a row in the decoding matrix, representing a check equation; connecting the variable node and the check node The line between the lines corresponds to an element other than 0 in the decoding matrix, which is called an edge.

As shown in FIG. 2, a typical flow of an iterative decoding method based on a bipartite graph is as follows: a bit information sequence or a symbol information vector sequence received by a channel is initialized into a bipartite graph via a variable node and transmitted to a corresponding check node. The check node performs check node update calculation, and performs codeword decision at the same time. If it is a legal codeword, the decoding is terminated, and the decoding result is output; otherwise, the check node update calculation result is transmitted to the variable node. Then the variable node update calculation is performed, and the calculation result is transmitted to the check node to perform the codeword decision again. This iteration is repeated until the correct codeword is translated or the maximum number of iterations is reached.

In the traditional iterative decoding process of multi-ary LDPC codes, the calculation is based on symbols. Taking the calculation of check nodes in the EMS algorithm as an example, the calculation formula is:

U and V are two symbol information vectors that have been sorted according to the confidence level. Each element in the vector has two attributes: symbol and confidence (LLR). The symbols of different elements in the same vector are different. The addition between symbols follows the addition of the Galois field, and the addition between confidences follows the addition of real numbers. E is also a symbol information vector that has been sorted according to the confidence level and is calculated from the U and V vectors. The confidence of the element with the symbol x in E is calculated as follows: For an element with the symbol x _u in U, if there is an element in the V with the symbol x _v such that x _u +x _v =x (galohua domain Addition, then add the confidence of the two; there may be a number of different x _u in _U can find the corresponding element x _{v in V} , then the confidence of the symbol x in the final E takes all these possible results The minimum value.

As shown in FIG. 3, the input symbol information vectors A, B have been sorted according to the confidence level from large to small. When the input data has n _m different symbols, each step needs to be calculated.

Times.

The present invention provides a set decoding method, which divides the symbol information vector into a plurality of sets according to different degrees of confidence of different symbols of the symbol information vector, and performs calculation in the decoding process in units of sets. Preferably, as shown in FIG. 4, the set decoding method may include the following steps: S1: receiving bit information, calculating a symbol information vector or directly receiving a symbol information vector; S2: initializing the symbol information vector into a bipartite graph And transmitting the symbol information vector to the corresponding check node; S3: dividing the symbol information vector into multiple sets according to different degrees of confidence of different symbols of the symbol information vector, in units of sets The check node performs a check node update calculation, and simultaneously performs a codeword decision. If it is a legal codeword, the decoding is terminated, and the decoding result is output; otherwise, the check node update calculation result is transmitted to the variable node. S4: Perform a variable node update calculation, and pass the calculation result to the check node to perform the codeword decision again, and then iterate until the correct codeword is decoded or until the maximum number of iterations is reached. Specifically, n _m different symbols of the symbol information vector in the iterative decoding process are divided into n _c (n _c <n _m ) sets, and the calculation in the iterative decoding process is performed in units of sets. Still taking the calculation of the check node as an example, as shown in Figure 5, at this time, only each step needs to be calculated.

Times.

Preferably, the dividing the symbol information vector into a plurality of sets comprises: dividing the symbol information vector into a plurality of sets according to different degrees of confidence of different symbols of the symbol information vector. And dividing the symbol information vector with a confidence level in a first preset range into a plurality of sets, each set containing one symbol; dividing the symbol information vector with a confidence level in a second preset range into multiple sets, each The set contains a plurality of symbols; wherein the confidence of the first preset range is higher than the confidence of the second preset range. Specifically, the information is divided according to the confidence of different symbols in the symbol information vector, and the high confidence is divided into multiple sets, each set contains one symbol, and the low confidence is also divided into multiple sets, and each set has multiple sets. For example, n _m =8, n _c =4, a possible division is shown in Figure 5. The first two elements with higher confidence in the original symbol information vector are separately divided into one set. The six elements are divided into two sets per three groups according to the level of confidence.

According to the difference of different sets of confidence, in the iterative decoding calculation process, we can select only the set with higher confidence to perform the operation, thus further reducing the computational complexity. Preferably, the calculating in the decoding process in units of sets comprises: calculating a set containing a plurality of symbols in one symbol information vector and a set having the highest confidence in another symbol information vector. Specifically, taking the division in FIG. 5 as an example, the symbol information vectors A, B are respectively divided into four sets A ₁ , A ₂ , A ₃ , A ₄ and B ₁ , B ₂ , B ₃ , B ₄ , wherein The set A ₁ , A ₂ , B ₁ , B ₂ all contain only one element, that is, the set A ₁ , A ₂ , B ₁ , B ₂ all contain only one symbol, and the set A ₃ , A ₄ , B ₃ , B ₄ contains a plurality of elements, that is, contains a plurality of symbols. Therefore, in the calculation process, the calculation results of A ₂ , A ₂ and B ₁ , B ₂ can be calculated between them, and for A ₃ , A ₄ only calculate the calculation result between them and the B ₁ set. For B ₃ , B ₄ only calculates the calculation results between them and the A ₁ set.

Preferably, in order to reduce computational complexity, multiple symbols of the same set use the same confidence in the calculation process. The confidence level of the set containing the plurality of symbols is the maximum value of the confidence of the plurality of symbols. Specifically, in the symbol-based decoding method, different symbols may have different degrees of confidence, but in the present invention, the set-based decoding method, the set is divided according to the confidence, and for the set containing multiple symbols, in order to reduce Computational complexity, all symbols of the same set use the same confidence in the calculation process. The confidence of the set containing multiple symbols is equal to the maximum value of the original confidence of all the symbols in the set. Taking the partition in Fig. 5 as an example, the confidence γ _{A1 of the} set A1 is equal to the confidence of the symbol a ₁ in the original symbol information vector. γ _a1 , Similarly, the confidence level γ _A2 = γ _a2 of the set A2 , the confidence γ _A3 = γ _a3 of the set _A3 , and the confidence γ _A4 = γ _{a6 of the} set A4.

In order to reduce the decoding complexity, the calculating the set of the plurality of symbols in one symbol information vector and the set having the highest confidence in the other symbol information vector comprises: including a set of the plurality of symbols in one symbol information vector The number is summed with the symbol of the set with the highest confidence in the other symbol information vector. Specifically, a set containing a plurality of symbols is represented by a unique number according to an element included in the set, and a set containing one symbol is still represented by the symbol, and the calculation in the iterative decoding process is a set number and a The summation operation of the symbols, the operation obtains a new set number, and the symbols in the set corresponding to the new set number are the sum of the symbols of all the elements in the set containing the plurality of symbols and the added symbols. Still taking the division in FIG. 5 as an example, the set A ₁ contains only one element a ₁ , then A ₁ is represented by the symbol a ₁ ; the set B ₃ contains three elements b ₃ , b ₄ , b ₅ , and B ₃ It is represented by the number β ₁ . The operation between the set B ₃ and the set A ₁ is β ₁ + a ₁ , the operation result is a new set number β ₂ , and the set corresponding to the number β ₂ also contains three elements, respectively a ₁ + b ₃ , a ₁ + b ₄ , a ₁ + b ₅ .

In addition, as shown in FIG. 6, the calculation in the decoding process in units of sets may also adopt a path-based calculation process: according to different sets of confidence, the set between the plurality of symbol information vectors is preset. The path is calculated. The calculation process shown in FIG. 6 and the calculation process shown in FIG. 5 are two different methods. FIG. 5 only performs operations on two ports at a time, and the operation results of the two ports need to be compared with other ports for multiple ports. Performing operations is a serial or semi-parallel method, and the calculation process shown in Figure 6 can directly operate on all ports at a time, which is a parallel method.

As shown in FIG. 6, for a computing unit having multiple input ports, the corresponding set can be selected according to the planned path, and one of the plurality of planning schemes can be selected according to the difference of the confidence distribution of the input information vector. The appropriate path plan.

Specifically, FIG. 6 shows a possible path planning diagram of a five-port input computing unit. According to the difference of the confidence distribution of the input information vector, the computing unit selects one of several fixed planned path schemes, and directly operates the corresponding set without comparing and sorting. The calculation process input must be a sorted information vector, but the output information vector may be unsorted, but as long as the elements do not contain the same symbol, whether or not the ordering does not affect the subsequent calculation process. Since the sorting comparison unit is saved, the resource consumption is greatly reduced.

On the other hand, the above-mentioned set decoding method, the present invention further provides a set decoder, comprising: a receiving unit, an initializing unit, a check node updating computing unit, and a variable node updating computing unit; Calculating the symbol information vector by receiving the bit information; or directly receiving the symbol information vector; the initializing unit is configured to initialize the symbol information vector into the bipartite graph, And transmitting the symbol information vector to a corresponding check node; the check node update calculation unit is configured to divide the symbol information vector into multiples according to different confidence levels of different symbols of the symbol information vector Collecting, performing check node update calculation on the check node in units of sets, and performing codeword decision at the same time, if it is a legal codeword, terminating decoding, and outputting the decoding result; otherwise, the check node is updated with the calculation result Passed to the variable node; the variable node update calculation unit is used to perform the variable node update calculation, and the calculation result is transmitted to the check node to perform the codeword decision again, and then iteratively iterates until the correct codeword is decoded or until the maximum number of iterations is reached. .

Preferably, the check node update calculation unit divides the symbol information vector into a plurality of sets according to different confidence levels of different symbols of the symbol information vector. The check node update calculation unit divides the symbol information vector with a confidence level in a first preset range into a plurality of sets, each set containing one symbol; and the symbol information with a confidence level in a second preset range The vector is divided into a plurality of sets, each set containing a plurality of symbols; wherein the confidence of the first preset range is higher than the confidence of the second preset range. The set containing a plurality of symbols is operated with the set with the highest degree of confidence. Multiple symbols of the same set use the same confidence in the calculation process. The confidence level of the set containing the plurality of symbols is the maximum value of the confidence of the plurality of symbols.

Preferably, the set decoder further includes a data storage unit, and the data storage unit is configured to store the symbol information vector. Specifically, in the aggregate decoder designed by the present invention, the symbol and the confidence of the symbol information vector are respectively saved by two sets of storage units S and L of n _m and n _c , and only the storage unit L of the confidence is saved. The data in the data participates in the process of calculation, comparison, etc. in the decoding iteration process, and the data in the storage unit S is only used to generate new symbols. Corresponding to the calculation of FIG. 5, as shown in FIG. 7, a possible example of a check node update calculation unit in the set decoder of the present invention, the check node update calculation unit has three data selection modules, and three data selection modules. Above the minimum module, the three data selection modules are initialized with γ _A1 + γ _B1 , γ _A1 + γ _B2 , γ _A2 + γ _{B1 ,} respectively, and the outputs of the three selection modules are compared, and finally the minimum through the minimum module The value is output and the data selection module that provides the minimum value is updated. When the minimum value is provided by the first data selection module, if the data in the module is γ _A2 + γ _B2 at this time, the module is no longer updated, and then the output of the module is no longer involved in the comparison, otherwise the module is The data is updated to γ _A2 + γ _B2 ; when the minimum value is provided by the second data selection module, assuming that the data in the module is γ _A1 + γ _Bi at this time, the module data is updated to γ _A1 + γ _{B (i +1)} ; When the minimum value is provided by the third data selection module, assuming that the data in the module is γ _Ai + γ _B1 at this time, the module data is updated to γ _A(i+1) + γ _B1 . When the output of the kth module is the smallest, the minimum module outputs the data, and controls the data selection module k to read the next data, and the remaining data selection module data does not change. At the same time, the minimum module generates an address according to k, and the control registers LU, LV, SU, and SV respectively output data for calculation. The address register stores the address of the next data of each of the three data selection modules, and controls the output of the two sets of registers according to the comparison result of the minimum value module. When the calculation is performed based on the path shown in FIG. 6, the decoding device only needs to use one storage unit to store a plurality of pre-planned path schemes, and directly selects the corresponding set in the input information vector according to the path scheme.

By adopting the set decoding method and the set decoder provided by the invention, the decoding complexity is reduced, and the computational complexity of the check node is especially reduced. At the same time, the present invention can provide performance of not less than the existing decoding method by reasonably dividing the set and adopting appropriate set confidence, providing high quality decoding results and improving decoding efficiency.

Those skilled in the art will appreciate that embodiments of the present application can be provided as a method, system, or computer program product. Thus, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment in combination of software and hardware. Moreover, the application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) including computer usable program code.

The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (system), and computer program products according to embodiments of the present application. It will be understood that each flow and/or block of the flowchart illustrations and/or FIG. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing device to produce a machine for the execution of instructions for execution by a processor of a computer or other programmable data processing device. Means for implementing the functions specified in one or more of the flow or in a block or blocks of the flow chart.

The computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing device to operate in a particular manner, such that the computer readable memory is stored in the computer readable memory. The instructions in the production result include an article of manufacture of the instruction device that implements the functions specified in one or more blocks of the flowchart or in a flow or block of the flowchart.

These computer program instructions can also be loaded onto a computer or other programmable data processing device such that a series of operational steps are performed on a computer or other programmable device to produce computer-implemented processing for execution on a computer or other programmable device. The instructions provide steps for implementing the functions specified in one or more of the flow or in a block or blocks of a flow diagram.

While the preferred embodiment of the present application has been described, those skilled in the art can make further changes and modifications to these embodiments once they are aware of the basic inventive concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and the modifications and

Finally, it should also be noted that in this context, relational terms such as first and second are used merely to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply these entities. There is any such actual relationship or order between operations. Furthermore, the term "comprises" or "comprises" or "comprises" or any other variations thereof is intended to encompass a non-exclusive inclusion, such that a process, method, article, or device that comprises a plurality of elements includes not only those elements but also Other elements, or elements that are inherent to such a process, method, item, or device. An element defined by the phrase "comprising a ..." without further limitation does not exclude the presence of additional identical elements in the process, method, article, or device that comprises the element.

The above embodiments are merely illustrative of the present invention and are not intended to be limiting of the invention, and various modifications and changes can be made without departing from the spirit and scope of the invention. Equivalent technical solutions are also within the scope of the invention, and the scope of the invention is defined by the claims.

Claims (10)

1. A set decoding method, comprising: dividing the symbol information vector into a plurality of sets according to different degrees of confidence of different symbols of the symbol information vector, and performing calculation in the decoding process in units of sets.
2. A set decoding method according to claim 1, comprising the steps of:

   S1: receiving bit information, calculating a symbol information vector or directly receiving a symbol information vector;

   S2: Initializing the symbol information vector into a bipartite graph, and transmitting the symbol information vector to a corresponding check node;

   S3: dividing the symbol information vector into a plurality of sets according to different confidence levels of different symbols of the symbol information vector, performing check node update calculation on the check node in units of sets, and performing code simultaneously Word decision, if it is a legal codeword, terminate the decoding, and output the decoding result; otherwise, the verification node update calculation result is transmitted to the variable node;

   S4: Perform a variable node update calculation, and pass the calculation result to the check node to perform the codeword decision again, and then iterate until the correct codeword is decoded or until the maximum number of iterations is reached.
3. The set decoding method according to claim 1 or 2, further comprising: dividing the symbol information vector of the confidence level in a first preset range into a plurality of sets, each The set contains a symbol; the symbol information vector with the confidence in the second preset range is divided into a plurality of sets, each set containing a plurality of symbols; wherein the confidence ratio of the first preset range is greater than The confidence of the second preset range is high.
4. The set decoding method according to claim 3, wherein the calculating in the decoding process in units of sets comprises: combining a set of symbols in one symbol information vector with another symbol information The set with the highest confidence in the vector is calculated.
5. The set decoding method according to claim 4, wherein the calculating the set of the plurality of symbols in one symbol information vector and the set having the highest confidence in the other symbol information vector comprises:

   The number of the set containing the plurality of symbols in one symbol information vector is summed with the symbol of the set having the highest confidence in the other symbol information vector.
6. The set decoding method according to claim 3, wherein the calculating in the decoding process in units of sets comprises:

   According to different sets of confidence, the set of multiple symbol information vectors is calculated according to a preset path.
7. The set decoding method according to claim 3, wherein the method further comprises: using a plurality of symbols of the same set to use the same confidence in the calculation process.
8. The set decoding method according to claim 3, wherein the confidence level of the set of the plurality of symbols is a maximum value of the confidence of the plurality of symbols.
9. A set decoder, comprising: a receiving unit, an initializing unit, a check node updating calculating unit, and a variable node updating calculating unit;

   The receiving unit is configured to receive bit information, calculate a symbol information vector, or directly receive a symbol information vector;

   The initialization unit is configured to initialize the symbol information vector into a bipartite graph, and transmit the symbol information vector to a corresponding check node;

   The check node update calculation unit is configured to divide the symbol information vector into a plurality of sets according to different degrees of confidence of different symbols of the symbol information vector, and perform verification on the check node in units of sets. The node updates the calculation and simultaneously performs the codeword decision. If it is a legal codeword, the decoding is terminated, and the decoding result is output; otherwise, the verification node update calculation result is transmitted to the variable node;

   The variable node update calculation unit is configured to perform a variable node update calculation, and pass the calculation result to the check node to perform a codeword decision again, and then iterate until the correct codeword is decoded or until the maximum number of iterations is reached.
10. The set decoder according to claim 9, wherein the check node update calculation unit is further configured to divide the symbol information vector with a confidence level in a first preset range into a plurality of sets. Each set contains one symbol; the symbol information vector with confidence in the second preset range is divided into a plurality of sets, each set containing a plurality of symbols; wherein, the confidence ratio of the first preset range is The confidence of the second preset range is high.

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