LDPC (Low Density Parity Check) code time sequence decoding method and device based on unstable variable node maximum residue silence mechanism

Abstract

The invention provides an LDPC (Low Density Parity Check) code time sequence decoding method and device based on an unstable variable node maximum residue silence mechanism. The method comprises the following steps of: initializing a message and a residual degree, positioning an unstable variable node, selecting a maximum residual degree edge of the unstable variable node, muting information update of the edge, transmitting input information of a muting edge, transmitting output information of the muting edge, updating the corresponding residual degree, updating an unstable variable node set and the like. Compared with a traditional IVC-RBP decoding method, the INP-VCRBP decoding method has the advantages that the decoding performance is remarkably improved while low complexity is kept, and particularly, the performance is more prominent under the requirements of low signal-to-noise ratio and low bit error rate. Experimental results show that the decoding method can maintain good performance under different code lengths, has good practical application value, and can promote application and development of LDPC codes in the fields of next-generation wireless communication, future ground orbit satellite communication and the like.

Description

Technical Field

[0001] The present invention belongs to the fields of electronics, communications and information engineering, and particularly relates to a LDPC code timing decoding method and device based on an unstable variable node maximum residual degree silent mechanism. Background Art

[0002] IVC-RBP (Improved Variable Node Classification Residual Belief Propagation) algorithm, improved variable node classification residual belief propagation algorithm. It is a dynamic decoding algorithm further improved on the basis of RBP algorithm. It not only considers the residual of the message, but also introduces the concept of unstable variable nodes, thereby optimizing the message update strategy. In the decoding process of LDPC code, the message transmission between variable nodes and check nodes is the key. IVC-RBP algorithm divides variable nodes into two categories: unstable variable nodes and stable nodes. Unstable variable nodes refer to those nodes whose messages change greatly during the iteration process, while stable nodes refer to those nodes whose messages change less. This classification method enables the algorithm to more accurately identify which nodes' message updates are more critical to the decoding process. When selecting the maximum residual edge, IVC-RBP algorithm gives priority to updating the edges related to unstable variable nodes. If the residuals of all the edges related to unstable variable nodes are small, the algorithm will consider the edges related to stable nodes. This strategy not only improves the decoding efficiency to a certain extent, but also further enhances the error correction ability of the algorithm. However, the decoding complexity of the IVC-RBP algorithm is relatively high, and the performance of the IVC-RBP algorithm needs to be further improved when dealing with complex communication channels and strong noise environments. Summary of the invention

[0003] Purpose of the invention: The technical problem to be solved by the present invention is to provide a LDPC code timing decoding method and device based on the unstable variable node maximum residual silence mechanism (INP-VCRBP) in view of the shortcomings of the existing IVC-RBP technology.

[0004] The method comprises the following steps: Step 1: Receive the codeword sequence of the LDPC code at the receiving end, and then perform initialization and residual calculation: initialize all messages from check nodes to variable nodes to 0, and initialize all messages from variable nodes to check nodes to the received log-likelihood ratio (LLR) value; at the same time, calculate the initial residual degree of each variable node to check node message; Step 2, locating unstable variable nodes: comparing the decision log-likelihood ratio LLR values ​​before and after the update of each variable node, so as to detect the stability of the variable node; an unstable variable node refers to a node whose decision log-likelihood ratio LLR value sign changes before and after the message is updated; the method of the present invention gives priority to processing unstable variable nodes, which is conducive to improving the convergence speed and accuracy of decoding; Step 3, silencing the maximum residual degree edge of the unstable variable node: by calculating and sorting the residual degrees of all edge information of the unstable variable node, locating the maximum residual degree edge of the unstable variable node, and silencing the state of the maximum residual degree edge; the unstable variable node is called a silent node, and the edge with the maximum residual degree is called a silent edge; the setting of the silent node reduces the number of information transmissions, which is conducive to improving the convergence speed of the decoding method and reducing the decoding complexity; Step 4, generating and transmitting input messages of silent edges: for a check node connected to a silent edge, generating and transmitting all input messages connected to the check node, but excluding input messages from variable nodes connected to the silent edge; Step 5, generating and transmitting output messages of silent edges: for variable nodes connected to silent edges, generating and transmitting all output messages connected to the variable nodes, but excluding those from check nodes connected to the silent edges; Step 6, update the remaining degree value and the unstable variable node set: update all the remaining degree changes caused by steps 4 and 5, and re-count and update the unstable variable node set; Step 7, check the stop condition: if all variable nodes satisfy the verification equation or reach the preset maximum number of iterations, stop decoding; otherwise, return to step 2 to detect unstable variable nodes and continue iteration.

[0005] In step 1, the information bit sequence to be transmitted is encoded by an LDPC encoder, and the information bit sequence to be transmitted is expanded into a codeword sequence of an LDPC code and then sent. The codeword sequence of the LDPC code is transmitted to the receiving end via a noisy channel. The information bit sequence to be transmitted comes from various data sources at the transmitting end, such as binary forms of text, images, audio or video sources; the decoding method and device of the present invention start error correction decoding of the received sequence at the receiving end.

[0006] Step 1 also includes: Initialization message: , ; Initialize the remainder: ; in Represents the message from check node a to variable node v; Represents the message from variable node v to check node a; Represents the log-likelihood ratio LLR value received by the variable node v; Indicates the message The residual degree; Indicates the absolute value of the received log-likelihood ratio (LLR).

[0007] In step 2, the following method is used to locate the unstable variable node: If sign( , then the variable node v is unstable, where Represents the message from variable node v to check node a before update, Represents the message from the updated variable node v to the check node a. sign is an information function used to determine the positive or negative zero sign of a number.

[0008] In step 3, the search range of the maximum residual degree edge is limited to the unstable variable nodes, which greatly narrows the search range; if there is no unstable variable node, search in all nodes; The maximum residual degree edge is selected as follows: For a non-empty set of unstable variable nodes, ; otherwise, ; in represents the maximum residual degree; Indicates the selection of the maximum remaining degree among the unstable variable nodes; Indicates selecting the maximum residual degree among all edges of the LDPC code; Represents a set of unstable nodes; Represents all edges.

[0009] In step 3, the state of the silent maximum residual degree edge means that the information value of the edge where the unstable variable node has the maximum residual degree is not updated or transmitted, that is, no processing is performed on the edge information, thereby reducing the complexity of the decoding device.

[0010] In step 4, the message is updated using the following formula: Update check node message: ,in Indicates the updated message value; The updated message value is calculated by the following formula : = ), Where u represents a node in the variable node set N(a) connected to the check node a, but does not include the specified variable node v; Represents the message from node u to variable node v; in represents the set of all variable nodes connected to the check node a; Represents the set after removing the variable node v; σ is a function, usually a hyperbolic tangent function, used to transform the message nonlinearly; Set the remainder to 0: =0.

[0011] Step 5 includes: Update variable node message: ; Stability judgment: If sign( , calculate the residual degree: = , If sign( , it means that the confidence of the variable node has not changed significantly, so no further processing is required. Specifically, the following measures can be taken: Do not update the residual degree: Since the confidence of the variable node has not changed, there is no need to calculate a new residual degree value; the residual degree value remains unchanged; Skip variable nodes: In subsequent iterations, you can skip updating the variable node and directly process other variable nodes; this can reduce unnecessary calculations and improve decoding efficiency;

[0012] Continue iterating: Although the confidence of the current variable node has not changed, other variable nodes may still need to be updated; therefore, continue iterating until the stopping condition is met.

[0013] Step 6 includes: If all variable nodes satisfy: =0, or the maximum number of iterations is reached: t≥ , then stop decoding; where N(v) represents the set of check nodes connected to the variable node v; t is the current iteration number; is the maximum number of iterations.

[0014] The present invention also provides an LDPC code timing decoding device based on the unstable variable node maximum remainder silence mechanism implemented according to the method, characterized in that it includes an initialization module, an unstable variable detection module, a maximum remainder selection module, a check node message update module, a variable node message propagation module, a stop condition check module and a control module; The initialization module is used to: Message initialization: Initialize all messages from check nodes to variable nodes to 0; LLR initialization: Initialize the message from the variable node to the check node to the received LLR value; Calculate the remaining degree: calculate the initial remaining degree of each variable node to check node message, and sort the remaining degrees in descending order; The unstable variable detection module is used for: Symbol comparison: compare the symbols before and after the variable node message is updated; Unstable variable node marking: detect unstable nodes and mark them; The maximum remainder selection module is used for: Residual degree comparison: select the edge with the largest residual degree among the unstable variable nodes; Global selection: If there is no unstable variable node, select the edge with the largest remaining degree in the entire system; The check node message update module is used for: Check node message update: Update the check node message corresponding to the selected edge; Clear the remaining degree: set the remaining degree of the selected edge to 0; The variable node message propagation module is used for: Variable node message update: update the variable node message corresponding to the selected edge; Stability judgment: judge the stability of variable nodes; Residue update: calculate the new residual value; The stop condition checking module is used for: Verification equation check: check whether all variable nodes satisfy the verification equation; Iteration number check: check whether the maximum number of iterations has been reached; The control module is used for: Process control: Control the entire decoding process, including module coordination and iterative control; State management: manages the states and conditions during the decoding process.

[0015] The present invention relates to the timing design of LDPC code decoding, aiming to improve the decoding performance of LDPC code and reduce the decoding complexity. The timing decoding method of LDPC code based on the maximum residual silence mechanism of unstable variable nodes (INP-VCRBP) significantly improves the decoding efficiency and error correction capability by optimizing the message update strategy and reducing unnecessary calculations.

[0016] Beneficial effects: The present invention proposes an LDPC code timing decoding method (INP-VCRBP) based on the maximum residual silence mechanism of unstable variable nodes, which has significant beneficial effects compared with traditional decoding methods.

[0017] First, the present invention significantly improves the decoding performance. By optimizing the message update strategy, the INP-VCRBP algorithm's silent approach to the maximum residual degree edge of the unstable variable node can more effectively reduce the number of unstable variable nodes. This strategy enables the algorithm to perform well under low signal-to-noise ratio and low bit error rate requirements, and can converge to the correct solution faster, reducing the bit error rate and frame error rate. Experimental results show that whether the code length is 155 or 576, the INP-VCRBP algorithm is superior to the IVC-RBP algorithm in BER (bit error rate) and FER (frame error rate), especially under high signal-to-noise ratio conditions, the performance advantage is more obvious.

[0018] Secondly, the present invention effectively reduces the decoding complexity. By silently processing the maximum residual degree edge of the unstable variable node, unnecessary message updates are reduced. At the same time, the INP-VCRBP algorithm avoids repeated updates to the initial selection edge and reduces the number of message recalculations. This mechanism significantly reduces the computational complexity and improves the decoding efficiency. At the same time, the algorithm introduces a dual decision-making mechanism, combining the concepts of residual degree and unstable variable nodes to more effectively locate the edges that need to be updated first, and further optimize the decoding process. This enables the INP-VCRBP algorithm to significantly improve the decoding performance while maintaining a low complexity and enhance the robustness of the algorithm.

[0019] In addition, the present invention has important practical application value. The INP-VCRBP algorithm can perform well under different signal-to-noise ratio conditions, especially under low signal-to-noise ratio and low bit error rate requirements. This makes the algorithm have broad application prospects in the fields of wireless communications, satellite communications, etc., and can promote the further application and development of LDPC codes in these fields. Future research can further explore the application of the algorithm in LDPC code decoding to further improve the performance and reliability of the coding scheme.

[0020] In summary, the present invention improves the decoding performance of LDPC codes and reduces the decoding complexity through innovative decoding methods and device designs, and has important practical application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a schematic diagram of the technical solution of the INP-VCRBP decoder proposed in the present invention.

[0022] Figure 2 It is a schematic diagram of the relationship between the bit error rate, frame error rate and signal-to-noise ratio of various decoding schemes for code A in a Gaussian white noise channel environment.

[0023] Figure 3 It is a schematic diagram of the relationship between the bit error rate, frame error rate and signal-to-noise ratio of various decoding schemes for code B in a Gaussian white noise channel environment.

[0024] Figure 4 It is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0025] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments, and the above and / or other advantages of the present invention will become more clear.

[0026] like Figure 4 As shown, the embodiment of the present invention provides a LDPC code timing decoding method based on the maximum residual silence mechanism of unstable variable nodes. To facilitate understanding of the decoding process, the symbols defined in the present invention are as follows: Represents a variable node To the verification node News The residual degree; σ is a function, usually a hyperbolic tangent function, used to transform the message nonlinearly; :Represents the number of nodes from the i-th check node Passed to the jth variable node information; :Represents the number of variables from the jth variable node Passed to the i-th check node information; N( )={ ∣ =1} represents the Tanner graph with the i-th check node A connected set of variable nodes; N( )={ ∣ =1} represents the node connected to the jth variable in the graph The set of all check nodes; N( )\ Represents the removal of the jth variable node The resulting subset; N( )\ Represents the removal of the i-th check node The resulting subset.

[0027] L( ) is a real value, indicating the received signal for the jth variable node confidence level.

[0028] The IVC-RBP algorithm can identify unstable variable nodes as an indicator of algorithm convergence. Based on the fact that the larger the residual degree, the faster the convergence speed, IVC-RBP accelerates convergence by first updating and propagating the message of the unstable variable node with the largest V2C residual degree.

[0029] The following is a simple modification of the IVC-RBP algorithm: without loss of generality, set the variable node Unstable, The remaining degree is the largest. The first step is to update and disseminate information , ; In order to make the node more reliable, rather than updating The second step is to set , update and spread the news , The last step is to determine Is it still unstable? Calculate the new residual degree , , , will be updated simultaneously.

[0030] The detailed calculation steps of the INP-VCRBP decoding algorithm include: Step 1, Initialization: , Step 2: Check whether there is an unstable variable node. If there is an unstable variable node, go to step 3; otherwise, go to step 5.

[0031] if then it is unstable; Step 3: Search for the maximum remaining degree of unstable variable nodes : , Among them, unstable variable node represents an unstable variable node; Step 4: If there is no unstable variable node, find the maximum remaining degree in the entire system : , Step 5: For each , generate and pass messages : , Step 6, set the remaining degree of the selected edge to 0, that is ; Step 7: For each , generate and pass messages , and for variable nodes For each , calculate the new residual degree , and update the message : , , Step 8: If the stop condition is not met, return to step 2 to continue iterating; otherwise, end the decoding process.

[0032] if , ,stop.

[0033] Code A used for simulation: a quasi-cyclic LDPC code with a code rate of 0.4 and a code length of 155.

[0034] Code B used for simulation: a quasi-cyclic LDPC code with a code rate of 0.5 and a code length of 576.

[0035] The computer used in the embodiment of the present invention is configured with 16GB of memory, a 12th Gen Intel(R) Core(TM) i5-1240P 1.70 GHz desktop computer, and the code used is developed in Matlab2024b language. There are two types of LDPC codes A and B used to test the invention scheme: code A is a quasi-cyclic LDPC code with a code rate of 0.4 and a code length of 155; code B is a quasi-cyclic LDPC code with a code rate of 0.5 and a code length of 576. After analyzing the performance of each decoding scheme, the following conclusions are drawn:

[0036] Figure 1 The core technology diagram of the INP-VCRBP decoder proposed in this invention is shown. , , , , , , , represents the check node. Triangle node: marked as , , , represents a variable node. And assume that the variable node is an unstable variable node, and The remaining degree is the largest. Then update and spread the message , set the residual degree r( )=0, update and propagate the message Then judge Is it still unstable, and calculate the new residual degree r( ).

[0037] Figure 2 The decoding performance comparison between the INP-VCRBP algorithm and the IVC-RBP algorithm is shown when the code length is 155. As can be seen from the figure, the INP-VCRBP algorithm is better than the IVC-RBP algorithm in terms of BER and FER. Specifically:

[0038] In the low SNR area, the bit error rate and frame error rate of the INP-VCRBP algorithm are lower than those of the IVC-RBP algorithm, which shows that the INP-VCRBP algorithm has better robustness under low SNR conditions. As the SNR increases, the performance gap between the two algorithms gradually increases. In the high SNR area, the performance advantage of the INP-VCRBP algorithm is more obvious. -4 At a bit error rate of , the INP-VCRBP algorithm has a gain of 0.15dB over the IVC-RBP algorithm. This means that under the same bit error rate requirement, the INP-VCRBP algorithm can be implemented at a lower signal-to-noise ratio, thereby saving transmission power or improving spectrum efficiency.

[0039] Figure 3 The decoding performance comparison between the INP-VCRBP algorithm and the IVC-RBP algorithm when the code length is 576 is shown. As can be seen from the figure, the INP-VCRBP algorithm is also better than the IVC-RBP algorithm in terms of BER and FER. Specifically: in the low signal-to-noise ratio area, the bit error rate and frame error rate of the INP-VCRBP algorithm are lower than those of the IVC-RBP algorithm, which shows that the INP-VCRBP algorithm also has better robustness under low signal-to-noise ratio conditions. As the signal-to-noise ratio increases, the performance gap between the two algorithms gradually increases. In the high signal-to-noise ratio area, the performance advantage of the INP-VCRBP algorithm is more obvious. Although the code length is increased to 576, the performance advantage of the INP-VCRBP algorithm still exists, which shows that the INP-VCRBP algorithm can maintain good performance under different code lengths.

[0040] In summary, through Figure 2 and Figure 3From the detailed analysis, it can be seen that the INP-VCRBP algorithm significantly improves the decoding performance while maintaining a low decoding complexity. Whether the code length is 155 or 576, the INP-VCRBP algorithm is superior to the IVC-RBP algorithm in terms of BER and FER, especially under low signal-to-noise ratio and low bit error rate requirements. In addition, the INP-VCRBP algorithm requires fewer average iterations and can complete the decoding process in a shorter time. This makes the INP-VCRBP algorithm of great value in practical applications and can promote the further application and development of LDPC codes in fields such as wireless communications. Future research can further explore the application of this algorithm in LDPC code decoding to further improve the performance and reliability of the coding scheme.

[0041] Although the IVC-RBP algorithm has excellent error correction performance, it also has some shortcomings. The implementation complexity of the IVC-RBP algorithm is relatively high, mainly because it needs to classify variable nodes and dynamically select unstable variable nodes for priority update in each iteration. Although this dynamic scheduling mechanism can improve decoding efficiency, it also increases the difficulty of algorithm design and implementation. Although the IVC-RBP algorithm improves decoding efficiency by giving priority to unstable variable nodes, in some cases, this strategy may cause the algorithm to fall into a local optimal solution. This is because the algorithm relies too much on residual information when selecting update nodes, while ignoring other factors that may affect decoding performance. For example, when dealing with certain specific error modes, the algorithm may not be able to effectively correct errors, thereby affecting the overall decoding performance. Under certain channel conditions, the performance improvement of the IVC-RBP algorithm may not be as significant as expected. For example, in a low-noise channel, the performance improvement of the algorithm may not be as obvious as in a high-noise channel. This means that under different channel conditions, the performance of the algorithm may vary greatly, which requires special attention in practical applications. The purpose of the INP-VCRBP (Informed and Non-Propagated variable-to-check Residual Belief Propagation) algorithm is to improve the original IVC-RBP algorithm to improve the decoding performance of low-density parity-check codes while reducing the decoding complexity. The INP-VCRBP algorithm significantly reduces the computational complexity, improves the decoding performance, and enhances the robustness of the algorithm by optimizing the dynamic scheduling strategy and reducing unnecessary message updates. The INP-VCRBP algorithm avoids repeated updates to the initial selected edges and reduces the number of message recalculations, thereby accelerating the convergence speed and reducing the bit error rate and frame error rate. In addition, the INP-VCRBP algorithm introduces a dual decision mechanism, combines the concepts of residual degree and unstable variable nodes, and more effectively locates the edges that need to be updated first, further optimizing the decoding process. This improvement enables INP-VCRBP to perform well under different signal-to-noise ratio conditions, especially under high signal-to-noise ratio, where its performance improvement is particularly significant. In summary, INP-VCRBP has comprehensively improved IVC-RBP in terms of computational complexity, decoding performance and robustness, making it more advantageous in practical applications.

[0042] The present invention provides a method and device for sequential decoding of LDPC codes based on the maximum residual degree silence mechanism of unstable variable nodes. There are many methods and ways to implement the technical solution. The above is only a preferred implementation of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the protection scope of the present invention. All components not specified in this embodiment can be implemented by existing technologies.

Claims

1. A method for sequential decoding of LDPC codes based on a maximum residual silence mechanism of unstable variable nodes, characterized in that: The following steps are involved: Step 1: Receive the codeword sequence of the LDPC code at the receiving end, and then perform initialization and residual calculation: initialize all messages from the check node to the variable node to 0, and initialize all messages from the variable node to the check node to the received log-likelihood ratio LLR value; at the same time, calculate the initial residual degree of each variable node to the check node message; Step 2, locate unstable variable nodes: compare the decision log-likelihood ratio (LLR) values ​​of each variable node before and after update to detect the stability of the variable node; Unstable variable nodes refer to nodes whose LLR value signs change before and after the message update; Step 3, silencing the maximum remaining degree edge of the unstable variable node: by calculating and sorting the remaining degrees of all edge information of the unstable variable node, locating the maximum remaining degree edge of the unstable variable node, and silencing the state of the maximum remaining degree edge; Unstable variable nodes are called silent nodes, and the edge with the maximum residual degree is called silent edge; Step 4, generating and transmitting input messages of silent edges: for a check node connected to a silent edge, generating and transmitting all input messages connected to the check node, but excluding input messages from variable nodes connected to the silent edge; Step 5, generating and transmitting output messages of silent edges: for variable nodes connected to silent edges, generating and transmitting all output messages connected to the variable nodes, but excluding those from check nodes connected to the silent edges; Step 6, update the remaining degree value and the unstable variable node set: update all the remaining degree changes caused by steps 4 and 5, and re-count and update the unstable variable node set; Step 7, check the stop condition: if all variable nodes satisfy the verification equation or reach the preset maximum number of iterations, stop decoding; otherwise, return to step 2 to detect unstable variable nodes and continue iteration.

2. The method according to claim 1, characterized in that In step 1, the information bit sequence to be transmitted is encoded by an LDPC encoder, and the information bit sequence to be transmitted is expanded into a codeword sequence of an LDPC code and then sent. The codeword sequence of the LDPC code is transmitted to the receiving end via a noisy channel.

3. The method according to claim 2, characterized in that Step 1 also includes: Initialization message: , ; Initialize the remainder: ; in Represents the message from check node a to variable node v; Represents the message from variable node v to check node a; Represents the log-likelihood ratio LLR value received by the variable node v; Indicates the message The residual degree; Indicates the absolute value of the received log-likelihood ratio (LLR).

4. The method according to claim 3, characterized in that In step 2, the following method is used to locate the unstable variable node: If sign( , then the variable node v is unstable, where Represents the message from variable node v to check node a before update, Represents the message from the updated variable node v to the check node a. sign is an information function used to determine the positive or negative zero sign of a number.

5. The method according to claim 4, characterized in that In step 3, the search range of the maximum residual degree edge is limited to unstable variable nodes; if there is no unstable variable node, it is searched in all nodes; The maximum residual degree edge is selected as follows: For a non-empty set of unstable variable nodes, ; otherwise, ; in represents the maximum residual degree; Indicates the selection of the maximum remaining degree among the unstable variable nodes; Indicates selecting the maximum residual degree among all edges of the LDPC code; Represents a set of unstable nodes; Represents all edges.

6. The method according to claim 5, characterized in that In step 3, the state of the silent maximum residual degree edge means that the information value of the edge where the maximum residual degree of the unstable variable node is located is not updated or transmitted.

7. The method according to claim 6, characterized in that In step 4, the message is updated using the following formula: Update check node message: ,in Indicates the updated message value; The updated message value is calculated by the following formula : = )， Where u represents a node in the variable node set N(a) connected to the check node a, but does not include the specified variable node v; Represents the message from node u to variable node v; in represents the set of all variable nodes connected to the check node a; Represents the set after removing the variable node v; σ is a function used to transform the message nonlinearly; Set the remainder to 0: =0.

8. The method according to claim 7, characterized in that Step 5 includes: Update variable node message: ; Stability judgment: If sign( , calculate the residual degree: = ， If sign( , it means that the confidence of the variable node has not changed significantly.

9. The method according to claim 8, characterized in that Step 6 includes: If all variable nodes satisfy: =0, or the maximum number of iterations is reached: t≥ , then stop decoding; where N(v) represents the set of check nodes connected to the variable node v; t is the current iteration number; is the maximum number of iterations.

10. An LDPC code sequential decoding device based on the unstable variable node maximum residual silence mechanism implemented according to the method according to any one of claims 1 to 9, characterized in that: It includes an initialization module, an unstable variable detection module, a maximum surplus degree selection module, a check node message update module, a variable node message propagation module, a stop condition check module and a control module; The initialization module is used to: Message initialization: Initialize all messages from check nodes to variable nodes to 0; LLR initialization: Initialize the message from the variable node to the check node to the received LLR value; Calculate the remaining degree: calculate the initial remaining degree of each variable node to check node message, and sort the remaining degrees in descending order; The unstable variable detection module is used for: Symbol comparison: compare the symbols before and after the variable node message is updated; Unstable variable node marking: detect unstable nodes and mark them; The maximum remainder selection module is used for: Residual degree comparison: select the edge with the largest residual degree among the unstable variable nodes; Global selection: If there is no unstable variable node, select the edge with the largest remaining degree in the entire system; The check node message update module is used for: Check node message update: Update the check node message corresponding to the selected edge; Clear the remaining degree: set the remaining degree of the selected edge to 0; The variable node message propagation module is used for: Variable node message update: update the variable node message corresponding to the selected edge; Stability judgment: judge the stability of variable nodes; Residue update: calculate the new residual value; The stop condition checking module is used for: Verification equation check: check whether all variable nodes satisfy the verification equation; Iteration number check: check whether the maximum number of iterations has been reached; The control module is used for: Process control: Control the entire decoding process, including module coordination and iterative control; State management: manages the states and conditions during the decoding process.

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