Adaptive LDPC decoding method based on node reliability

Abstract

The invention provides a self-adaptive LDPC (Low Density Parity Check) decoding method based on node reliability. The self-adaptive LDPC decoding method comprises the following steps: step 1, initializing parameter configuration; 2, calculating the value of a variable node in the iteration process, and judging the reliability of the variable node; 3, judging the reliability of the check node according to the reliability of the variable node, and selecting a proper algorithm to calculate the value of the check node by using the value of the reliability of the check node; 4, when the reliability of the check node is higher than a threshold value, resetting processing is carried out, and it is avoided that a decoding result has a high error code flat layer; step 5, checking the judgment result, if the check sum is zero, ending decoding, outputting a decoding result, and if the condition is not met, entering step 6; and 6, judging whether the current number of iterations reaches the preset maximum number of iterations, if the current number of iterations does not reach the preset maximum number of iterations, continuing loop iteration, and when the current number of iterations reaches the preset maximum number of iterations, ending decoding.

Description

Technical Field

[0001] The present invention relates to the technical field of channel coding and encoding, and in particular to an adaptive LDPC decoding method based on node reliability. Background Art

[0002] Low-density parity-check (LDPC) codes were first discovered in 1962 but were quickly neglected until their rediscovery in 1995. LDPC codes can approach the channel capacity when using the belief propagation (BP) decoding algorithm. When the traditional log-likelihood ratio belief propagation (LLR BP) decoding algorithm is applied to LDPC codes, the decoding information is iteratively exchanged between variable nodes and check nodes. It is a commonly used decoding algorithm and the most straightforward scheduling method, where all variable-to-check (V2C) messages and all check-to-variable (C2V) messages are continuously updated. As the decoding process proceeds, different nodes have different reliabilities. In general, the more reliable the nodes, the less computational resources are required for the nodes, as they do not change during the decoding process of subsequent iterations. Therefore, equal distribution of computations for all nodes and edges can lead to a waste of limited computational resources.

[0003] In order to make full use of the dynamic characteristics of the decoded information, more and more dynamic decoding algorithms based on different message selection and update strategies have been proposed, such as variable check RBP (VC RBP), dynamic silent variable node-free scheduling (D-SVNFS) and residual belief propagation based on residual decay (RD RBP).

[0004] Although the algorithms discussed above have attractive performance in terms of error correction and convergence speed, there is still a problem in decoding. When using dynamic scheduling for decoding, the node reliability assessment is inaccurate, resulting in a scheduling order that is not the optimal solution. The adaptive LDPC decoding method based on node reliability proposed in the present invention mainly solves the problems of inaccurate node reliability assessment and unclear node classification. The present invention aims to improve the efficiency and performance of LDPC decoding by comprehensively utilizing different node reliabilities and dynamic update strategies, reduce the bit error rate and reduce the computational complexity, thereby achieving better performance in actual engineering applications. Summary of the invention

[0005] In view of the defects in the prior art, the object of the present invention is to provide an adaptive LDPC decoding method based on node reliability.

[0006] The adaptive LDPC decoding method based on node reliability provided by the present invention comprises the following steps:

[0007] Step 1: Initialize parameter configuration;

[0008] Step 2: Calculate the value of the variable node during the iteration process and determine the reliability of the variable node;

[0009] Step 3: Determine the reliability of the check node according to the reliability of the variable node, and use the value of the check node reliability to select a suitable algorithm to calculate the value of the check node;

[0010] Step 4: When the reliability of the check node is higher than the threshold, a reset process is performed to avoid a high error floor in the decoding result;

[0011] Step 5: Check the judgment result. If the checksum is zero, the decoding is completed and the decoding result is output. If the condition is not met, go to step 6.

[0012] Step 6: Determine whether the current number of iterations has reached the preset maximum number of iterations. If it has not reached the maximum number of decoding times, continue to iterate in a loop. When the preset number of iterations is reached, the decoding ends.

[0013] Preferably, step 1 comprises the following steps:

[0014] Step 1.1: Configure the maximum number of iterations to iterMax based on historical data;

[0015] Step 1.2: For the initialization of channel information, the initialization information value of the check node is set to 0, and the initial information value of the variable node comes from the initial message of the channel.

[0016]

[0017] L(P i ) represents the information value initially defined by the channel, i.e., the external information transmitted from the variable node to the check node in the first iteration; P i (b) indicates that the receiving end receives y i After that, the corresponding codeword c i = the posterior probability of b, b = 0, 1; v i is the i-th variable node.

[0018] Preferably, step 2 comprises the following steps:

[0019] Step 2.1: The calculation formula of the variable node is as follows:

[0020]

[0021] Where C(i)\j represents the set of other check nodes connected to the i-th variable node except the j-th check node, C(i)\j={k:h ik =1,k≠j};

[0022] Step 2.2: If the sign of the variable node in two consecutive iterations, sign(Vij (l) (i, idx(j))), changes, then determine that this variable node is an unreliable node; otherwise, it is a reliable node.

[0023] Step 2.3: If the absolute value of the residual of the variable node in two consecutive iterations is less than the threshold threshoid, that is, it satisfies

[0024] |Vij (l) (i, idx(j)) - Vij (l-1) (i, idx(j))| < threshold, then determine that this variable node is a reliable node; otherwise, it is an unreliable node.

[0025] Preferably, the said Step 3 includes the following steps:

[0026] Step 3.1: In the formula for calculating the check node, the variable node needs to be used as the input of the formula. Therefore, when the reliability of the variable node is relatively high, the reliability of the check node calculated by substituting it into the check node equation is also relatively high at this time. If this variable node satisfies both the same sign in two consecutive iterations and the residual is within the specified threshold, then it is determined that the reliability of the corresponding check node is relatively high. If this variable node only satisfies the same sign in two consecutive iterations and does not satisfy the residual within the specified threshold, then it is determined that the reliability of the corresponding check node is average. If this variable node neither satisfies the same sign in two consecutive iterations nor satisfies the residual within the specified threshold, then it is determined that the reliability of the corresponding check node is relatively low.

[0027] Step 3.2: The check nodes with relatively high reliability keep the values of the previous iteration without updating, reducing the computational complexity of decoding and improving the decoding efficiency.

[0028] Step 3.3: The check nodes with average reliability are updated using the NMS decoding algorithm. This algorithm has much less computational complexity than the BP algorithm and slightly lower decoding performance. It is suitable for check nodes with medium reliability. The formula of its NMS algorithm is as follows:

[0029] r ji = α·Π i′∈V(j)\i sgn(q i′j )·min i′∈V(j)\i (q i′j )

[0030] Wherein, represents the extrinsic information passed from the check node j to the variable node i in the l-th iteration, b = 0, 1; Denote the extrinsic information passed from variable node \(i\) to check node \(j\) in the \(l\)-th iteration, \(b = 0, 1\); \(V(j)\setminus i\) represents the set of other variable nodes connected to the \(j\)-th check node except the \(i\)-th variable node, \(V(j)\setminus i=\{k:h kj = 1,k\neq i\}; \(\alpha\) is the scaling factor of the NMS algorithm.

[0031] Step 3.4: For unreliable check nodes, use the BP algorithm for updating. The decoding performance of the BP algorithm is very good, approaching the Shannon limit approximately, and can correct unreliable check nodes. The formula of the BP algorithm is as follows:

[0032]

[0033] Preferably, the said step 4 includes the following steps:

[0034] Step 4.1: When the reliability of the check node is continuously at a high level for three times during the iteration process, then set the check node to 0. This can avoid the situation that a small number of nodes stop updating, resulting in short cycles and a high error floor during decoding.

[0035] Preferably, the said step 5 includes the following steps:

[0036] Step 5.1: The calculation of the full information of the variable node and the hard decision formula are as follows:

[0037]

[0038] When \(q ij > 0\), that is, when the posterior probability is greater than 0, the \(i\)-th variable node \(v ij is judged as 0, otherwise it is judged as 1.

[0039] Preferably, the said step 6 includes the following steps:

[0040] Step 6.1: If \(iter < iterMax\), then continue the iterative decoding, otherwise stop the iterative decoding.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] (1) The present invention dynamically adjusts the decoding strategy according to the node reliability, improving the decoding efficiency and accuracy.

[0043] (2) The present invention effectively avoids a high error floor in the decoding result by performing a reset process when the reliability of the check node is higher than the threshold.

[0044] (3) The present invention adopts a simple calculation process and less calculation amount, and is easy to implement and integrate into various LDPC communication systems.

[0045] (4) The present invention can reduce resource consumption and power consumption, improve system performance and efficiency, and is suitable for actual engineering application scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0047] Figure 1 Flowchart of an adaptive LDPC decoding method based on node reliability.

[0048] Figure 2 BER data for each algorithm in Example 2.

[0049] Figure 3 is the complexity ratio of each algorithm in Example 2. DETAILED DESCRIPTION

[0050] The present invention is described in detail below in conjunction with specific embodiments. The following embodiments will help those skilled in the art to further understand the present invention, but are not intended to limit the present invention in any form. It should be noted that, for those of ordinary skill in the art, several changes and improvements can be made without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0051] Embodiment 1:

[0052] like Figure 1 According to the node reliability-based adaptive LDPC decoding method provided by the present invention, the method comprises the following steps:

[0053] Step 1: Initialize parameter configuration;

[0054] Step 2: Calculate the value of the variable node during the iteration process and determine the reliability of the variable node;

[0055] Step 3: Determine the reliability of the check node according to the reliability of the variable node, and use the value of the check node reliability to select a suitable algorithm to calculate the value of the check node;

[0056] Step 4: When the reliability of the check node is higher than the threshold, a reset process is performed to avoid a high error floor in the decoding result;

[0057] Step 5: Check the judgment result. If the checksum is zero, the decoding is completed and the decoding result is output. If the condition is not met, go to step 6.

[0058] Step 6: Determine whether the current number of iterations has reached the preset maximum number of iterations. If not, continue to iterate in a loop. When the preset number of iterations is reached, end the decoding.

[0059] The said step 1 includes the following steps:

[0060] Step 1.1: Configure the maximum number of iterations iterMax according to historical data;

[0061] Step 1.2: For the initialization process of channel information, set the initialization information value of the check node to 0, while the initial information value of the variable node comes from the channel initial message as

[0062]

[0063] L(P i ) represents the information value initially defined by the channel, that is, the extrinsic information passed from the variable node to the check node during the first iteration; P i (b) represents the posterior probability that the received codeword c i at the receiving end corresponds to the transmitted codeword c i =b after receiving y i , where b = 0, 1; v

[0064] The said step 2 includes the following steps:

[0065] Step 2.1: The calculation formula for the variable node is as follows:

[0066]

[0067] where C(i)\j represents the set of other check nodes connected to the i-th variable node except the j-th check node, C(i)\j = {k:h ik = 1, k ≠ j};

[0068] Step 2.2: If the sign of the variable node between two iterations sign(Vij (l) (i, idx(j))) changes, then determine that this variable node is an unreliable node, otherwise it is a reliable node;

[0069] Step 2.3: If the absolute value of the variable node residual between two consecutive iterations is less than the threshold threshoid, that is, it satisfies

[0070] |Vij (l) (i, idx(j)) - Vij (l-1) (i, idx(j))| < threshold, then determine that this variable node is a reliable node, otherwise it is an unreliable node.

[0071] The said step 3 includes the following steps:

[0072] Step 3.1: In the formula for calculating the check node, the variable node needs to be used as the input of the formula. Therefore, when the variable node reliability is high, the check node reliability calculated by substituting it into the check node equation is also high. If the variable node satisfies both the same sign of the previous and next iterations and the residual is within the specified threshold, the corresponding check node reliability is determined to be high. If the variable node only satisfies the same sign of the previous and next iterations and does not satisfy the residual within the specified threshold, the corresponding check node reliability is determined to be average. If the variable node does not satisfy both the same sign of the previous and next iterations and does not satisfy the residual within the specified threshold, the corresponding check node reliability is determined to be low.

[0073] Step 3.2: The check nodes with higher reliability keep the values ​​of the previous iteration and do not update them, which reduces the amount of decoding calculation and improves the decoding efficiency.

[0074] Step 3.3: Check nodes with average reliability are updated using the NMS decoding algorithm. This algorithm has much less computational complexity than the BP algorithm, but has slightly lower decoding performance. It is suitable for check nodes with medium reliability. The NMS algorithm formula is as follows:

[0075] r ji =α·Π i′∈V(j)\i sgn(q i′j )·min i′∈V(j)\i (q i′j )

[0076] in, Indicates the external information passed from check node j to variable node i in the lth iteration, b = 0, 1; represents the external information transmitted by variable node i to check node j in the lth iteration, b = 0, 1; V(j)\i represents the set of other variable nodes connected to the jth check node except the i-th variable node, V(j)\i = {k:h kj =1, k≠i}; α is the scaling factor of the NMS algorithm.

[0077] Step 3.4: For unreliable check nodes, the BP algorithm is used to update. The decoding performance of the BP algorithm is very good, approximately reaching the Shannon limit, and can correct unreliable check nodes. The BP algorithm formula is as follows:

[0078]

[0079] The step 4 comprises the following steps:

[0080] Step 4.1: When the reliability of the check node is at a high reliability for three consecutive times during the iteration process, the check node is set to 0. This can avoid a small number of nodes being no longer updated, resulting in a short loop and a high error floor during decoding.

[0081] Step 5 includes the following steps:

[0082] Step 5.1: The calculation of the full information of variable nodes and the hard decision formula are as follows:

[0083]

[0084] When q ij > 0, that is, when the posterior probability is greater than 0, the i-th variable node v ij is determined to be 0, otherwise it is determined to be 1.

[0085] Step 6 includes the following steps:

[0086] Step 6.1: If iter < iterMax, continue iterative decoding, otherwise stop iterative decoding.

[0087] Embodiment 2:

[0088] Embodiment 2 is a preferred example of Embodiment 1.

[0089] Next, taking the LDPC decoding scenario that conforms to the 3GPP R15 standard as an example, the adaptive LDPC decoding method based on node reliability proposed by the present invention is described. Specifically, the configuration of NR R15 LDPC is as follows: the information bit length kb = 18, the parity bit length mb = 18, the extended matrix Zc = 56, the code rate n = 1 / 2, the total bit length L = (kb + mb) * Zc = 2016, the modulation is BPSK modulation, and the transmission channel is an additive white Gaussian noise channel.

[0090] First, perform parameter initialization configuration. The signal-to-noise ratio range to be tested is -0.2 to 2. In order to obtain more detailed experimental results, the step value of the signal-to-noise ratio is set to 0.1. At each signal-to-noise ratio, the maximum number of decoding iterations iterMax = 30, the maximum number of simulation blocks maxBlocks = 1000 at each signal-to-noise ratio, and the maximum number of error blocks maxErrorBlocks = 50 at each signal-to-noise ratio. The above parameters are all empirical values obtained through a large number of simulation experiments. Specifically, it is necessary to evaluate the decoding performance by simulating different LDPC code lengths, code rates, channel conditions, and various parameter configurations. On the premise of maintaining the decoding performance, select the parameter combination that can perform optimally in terms of the bit error rate performance. These optimized parameter combinations will form a parameter table, which can be used to guide the selection and configuration of LDPC codes in actual applications. By systematically studying the influence of different parameter values on the performance of LDPC codes, the design and application of LDPC codes can be optimized, thereby improving the reliability and efficiency of communication systems. This method helps to ensure that LDPC codes can perform excellently in various communication environments and provides the best parameter setting suggestions for specific application scenarios.

[0091] During the LDPC iterative decoding process, as the iteration proceeds, the value of the iteration count variable iter will gradually increase. After each iteration, iter will increase by 1. When iter is less than the preset maximum number of iterations iterMax, the iteration process will continue; however, when iter exceeds iterMax, the decoding process will terminate immediately. This iterative control mechanism ensures that the LDPC decoding process is carried out effectively within a reasonable number of iterations, thereby improving the efficiency and accuracy of decoding.

[0092] In LDPC decoding, it is not sufficient to rely solely on the value of the check node to evaluate its reliability. Sometimes the value of some check nodes is 0, but this does not mean that these nodes are reliable. In fact, in this case, there may be errors in two or more variable nodes. Therefore, a more accurate evaluation of the reliability of the check node should consider the reliability of the variable nodes involved in the calculation of the check node. By comprehensively considering the situation of the variable nodes involved in the calculation of the check node, the reliability of the check node can be judged more accurately, thereby improving the accuracy and robustness of the LDPC decoding process.

[0093] Therefore, before evaluating the reliability of the check node, the following two steps need to be completed: first, calculate the information of the variable node; second, calculate the reliability of the variable node. When calculating the reliability of the variable node, it is necessary to evaluate from two perspectives. First, the reliability is judged based on whether the symbols of the variable node in the previous and subsequent iterations are consistent, and second, it is evaluated based on whether the absolute value of the residual between the previous and subsequent iterations is less than the set threshold. Meeting the first condition indicates that the variable node is at a medium reliability level, while meeting both conditions at the same time indicates that the variable node is relatively more reliable, and not meeting these two conditions indicates that the variable node is unreliable. This dual evaluation mechanism can more comprehensively evaluate the reliability of the variable node, thereby improving the accuracy and reliability of the LDPC decoding process.

[0094] according to Figure 1 In the flowchart of the adaptive LDPC decoding method based on node reliability, the next step is to use the calculated variable node reliability to evaluate the reliability of the check node. In the calculation algorithm of the check node, the value of the variable node is regarded as the variable input. When the variable node has a higher reliability, the calculated check node will also be more reliable. Therefore, based on the reliability of the variable node, the check node is classified into three levels: unreliable, generally reliable, and relatively reliable. By carefully considering the reliability of the variable node, the reliability of the check node can be more accurately evaluated, providing more reliable support for the decoding quality of the LDPC code.

[0095] After obtaining the reliability of the check node, the decoding method of the present invention adopts different decoding algorithms for check nodes with different reliability, and selects the BP algorithm and the NMS algorithm respectively.

[0096] a. BP algorithm, the calculation formula for information transmission from the check node to the variable node is as follows:

[0097]

[0098] b. NMS algorithm, the calculation formula for information transmission from the check node to the variable node is as follows:

[0099] r ji =α·Π i′∈V(j)\i sgn(q i′j )·min i′∈V(j)\i (q i′j )

[0100] The BP algorithm can converge to the correct solution quickly in many cases, has excellent error performance and convergence, and can usually approach the channel capacity limit. However, the BP algorithm may sometimes exhibit a slower convergence speed and higher computational complexity. In contrast, the NMS algorithm usually has a faster convergence speed, which helps to improve the decoding efficiency, but the implementation is relatively simple, and the decoding performance may not be as stable as the BP algorithm. Therefore, in the decoding process of LDPC codes, the present invention makes full use of the respective advantages and disadvantages of the BP algorithm and the NMS algorithm, and can achieve a balance between decoding performance and efficiency.

[0101] Different decoding algorithm strategies are adopted for check nodes with different reliability: for check nodes with low reliability, BP algorithm is adopted to improve their reliability; for check nodes with average reliability, NMS algorithm is selected to consolidate their reliability; and for more reliable nodes, updates are stopped, thereby saving decoding time and improving overall decoding efficiency.

[0102] Figure 2 The bit error rate (BER) of various algorithms in different channel signal-to-noise ratio environments in Example 2 is shown. It can be clearly seen from the figure that the adaptive LDPC decoding method based on node reliability (ANRA algorithm) proposed in this paper successfully reduces the decoding calculation amount while ensuring that the decoding performance is not inferior to the BP algorithm.

[0103] Figure 3 The complexity ratio of different algorithms in Example 2 is shown. The complexity ratio here is defined as the ratio of the number of check node updates to the maximum number of check node updates at each signal-to-noise ratio. It can be clearly seen from the figure that the ANRA algorithm has the lowest complexity, which significantly reduces the burden of decoding calculations.

[0104] Those skilled in the art know that, in addition to implementing the system, device and its various modules provided by the present invention in a purely computer-readable program code, it is entirely possible to implement the same program in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers and embedded microcontrollers by logically programming the method steps. Therefore, the system, device and its various modules provided by the present invention can be considered as a hardware component, and the modules included therein for implementing various programs can also be regarded as structures within the hardware component: the modules for implementing various functions can also be regarded as both software programs for implementing the method and structures within the hardware component.

[0105] The above describes the specific embodiments of the present invention. It should be understood that the present invention is not limited to the above specific embodiments, and those skilled in the art can make various changes or modifications within the scope of the claims, which does not affect the essence of the present invention. In the absence of conflict, the embodiments of the present application and the features in the embodiments can be combined with each other arbitrarily.

Claims

1. An adaptive LDPC decoding method based on node reliability, characterized in that: The steps include: Step 1: Initialize parameter configuration; Step 2: Calculate the value of the variable node during the iteration process and determine the reliability of the variable node; Step 3: Determine the reliability of the check node according to the reliability of the variable node, and use the value of the check node reliability to select a suitable algorithm to calculate the value of the check node; Step 4: When the reliability of the check node is higher than the threshold, a reset process is performed to avoid a high error floor in the decoding result; Step 5: Check the judgment result. If the checksum is zero, the decoding is completed and the decoding result is output. If the condition is not met, go to step 6. Step 6: Determine whether the current number of iterations has reached the preset maximum number of iterations. If it has not reached the maximum number of decoding times, continue to iterate in a loop. When the preset number of iterations is reached, the decoding ends.

2. The node reliability-based adaptive LDPC decoding method according to claim 1, characterized in that: The step 1 comprises the following steps: Step 1.1: Configure the maximum number of iterations to iterMax based on historical data; Step 1.2: For the initialization of channel information, the initialization information value of the check node is set to 0, and the initial information value of the variable node comes from the initial message of the channel. L(P i ) represents the information value initially defined by the channel, i.e., the external information transmitted from the variable node to the check node in the first iteration; P i (b) indicates that the receiving end receives y i After that, the corresponding codeword c i = the posterior probability of b, b = 0, 1; v i is the i-th variable node.

3. The node reliability-based adaptive LDPC decoding method according to claim 2, characterized in that: The step 2 comprises the following steps: Step 2.1: The calculation formula of the variable node is as follows: Where C(i)\j represents the set of other check nodes connected to the i-th variable node except the j-th check node, C(i)\j={k:h ik =1,k≠j}; Step 2.2: If the variable node symbol sign(Vij of the two iterations (l) If (i,idx(j))) changes, the variable node is determined to be an unreliable node, otherwise it is a reliable node; Step 2.3: If the absolute value of the variable node residual in the two iterations is less than the threshold threshoid, then |Vij (l) (i, idx(j)) - Vij (l-1) (i, idx(j)) < threshold, then determine that the variable node is a reliable node, otherwise it is an unreliable node.

4. The node reliability-based adaptive LDPC decoding method according to claim 3, characterized in that: The step 3 comprises the following steps: Step 3.1: In the formula for calculating the check node, the variable node needs to be used as the input of the formula. Therefore, when the variable node reliability is high, the check node reliability calculated by substituting it into the check node equation is also high. If the variable node satisfies both the same sign of the previous and next iterations and the residual is within the specified threshold, the corresponding check node reliability is determined to be high. If the variable node only satisfies the same sign of the previous and next iterations and does not satisfy the residual within the specified threshold, the corresponding check node reliability is determined to be average. If the variable node neither satisfies the same sign of the previous and next iterations nor the residual is within the specified threshold, the corresponding check node reliability is determined to be low. Step 3.2: The check nodes with higher reliability keep the values ​​of the previous iteration and do not update them, which reduces the amount of decoding calculation and improves the decoding efficiency; Step 3.3: Check nodes with average reliability are updated using the NMS decoding algorithm. This algorithm has much less computational complexity than the BP algorithm, but has slightly lower decoding performance. It is suitable for check nodes with medium reliability. The NMS algorithm formula is as follows: r ji =a·P i′∈V(j)\i sgn(q i′j )·min i′∈V(j)\i (q i′j ) in, Indicates the external information passed from check node j to variable node i in the lth iteration, b = 0, 1; represents the external information transmitted by variable node i to check node j in the lth iteration, b = 0, 1; V(j)\i represents the set of other variable nodes connected to the jth check node except the i-th variable node, V(j)\i = {k:h kj =1, k≠i}; α is the scaling factor of the NMS algorithm. Step 3.4: For unreliable check nodes, the BP algorithm is used to update. The decoding performance of the BP algorithm is very good, approximately reaching the Shannon limit, and can correct unreliable check nodes. The BP algorithm formula is as follows:

5. The node reliability-based adaptive LDPC decoding method according to claim 4, characterized in that: The step 4 comprises the following steps: Step 4.1: When the reliability of the check node is at a high reliability for three consecutive times during the iteration process, the check node is set to 0. This can avoid a small number of nodes being no longer updated, resulting in a short loop and a high error floor during decoding.

6. The node reliability-based adaptive LDPC decoding method according to claim 5, characterized in that: The step 5 comprises the following steps: Step 5.1: The calculation of the full information of the variable node and the hard decision formula are as follows: When ij >0, that is, when the posterior probability is greater than 0, the i-th variable node v ij The judgment is 0, otherwise the judgment is 1.

7. The node reliability-based adaptive LDPC decoding method according to claim 6, characterized in that: The step 6 comprises the following steps: Step 6.1: If iter < iterMax, continue iterative decoding; otherwise, stop iterative decoding.

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