LDPC decoding method based on node reliability dynamic correction criterion

Abstract

The application provides an LDPC decoding method based on a node reliability dynamic correction criterion, which comprises the following steps: step 1, initializing channel information; step 2, judging whether the maximum iteration number is reached, if yes, outputting a result, otherwise, entering step 3; step 3, calculating the extrinsic information of a check node to a variable node; step 4, calculating the extrinsic information of the variable node to the check node; step 5, adjusting a correction factor according to the matching condition of the variable node symbol and the symbol of the last iteration; step 6, if the correction factor exceeds a threshold value, limiting the correction factor to the threshold value, otherwise, continuing the next step; step 7, multiplying the correction factor and the variable node information to adjust the weight of the node information; step 8, calculating the full information of the variable node and performing hard decision; step 9, checking whether the decision result satisfies the condition that the check sum is zero, if yes, outputting a decoding result, otherwise, continuing iteration; and step 10, if the condition is not satisfied, entering the next iteration and updating the check node information.

Description

Technical Field

[0001] This invention relates to the technical field of LDPC decoding methods for error-correcting codes, and specifically to an LDPC decoding method based on a dynamic correction criterion for node reliability. Background Technology

[0002] With the increasing complexity of communication service scenarios and applications, and the improvement of communication performance indicators, people have placed higher demands on the transmission security and transmission rate of mobile communication technology. Channel coding and decoding technology is an important component of modern mobile communication. Low-density parity-check (LDPC) codes are coding schemes that approach the Shannon limit. Their coding method is simple and clear, while their decoding complexity is relatively low. They also possess parallel operation capabilities, making them very suitable for hardware implementation in practical applications. Classic LDPC decoding algorithms include SPA and BP algorithms. This decoding algorithm uses soft information for iterative decoding, where probability information and log-likelihood ratio (LLR) information are used. Through continuous iterative calculations, it can achieve decoding performance close to the Shannon limit. However, because soft-decision algorithms use a large number of multiplication operations when calculating probability information, they require high computational complexity and resources, making hardware implementation difficult.

[0003] Using log-likelihood ratio information to represent probability information can transform a large number of multiplication operations into addition operations, greatly reducing computational complexity. Subsequently, scholars proposed various improved algorithms, such as the minimum sum (MS) algorithm and BP-based approximation algorithms, to further reduce decoding complexity, but these incur a loss of decoding accuracy. Therefore, reliability-based binary LDPC decoding algorithms were proposed. These algorithms extract reliability information based on the bit information before and after the decision to aid decoding, improving decoding performance to some extent while reducing decoding complexity. The most typical examples are the weighted bit-flipping (WBF) algorithm and its improved algorithms, such as the modified MWBF (MWBF) algorithm and the IMWBF algorithm. These two algorithms reduce computational complexity while minimizing the loss of decoding performance; however, because their weighting coefficients are fixed, they can lead to pseudo-local maxima phenomena and error planes in some multi-element LDPC codes. Furthermore, they also exhibit oscillation phenomena and may produce problems such as lower bound errors. Summary of the Invention

[0004] The purpose of this invention is to provide an LDPC decoding method based on a dynamic node reliability correction criterion, which aims to solve the technical problems of existing LDPC decoding methods, such as reduced decoding performance and non-convergence due to variable node oscillations when reducing algorithm complexity.

[0005] To achieve the above objectives, this invention provides an LDPC decoding method based on a dynamic node reliability correction criterion, comprising the following steps:

[0006] Step 1: Initialize the incoming channel information value, which serves as the initialization information for the variable node;

[0007] Step 2: Determine whether the current iteration count has reached the preset maximum iteration count. If it has, exit the loop and output the decoded codeword; otherwise, proceed to Step 3.

[0008] Step 3: Pass in the variable node information and calculate the external information passed from the verification node to the variable node;

[0009] Step 4: Pass in the verification node information and calculate the external information passed from the variable node to the verification node;

[0010] Step 5: Determine the sign of the current variable node information value. If it is the same as the sign in the previous iteration, increase the value of the correction factor; otherwise, decrease the value of the correction factor.

[0011] Step 6: Determine whether the value of the correction factor has exceeded the set threshold. If it exceeds the maximum threshold, set the value of the correction factor and the value of the corresponding variable node to zero; if it exceeds the minimum threshold, set the correction factor to the minimum threshold.

[0012] Step 7: Multiply the variable node by the correction factor. The information value of the variable node with high reliability is amplified, and the information value of the variable node with low reliability is reduced.

[0013] Step 8: Calculation of full information for variable nodes and hard decision;

[0014] Step 9: Verify the judgment result. If the checksum is zero, the decoding ends and the decoding result is output. If the condition is not met, proceed to step S10.

[0015] Step 10: Return to the decoding iteration process of step S2, enter the next round of iteration, and at the same time pass the information of the previous iteration to the check nodes that need to be updated for the allocation of check nodes;

[0016] Optionally, for the initialization processing of channel information, the initialization information value of the verification node is set to 0, while the initial information value of the variable node comes from the channel initialization message.

[0017]

[0018] L(P i ) represents the information value initially defined for the channel, i.e., the external information passed from the variable node to the check node during the first iteration; P i (b) indicates that the receiving end received y i Then, the corresponding sender codeword c i =The posterior probability of b, where b = 0, 1; v iLet i be the i-th variable node.

[0019] Optionally, the update rules for the verification nodes include the following calculation formula:

[0020] a. The BP algorithm, the calculation formula for the information transmitted from the check node to the variable node is as follows:

[0021]

[0022] The calculation formula for the information transmitted from the verification node to the variable node in the b.MS algorithm is as follows:

[0023] r ji =Π i′∈V(j)\i sgn(q i′j )·min i′∈V(j)\i (|q i′j |)

[0024] c. The NMS algorithm uses the following formula to calculate the information transmitted from the verification node to the variable node:

[0025] r ji =α·Π i′∈V(j)\i sgn(q i′j )·min i′∈V(j)\i (q i′j )

[0026] d. The OMS algorithm calculates the information transmitted from the verification node to the variable node using the following formula:

[0027] r ji =max(min i′∈V(j)\i (|q i′j |)-β,0)·Π i′∈V(j)\i sgn(q i′j )

[0028] in, This represents the external information passed from node j to variable node i in the l-th iteration, where b = 0, 1; V(j) represents the external information passed from variable node i to check node j in the l-th iteration, b = 0, 1; V(j) represents the set of other variable nodes connected to check node j, excluding the i-th variable node, V(j) = {k:h} kj =1,k≠i}; α is the scaling factor of the NMS algorithm, and β is the offset factor of the OMS algorithm.

[0029] Optionally, the external information passed from the variable node to the verification node is calculated using the following formula in V2C:

[0030]

[0031] q ij =qij *α ij

[0032] α ij = 1 / num(i,j)

[0033] Where C(i)\j represents the set of other check nodes connected to the i-th variable node, excluding the j-th check node, C(i)\j={k:h ik =1,k≠j};α ij This represents the dynamic correction factor for each variable node; num(i,j) is used to measure the reliability of the external information passed from variable node i to check node j.

[0034] Optionally, α ij This is a dynamic offset correction factor that passes information from variable nodes to verification nodes. In each new iteration, each variable node will have a corresponding correction factor α. ij The multiplication is performed, and the correction factor is determined by the reliability of the node. If the node is highly reliable, the correction factor is amplified; otherwise, it is reduced. The reliability of the variable node is determined by whether the sign of the information passed from the variable node to the verification node is consistent in two iterations. If the signs are the same, the node is reliable; if the signs are different, the node is unstable. The formula is as follows:

[0035]

[0036] Among them, Vij (l) (i, idx(j)) represents the information passed from the current variable node to the external verification node, Vij (l-1) (i, idx(j)) represents the information passed from the variable node to the verification node in the previous iteration.

[0037] Optionally, the dynamic offset correction factor α that is passed from the variable node to the external information of the verification node. ij The size has a specified threshold, which limits the variation of the external information passed from the variable node to the verification node to a stable range.

[0038] Optionally, the calculation of the full information of the variable node and the hard decision formula are as follows:

[0039]

[0040] When q ij When the posterior probability is greater than 0, i.e., the i-th variable node v is... ij The judgment is 0, otherwise the judgment is 1.

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

[0042] (1) This invention improves the accuracy and convergence speed of decoding by dynamically adjusting the node reliability correction factor.

[0043] (2) The present invention sets a correction factor threshold to avoid over-adjustment, ensure decoding stability and improve noise resistance.

[0044] (3) This invention can quickly exit the iteration by intelligently judging the checksum, reducing decoding delay and improving real-time performance.

[0045] (4) The present invention adaptively adjusts the decoding strategy under different channel conditions to improve the robustness and fault tolerance of the system. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 This is a flowchart of an LDPC decoding method based on a dynamic node reliability correction criterion according to the present invention.

[0048] Figure 2 It contains BER data for various commonly used LDPC decoding algorithms.

[0049] Figure 3 It is BER data with the addition of various LDPC decoding algorithms of this invention. Detailed Implementation

[0050] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0051] Please see Figure 1 This invention provides an LDPC decoding method based on a dynamic node reliability correction criterion, comprising the following steps:

[0052] S1: Initializes the incoming channel information value, serving as the initialization information for the variable node;

[0053] S2: Determine whether the current iteration count has reached the preset maximum iteration count. If it has, exit the loop and output the decoded codeword; otherwise, proceed to step 3.

[0054] S3: Pass in the variable node information and calculate the external information passed from the verification node to the variable node;

[0055] S4: Pass in the verification node information and calculate the external information passed from the variable node to the verification node;

[0056] S5: Determine the sign of the current variable node information value. If it is the same as the sign in the previous iteration, increase the value of the correction factor. If it is different, decrease the value of the correction factor.

[0057] S6: Determine whether the value of the correction factor has exceeded the set threshold. If it exceeds the maximum threshold, set the value of the correction factor and the value of the corresponding variable node to zero; if it exceeds the minimum threshold, set the correction factor to the minimum threshold.

[0058] S7: Multiplying the variable node by the correction factor amplifies the information value of the variable node with high reliability and reduces the information value of the variable node with low reliability.

[0059] S8: Calculation of full information of variable nodes and hard decision;

[0060] S9: Verify the judgment result. If the checksum is zero, the decoding ends and the decoding result is output. If the condition is not met, proceed to step S10.

[0061] S10: Return to the decoding iteration process of step S2, enter the next iteration, and pass the information from the previous iteration to the check nodes that need to be updated for the allocation of check nodes; iteration count +1.

[0062] Specifically, research on Low Density Parity Check (LDPC) decoding algorithms has revealed that the reliability of variable nodes varies. Nodes with poor reliability degrade decoding performance, causing oscillations in the decoding process, hindering rapid convergence, and generating non-negligible error layers. To address these shortcomings, an LDPC decoding method based on a dynamic node reliability correction criterion is proposed. This algorithm rationally judges the reliability of a variable node by analyzing the difference in the sign of its information value between two iterations. A threshold is then used to determine the degree of reliability. The algorithm adaptively derives a correction factor for each variable node based on its reliability information. Multiplying this correction factor by the variable node improves its reliability and stability, thereby enhancing decoding performance.

[0063] Furthermore, the following explanation is provided in conjunction with the specific implementation steps:

[0064] Step S1: During information transmission, the original binary codeword c = (c1, c2, ..., c nBinary Phase Shift Keying (BPSK) is used; the BPSK mapping rule is x. i =2c i -1) Modulation method: After modulation, the received signal sequence y = (y1, y2, ..., y) after passing through an additive white Gaussian noise channel is y = (y1, y2, ..., y) n )for

[0065] y i =x i +n i i = 1, 2, ..., n

[0066] Where, n i It follows a mean of 0 and a variance of σ. 2 =N0 / 2 Gaussian white noise.

[0067] The following symbols are redefined:

[0068] v i Represents the i-th variable node; c j This represents the j-th verification node; This represents the external information passed from node j to variable node i in the l-th iteration, where b = 0, 1; b represents the external information passed from variable node i to variable node j in the l-th iteration, where b = 0, 1; C(i) represents the set of all verification nodes connected to the i-th variable node, C(i) = {i:h} ij =1}; V(j) represents the set of all variable nodes connected to the j-th check node, V(j) = {j:h} ij =1}; C(i)\j represents the set of other check nodes connected to the i-th variable node, excluding the j-th check node, C(i)\j={k:h ik =1,k≠j}; V(j)\i represents the set of other variable nodes connected to the j-th check node, excluding the i-th variable node, V(j)\i={k:h kj =1,k≠i};P i (b) indicates that the receiving end received y i Then, the corresponding sender codeword c i = the posterior probability of b, where b = 0, 1; Let b represent the posterior probability information of the i-th variable node in the l-th iteration, where b = 0, 1;

[0069] Channel initialization process: The information value of the check node is initialized to 0, and the variable node is initialized with the following information from the channel:

[0070]

[0071] Simultaneously set the maximum number of iterations I for the entire algorithm. max .

[0072] Step S3: Input the variable node information and calculate the extrinsic information passed from the check node to the variable node. This invention proposes an LDPC decoding method based on a dynamic node reliability correction criterion. This method is applicable to optimizing various decoding algorithms and demonstrates the check node update rules for commonly used BP, MS, NMS, and OMS algorithms.

[0073] The update rules for the verification nodes include the following calculation formulas:

[0074] a. The BP algorithm, the calculation formula for the information transmitted from the check node to the variable node is as follows:

[0075]

[0076] The calculation formula for the information transmitted from the verification node to the variable node in the b.MS algorithm is as follows:

[0077] r ji =Π i′∈V(j)\i sgn(q i′j )·min i′∈V(j)\i (q i′j )

[0078] c. The NMS algorithm uses the following formula to calculate the information transmitted from the verification node to the variable node:

[0079] r ji =α·Π i′∈V(j)\i sgn(q i′j )·min i′∈V(j)\i (|q i′j |)

[0080] d. The OMS algorithm calculates the information transmitted from the verification node to the variable node using the following formula:

[0081] r ji =max(min i′∈V(j)\i (|q i′j |)-β,0)·Π i′∈V(j)\i sgn(q i′j )

[0082] Step S4: Input the verification node information and calculate the extrinsic information passed from the variable node to the verification node. The process of inputting verification node information involves calculating extrinsic information based on constraints. Extrinsic information refers to the information calculated by the verification node based on the received variable node information and constraints; it reflects the verification node's expectations and constraints regarding the variable node. This extrinsic information will be passed back to the variable node to update its information, and further verification node information input and calculation will be performed in the next iteration. The calculation formula is as follows:

[0083]

[0084] Step S5: During the decoding process, some variable nodes are reliable, while others are unreliable. This paper proposes an operation based on a dynamic node reliability correction criterion during decoding. Specifically, this operation involves determining the sign of the current variable node information value and adjusting the correction factor value based on whether it is the same as the sign in the previous iteration. If the signs are the same, the correction factor value will be amplified; if the signs are different, the correction factor value will be reduced. The specific logic for determining node reliability is as follows:

[0085]

[0086] α ij = 1 / num(i,j)

[0087] Among them, Vij (l) (i, idx(j)) represents the external information passed from the current variable node to the verification node, Vij (l-1) (i, idx(j)) represents the external information passed from the variable node to the check node in the previous iteration, α ij This represents the dynamic correction factor for each variable node; num(i,j) is used to measure the reliability of the external information passed from variable node i to check node j.

[0088] The purpose of this dynamic correction criterion is to adaptively adjust the magnitude of the correction factor based on changes in the sign of the variable node information value. By increasing or decreasing the value of the correction factor, more precise adjustments can be made to different variable node information values ​​during the decoding process. The advantage of this is that if the current sign is the same as the sign in the previous iteration, it indicates that the variable node information value is consistent, so the correction factor value can be increased to strengthen the reliability and influence of that information value. Conversely, if the current sign is different from the sign in the previous iteration, it indicates that the variable node information value has changed, potentially indicating some error or uncertainty; therefore, the correction factor value needs to be decreased to reduce the impact on that information value.

[0089] By comparison Figure 2 and Figure 3 The data clearly demonstrate that the LDPC decoding method (NRDC method) based on the dynamic node reliability correction criterion proposed in this paper exhibits superior performance advantages among various soft-decision decoding algorithms. Figure 3 This paper clearly demonstrates the significant performance improvement in bit error rate achieved by the LDPC decoding algorithm improved by the method presented in this paper. This innovative technique not only performs exceptionally well among various soft-decision decoding algorithms but also greatly enhances their performance. The introduction of this method makes a significant contribution to improving the reliability and efficiency of communication systems and opens up new prospects for the future development of communication technologies.

Claims

1. An LDPC decoding method based on a dynamic node reliability correction criterion, characterized in that, Includes the following steps: Step 1: Initialize the incoming channel information value, which serves as the initialization information for the variable node; Step 2: Determine whether the current iteration count has reached the preset maximum iteration count. If it has, exit the loop and output the decoded codeword; otherwise, proceed to Step 3. Step 3: Pass in the variable node information and calculate the external information passed from the verification node to the variable node; Step 4: Pass in the verification node information and calculate the external information passed from the variable node to the verification node; Step 5: Determine the sign of the current variable node information value. If it is the same as the sign in the previous iteration, increase the value of the correction factor; otherwise, decrease the value of the correction factor. Step 6: Determine whether the value of the correction factor has exceeded the set threshold. If it is less than the minimum threshold, then set the value of the correction factor and the value of the corresponding variable node to zero. If the maximum threshold is exceeded, the correction factor is set to equal the maximum threshold. Step 7: Multiply the variable node by the correction factor. The information value of the variable node with high reliability is amplified, and the information value of the variable node with low reliability is reduced. Step 8: Calculation of full information for variable nodes and hard decision; Step 9: Verify the judgment result. If the checksum is zero, the decoding ends and the decoding result is output. If the condition is not met, proceed to step S10. Step 10: Return to the decoding iteration process of step S2, enter the next round of iteration, and at the same time pass the information of the previous iteration to the check nodes that need to be updated for the allocation of check nodes.

2. The LDPC decoding method based on a dynamic node reliability correction criterion as described in claim 1, characterized in that, For the initialization of channel information, the initialization information value of the check node is set to 0, while the initial information value of the variable node comes from the channel initialization message. This represents the information value initially defined for the channel, i.e., the external information passed from the variable node to the verification node during the first iteration; This indicates that the receiving end has received... Then, the corresponding sender codeword The posterior probability, b = 0, 1; Let i be the i-th variable node.

3. The LDPC decoding method based on a dynamic node reliability correction criterion as described in claim 2, characterized in that, The update rules for the verification nodes include the following calculation formulas: a. The BP algorithm, the calculation formula for the information transmitted from the check node to the variable node is as follows: The calculation formula for the information transmitted from the verification node to the variable node in the b.MS algorithm is as follows: c. The NMS algorithm uses the following formula to calculate the information transmitted from the verification node to the variable node: d. The OMS algorithm calculates the information transmitted from the verification node to the variable node using the following formula: in, This represents the external information passed from node j to variable node i in the first iteration, where b = 0, 1; This represents the external information passed from variable node i to check node j in the first iteration, where b = 0, 1; This represents the set of other variable nodes connected to the j-th check node, excluding the i-th variable node. ; This is the scaling factor for the NMS algorithm. This is the offset factor for the OMS algorithm.

4. The LDPC decoding method based on a dynamic node reliability correction criterion as described in claim 3, characterized in that, The external information passed from the variable node to the verification node is calculated using the following formula in V2C: in, This represents the set of other check nodes connected to the i-th variable node, excluding the j-th check node. ; The dynamic offset correction factor represents the information passed from the variable node to the external verification node. Used to measure the reliability of external information passed from variable node i to check node j.

5. The LDPC decoding method based on a dynamic node reliability correction criterion as described in claim 4, characterized in that, This is a dynamic offset correction factor that passes information from variable nodes to verification nodes. In each new iteration, each variable node will have a corresponding correction factor. The value of the correction factor is determined by the reliability of the node. If the node has high reliability, the value of the correction factor is increased; otherwise, it is decreased. The reliability of this variable node is determined by whether the sign of the information passed from the variable node to the verification node is consistent in two iterations. If the signs are the same, it is a reliable node; if the signs are different, it is a volatile node. The formula is as follows: in, This indicates that the current variable node passes information to the external verification node. This indicates that the variable node passed information to the check node in the previous iteration.

6. The LDPC decoding method based on a dynamic node reliability correction criterion as described in claim 5, characterized in that, Its variable node passes the dynamic offset correction factor to the external information of the check node. The size of the variable node has a specified threshold, which limits the variation of the external information passed from the variable node to the verification node to a stable range.

7. The LDPC decoding method based on a dynamic node reliability correction criterion as described in claim 6, characterized in that, The calculation of the full information of the variable node and the hard decision formula are as follows: when That is, when the posterior probability is greater than 0, the first... Variable nodes The judgment is 0, otherwise the judgment is 1.

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