Quick list decoding method for polarization-adjusted convolutional code special nodes

Abstract

Provided are a quick list decoding method and apparatus for polarization-adjusted convolutional code special nodes, a computer electronic device, a computer-readable storage medium, a computer program product, and a computer program. The method comprises: determining special nodes in a decoding tree for list decoding of a polarization-adjusted convolutional code, wherein the special nodes comprise a source node and a code rate-0 or repetition code sub-node sequence, and the special nodes are identified by means of a sequence set; calculating a log-likelihood ratio of the source node corresponding to the special node, and determining the number of paths; on the basis of the log-likelihood ratio and the sequence set corresponding to the special nodes, calculating the metrics of the paths, and reserving the paths having the minimum metric, the number of reserved paths being the set number of lists; decoding the source node by taking the reserved paths as inputs to obtain an estimated code word of each path of the source node; and calculating an estimated output bit and an estimated information bit of the special node by means of the estimated code word.

Description

A fast list decoding method for special nodes of polarization-adjusted convolutional codes

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This disclosure claims priority to Chinese Patent Application No. 2023106325792 filed in China on May 30, 2023, the entire contents of which are incorporated herein by reference. Technical Field

[0003] The present disclosure belongs to the field of wireless communication technology, and particularly relates to a method and device for fast list decoding of special nodes of a polarization-adjusted convolutional code, a computer electronic device, a computer-readable storage medium, a computer program product, and a computer program. Background Art

[0004] Polar codes are a channel coding scheme theoretically proven to achieve the Shannon capacity and have been adopted by fifth-generation mobile communication systems (5G). In his Shannon Lecture at the 2019 International Symposium on Information Technology (ISIT), Arikan proposed a new polar coding scheme, named polarization-adjusted convolutional (PAC) codes. This scheme represents a significant breakthrough in polar coding, further improving its performance. For shorter code lengths, polarization-adjusted convolutional codes outperform polar codes without CRC concatenation; for longer code lengths, CRC-assisted polarization-adjusted convolutional codes outperform CRC-assisted polar codes. The mainstream decoding algorithms for polarization-adjusted convolutional codes include Fano sequential decoding and list decoding. Notably, under sequential decoding, the performance of polarization-adjusted convolutional codes closely approximates the binary additive white Gaussian noise (BIAWGN) dispersion bound. Compared to sequential decoding, successive cancellation list (SCL) decoding is a non-backtracking tree search method with fixed complexity. Therefore, it has lower worst-case complexity and offers advantages under low signal-to-noise ratio (SNR) conditions. List decoding significantly improves the error correction performance of offset list SC decoding by maintaining a list that retains multiple most reliable codeword sequences. By combining polar codes with a cyclic redundancy check (CRC), a CRC-aided SCL (CA-SCL) decoder can achieve decoding performance approaching that of a maximum-likelihood (ML) decoder. However, the bit-by-bit sequential nature of SC decoding results in high decoding latency and low throughput, hindering its application in low-latency communication scenarios such as ultra-reliable low-latency communication (URLLC). To achieve efficient decoding, a common approach is to parallelize the decoding process, performing decoding at the intermediate nodes of the decoding tree rather than at the leaf node level. This avoids traversing the entire code tree and improves decoding speed. To ensure decoding performance does not degrade, node-level decoding must consider the constraints imposed by the distribution of information and frozen bits within the node to ensure effective decoding results. Therefore, the higher the level of the decoding tree a node is in, the higher the degree of parallelism, the lower the decoding latency, and the higher the throughput. However, the more bits a node contains, the more complex the constraints, making it difficult to implement a performance-free decoding algorithm.

[0005] Summary of the Invention

[0006] The present disclosure aims to solve one of the technical problems in the related art at least to a certain extent.

[0007] To this end, the present disclosure provides a method and apparatus, computer electronic device, computer-readable storage medium, computer program product, and computer program for fast list decoding of special nodes of polarization-adjusted convolutional codes. This method is used to reduce time complexity without sacrificing decoding performance.

[0008] To achieve the above objectives, the first embodiment of the present disclosure proposes a fast list decoding method for special nodes of a polarization-adjusted convolutional code, including:

[0009] Determining the special node in a decoding tree for polarization-adjusted convolutional code list decoding; wherein the special node includes a source node and a sequence of rate-zero or rate-repeat code sub-nodes, and identifying the special node by a sequence set;

[0010] Calculating the log-likelihood ratio of the source node corresponding to the special node to determine the number of paths;

[0011] Calculating the metric of each path based on the log-likelihood ratio and the sequence set corresponding to the special node, retaining the path with the smallest metric, and the number of retained paths being the set number of lists;

[0012] Decoding the source node using the retained path as input to obtain an estimated codeword for each path of the source node; and

[0013] The estimated output bits and estimated information bits of the special node are calculated using the estimated codeword.

[0014] In addition, the fast list decoding method for a special node of a polarization-adjusted convolutional code according to the above embodiment of the present disclosure may also have the following additional technical features:

[0015] In one embodiment of the present disclosure, the special node includes a polarization-adjusted convolutional code subcode consisting of the one source node and the string of code rate zero or repeat code subcode subcode sequences, and is located in the middle layer of the decoding tree of the polarization-adjusted convolutional code continuous offset decoding, wherein the source node is located at the rightmost node of the special node and has an arbitrary code rate and structure, and the source node length N r Satisfy N r ≥p-1, where p is the length of the convolution polynomial of the polarization-adjusted convolutional code.

[0016] In one embodiment of the present disclosure, the sequence set includes:

[0017] For the mth sequence set set up is a function that performs recursive calculations, is the result after a times of calculation, then Where s and r are the levels of the special node and the source node in the decoding tree for decoding the polarization-adjusted convolutional code list, respectively. m The calculation is as follows:

[0018] in, represents the Kronecker product, x s-a is the first (p-1) bit value of the a-th child node. For the child node x at level t in the decoding tree, t It can be calculated as follows:

[0019] Where q = 2t-p + 1,0 q is an all-zero vector of length q, is a vector of length q and

[0020] In one embodiment of the present disclosure, calculating the log-likelihood ratio of the source node corresponding to the special node includes:

[0021] Log-likelihood ratio based on special nodes And the log-likelihood ratio of the source node for the mth sequence set The calculation is as follows:

[0022] In one embodiment of the present disclosure, calculating the metric of each path includes:

[0023] in, and They are and λ s,l A hard judgment.

[0024] In one embodiment of the present disclosure, decoding the source node includes:

[0025] Based on the special structure of the source node, an estimated codeword sequence of the source node is calculated by a fast list decoding algorithm corresponding to the source node; and

[0026] Based on the fact that the source node only has a common structure, the estimated codeword sequence of the source node is calculated by a traditional serial cancellation decoding list algorithm.

[0027] In one embodiment of the present disclosure, the calculating the estimated output bit and the estimated information bit of the special node using the estimated codeword includes:

[0028] Calculate the output bits:

[0029] Calculate the estimated information bits:

[0030] in, is the estimated bit of a special node, which is calculated as follows:

[0031] in, is the polarization generator matrix and F is a polarizable nucleus and SR node estimates information bits The calculation is as follows:

[0032] To achieve the above objectives, the second embodiment of the present disclosure proposes a fast list decoding device for a special node of a polarization-adjusted convolutional code, comprising the following modules:

[0033] a screening module, configured to determine a special node in a decoding tree for decoding a polarization-adjusted convolutional code list; wherein the special node includes a source node and a sequence of rate-zero or rate-repeated code sub-nodes, and the special node is identified by a sequence set;

[0034] A first calculation module is used to calculate the log likelihood ratio of the source node corresponding to the special node to determine the number of paths;

[0035] a second calculation module, configured to calculate a metric of each of the paths based on the log-likelihood ratio and the sequence set corresponding to the special node, and retain the path with the smallest metric, wherein the number of retained paths is the set number of lists;

[0036] a decoding module, configured to decode the source node using the retained path as input to obtain an estimated codeword for each path of the source node; and

[0037] An output module is configured to calculate the estimated output bits and estimated information bits of the special node using the estimated codeword.

[0038] To achieve the above-mentioned objectives, an embodiment of the third aspect of the present disclosure proposes a computer electronic device, characterized in that it includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, it implements a fast list decoding method for special nodes of a polarization-adjusted convolutional code as described in any embodiment of the first aspect of the present disclosure.

[0039] To achieve the above-mentioned purpose, the fourth aspect embodiment of the present disclosure proposes a computer-readable storage medium on which a computer program is stored, characterized in that when the computer program is executed by a processor, a fast list decoding method for special nodes of a polarization-adjusted convolutional code as described in any embodiment of the first aspect of the present disclosure is implemented.

[0040] To achieve the above-mentioned purpose, an embodiment of the fifth aspect of the present disclosure provides a computer program product, including a computer program, which, when executed by a processor, is used to implement the fast list decoding method for special nodes of the polarization-adjusted convolutional code as described in any embodiment of the first aspect of the present disclosure.

[0041] To achieve the above-mentioned purpose, an embodiment of the sixth aspect of the present disclosure provides a computer program, including computer program code. When the computer program code is run on a computer, the computer executes the fast list decoding method for special nodes of the polarization-adjusted convolutional code as described in any embodiment of the first aspect of the present disclosure.

[0042] The fast list decoding method for special nodes of polarization-adjusted convolutional codes proposed in the embodiments of the present disclosure introduces a special node and fully utilizes the properties of special nodes in polarization-adjusted convolutional codes. A given special node can be determined by a sequence set, and the sequence set can be generated offline. In addition, list decoding takes special nodes into account, improves decoding parallelism, and significantly reduces decoding time complexity without sacrificing decoding performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The above and / or additional aspects and advantages of the present disclosure will become apparent and readily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0044] FIG1 is a schematic flow chart of a fast list decoding method for a special node of a polarization-adjusted convolutional code provided by an embodiment of the present disclosure.

[0045] FIG2 is a schematic diagram of a special node binary tree structure provided by an embodiment of the present disclosure.

[0046] FIG3 is a schematic diagram showing a comparison of theoretical decoding performance between a fast list decoding algorithm for a special node of a polarization-adjusted convolutional code provided by an embodiment of the present disclosure and a traditional list decoding algorithm.

[0047] FIG4 is a schematic diagram showing a comparison of theoretical decoding delays between a fast list decoding algorithm for a special node of a polarization-adjusted convolutional code provided by an embodiment of the present disclosure and a traditional list decoding algorithm.

[0048] FIG5 is a flow chart of a fast list decoding device for a special node of a polarization-adjusted convolutional code provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0049] The following describes in detail embodiments of the present disclosure, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present disclosure, and should not be construed as limiting the present disclosure.

[0050] The following describes a fast list decoding method for a special node of a polarization-adjusted convolutional code according to an embodiment of the present disclosure with reference to the accompanying drawings.

[0051] Example 1

[0052] FIG1 is a schematic flow chart of a fast list decoding method for a special node of a polarization-adjusted convolutional code provided by an embodiment of the present disclosure.

[0053] As shown in FIG1 , the fast list decoding method for a special node of a polarization-adjusted convolutional code includes steps S101 to S105 .

[0054] S101, determining a special node in a decoding tree for decoding a polarization-adjusted convolutional code list; wherein the special node includes a source node and a sequence of rate-zero or repeat code sub-nodes, and the special node is identified by a sequence set.

[0055] In some embodiments, the continuous cancellation decoding process in S101 is specifically as follows: the rapid list decoding algorithm is based on rapid continuous cancellation decoding, which can be regarded as a depth-first search process from the root node of the decoding tree to the leaf node. There are two types of information that are transmitted between nodes in the decoding tree. The first type is the log-likelihood ratio sequence transmitted from the upper node to the lower node (especially, at the root node, the decoding will calculate the channel log-likelihood ratio sequence provided by the previous module), and the second type is the estimated codeword sequence transmitted from the lower node to the upper node. During the decoding process, the log-likelihood ratio will be calculated step by step and passed to the next level node in sequence. When the decoding reaches the level of a special node, the estimated codeword sequence is directly calculated based on the log-likelihood ratio sequence of the special node. The estimated codeword sequence will be calculated step by step and passed to the upper level node in sequence until the root node. The above process will be repeated until the last leaf node is decoded. On the basis of rapid continuous cancellation decoding, list decoding splits the decoding path into parallel decoding branches and retains the most likely correct decoding path of the number of lists according to the path metric.

[0056] In one embodiment of the present disclosure, a special node includes a polarization-adjusted convolutional code subcode consisting of a source node and a sequence of code rate zero or repeat code subcode subcodes, located in the middle layer of the decoding tree of polarization-adjusted convolutional code continuous offset decoding, wherein the source node is located at the rightmost node of the special node and has an arbitrary code rate and structure, and the source node length N r Satisfy Nr ≥p-1, where p is the length of the convolution polynomial of the polarization-adjusted convolutional code.

[0057] In one embodiment of the present disclosure, the sequence set includes:

[0058] For the mth sequence set set up is a function that performs recursive calculations, is the result after a times of calculation, then Where s and r are the levels of the special node and the source node in the decoding tree, respectively. m The calculation is as follows:

[0059] in, represents the Kronecker product, x s-a is the first (p-1) bit value of the a-th child node. For the child node x at level t in the decoding tree, t It can be calculated as follows:

[0060] Where q = 2t-p + 1,0 q is an all-zero vector of length q, is a vector of length q and

[0061] Once the special node is determined, the sequence set is also determined. And by enumerating the possible values ​​of the first p bits, the sequence set can be generated offline.

[0062] S102, calculating the log-likelihood ratio of the source node corresponding to the special node, and determining the number of paths.

[0063] In one embodiment of the present disclosure, calculating the log-likelihood ratio of the source node corresponding to the special node includes:

[0064] Log-likelihood ratio based on special nodes And the log-likelihood ratio of the source node for the mth sequence set The calculation is as follows:

[0065] S103 , based on the log-likelihood ratio and the sequence set corresponding to the special node, calculate the metric of each path, retain the path with the smallest metric, and the number of retained paths is the set number of lists.

[0066] In one embodiment of the present disclosure, calculating the metric of each path includes:

[0067] in, and They are and λ s,l A hard judgment.

[0068] After obtaining the path metric of each path, the path with the smallest number of paths is selected.

[0069] S104, using the retained path as input, decoding the source node to obtain an estimated codeword for each path of the source node;

[0070] In one embodiment of the present disclosure, decoding a source node includes:

[0071] If the source node has a special structure, the estimated codeword sequence of the source node is calculated by the fast list decoding algorithm corresponding to the node;

[0072] If the source node only has a common structure, the estimated codeword sequence of the source node is calculated using a traditional serial cancellation decoding list algorithm.

[0073] S105 , calculating the estimated output bits and estimated information bits of the special node by estimating the codeword.

[0074] In one embodiment of the present disclosure, calculating the estimated output bits and estimated information bits of a special node by estimating a codeword includes:

[0075] Calculate the output bits:

[0076] Calculate the estimated information bits:

[0077] in, is the estimated bit of a special node, which is calculated as follows:

[0078] in, is the polarization generator matrix and F is a polarizable nucleus and SR node estimates information bits The calculation is as follows:

[0079] Example 2

[0080] Figure 2 is a schematic diagram of the binary tree structure of special nodes. For the node at the sth level in the continuous offset decoding tree, define λ s,l [1:2 s ] and β s,l [1:2 sThey are respectively the log-likelihood ratio (LLR) sequence and the codeword estimation sequence of the l-th path of the node. Define a special node of the i-th node at the p-th layer in the decoding tree. This node is composed of a source node and a sequence of rate-zero (Rate-0, R0) or repetition code (Repetition, REP) child nodes. The source node is located at the rightmost end of the r-th layer, and the left nodes of each layer where r ≤ t < s are R0 or REP child nodes. Thus, this special node is called a sequence repetition node (Sequence Repitition, SR).

[0081] Consider transmitting signals over an additive white Gaussian noise channel with binary phase shift keying (BPSK) modulation, and consider a polar-modulated convolutional code with a code pattern of (128, 64). The construction method is the RM construction, the convolutional polynomial is c = (1, 0, 0, 1, 1), and the number of paths L = 4. A fast list decoding method for special nodes of a polar-modulated convolutional code proposed for this system includes steps S201 to S205.

[0082] S201, construct a sequence set offline. For a given There are three such special nodes in the decoding binary tree. Taking the special node with two Rate-0 and one repetition code child node and a source node length of 8 as an example, there are a total of 2 W = 2 sequence sets. For the child node x at the t-th level of the decoding tree t it can be calculated as follows:

[0083] Enumerate the possible values of the first p bits of the SR node to obtain x s-1 ; for other left child nodes, x t is as follows:

[0084] where η[t] is the value of the rightmost bit of the left child node at the t-th level. Thus, F m The calculation is specifically:

[0085] S202, based on the log-likelihood ratio λ of the special node s,l and the m-th (m ∈ {1, 2}) sequence set, for each path and each sequence set, the log-likelihood ratio of the source node is calculated as follows:

[0086] At this time, there are a total of L·m = 8 paths. <C000260>

[0087] S203, calculate the path metrics of the eight paths respectively:

[0088] Select the four paths with the smallest path metrics.

[0089] S204: Using the four paths selected in S203 as input, the source node is decoded. If the source node has a special structure, the estimated output codeword for the source node is calculated using the corresponding fast list decoding algorithm. If the source node has only a general structure, the estimated output codeword for the source node is calculated using the traditional serial cancellation decoding list algorithm.

[0090] S205, based on the source node estimated output bits obtained in S204 and the mth sequence set, the estimated output bits of the special nodes of each path are calculated as follows:

[0091] Based on the special node estimated output bits, SR node estimated bits The calculation is as follows:

[0092] SR node estimated information bits for each path The calculation is as follows:

[0093] At this point, the fast list decoding of the SR node is completed.

[0094] Figure 3 shows a comparison of the theoretical decoding performance of the fast list decoding algorithm for special nodes of the polarization-adjusted convolutional code in this embodiment and the traditional list decoding algorithm. As can be seen from the figure, the fast list decoding algorithm based on SR nodes does not suffer from any loss in error control performance compared to the traditional list decoding algorithm.

[0095] Figure 4 compares the theoretical decoding delays of the fast list decoding algorithm for polarization-adapted convolutional codes with those of the traditional list decoding algorithm. The figure shows that the fast list decoding algorithm based on SR nodes significantly reduces decoding delay compared to the traditional list decoding algorithm, especially at low bit rates.

[0096] The fast list decoding method for special nodes of polarization-adjusted convolutional codes proposed in the embodiments of the present disclosure introduces a special node to fully utilize the properties of special nodes in polarization-adjusted convolutional codes. A given special node can be determined by a sequence set, and the sequence set can be generated offline. In addition, list decoding takes special nodes into account, improves decoding parallelism, and significantly reduces decoding time complexity without sacrificing decoding performance.

[0097] In order to implement the above embodiment, the embodiment of the present disclosure further proposes a fast list decoding device for special nodes of polarization-adjusted convolutional codes.

[0098] FIG5 is a schematic structural diagram of a fast list decoding device for a special node of a polarization-adjusted convolutional code provided by an embodiment of the present disclosure.

[0099] As shown in FIG5 , the fast list decoding device for a special node of a polarization-adjusted convolutional code includes a screening module 100 , a first calculation module 200 , a second calculation module 300 , a decoding module 400 and an output module 500 .

[0100] The screening module 100 is configured to determine a special node in a decoding tree for decoding a polarization-adjusted convolutional code list, wherein the special node includes a source node and a sequence of rate-zero or repeat code sub-nodes, and the special node is identified by a sequence set.

[0101] The first calculation module 200 is used to calculate the log likelihood ratio of the source node corresponding to the special node and determine the number of paths.

[0102] The second calculation module 300 is used to calculate the metric of each path based on the log-likelihood ratio and the sequence set corresponding to the special node, and retain the path with the smallest metric. The number of retained paths is the set number of lists.

[0103] The decoding module 400 is configured to decode the source node using the retained path as input to obtain an estimated codeword for each path of the source node.

[0104] The output module 500 is configured to calculate the estimated output bits and estimated information bits of the special node by estimating the codeword.

[0105] To achieve the above-mentioned objectives, an embodiment of the third aspect of the present disclosure proposes a computer device, characterized in that it includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, it implements the fast list decoding method for special nodes of the polarization-adjusted convolutional code as described in any embodiment of the first aspect of the present disclosure.

[0106] To achieve the above-mentioned objectives, an embodiment of the fourth aspect of the present disclosure proposes a computer-readable storage medium on which a computer program is stored, characterized in that when the computer program is executed by a processor, it implements the fast list decoding method for special nodes of the polarization-adjusted convolutional code as described in any embodiment of the first aspect of the present disclosure.

[0107] To achieve the above-mentioned purpose, an embodiment of the fifth aspect of the present disclosure provides a computer program product, including a computer program, which, when executed by a processor, is used to implement the fast list decoding method for special nodes of the polarization-adjusted convolutional code as described in any embodiment of the first aspect of the present disclosure.

[0108] To achieve the above-mentioned purpose, an embodiment of the sixth aspect of the present disclosure provides a computer program, including computer program code. When the computer program code is run on a computer, the computer executes the fast list decoding method for special nodes of the polarization-adjusted convolutional code as described in any embodiment of the first aspect of the present disclosure.

[0109] It should be noted that the above explanations of the method and device embodiments are also applicable to the computer electronic equipment, computer-readable storage medium, computer program product and computer program of the above embodiments, and will not be repeated here.

[0110] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present disclosure. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine different embodiments or examples described in this specification and features of different embodiments or examples, unless they are mutually inconsistent.

[0111] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. Throughout the present disclosure, "plurality" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0112] Although the embodiments of the present disclosure have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present disclosure. A person skilled in the art may change, modify, replace, and modify the above embodiments within the scope of the present disclosure.

[0113] All embodiments of the present disclosure may be implemented individually or in combination with other embodiments, and are all considered to be within the scope of protection claimed by the present disclosure.

Claims

1. A fast list decoding method for special nodes of polarization-adjusted convolutional codes, It is characterized in that The following steps are involved: Determine the special node in the decoding tree of the polarization adjusted convolutional code list decoding; wherein the special node includes a source node and a sequence of rate zero or repeat code sub-nodes, and the special node is identified by a sequence set; Calculating the log-likelihood ratio of the source node corresponding to the special node to determine the number of paths; Based on the log-likelihood ratio and the sequence set corresponding to the special node, the metric of each path is calculated, and the path with the smallest metric is retained, and the number of retained paths is the set number of lists; Taking the retained path as input, decoding the source node to obtain an estimated codeword for each of the paths of the source node; and The estimated output bits and the estimated information bits of the special node are calculated using the estimated codeword.

2. The method according to claim 1, It is characterized in that The special node includes a polarization-adjusted convolutional code subcode consisting of the one source node and the sequence of rate-zero or repeat code subcode subcode subcodes, and is located in the middle layer of the decoding tree of polarization-adjusted convolutional code continuous offset decoding, wherein the source node is located at the rightmost node of the special node and has an arbitrary code rate and structure, and the source node Length N r Satisfy N r ≥p-1, where p is the length of the convolution polynomial of the polarization adjusted convolutional code.

3. The method according to claim 1 or 2, It is characterized in that The sequence set includes: For the mth sequence set set up is a function that performs recursive calculations, is the result after a times of calculation, then Where s and r are the levels of the special node and the source node in the decoding tree of the polarization-adjusted convolutional code list decoding, respectively. m The calculation is as follows: in, represents the Kronecker product, x s-a is the first (p-1) bit value of the a-th child node. For the child node x at level t in the decoding tree, t It can be calculated as follows: Where q = 2t-p + 1, 0 q is an all-zero vector of length q, is a vector of length q and 4. The method according to any one of claims 1 to 3, It is characterized in that The calculating the log-likelihood ratio of the source node corresponding to the special node includes: Log-likelihood ratio based on special nodes and the mth sequence set, the log-likelihood ratio of the source node The calculation is as follows As shown:

5. The method according to any one of claims 1 to 4, It is characterized in that The calculation of the metric of each path includes: in, and They are and λ s,l A hard verdict.

6. The method according to any one of claims 1 to 5, It is characterized in that The decoding of the source node comprises: Based on the special structure of the source node, an estimated codeword sequence of the source node is calculated by a fast list decoding algorithm corresponding to the source node; and Based on the fact that the source node only has a common structure, the estimated codeword sequence of the source node is calculated by a traditional serial cancellation decoding list algorithm.

7. The method according to any one of claims 1 to 6, It is characterized in that The calculating the estimated output bit and the estimated information bit of the special node by using the estimated codeword includes: Calculate the output bits: Calculate the estimated information bits: in, is the estimated bit of a special node, which is calculated as follows: in, is the polarization generator matrix and F is a polarizable core and SR node estimation information Bit The calculation is as follows:

8. A fast list decoding device for special nodes of polarization adjustment convolutional code, It is characterized in that include: A screening module, used to determine the special node in the decoding tree of the polarization adjusted convolutional code list decoding; wherein the special node includes a source node and a sequence of code rate zero or repeat code sub-nodes, and the special node is identified by a sequence set; A first calculation module, used to calculate the log likelihood ratio of the source node corresponding to the special node to determine the number of paths; A second calculation module is used to calculate the metric of each of the paths based on the log-likelihood ratio and the sequence set corresponding to the special node, and retain the path with the smallest metric, and the number of retained paths is the set number of lists; a decoding module, configured to decode the source node using the retained path as input to obtain an estimated codeword for each of the paths of the source node; and An output module is used to calculate the estimated output bit and the estimated information bit of the special node through the estimated codeword.

9. A computer electronic device, It is characterized in that The invention comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the fast list decoding method for special nodes of the polarization adjusted convolutional code according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, It is characterized in that When the computer program is executed by a processor, the fast list decoding method for a special node of a polarization adjusted convolutional code according to any one of claims 1 to 7 is implemented.

11. A computer program product, comprising a computer program, which, when executed by a processor, implements the fast list decoding method for special nodes of a polarization adjusted convolutional code according to any one of claims 1 to 7.

12. A computer program, It is characterized in that The computer program comprises computer program codes, and when the computer program codes are run on a computer, the computer is enabled to execute the fast list decoding method for special nodes of polarization-adjusted convolutional codes according to any one of claims 1 to 7.

Images