CN115276671A - BP decoding method, equipment and medium based on polarization code - Google Patents

BP decoding method, equipment and medium based on polarization code Download PDF

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CN115276671A
CN115276671A CN202210721970.5A CN202210721970A CN115276671A CN 115276671 A CN115276671 A CN 115276671A CN 202210721970 A CN202210721970 A CN 202210721970A CN 115276671 A CN115276671 A CN 115276671A
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decoding
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right information
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董心洁
王宇航
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Beijing Institute of Electronic System Engineering
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    • H—ELECTRICITY
    • H03—ELECTRONIC CIRCUITRY
    • H03M—CODING; DECODING; CODE CONVERSION IN GENERAL
    • H03M13/00—Coding, decoding or code conversion, for error detection or error correction; Coding theory basic assumptions; Coding bounds; Error probability evaluation methods; Channel models; Simulation or testing of codes
    • H03M13/03—Error detection or forward error correction by redundancy in data representation, i.e. code words containing more digits than the source words
    • H03M13/05—Error detection or forward error correction by redundancy in data representation, i.e. code words containing more digits than the source words using block codes, i.e. a predetermined number of check bits joined to a predetermined number of information bits
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Abstract

One embodiment of the invention discloses a BP decoding method, equipment and medium based on polarization codes, wherein the method comprises the following steps: s1: initializing left information and right information of each layer according to a preset factor graph; s2: the n-m layers close to the channel end update and calculate the left information layer by layer from the channel end to the n-m layers, and after one traversal update is completed, the right information is updated and calculated layer by layer from the n-m layers to the channel end; s3: each layer of m layers of groups close to the decoding end recurs according to a certain sequence and updates the left information and the right information of each node in the calculation group in parallel; s4: repeating S2 and S3 until the decoding iteration times meet the early termination condition or reach the set maximum times, and acquiring B stored in the right information matrixλ＝0Information data of this column, according to Bλ＝n‑1(i)+Lλ＝n‑1(i) Hard decision is made to determine the decoding result of the ith bit. The invention solves the problems of high parallelism, large space complexity and high iteration number required for achieving ideal performance of the traditional BP decoding method of the polarization code.

Description

BP decoding method, equipment and medium based on polarization code
Technical Field
The invention relates to the technical field of wireless communication. And more particularly, to a BP decoding method, apparatus, and medium based on a polar code.
Background
In 2009 e.arikan proposed a new channel coding scheme-polar code, i.e. polar code. polar codes are the only channel codes that have been proven to achieve channel capacity, and have lower coding complexity and better coding performance than conventional LDPC codes. Therefore, the polar code has great research and practical values, and has been determined as a control channel coding scheme for the eMBB (enhanced mobile broadband) scenario in 5G in the conference of 3 GPP. Therefore, the polarization code has a very wide application prospect and has the potential of continuing in-depth research.
The traditional BP decoding method can perform parallel operation in each frame, has high space complexity, and further optimizes the BP decoding method from the aspect of reducing the decoding complexity in order to improve the practicability of the BP decoding method.
Some researchers have introduced an advanced stop mechanism [ Simsek C., turk K. Simplicized Early Stopping Criterion for Belief-Propagation Polar codes Letters,2016,20 (8): 1515-1518 ], that is, each time an iteration is completed, an advanced stop condition is used for judgment, if the condition is satisfied, the next iteration is not performed, and the number of iterations can be reduced on the premise of reaching the same performance through the mechanism, so that the time complexity of the decoding method is reduced, but the decoding iteration number is obviously reduced under a high signal-to-noise ratio, and the iteration number is still higher under a low signal-to-noise ratio, so that the decoding delay reduction effect is limited. Lin Jun et al further improve decoding throughput [ Lin J., yan Z.and Wang Z.efficient Soft cancellation Decoder architecture for Polar Codes [ J ]. IEEE Transactions on Virtual Large Scale Integration (VLSI) Systems,2017,25 (1): 87-99 ], introduce special nodes to prune the decoding binary tree, which can effectively reduce decoding delay but sacrifice certain performance.
Disclosure of Invention
In view of the above, a first embodiment of the present invention provides a BP decoding method based on a polar code, including:
s1: initializing left information and right information of each layer according to a preset factor graph;
s2: the n-m layers close to the channel end update and calculate the left information layer by layer from the channel end to the n-m layers, and after one traversal update is completed, the n-m layers update and calculate the right information layer by layer from the n-m layers to the channel end;
s3: each layer of m layers of groups close to the decoding end recurs according to a certain sequence and updates the left information and the right information of each node in the calculation group in parallel;
s4: repeating S2 and S3 until the decoding iteration times meet the early termination condition or reach the set maximum times, and acquiring B stored in the right information matrixλ＝0The information data of this column, i.e. the soft information output by the decoder, is according to Bλ＝n-1(i)+Lλ＝n-1(i) Hard decision is made to determine the decoding result of the ith bit.
In one embodiment, the early termination condition is:
each time an iteration is completed, if the estimation of the decoding result
Figure BDA0003711744690000021
Satisfy the requirements of
Figure BDA0003711744690000022
Wherein the content of the first and second substances,
Figure BDA0003711744690000023
for estimating the log-likelihood ratio LLR information of the channel end, G is a generating matrix preset according to the polarization code coding, then the decoding is terminated in advance, and the decoding result is output
Figure BDA0003711744690000024
Otherwise, the next iteration is continued.
In a specific embodiment, the S2 includes:
and (3) updating and calculating the left information layer by layer from the channel end to the n-m layers of the n-m layers close to the channel end, and updating and calculating the right information layer by layer from the n-m layers to the channel end after completing one traversal updating:
Figure BDA0003711744690000025
Figure BDA0003711744690000026
Figure BDA0003711744690000027
Figure BDA0003711744690000028
wherein the content of the first and second substances,
Figure BDA0003711744690000029
and
Figure BDA00037117446900000210
respectively representing left information and right information of nodes (i, j) based on log-likelihood ratios (LLR) in the process of the t-th iteration, and alpha is a constant coefficient.
In a specific embodiment, the S3 includes:
and (3) carrying out recursive parallel updating calculation on each layer of m layers of groups close to the decoding end according to a certain sequence, and updating left information of the nodes in the groups:
Figure BDA00037117446900000211
phi is an even number
Figure BDA00037117446900000212
Phi is an odd number
And updating right information of the nodes in the group:
Figure BDA00037117446900000213
Figure BDA00037117446900000214
wherein phi represents the group number of the node, omega represents the serial number in the group,
Figure BDA00037117446900000215
in a specific embodiment, the S1 includes:
initializing left information Lλ＝0Is the channel log-likelihood ratio information;
initializing the right information Bλ＝n-1The information bit is initialized to 0, the freezing bit is initialized to infinity, and the left information L and the right information B of the rest layers are initialized to 0.
In a specific embodiment, the set maximum iteration number is obtained by artificial setting or performing a simulation experiment in advance according to the code length and code rate of the polarization code.
A second embodiment of the present invention provides a computer device, comprising a processor and a memory storing a computer program, wherein the processor executes the computer program to implement the method according to the first embodiment.
A third embodiment of the invention provides a computer-readable storage medium, on which a computer program is stored, characterized in that the program, when executed by a processor, implements the method according to the first embodiment.
The invention has the following beneficial effects:
the invention provides a BP decoding method, equipment and a medium based on a polarization code, wherein n-m layers close to a channel end are used for updating and calculating left information layer by layer from the channel end to the n-m layers, after one-time traversal updating is completed, the n-m layers are used for updating and calculating right information layer by layer from the n-m layers to the channel end, and m layers close to the decoding end are grouped according to a certain sequence to recur and update the left information and the right information of each node in a calculation group in parallel, so that the problems of high parallelism, high space complexity and high iteration times required for achieving ideal performance of the traditional BP decoding method of the polarization code are solved, the convergence speed of the BP decoding method is accelerated by changing the calculation and update time sequence of a plurality of layers of nodes close to the decoding end, the purpose of reducing time delay is achieved, and meanwhile memory resources can be saved.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a flowchart illustrating a BP decoding method based on polar codes according to an embodiment of the present invention;
FIG. 2 illustrates a preset factor graph diagram according to one embodiment of the present invention;
FIG. 3 is a diagram illustrating performance curves of a BP decoding method based on polar codes according to an embodiment of the present invention;
fig. 4 shows a schematic structural diagram of a computer device according to another embodiment of the present invention.
Detailed Description
In order to make the technical solutions and advantages of the present invention more apparent, the following detailed description of the embodiments of the present invention will be made with reference to the accompanying drawings.
As shown in fig. 1, an embodiment of the present invention provides a BP decoding method based on a polar code, including:
s1: initializing left information and right information of each layer according to a preset factor graph;
in a specific embodiment, the S1 includes:
initializing the left information Lλ＝0Is the channel log likelihood ratio information;
initializing the right information Bλ＝n-1The information bit is initialized to 0, the frozen bit is initialized to infinity, and the left information L and the right information B in the remaining layers are initialized to 0.
S2: the n-m layers close to the channel end update and calculate the left information layer by layer from the channel end to the n-m layers, and after one traversal update is completed, the n-m layers update and calculate the right information layer by layer from the n-m layers to the channel end;
in a specific embodiment, the S2 includes:
and (3) updating and calculating the left information layer by layer from the channel end to the n-m layers of the n-m layers close to the channel end, and after one traversal updating is completed, updating and calculating the right information layer by layer from the n-m layers to the channel end:
Figure BDA0003711744690000041
Figure BDA0003711744690000042
Figure BDA0003711744690000043
Figure BDA0003711744690000044
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003711744690000045
and
Figure BDA0003711744690000046
respectively representing the left information and the right information of the nodes (i, j) based on the log-likelihood ratio (LLR) in the t-th iteration process, wherein alpha is a constant coefficient, and the specific setting can be carried out by a person skilled in the art according to the actual situation.
S3: calculating time sequences of m layers close to a decoding end according to new nodes, namely recurrently and parallelly updating left information and right information of each node in a calculation group according to a certain sequence in each layer of grouping;
in the embodiment, m layers close to the decoding end can update the left information and the right information of each node according to the node calculation time sequence of the polar code SC decoding method in the prior art, namely for the node of the ith (1 ≦ i ≦ m) layer, 2 is addedi-1Dividing each node into a group, and updating left information or right information of nodes in a computation group in parallel in a time sequence period, for example, in a specific example, the S3 includes:
the m layers close to the decoding end calculate the time sequence according to the new node, namely, each layer of grouping recurs according to a certain sequence and updates the left information and the right information of each node in the calculation group in parallel, when the left information of the node in the group is updated:
Figure BDA0003711744690000047
phi is an even number
Figure BDA0003711744690000048
Phi is an odd number
When updating right information of the intra-group node:
Figure BDA0003711744690000049
Figure BDA0003711744690000051
wherein phi represents the group number of the node, omega represents the serial number in the group,
Figure BDA0003711744690000052
in the embodiment, when each node of m layers close to the decoding end transmits information to the left, the left information is updated, so that the information of the frozen bit and the information from the channel pass through the whole factor graph in any iteration, the convergence speed is increased, and the iteration times are reduced.
In a specific example, taking the preset factor graph shown in fig. 2 and m =2 as an example, the update timing of the left information L and the right information B of each node in m layers close to the decoding end is shown in the following table:
Figure BDA0003711744690000053
for these layers, the left information calculation formula is as follows:
Figure BDA0003711744690000054
phi is an even number
Figure BDA0003711744690000055
Phi is an odd number
The right information calculation formula is as follows:
Figure BDA0003711744690000056
Figure BDA0003711744690000057
wherein phi represents the group number of the node, omega represents the serial number in the group,
Figure BDA0003711744690000058
when the information is updated from right to left and then from left to right, namely, one iteration is completed, the condition that the information of the frozen bit and the information from the channel pass through the whole factor graph in any iteration is ensured, the convergence speed is increased, and the iteration times are reduced.
S4: repeating S2 and S3 until the number of decoding iterations meets the early termination condition or reaches the set maximum number, and acquiring B stored in the right information matrixλ＝0The information data of this column, i.e. the soft information output by the decoder, is according to Bλ＝n-1(i)+Lλ＝n-1(i) Hard decision is made to determine the decoding result of the ith bit.
In the embodiment, the convergence rate of the BP decoding method is increased by changing the calculation and update time sequence of a plurality of layers of nodes close to the decoding end, and the problems that the traditional BP decoding method of the polarization code is high in parallelism, large in space complexity and high in iteration times required for achieving ideal performance are solved, so that the purpose of reducing time delay is achieved, and meanwhile, memory resources can be saved.
In a specific embodiment, the set maximum iteration number is obtained by artificial setting or performing a simulation experiment in advance according to the code length and code rate of the polarization code.
The physical meaning of setting the maximum iteration number M in this embodiment is: polarization code for determining code length code rate (polarization code length N = 2)n) The maximum number of times required to achieve a certain decoding performance in BP decoding. The determination of the set maximum iteration times M can be manually set according to experience, and can also be determined through a large number of simulation experiments according to different code length code rates, so that meaningless calculation can be avoided, and the overall decoding efficiency is improved.
In one embodiment, the early termination condition is:
if the estimation of the decoding result is performed every time an iteration is performed
Figure BDA0003711744690000061
Satisfy the requirements of
Figure BDA0003711744690000062
Wherein the content of the first and second substances,
Figure BDA0003711744690000063
for estimating the log-likelihood ratio LLR information of the channel end, G is a generating matrix preset according to the polarization code coding, then the decoding is terminated in advance, and the decoding result is output
Figure BDA0003711744690000066
Otherwise, the next iteration is continued.
In the embodiment, the estimation of the decoding result is judged by completing iteration once, if the judgment condition is met, the decoding is terminated in advance and the decoding result is output, otherwise, the next iteration is continued, so that most redundant iterations are reliably and efficiently reduced, the time complexity of the decoding method is reduced, and the BP decoding efficiency of the polar code is improved.
In the invention, as can be seen from the formula and the time sequence table, the right information B with even group number does not need to be recalculated (can be covered, does not need to be stored completely) when being used for calculating the left information L in the next iterationStoring); the odd-numbered right information B is used (not coverable) in the next iteration of calculating the left information L. Therefore, if m layers of nodes are updated with a new time sequence, as shown in fig. 1, each layer of L information only needs to open up the storage space of the first region, and each layer of B information only needs to open up the storage spaces of the first region and the second region, so that the storage capacity needed by the left information is reduced from the original N (N + 1) to the original storage capacity
Figure BDA0003711744690000064
The storage capacity needed by the right information is reduced from the original N (N + 1) to
Figure BDA0003711744690000065
In a specific example, as shown in fig. 3, taking (1024, 512) polar codes as an example, frame error rate performance curves decoded with new timing sequences by simulating different numbers of layers, for example, FER performance curves after the polar code-based BP decoding method described in this embodiment is adopted by simulating 3 layers, 4 layers, and 5 layers, respectively, all adopt an early stop mechanism. It can be seen that when 3 layers are selected and decoded with a new timing sequence, the performance of the curve shown by the blue five-pointed star in the figure is basically identical to that of the conventional BP decoding method.
Therefore, we have counted the average iteration times of selecting 3 layers of the BP decoding method based on the polar code according to the present embodiment under different signal-to-noise ratios and the average iteration times of the conventional BP decoding method, and still take (1024,512) polar codes as an example, and the degree of reduction is shown in the following table.
1dB 1.5dB 2dB 2.5dB 3dB 3.5dB 4dB
BP decoding method 48.16 25.12 11.84 6.75 4.98 4.17 3.49
BP decoding method provided by the invention 42.94 24.68 11.22 6.66 4.89 4.07 3.35
Degree of reduction 10.8％ 1.8％ 5.2％ 1.3％ 1.8％ 2.4％ 4.0％
As can be seen from the statistical results given in the above table in combination with the performance graph, the average iteration number of the BP decoding method based on the polar code proposed in this embodiment can be significantly reduced compared with the conventional BP decoding method on the premise of ensuring the lossless performance, thereby achieving the purpose of reducing the decoding delay. In addition, the total memory space of the BP decoding method based on the polar code proposed in this embodiment can be reduced, for example, taking the polar code of (1024, 512) as an example, the total memory space of the conventional BP decoding method is:
N(n+1)+N(n+1)＝1024*(10+1)+1024*(10+1)＝22528，
the total memory space of the BP decoding method provided by the invention is as follows:
Figure BDA0003711744690000071
the total memory capacity can be reduced by 20.3% compared with the conventional BP decoder,
therefore, it can be seen that in the embodiment, through enabling the n-m layers close to the channel end to update and calculate the left information layer by layer from the channel end to the n-m layers, after one traversal update is completed, the right information is updated and calculated layer by layer from the n-m layers to the channel end, and the m layers of groups close to the decoding end recur in a certain sequence and update the left information and the right information of each node in the calculation group in parallel, the problems of high parallelism, large space complexity and high iteration number required for achieving ideal performance of the traditional BP decoding method of the polarization code are solved, the convergence speed of the BP method is accelerated by changing the calculation update time sequence of the nodes close to the decoding end, the purpose of reducing time delay is achieved, and meanwhile, memory resources can be saved.
Another embodiment of the present invention provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements: s1: initializing left information and right information of each layer according to a preset factor graph; s2: the n-m layers close to the channel end update and calculate the left side from the channel end to the n-m layers layer by layerInformation, after one traversal update is completed, updating and calculating right information layer by layer from the n-m layers to the channel end; s3: grouping m layers close to a decoding end according to a certain sequence, recurrently and parallelly updating left information and right information of each node in a calculation group; s4: repeating S2 and S3 until the decoding iteration times meet the early termination condition or reach the set maximum times, and acquiring B stored in the right information matrixλ＝0The information data of this column, i.e. the soft information output by the decoder, is according to Bλ＝n-1(i)+Lλ＝n-1(i) Hard decision is made to determine the decoding result of the ith bit.
In practice, the computer-readable storage medium may take any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present embodiment, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
As shown in fig. 4, another embodiment of the present invention provides a schematic structural diagram of a computer device. The computer device 12 shown in FIG. 4 is only one example and should not bring any limitations to the functionality or scope of use of embodiments of the present invention.
As shown in FIG. 4, computer device 12 is in the form of a general purpose computing device. The components of computer device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that couples various system components including the system memory 28 and the processing unit 16.
Bus 18 represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures. By way of example, such architectures include, but are not limited to, industry Standard Architecture (ISA) bus, micro-channel architecture (MAC) bus, enhanced ISA bus, video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.
Computer device 12 typically includes a variety of computer system readable media. Such media can be any available media that is accessible by computer device 12 and includes both volatile and nonvolatile media, removable and non-removable media.
The system memory 28 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM) 30 and/or cache memory 32. Computer device 12 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 34 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 4, and commonly referred to as a "hard drive"). Although not shown in FIG. 4, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 by one or more data media interfaces. Memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention.
A program/utility 40 having a set (at least one) of program modules 42 may be stored, for example, in memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may comprise an implementation of a network environment. Program modules 42 generally carry out the functions and/or methodologies of embodiments of the invention as described.
Computer device 12 may also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), with one or more devices that enable a user to interact with computer device 12, and/or with any devices (e.g., network card, modem, etc.) that enable computer device 12 to communicate with one or more other computing devices. Such communication may be through an input/output (I/O) interface 22. Also, computer device 12 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the Internet) through network adapter 20. As shown in FIG. 4, the network adapter 20 communicates with the other modules of the computer device 12 via the bus 18. It should be understood that although not shown in FIG. 4, other hardware and/or software modules may be used in conjunction with computer device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
The processor unit 16 executes programs stored in the system memory 28 to perform various functional applications and data processing, such as implementing a BP decoding method based on polar codes provided by the embodiment of the present invention.
It should be understood that the above-described embodiments of the present invention are examples for clearly illustrating the invention, and are not to be construed as limiting the embodiments of the present invention, and it will be obvious to those skilled in the art that various changes and modifications can be made on the basis of the above description, and it is not intended to exhaust all embodiments, and obvious changes and modifications can be made on the basis of the technical solutions of the present invention.

Claims (8)

1. A BP decoding method based on polarization code is characterized by comprising the following steps:
s1: initializing left information and right information of each layer according to a preset factor graph;
s2: the n-m layers close to the channel end update and calculate the left information layer by layer from the channel end to the n-m layers, and after one traversal update is completed, the right information is updated and calculated layer by layer from the n-m layers to the channel end;
s3: each layer of m layers of groups close to the decoding end recurs according to a certain sequence and updates the left information and the right information of each node in the calculation group in parallel;
s4: repeating S2 and S3 until the number of decoding iterations meets the early termination condition or reaches the set maximum number, and acquiring B stored in the right information matrixλ＝0The information data of this column, i.e. the soft information output by the decoder, is according to Bλ＝n-1(i)+Lλ＝n-1(i) Hard decision is made to determine the decoding result of the ith bit.
2. The method of claim 1, wherein the early termination condition is:
each time an iteration is completed, if the estimation of the decoding result
Figure FDA0003711744680000011
Satisfy the requirements of
Figure FDA0003711744680000012
Wherein the content of the first and second substances,
Figure FDA0003711744680000013
for estimating the log-likelihood ratio LLR information of the channel end, G is a generating matrix preset according to the polarization code coding, then the decoding is terminated in advance, and the decoding result is output
Figure FDA0003711744680000014
Otherwise, the next iteration is continued.
3. The method of claim 1, wherein the S2 comprises:
updating and calculating the left information layer by layer from the layer close to the channel end to the layer n-m, and updating and calculating the right information layer by layer from the layer n-m to the channel end after one traversal updating is completed:
Figure FDA0003711744680000015
Figure FDA0003711744680000016
Figure FDA0003711744680000017
Figure FDA0003711744680000018
wherein the content of the first and second substances,
Figure FDA0003711744680000019
and
Figure FDA00037117446800000110
respectively representing left information and right information of nodes (i, j) based on log-likelihood ratios (LLRs) in the process of the t-th iteration, and alpha is a constant coefficient.
4. The method of claim 1, wherein the S3 comprises:
and (3) carrying out recursive parallel updating calculation on each layer of m layers of groups close to the decoding end according to a certain sequence, and updating left information of the nodes in the groups:
Figure FDA00037117446800000111
phi is an even number
Figure FDA00037117446800000112
Phi is an odd number
And updating right information of the nodes in the group:
Figure FDA00037117446800000113
Figure FDA00037117446800000114
wherein phi represents the group number of the node, omega represents the serial number in the group,
Figure FDA00037117446800000115
5. the method of claim 1, wherein the S1 comprises:
initializing left information Lλ＝0Is the channel log-likelihood ratio information;
initializing the right information Bλ＝n-1The information bit is initialized to 0, the freezing bit is initialized to infinity, and the left information L and the right information B of the rest layers are initialized to 0.
6. The method of claim 1, wherein the set maximum number of iterations is obtained by artificial setting or pre-simulation experiment according to a code length and a code rate of a polarization code.
7. A computer device comprising a processor and a memory having stored thereon a computer program, characterized in that the processor, when executing the program, implements the method according to any of claims 1-6.
8. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-6.
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CN117375635A (en) * 2023-11-09 2024-01-09 中国人民解放军军事科学院系统工程研究院 Geometric representation method and device for BP decoding of satellite communication polarization code

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117375635A (en) * 2023-11-09 2024-01-09 中国人民解放军军事科学院系统工程研究院 Geometric representation method and device for BP decoding of satellite communication polarization code
CN117375635B (en) * 2023-11-09 2024-05-03 中国人民解放军军事科学院系统工程研究院 Geometric representation method and device for BP decoding of satellite communication polarization code

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