Polarization adjustment convolutional code pre-transformation method and device, terminal and storage medium
By optimizing the convolutional polymorphism of polarization-adjusted convolutional codes through tree-structure modeling, computational complexity is reduced, code redistribution is simplified, and error correction performance is improved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-04
- Publication Date
- 2026-03-27
AI Technical Summary
In existing technologies, optimizing the code weight distribution of polarization-adjusted convolutional codes suffers from high computational complexity and difficulty, especially when exhaustive search, the computational complexity increases exponentially with the code length.
A tree-structured modeling approach is used to perform convolutional multivariate modeling on the first convolutional expression and multiple expressions to be processed. By setting the coefficients to 0 or 1, a tree structure is formed, and only the optimal branch is retained for expansion, thereby reducing computational complexity.
It effectively reduces the computational complexity of optimizing convolutional polymorphisms, simplifies the optimization difficulty of code redistribution, and improves the error correction performance of polarization-adjusted convolutional codes.
Smart Images
Figure CN121750001A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of channel coding and decoding technology. More specifically, this application relates to a pre-transformation method, apparatus, terminal, and storage medium for polarization-adjusted convolutional codes. Background Technology
[0002] Polarization-Adjusted Convolutional (PAC) codes combine polar codes with convolutional codes to construct a hybrid coding architecture that significantly enhances error correction performance. In the PAC code structure, the convolutional pretransform, determined by a single convolutional polynomial, is a key factor controlling the code redistribution of PAC codes. Therefore, existing techniques typically optimize the code redistribution of PAC codes by optimizing the convolutional polynomial. Specifically, existing techniques usually employ exhaustive search to optimize the convolutional polynomial. The core of exhaustive search is to not overlook any possible solutions; by traversing all candidate combinations, calculating the objective function value (such as bit error rate, channel capacity, etc.), and finally selecting the solution with the best performance as the convolutional polynomial. However, unconstrained exhaustive search requires enumerating all possible polynomials, which leads to an exponential increase in computational complexity for optimizing the convolutional polynomial with the code length, increasing the difficulty of optimizing the code redistribution of PAC codes. Summary of the Invention
[0003] The purpose of this application is to provide a pre-transformation method, apparatus, terminal, and storage medium for polarization-adjusted convolutional codes, which can reduce the computational complexity of optimizing convolutional polynomials and reduce the difficulty of optimizing the code weight distribution of PAC codes. This application is mainly achieved through the following technical solutions: A first aspect of this application provides a pre-transformation method for polarization-adjusted convolutional codes, comprising: Set the first convolutional expression and multiple expressions to be processed; A tree-structured modeling method is used to perform convolutional polynomial modeling on the first convolutional expression and the plurality of expressions to be processed, to obtain the target convolutional polynomial of the polarization-adjusted convolutional code. The tree-structured modeling method sets the coefficients in the target expression to be processed to 0 or 1. When the coefficients of the target expression to be processed are 0, the expression to be processed corresponding to the target expression to be processed is not used to construct the target convolutional polynomial. The target expression to be processed is any one of the plurality of expressions to be processed.
[0004] According to an embodiment of this application, the step of performing convolutional polynomial modeling on the first convolutional expression and the plurality of expressions to be processed using a tree structure modeling method to obtain the target convolutional polynomial of the polarization-adjusted convolutional code includes: Set the information set, maximum convolution constraint length, first minimum code weight, first error coefficient, number of iterations, and first expression to be processed, and initialize the first convolution expression as follows: The iteration count is set to 1, and the first expression to be processed is initialized as the first expression to be processed among the plurality of expressions to be processed; If the number of iterations is not equal to the maximum convolution constraint length, the following steps are executed repeatedly: A second convolutional expression is constructed based on the first convolutional expression and the first expression to be processed; a preset algorithm is used to calculate the minimum code weight and error coefficient of the information set to obtain the second minimum code weight and the second error coefficient; the first minimum code weight, the first error coefficient, the second minimum code weight, and the second error coefficient are judged based on preset conditions to obtain a judgment result; if the judgment result meets the conditions, the second convolutional expression is used as the target convolutional polynomial of the polarization-adjusted convolutional code, and the loop ends; if the judgment result does not meet the conditions, the iteration count is incremented by 1, the value of the first convolutional expression is modified to the second convolutional expression, and the first expression to be processed is modified to the next expression to be processed among the multiple expressions to be processed.
[0005] According to one embodiment of this application, after the step of using the second convolutional expression as the target convolutional polynomial of the polarization-adjusted convolutional code, the step of performing convolutional polynomial modeling processing on the first convolutional expression and the plurality of expressions to be processed using a tree structure modeling method to obtain the target convolutional polynomial of the polarization-adjusted convolutional code further includes: The second minimum code weight is taken as the target minimum code weight of the target convolution polynomial; The second error coefficient is used as the target error coefficient of the target convolution polynomial; Both the target minimum code weight and the target error coefficient are used to quantify the error correction capability of the target convolutional polynomial.
[0006] According to one embodiment of this application, the calculation formula for the step of constructing the second convolutional expression based on the first convolutional expression and the first expression to be processed is as follows: ; in, It is the second convolution expression; It is the first convolution expression; yes coefficient, The tree structure modeling method is randomly set to 0 or 1; It is the first expression to be processed, and also the first of the plurality of expressions to be processed. One pending expression; , It is the maximum convolution constraint length.
[0007] According to one embodiment of this application, the preset conditions include a first condition and a second condition, wherein... The first condition is ; The second condition is: ,and ; in, It is the second minimum code weight; It is the first minimum code weight; It is the second error coefficient; It is the first error coefficient.
[0008] According to one embodiment of this application, the step of judging the first minimum code weight, the first error coefficient, the second minimum code weight, and the second error coefficient based on preset conditions to obtain the judgment result includes: If the first minimum code weight, the first error coefficient, the second minimum code weight, and the second error coefficient meet the first condition or the second condition, then the judgment result is that the condition is met. If the first minimum code weight, the first error coefficient, the second minimum code weight, and the second error coefficient do not meet the first condition or the second condition, then the judgment result is that the condition is not met.
[0009] According to one embodiment of this application, the information set is constructed by a method combining Reid-Muller coding and Gaussian approximation.
[0010] A second aspect of this application provides a pre-transformation apparatus for polarization-adjusted convolutional codes, comprising: The settings module is used to set the first convolutional expression and multiple expressions to be processed; The modeling processing module is used to perform convolutional polynomial modeling processing on the first convolutional expression and the plurality of expressions to be processed using a tree structure modeling method to obtain the target convolutional polynomial of the polarization-adjusted convolutional code. The tree structure modeling method sets the coefficients in the target expression to be processed to 0 or 1. When the coefficients of the target expression to be processed are 0, the expression to be processed corresponding to the target expression to be processed is not used to construct the target convolutional polynomial. The target expression to be processed is any one of the plurality of expressions to be processed.
[0011] A third aspect of this application provides a terminal device, including a processor and a memory, the memory being used to store a computer program, and the processor being used to call and run the computer program stored in the memory to perform the steps of the pre-transformation method for polarization-adjusted convolutional codes provided in the first aspect of this application.
[0012] A fourth aspect of this application provides a computer-readable storage medium for storing a computer program that causes a computer to perform the steps of the pre-transformation method for polarization-adjusted convolutional codes provided in the first aspect of this application.
[0013] The beneficial effects of the embodiments of this application include: This application proposes a tree-structured modeling method to generate the target convolutional polynomial of polarization-adjusted convolutional codes. The coefficient selection process for the target convolutional polynomial is modeled as a progressively expanding tree search problem. Specifically, this application involves setting a first convolutional polynomial and multiple unprocessed polynomials; using a tree-structured modeling method to perform convolutional polynomial modeling on the first convolutional polynomial and the multiple unprocessed polynomials to obtain the target convolutional polynomial of the polarization-adjusted convolutional codes. The tree-structured modeling method sets the coefficients in the target unprocessed polynomials to 0 or 1. When the coefficients of the target unprocessed polynomial are 0, the unprocessed polynomial corresponding to the target unprocessed polynomial is not used to construct the target convolutional polynomial. The target unprocessed polynomial is any one of the multiple unprocessed polynomials. Compared with existing technologies that use exhaustive search to optimize convolutional polynomials, the embodiments of this application use the coefficients of the first convolutional polynomial as the root and the coefficients of each polynomial to be processed (0 or 1) as tree nodes to form a tree structure. During the tree search process, only the currently optimal polynomial branch is retained for further expansion. That is, only the polynomial to be processed with a coefficient of 1 can be expanded with the first convolutional polynomial to form the target convolutional polynomial. This allows the embodiments of this application to obtain the target convolutional polynomial without enumerating all possible polynomials. Therefore, the embodiments of this application can reduce the computational complexity of optimizing convolutional polynomials, thereby reducing the difficulty of optimizing the code redistribution of PAC codes. Attached Figure Description
[0014] To more clearly illustrate the technical solutions in the embodiments of this application or the conventional technology, the drawings used in the description of the embodiments or the conventional technology will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0015] Figure 1 The flowcharts for the pre-transformation method of the polarization-adjusted convolutional code in this application are shown in some embodiments. Figure 2 A reference figure comparing the block error rates of the polarization-adjusted convolutional code and the underlying polarization code in this application; Figure 3 The diagram below shows the principle block diagram of the pre-transformation device for the polarization-adjusted convolutional code in some embodiments of this application. Figure 4 This is a schematic block diagram of the terminal device of this application in some embodiments. Detailed Implementation
[0016] To make the above-mentioned objectives, features, and advantages of this application more apparent and understandable, the specific embodiments of this application are described in detail below with reference to the accompanying drawings. Many specific details are set forth in the following description to provide a thorough understanding of this application. However, this application can be implemented in many other ways different from those described herein, and those skilled in the art can make similar modifications without departing from the spirit of this application. Therefore, this application is not limited to the specific embodiments disclosed below.
[0017] It should be noted that 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 indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.
[0018] The terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or illustration. Any embodiment or design described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.
[0019] The terms “comprising,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, system, product, or apparatus that includes a series of steps or units is not necessarily limited to those steps or units that are expressly listed, but may include other steps or units that are not expressly listed or that are inherent to such process, method, product, or apparatus.
[0020] Unless otherwise defined, all technical and scientific terms used in this application have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The term "and / or" as used in this application includes any and all combinations of one or more of the associated listed items.
[0021] The specific embodiments of this application will be further described below with reference to the accompanying drawings.
[0022] refer to Figure 1 The diagram shown is a flowchart of a pre-transformation method for polarization-adjusted convolutional codes provided in the first aspect of an embodiment of this application. Figure 1 In the above, the pre-transformation method for the polarization-adjusted convolutional code includes: S1. Set the first convolution expression and multiple expressions to be processed.
[0023] The number of the multiple unprocessed expressions is equal to the maximum convolution constraint length.
[0024] The maximum convolution constraint length can be set to 6. In other embodiments, the maximum convolution constraint length can be other values, which can be set by those skilled in the art according to actual needs.
[0025] S2. A tree-structured modeling method is used to perform convolutional polynomial modeling on the first convolutional expression and the plurality of expressions to be processed, to obtain the target convolutional polynomial of the polarization-adjusted convolutional code. The tree-structured modeling method sets the coefficients in the target expression to be processed to 0 or 1. When the coefficients of the target expression to be processed are 0, the expression to be processed corresponding to the target expression to be processed is not used to construct the target convolutional polynomial. The target expression to be processed is any one of the plurality of expressions to be processed.
[0026] Furthermore, the step of performing convolutional polynomial modeling on the first convolutional expression and the plurality of expressions to be processed using a tree structure modeling approach to obtain the target convolutional polynomial of the polarization-adjusted convolutional code includes: setting the information set, maximum convolution constraint length, first minimum code weight, first error coefficient, number of iterations, and first expression to be processed, and initializing the first convolutional expression as follows: The iteration count is set to 1, and the first expression to be processed is initialized as the first expression among the plurality of expressions to be processed. If the iteration count is not equal to the maximum convolution constraint length, the following steps are executed iteratively: a second convolution expression is constructed based on the first convolution expression and the first expression to be processed; a preset algorithm is used to calculate the minimum code weight and error coefficient of the information set to obtain a second minimum code weight and a second error coefficient; the first minimum code weight, the first error coefficient, the second minimum code weight, and the second error coefficient are judged based on preset conditions to obtain a judgment result; if the judgment result meets the conditions, the second convolution expression is used as the target convolution polynomial of the polarization-adjusted convolutional code, and the loop ends; if the judgment result does not meet the conditions, the iteration count is incremented by 1, the value of the first convolution expression is modified to the second convolution expression, and the first expression to be processed is modified to the next expression among the plurality of expressions to be processed.
[0027] The information set is constructed using a combination of Reed-Muller coding and Gaussian approximation. Reed-Muller coding (RM code) is a linear block error correction coding technique widely used in communications. Gaussian approximation is a mathematical method that simplifies complex problems into approximate solutions by introducing the mean and variance of a probability distribution to approximate variable deviations, thereby transforming a linear optimization problem into a nonlinear equation solving problem.
[0028] The information set is the information bit channel index set of the polarization-adjusted convolutional code, which refers to the set of sub-channel indices selected for transmitting information bits during the polarization-adjusted convolutional code process.
[0029] The first convolutional expression is initialized as follows: In this case, the first convolution is a bottom-level polarization code, and the first convolution is also a polarization-adjusted convolution code.
[0030] The tree-structure modeling method aims to break the constraint length limitation and fully utilize the potential of convolutional polynomials across the entire code length. The idea behind this method is to model each choice of a convolutional polynomial as a branch in a search tree, with the first term's coefficient fixed at 1 (i.e., the first convolutional polynomial is initialized to...). Each subsequent term has its corresponding coefficient set to 0 or 1, thus forming a tree structure. During the tree search process, only the currently optimal polynomial branch is retained for further expansion, which reduces the computational complexity to a polynomial growth with the code length. Furthermore, this method allows setting the maximum constraint length for convolution, thereby further controlling the computational complexity.
[0031] The calculation methods for the first minimum code weight and the first error coefficient can be the same as those for the second minimum code weight and the second error coefficient.
[0032] Furthermore, the calculation formula for constructing the second convolutional expression based on the first convolutional expression and the first expression to be processed is as follows: ; in, It is the second convolution expression; It is the first convolution expression; yes coefficient, The tree structure modeling method is randomly set to 0 or 1; It is the first expression to be processed, and also the first of the plurality of expressions to be processed. One pending expression; , It is the maximum convolution constraint length.
[0033] Furthermore, the step of using a preset algorithm to calculate the minimum code weight and error coefficient of the information set to obtain the second minimum code weight and the second error coefficient can be implemented using existing technologies.
[0034] In other embodiments, the step of using a preset algorithm to calculate the minimum code weight and error coefficient of the information set to obtain the second minimum code weight and the second error coefficient includes: using a first sub-algorithm of the preset algorithm to calculate the minimum code weight of the information set to obtain the third minimum code weight of each element in the information set; extracting the minimum value from all the third minimum code weights to obtain the second minimum code weight; using a second sub-algorithm of the preset algorithm to calculate the error coefficient of the information set to obtain the third error coefficient of each element in the information set; and taking the maximum value or average value of all the third error coefficients as the second error coefficient.
[0035] The second sub-algorithm can be a bit error rate calculation method or other calculation methods, which can be set by those skilled in the art according to actual needs.
[0036] Furthermore, the calculation formula for the step of performing minimum code weight calculation on the information set using the first sub-algorithm of the preset algorithm to obtain the third minimum code weight of each element in the information set is as follows: ; ; in, It is the third minimum code weight of each element in the information set; Is to take The minimum value in; yes The OK; It is a binary Wol-Hadamard matrix. of Second Kronecker power, It is the tensor product operator; It is the information set mentioned above; It is the first polarization-adjusted convolutional code bits, ; It is a polarization-adjusted convolutional code length; It is the set of indices of the non-zero elements of the vector; It is a symbol for "defined as".
[0037] Furthermore, the preset conditions include a first condition and a second condition, wherein, The first condition is ; The second condition is: ,and ; in, It is the second minimum code weight; It is the first minimum code weight; It is the second error coefficient; It is the first error coefficient.
[0038] The first condition represents an improvement in the minimum code weight corresponding to the second convolution. The second condition represents a reduction in the error coefficient corresponding to the second convolution. In this case, the second convolution is the optimal solution.
[0039] Further, the step of judging the first minimum code weight, the first error coefficient, the second minimum code weight, and the second error coefficient based on preset conditions to obtain the judgment result includes: if the first minimum code weight, the first error coefficient, the second minimum code weight, and the second error coefficient meet the first condition or the second condition, then the judgment result is that the condition is met; if the first minimum code weight, the first error coefficient, the second minimum code weight, and the second error coefficient do not meet the first condition or the second condition, then the judgment result is that the condition is not met.
[0040] Furthermore, the step of using the second convolutional expression as the target convolutional polynomial of the polarization-adjusted convolutional code can be expressed as follows: ;in, It is the target convolution polynomial.
[0041] Through the above implementation methods, this application embodiment uses the coefficients of the first convolutional expression as the root and the coefficients of each expression to be processed (0 or 1) as tree nodes to form a tree structure. During the tree search process, only the currently optimal polynomial branch is retained for further expansion. That is, only the expression to be processed with a coefficient of 1 can be expanded with the first convolutional expression into the target convolutional polynomial. This allows this application embodiment to obtain the target convolutional polynomial without enumerating all possible polynomials. Thus, this application embodiment can reduce the computational complexity of optimizing the convolutional polynomial, thereby reducing the optimization difficulty of the code redistribution of PAC codes.
[0042] The embodiments of this application reduce the computational complexity to a polynomial growth with the code length. At the same time, the method can set the maximum constraint length of the convolution, thereby further controlling the computational complexity.
[0043] In some implementations, after the step of using the second convolutional expression as the target convolutional polynomial of the polarization-adjusted convolutional code, the step of performing convolutional polynomial modeling processing on the first convolutional expression and the plurality of expressions to be processed using a tree structure modeling method to obtain the target convolutional polynomial of the polarization-adjusted convolutional code further includes: using the second minimum code weight as the target minimum code weight of the target convolutional polynomial; using the second error coefficient as the target error coefficient of the target convolutional polynomial; both the target minimum code weight and the target error coefficient are used to quantify the error correction capability of the target convolutional polynomial.
[0044] Furthermore, the step of using the second minimum codeweight as the target minimum codeweight of the target convolution polynomial can be expressed as: ; It is the minimum code weight of the target.
[0045] Furthermore, the step of using the second error coefficient as the target error coefficient of the target convolution polynomial can be expressed as: ; It is the target error coefficient.
[0046] In other implementations, list search and theoretical calculation methods can be used to calculate the target minimum code weight and the target error coefficient.
[0047] The tree structure modeling method in this application adopts a single-step path-preserving search strategy, which is the key to achieving efficient and deterministic optimization. By reducing the exponential complexity of convolutional polynomial optimization to polynomial complexity, the feasibility of the pre-transformation method of polarization-adjusted convolutional codes in practice is guaranteed.
[0048] In practical applications, this embodiment employs binary phase-shift keying modulation in a Gaussian white noise channel. The block error rate performance of both the designed polarization-adjusted convolutional code and the underlying polarization code was simulated, and the simulation results were based on a list size. The 32-bit SCL (Successive Cancellation List) decoding was evaluated. The information sets of both the polarization-adjusted convolutional code and the underlying polarization code were constructed using a combination of Reed-Müller coding and Gaussian approximation. A comparison of the target convolutional polynomial, target minimum code weight, and target error coefficients of the polarization-adjusted convolutional code and the underlying polarization code designed in this application embodiment is shown in Table 1.
[0049] Table 1
[0050] When the code dimension is 16, by reducing the target error coefficient from 300 to 140, the polarization-adjusted convolutional code designed in this embodiment achieves a lower block error rate compared to the underlying polarization code. This increases the gain by approximately 0.2 dB (dB is a unit of decibels); with a code dimension of 32, by reducing the target error coefficient from 664 to 472, the polarization-adjusted convolutional code designed in this application embodiment achieves a lower block error rate compared to the underlying polarization code. This increases the gain by approximately 0.1 dB. With a code dimension of 48, by reducing the target error coefficient from 432 to 320, the polarization-adjusted convolutional code designed in this embodiment achieves a lower block error rate compared to the underlying polarization code. This results in a gain improvement of approximately 0.1 dB. In summary, the method for obtaining the target convolutional polynomial in this embodiment effectively optimizes the code weight distribution of polarization-adjusted convolutional codes and improves block error rate performance with extremely low complexity. A comparison of the block error rate performance of the polarization-adjusted convolutional codes and the underlying polarization codes at code lengths of 64 and code dimensions of 16, 32, and 48 can be found in [reference needed]. Figure 2 As shown.
[0051] refer to Figure 3 The diagram shown is a schematic block diagram of a pre-transformation device for polarization-adjusted convolutional codes provided in the second aspect of an embodiment of this application. Figure 3 In the process, the pre-transformation device 100 for the polarization-adjusted convolutional code includes: Setting module 101 is used to set the first convolutional expression and multiple expressions to be processed; The modeling processing module 102 is used to perform convolutional polynomial modeling processing on the first convolutional expression and the plurality of expressions to be processed using a tree structure modeling method to obtain the target convolutional polynomial of the polarization-adjusted convolutional code. The tree structure modeling method sets the coefficients in the target expression to be processed to 0 or 1. When the coefficients of the target expression to be processed are 0, the expression to be processed corresponding to the target expression to be processed is not used to construct the target convolutional polynomial. The target expression to be processed is any one of the plurality of expressions to be processed.
[0052] A third aspect of this application provides a terminal device, the schematic diagram of which is as follows: Figure 4As shown. The terminal device includes a processor, memory, network interface, display screen, and temperature sensor connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface of the terminal device is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a pre-transformation method for polarization-adjusted convolutional codes. The display screen can be a liquid crystal display (LCD) or an electronic ink display. The temperature sensor is pre-installed inside the terminal device to detect the operating temperature of the internal components.
[0053] Those skilled in the art will understand that Figure 4 The schematic diagram shown is only a partial structural diagram related to the present invention and does not constitute a limitation on the terminal device to which the present invention is applied. The specific terminal device may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.
[0054] In some embodiments, this application provides a terminal device including a processor and a memory. The memory stores a computer program, and the processor calls and runs the computer program stored in the memory to perform the steps of the pre-transformation method for polarization-adjusted convolutional codes provided in the first aspect of this application.
[0055] A fourth aspect of this application provides a computer-readable storage medium for storing a computer program that causes a computer to perform the steps of the pre-transformation method for polarization-adjusted convolutional codes provided in the first aspect of this application.
[0056] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided by this invention can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.
[0057] The technical features of the above embodiments can be combined without changing the basic principles of this application. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0058] The above embodiments merely illustrate several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the patent protection scope of this application should be determined by the appended claims.
Claims
1. A pre-transformation method for polarization-adjusted convolutional codes, characterized in that, include: Set the first convolutional expression and multiple expressions to be processed; A tree-structured modeling method is used to perform convolutional polynomial modeling on the first convolutional expression and the plurality of expressions to be processed, to obtain the target convolutional polynomial of the polarization-adjusted convolutional code. The tree-structured modeling method sets the coefficients in the target expression to be processed to 0 or 1. When the coefficients of the target expression to be processed are 0, the expression to be processed corresponding to the target expression to be processed is not used to construct the target convolutional polynomial. The target expression to be processed is any one of the plurality of expressions to be processed.
2. The pre-transformation method for polarization-adjusted convolutional codes according to claim 1, characterized in that, The steps of performing convolutional polynomial modeling on the first convolutional expression and the plurality of expressions to be processed using a tree structure modeling approach to obtain the target convolutional polynomial of the polarization-adjusted convolutional code include: Set the information set, maximum convolution constraint length, first minimum code weight, first error coefficient, number of iterations, and first expression to be processed, and initialize the first convolution expression as follows: The iteration count is set to 1, and the first expression to be processed is initialized as the first expression to be processed among the plurality of expressions to be processed; If the number of iterations is not equal to the maximum convolution constraint length, the following steps are executed repeatedly: A second convolutional expression is constructed based on the first convolutional expression and the first expression to be processed; a preset algorithm is used to calculate the minimum code weight and error coefficient of the information set to obtain the second minimum code weight and the second error coefficient; the first minimum code weight, the first error coefficient, the second minimum code weight, and the second error coefficient are judged based on preset conditions to obtain a judgment result; if the judgment result meets the conditions, the second convolutional expression is used as the target convolutional polynomial of the polarization-adjusted convolutional code, and the loop ends; if the judgment result does not meet the conditions, the iteration count is incremented by 1, the value of the first convolutional expression is modified to the second convolutional expression, and the first expression to be processed is modified to the next expression to be processed among the multiple expressions to be processed.
3. The pre-transformation method for polarization-adjusted convolutional codes according to claim 2, characterized in that, After the step of using the second convolutional expression as the target convolutional polynomial of the polarization-adjusted convolutional code, the step of performing convolutional polynomial modeling on the first convolutional expression and the plurality of expressions to be processed using a tree structure modeling method to obtain the target convolutional polynomial of the polarization-adjusted convolutional code further includes: The second minimum code weight is taken as the target minimum code weight of the target convolution polynomial; The second error coefficient is used as the target error coefficient of the target convolution polynomial; Both the target minimum code weight and the target error coefficient are used to quantify the error correction capability of the target convolutional polynomial.
4. The pre-transformation method for polarization-adjusted convolutional codes according to claim 2, characterized in that, The calculation formula for constructing the second convolutional expression based on the first convolutional expression and the first expression to be processed is as follows: ； in, It is the second convolution expression; It is the first convolution expression; yes coefficient, The tree structure modeling method is randomly set to 0 or 1; It is the first expression to be processed, and also the first of the plurality of expressions to be processed. One pending expression; , It is the maximum convolution constraint length.
5. The pre-transformation method for polarization-adjusted convolutional codes according to claim 2, characterized in that, The preset conditions include a first condition and a second condition, wherein... The first condition is ; The second condition is: ,and ; in, It is the second minimum code weight; It is the first minimum code weight; It is the second error coefficient; It is the first error coefficient.
6. The pre-transformation method for polarization-adjusted convolutional codes according to claim 5, characterized in that, The steps for judging the first minimum code weight, the first error coefficient, the second minimum code weight, and the second error coefficient based on preset conditions to obtain the judgment result include: If the first minimum code weight, the first error coefficient, the second minimum code weight, and the second error coefficient meet the first condition or the second condition, then the judgment result is that the condition is met. If the first minimum code weight, the first error coefficient, the second minimum code weight, and the second error coefficient do not meet the first condition or the second condition, then the judgment result is that the condition is not met.
7. The pre-transformation method for polarization-adjusted convolutional codes according to claim 2, characterized in that, The information set is constructed using a combination of Reed-Muller coding and Gaussian approximation.
8. A pre-transformation device for polarization-adjusted convolutional codes, characterized in that, include: The settings module is used to set the first convolutional expression and multiple expressions to be processed; The modeling processing module is used to perform convolutional polynomial modeling processing on the first convolutional expression and the plurality of expressions to be processed using a tree structure modeling method to obtain the target convolutional polynomial of the polarization-adjusted convolutional code. The tree structure modeling method sets the coefficients in the target expression to be processed to 0 or 1. When the coefficients of the target expression to be processed are 0, the expression to be processed corresponding to the target expression to be processed is not used to construct the target convolutional polynomial. The target expression to be processed is any one of the plurality of expressions to be processed.
9. A terminal device, characterized in that, include: A processor and a memory, the memory for storing a computer program, the processor for calling and running the computer program stored in the memory to perform the steps of the pre-transformation method for polarization-adjusted convolutional codes as described in any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that, Used to store a computer program that causes a computer to perform the steps of the pre-transformation method for polarization-adjusted convolutional codes as described in any one of claims 1 to 7.