Method and device for constructing multi-code-rate low-density parity check code

Abstract

According to the multi-code-rate low-density parity check code construction method and device, the number of non-zero elements in a multi-code-rate check matrix and the number of decoding iterations of each code rate are comprehensively considered, a global normalization throughput index is introduced, the number of decoding iterations is set to be smaller than a preset iteration number threshold value, and the number of decoding iterations is set to be smaller than the number of decoding iterations. And setting a basic model graph matrix which has the maximum global normalized throughput index value and meets the first constraint condition and the second constraint condition when the column weight of the check matrix is greater than a preset minimum column weight value, and constructing a multi-code-rate low-density parity check (LDPC) code based on the matrix, the decoding throughput is further improved while hardware resource occupation is reduced, it is ensured that the generated multi-code-rate LDPC code is low in hardware resource occupation, the application requirement of a resource limited scene is met, and the applicability of the LDPC code is improved.

Description

Technical Field

[0001] This application relates to channel coding technology, and particularly to a construction method and device for multi-rate low-density parity-check (LDPC) codes. Background Art

[0002] With the rapid development of emerging communication technologies such as high-speed satellite optical communication and millimeter-wave communication, modern communication systems have an increasing demand for high data throughput and low latency. In these high-speed communication applications, channel coding technology plays a crucial role. It can effectively ensure the reliable transmission of data and protect the signal during the transmission process to cope with various channel interference and noise problems. Especially with the continuous increase in communication rates, how to achieve efficient and reliable data transmission in a complex channel environment has become one of the core challenges in the development of communication technology.

[0003] In the face of terminal miniaturization and limited hardware resources, existing multi-rate low-density parity-check (LDPC) coding schemes have certain limitations. Although multi-rate LDPC codes have been widely used in many communication standards due to their excellent decoding performance and flexible code rate adjustment capabilities, under the requirements of high data rates and low resource consumption, these schemes often require a large amount of hardware resources and multiple iterative decoding processes, and it is difficult to meet the requirements of low resource occupancy and high decoding throughput in emerging communication scenarios. Therefore, optimizing LDPC codewords under hardware constraints to achieve efficient data transmission has become the main research direction. It has been found in related technologies that the resource occupancy of the hardware implementation of an LDPC code decoder is approximately proportional to the number of non-zero elements in the parity-check matrix, while the decoding throughput is inversely proportional to the required number of decoding iterations. Based on this relationship, some studies have reduced the complexity of the hardware implementation of the decoder by reducing the number of non-zero elements in the LDPC parity-check matrix. However, this usually leads to an increase in the number of decoding iterations, resulting in a decrease in decoding throughput. On the other hand, some studies focus on optimizing the number of decoding iterations to improve decoding throughput, but often neglect the control of the hardware implementation complexity of the decoder. Therefore, although the research in related technologies has made progress in their respective optimization directions, it has not achieved the comprehensive optimization of hardware resource consumption and decoding throughput, and most of them focus on the construction optimization of single-rate LDPC codewords, lacking the comprehensive optimization of the hardware implementation complexity and decoding throughput of multi-rate LDPC codes.

[0004] In summary, how to improve the coding quality of multi-rate LDPC codes has become a problem to be solved. Summary of the Invention

[0005] The embodiments of this application provide a construction method for multi-rate low-density parity-check codes, including:

[0006] Construct a global normalized throughput metric based on the number of non-zero elements in the parity-check matrix and the number of decoding iterations for each code rate; wherein, the global normalized throughput metric is used to quantify the decoding throughput under unit hardware resource consumption;

[0007] Determine the base graph matrix when the global normalized throughput metric takes the maximum value and satisfies the first constraint condition and the second constraint condition through a global search method; wherein, the first constraint condition is that the number of decoding iterations is less than a preset iteration number threshold, and the second constraint condition is that the column weight of the parity-check matrix is greater than a preset minimum column weight;

[0008] Construct a multi-code rate parity-check (LDPC) code according to the determined base graph matrix. On the other hand, an embodiment of the present application also provides a computer storage medium, in which a computer program is stored, and when the computer program is executed by a processor, the above-mentioned method for constructing a multi-code rate low-density parity-check code is implemented.

[0009] On yet another aspect, an embodiment of the present application also provides a terminal, including: a memory and a processor, and a computer program is stored in the memory; wherein,

[0010] The processor is configured to execute the computer program in the memory;

[0011] When the computer program is executed by the processor, the above-mentioned method for constructing a multi-code rate low-density parity-check code is implemented.

[0012] On still another aspect, an embodiment of the present application also provides a device for constructing a multi-code rate low-density parity-check code, including: a construction unit, a search unit, and an encoding unit; wherein,

[0013] The construction unit is configured to: construct a global normalized throughput metric based on the number of non-zero elements in the parity-check matrix and the number of decoding iterations for each code rate; wherein, the global normalized throughput metric is used to quantify the decoding throughput under unit hardware resource consumption;

[0014] The search unit is configured to: determine the base graph matrix when the global normalized throughput metric takes the maximum value and satisfies the first constraint condition and the second constraint condition through a global search method; wherein, the first constraint condition is that the number of decoding iterations is less than a preset iteration number threshold, and the second constraint condition is that the column weight of the parity-check matrix is greater than a preset minimum column weight;

[0015] The encoding unit is configured to: construct a multi-code rate parity-check LDPC code according to the determined base graph matrix.

[0016] In the embodiments of the present disclosure, considering the number of non-zero elements in the multi-rate parity-check matrices and the number of decoding iterations for each rate comprehensively, a global normalized throughput metric is introduced. A first constraint condition is set that the number of decoding iterations is less than a preset iteration threshold, and a second constraint condition is set that the column weight of the parity-check matrix is greater than a preset minimum column weight. A base graph matrix is determined by a global search method when the value of the global normalized throughput metric is the largest and satisfies the first and second constraint conditions. A multi-rate parity-check (LDPC) code is constructed based on the determined base graph matrix, which reduces the hardware resource occupation and further improves the decoding throughput while ensuring a relatively low hardware resource occupation of the generated multi-rate LDPC code, meets the actual application requirements in resource-constrained scenarios, and improves the applicability of the constructed multi-rate parity-check matrices.

[0017] Other features and advantages of the present application will be described in the following specification, and will, in part, be obvious from the specification, or will be learned by practicing the present application. Other advantages of the present application can be realized and obtained by the solutions described in the specification and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings are used to provide an understanding of the technical solutions of the present application and constitute a part of the specification. They are used together with the embodiments of the present application to explain the technical solutions of the present application and do not constitute a limitation to the technical solutions of the present application.

[0019] Figure 1 is a flowchart of a method for constructing a multi-rate low-density parity-check code according to an embodiment of the present disclosure;

[0020] Figure 2 is a structural block diagram of an apparatus for constructing a multi-rate low-density parity-check code according to an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0021] The present application describes multiple embodiments, but the description is exemplary rather than restrictive, and it will be obvious to those of ordinary skill in the art that there can be more embodiments and implementation solutions within the scope of the embodiments described in the present application. Although many possible feature combinations are shown in the drawings and discussed in the detailed description, many other combination ways of the disclosed features are also possible. Unless specifically restricted, any feature or element of any embodiment can be combined with any other feature or element in any other embodiment, or can replace any other feature or element in any other embodiment.

[0022] This application includes and contemplates combinations of features and elements known to those of ordinary skill in the art. The disclosed embodiments, features, and elements of this application can also be combined with any conventional features or elements to form a unique inventive solution. Any feature or element of any embodiment can also be combined with features or elements from other inventive solutions to form another unique inventive solution. Therefore, it should be understood that any feature shown and / or discussed in this application can be implemented alone or in any suitable combination. Therefore, the embodiments are not subject to other limitations except those made in accordance with the appended claims and their equivalents. In addition, various modifications and changes can be made within the scope of the appended claims.

[0023] In addition, when describing representative embodiments, the specification may have presented the method and / or process as a specific sequence of steps. However, to the extent that the method or process does not depend on the specific order of the steps described herein, the method or process should not be limited to the specific order of steps described. As will be understood by those of ordinary skill in the art, other step sequences are possible. Therefore, the specific order of steps set forth in the specification should not be construed as a limitation on the claims. In addition, the claims directed to the method and / or process should not be limited to performing their steps in the order written, as those skilled in the art can readily understand that these orders can vary and still remain within the spirit and scope of the embodiments of this application.

[0024] The inventors of this application analyzed and found that the implementation of multi-rate LDPC code decoders in the related art usually faces the problem of high hardware resource occupancy. At the same time, its relatively large number of decoding iterations significantly limits the decoding throughput, making it impossible to meet the requirements of low resource consumption and high throughput while ensuring decoding performance under the condition of limited hardware resources.

[0025] Figure 1 It is a flowchart of a method for constructing a multi-rate low-density parity-check code according to an embodiment of the present disclosure. As Figure 1 shown, it includes:

[0026] Step 101, construct a global normalized throughput metric according to the number of non-zero elements in the parity-check matrix and the number of decoding iterations for each code rate; wherein, the global normalized throughput metric is used to quantify the decoding throughput under unit hardware resource consumption;

[0027] Step 102, through a global search method, determine the base graph matrix when the value of the global normalized throughput metric is the largest and satisfies the first constraint condition and the second constraint condition; wherein, the first constraint condition is that the number of decoding iterations is less than a preset iteration threshold, and the second constraint condition is that the column weight of the parity-check matrix is greater than a preset minimum column weight;

[0028] Step 103: Construct a multi-rate parity check (LDPC) code according to the determined base graph matrix.

[0029] In the embodiments of the present disclosure, considering the number of non-zero elements in the multi-rate parity check matrix and the number of decoding iterations for each code rate, a global normalized throughput metric is introduced. A first constraint condition is set that the number of decoding iterations is less than a preset iteration threshold, and a second constraint condition is set that the column weight of the parity check matrix is greater than a preset minimum column weight. The base graph matrix is determined through a global search method when the global normalized throughput metric takes the maximum value and satisfies the first and second constraint conditions. The multi-rate parity check (LDPC) code is constructed based on the determined base graph matrix, reducing the hardware resource occupancy while further improving the decoding throughput, ensuring a relatively low hardware resource occupancy of the generated multi-rate LDPC code, meeting the actual application requirements in resource-constrained scenarios, and improving the applicability of the constructed multi-rate parity check matrix.

[0030] In an exemplary instance, the global normalized throughput metric in the embodiments of the present disclosure is and its expression is:

[0031]

[0032] In the formula, ∑ i,j b i,j represents the sum of elements in the base graph matrix; w d is a preset throughput weighting coefficient; D represents the number of code rates of the multi-rate LDPC code; K d represents the information bit length corresponding to the code rate r d ; I d represents the number of decoding iterations required under the decoding threshold constraint value d for the code rate r ; represents the minimum number of iterations required for successful decoding of the LDPC code with as the base graph matrix under ; is the base graph matrix corresponding to the code rate r d ; represents the decoding threshold constraint value corresponding to the code rate r d ; represents the throughput.

[0033] In an exemplary instance, the embodiments of the present disclosure can be calculated by LDPC codeword performance analysis methods such as the EXIT algorithm; ∑ i,j b i,j in the embodiments of the present disclosure can represent the number of non-zero vectors.

[0034] In an exemplary instance, the first constraint condition of the embodiment of the present disclosure is: I d ≤I max , d = 1, …, D; The second constraint condition is: 1 T b u -d v,min ≥0, u = 1, …, U;

[0035] Wherein, I max is the maximum limit value of the pre-set code rate decoding iteration times I d , 1 T represents a column vector with all elements being 1 and having the same length as b u , b u is the column vector to be optimized in the base graph matrix, 1 T b u represents the sum of the elements of the column vector b u to be optimized, and d v,min is the minimum column weight of the column vector to be optimized.

[0036] In an exemplary instance, the second constraint condition of the embodiment of the present disclosure can be set based on the empirical criterion during the LDPC codeword design, that is, the column weight of the parity check matrix needs to be not less than a constant (generally 3), so as to ensure that the minimum code distance of the constructed LDPC code increases linearly with the code length, and thus has better decoding performance.

[0037] In an exemplary instance, the throughput weighting coefficient of the embodiment of the present disclosure is set according to the usage scenario analysis; for example, it can include the frequency setting of using multi-code rate LDPC codewords in different scenarios.

[0038] In an exemplary instance, the global search method of the embodiment of the present disclosure includes:

[0039] The global search method based on the differential evolution algorithm; or,

[0040] The global search method based on simulated annealing.

[0041] In an exemplary instance, the global search method based on the differential evolution algorithm of the embodiment of the present disclosure generally includes: initialization, individual mutation, individual crossover, and next-generation individual selection, etc.[[ID=5@]]

[0042] In an exemplary instance, when the global search method of the embodiment of the present disclosure is the global search method based on the differential evolution algorithm, the number of individuals in the base graph matrix is S, the number of evolution rounds of the differential evolution algorithm is G, and the crossover probability is p c , based on the global search method, determining the base graph matrix when the global normalized throughput metric takes the maximum value and satisfies the first constraint condition and the second constraint condition, includes:

[0043] Randomly generate S initial candidate motif graph matrices The subscript 0 represents the initialization stage; the Q variables of each motif graph matrix are randomly taken from the set of non - negative integers {x min , x min + 1, …, x max}, x min and x max represent the minimum and maximum values of the variables to be optimized respectively;

[0044] In the g - th iteration of the G - round evolution, perform the following processing:

[0045] According to the S candidate motif graph matrices Generate S mutant motif graph matrices The subscript g represents the round of the evolutionary iteration; where g = 0, 1, …, G - 1, is the q - th variable of, t1, t2, t3 are three different integers randomly selected from the set {1, 2, …, S}, and the function Φ(x) returns the non - negative integer in the set {x min , x min + 1, …, x max} that is closest to x;

[0046] According to the S candidate motif graph matrices and the S mutant motif graph matrices Generate S crossover motif graph matrices where, is the q - th variable of, takes the value of c with probability p takes the value of c with probability 1 - p

[0047] According to the S candidate motif graph matrices and the S crossover motif graph matrices Produce the S candidate motif graph matrices for the (g + 1) - th round where, the s - th candidate motif graph matrix is generated as follows: when the global normalized throughput corresponding to the individual is higher than (better than ), and the number of inequalities that satisfy the first constraint condition and the second constraint condition is greater than or equal to the number of inequalities that satisfy the first constraint condition and the second constraint condition, let Otherwise, when the individual The corresponding global normalized throughput value is less than or equal to and / or, When the number of inequalities satisfying the first constraint condition and the second constraint condition is less than the number of inequalities satisfying the first constraint condition and the second constraint condition, let

[0048] Through G rounds of evolution, S candidate base graph matrices are obtained

[0049] Select from the S candidate base graph matrices the base graph matrix with the largest normalized throughput value and satisfying the first constraint condition and the second constraint condition.

[0050] The first constraint condition of the embodiment of the present disclosure is: I d ≤I max , d = 1, …, D. By taking the values of d from 1 to D respectively, the number of inequalities satisfying the first constraint condition can be obtained; the second constraint condition of the embodiment of the present disclosure is: 1 T b u -d v,min ≥0, u = 1, …, U. By taking the values of u from 1 to U respectively, the number of inequalities satisfying the second constraint condition can be obtained;

[0051] In each iteration of the embodiment of the present disclosure, the parity check matrix with a higher global normalized throughput is preferentially selected to ensure that the optimization process gradually converges to the optimal solution; at the same time, by strictly maintaining the first constraint condition and the second constraint condition, it is ensured that the number of satisfied constraint conditions does not decrease during the optimization process, so as to generate a parity check matrix with the largest global normalized throughput and good decoding performance.

[0052] In an exemplary example, the embodiment of the present disclosure constructs a multi-rate low-density parity-check (LDPC) code according to the determined base graph matrix, including:

[0053] According to the selected base graph matrix, use the LDPC base graph expansion algorithm for two expansions; wherein, the first expansion factor is L1, which is used to eliminate the elements greater than 1 in the base graph matrix to obtain a basic parity check matrix composed of all 0 and 1 elements; the second expansion factor is L2, which is used to perform a quasi-cyclic expansion on the basic parity check matrix, and finally obtain a multi-rate parity check matrix code of size (mL1L2) × (nL1L2); the shape of the multi-rate parity check matrix code in the embodiment of the present disclosure is M d ×N d , where M d = m d L1L2, N d = n d L1L2.

[0054] The embodiments of the present disclosure also provide a computer storage medium, in which a computer program is stored, and when the computer program is executed by a processor, the above method for constructing a multi-rate low-density parity-check code is implemented.

[0055] The embodiments of the present disclosure also provide a terminal, including: a memory and a processor, where a computer program is stored in the memory; wherein,

[0056] The processor is configured to execute the computer program in the memory;

[0057] When the computer program is executed by the processor, the above method for constructing a multi-rate low-density parity-check code is implemented.

[0058] Figure 2 FIG. is a structural block diagram of a device for constructing a multi-rate low-density parity-check code according to an embodiment of the present disclosure. As Figure 2 shown, it includes: a construction unit, a search unit, and an encoding unit; wherein,

[0059] The construction unit is configured to: construct a global normalized throughput metric according to the number of non-zero elements in the parity-check matrix and the number of decoding iterations for each code rate; wherein, the global normalized throughput metric is used to quantify the decoding throughput under the consumption of unit hardware resources;

[0060] The search unit is configured to: determine a base graph matrix when the value of the global normalized throughput metric is the largest and satisfies a first constraint condition and a second constraint condition through a global search method; wherein, the first constraint condition is that the number of decoding iterations is less than a preset iteration number threshold, and the second constraint condition is that the column weight of the parity-check matrix is greater than a preset minimum column weight;

[0061] The encoding unit is configured to: construct a multi-rate parity-check LDPC code according to the determined base graph matrix.

[0062] In an exemplary example, the global normalized throughput metric of the embodiments of the present disclosure is The expression is:

[0063]

[0064] In the formula, ∑ i,j b i,j represents the sum of elements in the base graph matrix; w d is a preset throughput weighting coefficient; D represents the number of code rates of the multi-rate LDPC code; K d represents the information bit length corresponding to the code rate r d ; I d represents the number of decoding iterations required for the code rate r d under the decoding threshold constraint value , Denote the minimum number of iterations required for successful decoding of the LDPC code with as the base graph matrix at ; B rd is the code rate r d corresponding base graph matrix, Denote the decoding threshold constraint value corresponding to the code rate r d ; Denote the throughput.

[0065] In an exemplary instance, the first constraint condition in the embodiments of the present disclosure is: I d ≤I max , d = 1, …, D, and the second constraint condition is: 1 T b u -d v,min ≥0, u = 1, …, U;

[0066] wherein, I max is the maximum limit value of the preset code rate decoding iteration number I d , 1 T represents a column vector with all elements being 1 and having the same length as b u , b u is the column vector to be optimized in the base graph matrix, 1 T b u represents the sum of the elements of the column vector b u to be optimized, and d v,min is the minimum column weight of the column vector to be optimized.

[0067] In an exemplary instance, the global search method in the embodiments of the present disclosure includes:

[0068] A global search method based on the differential evolution algorithm; or,

[0069] A global search method based on simulated annealing.

[0070] In an exemplary instance, when the global search method in the embodiments of the present disclosure is a global search method based on the differential evolution algorithm, the number of individuals of the base graph matrix is S, the number of evolution rounds of the differential evolution algorithm is G, and the crossover probability is p c , and the search unit is set as:

[0071] Randomly generate S initial candidate base graph matrices The Q variables of each base graph matrix are randomly taken from the set of non-negative integers {x min , x min +1, …, x max}, where x min and x max respectively represent the minimum and maximum values of the variables to be optimized;

[0072] In the g-th iteration of the G-round evolution, the following processing is performed:

[0073] According to S candidate schema graph matrices Generate S mutated schema graph matrices where g = 0, 1, …, G - 1, is the q-th variable of, t1, t2, t3 are three different integers randomly selected from the set {1, 2, …, S}, and the function Φ(x) returns the integer in the set of non-negative integers {x min , x min + 1, …, x max} that is closest to x;

[0074] According to S candidate schema graph matrices and S mutated schema graph matrices Generate S crossover schema graph matrices where, is the q-th variable of, takes the value of c with probability p and takes the value of c with probability 1 - p

[0075] According to S candidate schema graph matrices and S crossover schema graph matrices Generate the S candidate schema graph matrices for the (g + 1)-th round where the s-th candidate schema graph matrix is generated as follows: When the global normalized throughput value corresponding to the individual is higher than (i.e., is better than ), and the number of inequalities satisfying the first constraint condition and the second constraint condition is greater than or equal to the number of inequalities satisfying the first constraint condition and the second constraint condition, let Otherwise, when the global normalized throughput value corresponding to the individual is less than or equal to and / or, the number of inequalities satisfying the first constraint condition and the second constraint condition is less than the number of inequalities satisfying the first constraint condition and the second constraint condition, let

[0076] Through the G - round evolution, S candidate basic pattern graph matrices are obtained.

[0077] Select the basic pattern graph matrix with the largest normalized throughput value from the S candidate basic pattern graph matrices and satisfying the first constraint condition and the second constraint condition.

[0078] The following briefly describes the embodiments of the present disclosure through application examples. The application examples are only used to state the embodiments of the present disclosure and do not limit the protection scope of the embodiments of the present disclosure.

[0079] Application Example

[0080] The embodiments of the present disclosure are set as follows:

[0081] The number of code - rate values D = 4, and the code - rates include

[0082] The shape of the basic pattern graph matrix B to be optimized is 4×20; the basic pattern graph matrices B corresponding to the code - rates 1 / 2, 2 / 3, 3 / 4, and 4 / 5 1 / 2 , B 2 / 3 , B 3 / 4 , B 4 / 5 are the first 8 columns, the first 12 columns, the first 16 columns, and the first 20 columns of B respectively, and the lengths of the encoded information bits k1, k2, k3, and k4 are 4, 8, 12, and 16 respectively.

[0083] The first 3 columns of the basic pattern graph matrix B are fixed columns, and the last 17 columns are columns to be optimized, which can be expressed as:

[0084]

[0085] where the non - negative integer x i , i = 1, 2, … 68 are variables to be optimized, and it is limited that the minimum value x min = 0, and the maximum value x max = 3, that is, 0 ≤ x i ≤ 3.

[0086] The minimum column weight of the columns to be optimized in the basic pattern graph matrix B is d v,min = 3;

[0087] The decoding threshold constraint values corresponding to the code - rates 1 / 2, 2 / 3, 3 / 4, and 4 / 5 are respectively and The throughput weighting coefficients are set as w1 = w2 = w3 = w4 = 1 / 4.

[0088] The global normalized throughput index is defined as:

[0089]

[0090] In the global search method based on differential evolution, the number of individuals in the base graph matrix S = 700, the number of differential evolution rounds G = 200, and the crossover probability p c = 0.3.

[0091] The parity-check matrix H is extended from the base graph matrix B by a two-step lifting method, and the first extension factor L1 = 4 and the second extension factor L2 = 256; the parity-check matrices H 1 / 2 、H 2 / 3 、H 3 / 4 and H 4 / 5 corresponding to code rates 1 / 2, 2 / 3, 3 / 4, and 4 / 5 have shapes of 4096×8192, 4096×12288, 4096×16384, and 4096×20480 respectively, and the information-bit lengths are 4096, 8192, 12288, and 16384 respectively.

[0092] Under the above settings, the embodiments of the present disclosure include:

[0093] Step 1. Determine the constrained optimization problem:

[0094] The optimization objective is:

[0095]

[0096] The first constraint condition and the second constraint condition are:

[0097] I d ≤25, d = 1, …, 4;

[0098]

[0099] where the code rate r d The required number of decoding iterations I under the decoding threshold constraint value d is obtained by the EXIT algorithm, that is

[0100] Step 2. Solve the constrained optimization problem:

[0101] Use the differential evolution algorithm to solve the constrained optimization problem, including:

[0102] 2.1) Algorithm initialization: Randomly generate 700 initial candidate base graph matrices Each of the 68 variables of each base graph matrix is randomly taken from the set of non-negative integers {0, 1, 2, 3}.

[0103] 2.2) Individual mutation: In the g-th iteration (g = 0, 1, …, 199), generate the mutant base graph matrix For the q-th variable where Determined by the following formula: Wherein, t1, t2, t3 are three different integers randomly selected from the set {1, 2, …, 700}, and the function Φ(x) returns the integer in the set of non - negative integers {0, 1, 2, 3} that is closest to x.

[0104] 2.3) Individual crossover: In the g - th iteration (g = 0, 1, …, 199), generate the crossover base - mode graph matrix Wherein, The q - th variable of Takes the value of With a probability of 0.3 and takes the value of

[0105] 2.4) Next - generation individual selection: Generate the candidate base - mode graph matrix for the (g + 1)-th round Wherein, the s - th candidate base - mode graph matrix Is generated as follows: When the global normalized throughput value corresponding to the individual Is higher than (That is Is better than ), and The number of inequalities satisfying the first constraint condition and the second constraint condition is greater than or equal to The number of inequalities satisfying the first constraint condition and the second constraint condition, let Otherwise, when the global normalized throughput value corresponding to the individual Is less than or equal to And / or, The number of inequalities satisfying the first constraint condition and the second constraint condition is less than The number of inequalities satisfying the first constraint condition and the second constraint condition, let

[0106] After 200 rounds of evolutionary iteration, obtain the final 700 candidate base - mode graph matrices Select the base - mode graph matrix B with the maximum normalized throughput and satisfying all inequality constraints from them as the final optimization result.

[0107] Step 3: Selection of the final parity - check matrix:

[0108] The base - mode graph matrix B selected in Step 2 is:

[0109]

[0110] Each code - rate base - mode graph matrix can be respectively expressed as:

[0111]

[0112] In the embodiment of the present disclosure, the final check matrix H is obtained by two-step lifting of the base graph matrix B through the progressive edge grow (PEG) algorithm. The first lifting (lifting factor L1 = 4) is used to eliminate the elements greater than 1 in the base graph matrix to obtain a basic check matrix consisting entirely of 0s and 1s. The second lifting (lifting factor L2 = 256) is used to perform a cyclic matrix expansion on the basic matrix to obtain the final check matrix.

[0113] To illustrate the effectiveness of the coding method of the embodiment of the present disclosure in realizing low decoding complexity and high throughput multi-rate LDPC codes, AR4JA LDPC codes and Delay-limited LDPC codes with the same code rate and information bit length are selected for comparison.

[0114] 1) Comparison of hardware implementation complexity:

[0115] Referring to Table 1, the numbers of "1" elements in the check matrices of the three codewords are as follows:

[0116]

[0117]

[0118] Table 1

[0119] Compared with AR4JA LDPC codes and Delay-limited LDPC codes, the E total values of the codewords proposed in the embodiment of the present disclosure are reduced by 3.85% and 19.35% respectively, having the lowest decoder hardware implementation complexity.

[0120] 2) Comparison of the number of iterations required for decoding:

[0121] The LDPC BP decoding algorithm is used for decoding; the reference signal-to-noise ratios for code rates 1 / 2, 2 / 3, 3 / 4, and 4 / 5 are set respectively Set the attenuation value of E b / N0 to be Δ = 0, 0.1, 0.2, 0.3 dB. Let the codewords with code rates 1 / 2, 2 / 3, 3 / 4, and 4 / 5 have E b / N0 values respectively at which the bit error rate (BER) is less than 10 -6 The number of BP decoding iterations required is I1, I2, I3, and I4.

[0122] Table 2 to Table 5 respectively correspond to the number of iterations required for decoding with Δ = 0 dB, the number of iterations required for decoding with Δ = 0.1 dB, the number of iterations required for decoding with Δ = 0.2 dB, and the number of iterations required for decoding with Δ = 0.3 dB. According to the simulation results, different E bThe number of decoding iterations required for the three codewords at the / N0 level is as follows:

[0123] Codeword <![CDATA[I1]]> <![CDATA[I2]]> <![CDATA[I3]]> <![CDATA[I4]]> AR4JA Code 29 32 39 49 Delay-limited Code 20 20 20 20 Codeword proposed in the embodiments of the present disclosure 23 21 23 25

[0124] Table 2

[0125] Codeword <![CDATA[I1]]> <![CDATA[I2]]> <![CDATA[I3]]> <![CDATA[I4]]> AR4JA Code 24 25 28 27 Delay-limited Code 18 19 18 18 Codeword proposed in the embodiments of the present disclosure 20 18 17 19

[0126] Table 3

[0127] Codeword <![CDATA[I1]]> <![CDATA[I2]]> <![CDATA[I3]]> <![CDATA[I4]]> AR4JA Code 23 22 22 23 Delay-limited Code 17 16 15 15 [[ID= 18 16 15 16

[0128] Table 4

[0129] ​ <![CDATA[I1]]> <![CDATA[I2]]> <![CDATA[I3]]> <![CDATA[I4]]> ​ 20 19 19 19 ​ 15 15 14 13 ​ 17 14 13 14

[0130] Table 5

[0131] It can be seen from the above table data that under the same decoding performance requirements, the proposed codewords require significantly fewer decoding iterations than the AR4JA LDPC code and are slightly higher than the Delay-limited LDPC code.

[0132] Global normalized throughput comparison:

[0133] When comprehensively considering the hardware implementation complexity and the number of decoding iterations, the global normalized throughput comparison of each codeword is shown in Table 5:

[0134]

[0135] Table 6

[0136] When Δ = 0, 0.1, 0.2, 0.3 dB: Compared with the AR4JA LDPC code, the global normalized throughput of the proposed LDPC codewords is increased by 76.44%, 51.22%, 47.48%, and 42.62% respectively; compared with the Delay-limited LDPC code, the global normalized throughput of the proposed LDPC codewords is increased by 6.43%, 23.64%, 20.20%, and 23.34% respectively.

[0137] The above analysis shows that using the encoding method of the embodiments of the present disclosure, the obtained LDPC code not only has a lower hardware implementation complexity of the decoder, but also requires fewer decoding iterations, thereby achieving the maximization of the global normalized throughput and having important application value for high-throughput communication scenarios with resource constraints.

[0138] Those of ordinary skill in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, and appropriate combinations thereof. In the hardware implementation, the division of functional modules / units mentioned above does not necessarily correspond to the division of physical components; for example, one physical component may have multiple functions, or one function or step may be executed by several physical components in cooperation. Some or all of the components may be implemented as software executed by a processor, such as a digital signal processor or a microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include a computer storage medium (or non-transitory medium) and a communication medium (or transitory medium). As is well known to those of ordinary skill in the art, the term "computer storage medium" includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information, such as computer-readable instructions, data structures, program modules, or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disk (DVD) or other optical disk storage, magnetic cassettes, tapes, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer. In addition, it is well known to those of ordinary skill in the art that communication media typically contain computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transmission mechanism, and can include any information delivery medium.

Claims

1. A construction method of a multi - rate low - density parity - check code, characterized in that, Including: Construct a global normalized throughput metric according to the number of non-zero elements in the parity check matrix and the number of decoding iterations for each code rate; wherein, the global normalized throughput metric is used to quantify the decoding throughput under the consumption of unit hardware resources; Determine the base graph matrix when the value of the global normalized throughput metric is the largest and satisfies the first constraint condition and the second constraint condition through a global search method; wherein, the first constraint condition is that the number of decoding iterations is less than a preset iteration number threshold, and the second constraint condition is that the column weight of the parity check matrix is greater than a preset minimum column weight; Construct a multi-code rate parity check LDPC code according to the determined base graph matrix.

2. The construction method according to claim 1, wherein The global normalized throughput metric is The expression of which is: where, ∑ i,j b i,j represents the sum of elements in the base graph matrix; w d is a pre-set throughput weighting coefficient; D represents the number of code rates of the multi-rate LDPC code; K d represents the information bit length corresponding to the code rate r d ; I d represents the number of decoding iterations required for the code rate r d under the decoding threshold constraint value ; represents the minimum number of iterations required for successful decoding of the LDPC code with as the base graph matrix under ; is the base graph matrix corresponding to the code rate r d ; represents the decoding threshold constraint value corresponding to the code rate r d ; represents the throughput.

3. The construction method according to claim 2, wherein: The first constraint condition is: I d ≤I max , d = 1, …, D; The second constraint is: 1 T b u -d v,min ≥0, u = 1, …, U; Among them, I max is the maximum limit value of the pre-set code rate decoding iteration times I d of, 1 T represents a column vector with all elements being 1 and the same length as b u , b u is the column vector to be optimized in the base mode graph matrix, 1 T b u represents the sum of the elements of the column vector b to be optimized u , d v,min is the minimum column weight of the column vector to be optimized.

4. The construction method according to any one of claims 1 to 3, characterized in that The global search method includes: A global search method based on the differential evolution algorithm; or, A global search method based on simulated annealing.

5. The construction method according to claim 4, wherein, When the global search method is the global search method based on the differential evolution algorithm, the number of individuals in the basis graph matrix is S, the number of evolution rounds of the differential evolution algorithm is G, and the crossover probability is p c , determining, based on the global search method, the basis graph matrix when the value of the global normalized throughput metric is the largest and satisfies the first constraint condition and the second constraint condition, includes: Randomly generate S initial candidate motif graph matrices The Q variables of each motif graph matrix are randomly taken from the set of non-negative integers {x min , x min +1, …, x max}, where x min and x max represent the minimum and maximum values of the variables to be optimized, respectively; In the g-th iteration of the G-round evolution, perform the following processing: According to S candidate schema graph matrices Generate S mutant schema graph matrices where g = 0, 1, …, G - 1, is the q-th variable of t1, t2, t3 are three different integers randomly selected from the set {1, 2, …, S}, and the function Φ(x) returns the integer in the set of non-negative integers {x min , x min + 1, …, x max} that is closest to x; According to S candidate schema graph matrices and S mutant schema graph matrices generate S crossover schema graph matrices where is the q-th variable of takes the value of c with probability p and takes the value of c with probability 1 - p According to S candidate schema graph matrices and S cross-schema graph matrices generate the S candidate schema graph matrices in the (g + 1)-th round where the generation method of the s-th candidate schema graph matrix is as follows: when the global normalized throughput value corresponding to the individual is higher than the global normalized throughput value corresponding to and the number of inequalities that satisfies the first constraint condition and the second constraint condition is greater than or equal to the number of inequalities that satisfies the first constraint condition and the second constraint condition, let Otherwise, when the global normalized throughput value corresponding to the individual is less than or equal to the global normalized throughput value corresponding to and / or the number of inequalities that satisfies the first constraint condition and the second constraint condition is less than the number of inequalities that satisfies the first constraint condition and the second constraint condition, let Through G-round evolution, S candidate schema graph matrices are obtained Select the base graph matrix with the largest normalized throughput value and satisfying the first constraint condition and the second constraint condition from S candidate base graph matrices.

6. A computer storage medium, in which a computer program is stored, and when the computer program is executed by a processor, it implements the construction method of the multi-code rate low-density parity check code according to any one of claims 1 to 5.

7. A terminal, comprising: A memory and a processor, wherein a computer program is stored in the memory; wherein, The processor is configured to execute the computer program in the memory; When the computer program is executed by the processor, it implements the construction method of the multi-code rate low-density parity check code according to any one of claims 1 to 5.

8. An apparatus for constructing a multi-rate low-density parity-check code, comprising: A construction unit, a search unit and an encoding unit; wherein, The construction unit is configured to: construct a global normalized throughput metric according to the number of non-zero elements in the parity check matrix and the number of decoding iterations for each code rate; wherein, the global normalized throughput metric is used to quantify the decoding throughput under the consumption of unit hardware resources; The search unit is configured to: determine the base graph matrix when the value of the global normalized throughput metric is the largest and satisfies the first constraint condition and the second constraint condition through a global search method; wherein, the first constraint condition is that the number of decoding iterations is less than a preset iteration number threshold, and the second constraint condition is that the column weight of the parity check matrix is greater than a preset minimum column weight; The encoding unit is configured to: construct a multi-code rate parity check LDPC code according to the determined base graph matrix.

9. The construction device according to claim 8, characterized in that, The global normalized throughput metric is The expression for is: Where, ∑ i,j b i,j represents the sum of elements in the base graph matrix; w d is a pre-set throughput weighting coefficient; D represents the number of code rates of the multi-code rate LDPC code; K d represents the information bit length corresponding to the code rate r d ; I d represents the number of decoding iterations required for the code rate r d under the decoding threshold constraint value ; represents the minimum number of iterations required for successful decoding of the LDPC code with as the base graph matrix under ; is the base graph matrix corresponding to the code rate r d ; represents the decoding threshold constraint value corresponding to the code rate r d ; represents the throughput.

10. The construction device according to claim 9, wherein: The first constraint condition is: I d ≤I max , d = 1, …, D; The second constraint is: 1 T b u -d v,min ≥0, u = 1, …, U; Among them, I max is the maximum limit value of the pre-set code rate decoding iteration times I d of, 1 T represents a column vector with all elements being 1 and the same length as b u , b u is the column vector to be optimized in the base graph matrix, 1 T b u represents finding the sum of the elements of the column vector b to be optimized u , d v,min is the minimum column weight of the column vector to be optimized.

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