Encoding optimization method and device of dual-generator matrix LDPC code, equipment and storage medium

Abstract

The application provides a double protograph LDPC code encoding optimization method, device and equipment and a storage medium, including: constructing a multiple connection base matrix and optimizing the multiple connection base matrix; providing a set of joint encoding base matrices based on the double protograph LDPC code, replacing a single edge connection base matrix of the joint encoding base matrix with the optimized multiple connection base matrix to generate a joint encoding matrix; expanding each part of the joint encoding matrix to generate a channel encoding matrix and a predetermined size connection matrix; source encoding a given original source bit to generate source compression information; and generalizing the joint encoding matrix according to the source compression information, the channel encoding matrix and the predetermined size connection matrix, aiming to increase the possibility that source redundancy information and channel state information can be fully utilized.

Description

Technical Field

[0001] This invention relates to the field of computers, and in particular to a method, apparatus, device, and storage medium for encoding optimization of dual-prototype LDPC codes. Background Technology

[0002] Compared to traditional separate source or channel coding schemes, the goal of joint source-channel coding schemes is to fully utilize source redundancy and channel state information to improve overall system performance. A joint coding system consisting of two LDPC codes was proposed by M. Fresia et al. in "Joint source and channel coding" [IEEE Signal Processing Magazine, 2010, 27(6):103-114]. One LDPC code is used for source compression, and the other for channel error control; this is called a dual LDPC coding system. Compared to separate LDPC coding systems, the core of the dual LDPC coding structure is the introduction of a connection matrix, which establishes a link between the two LDPC codes. At the decoding end, the joint confidence propagation decoding algorithm uses this connection matrix to iteratively exchange source redundancy and channel state information to improve system performance.

[0003] The specific structure and encoding algorithm are described below:

[0004] A joint source-channel coding based on dual LDPC codes can be represented by a joint matrix.

[0005]

[0006] Where H S It is a size M s ×N s The source coding matrix, H C It is a size M s ×N s The channel coding matrix, H L1 It is a size M s ×N C It is a first-class connection matrix, H L2 It is a size M C ×N S It is a connection matrix of the second kind. Furthermore, H... L1 Composed of a zero matrix 0 and an identity matrix H I This identity matrix is ​​composed of [variables]. It implies that there is a one-to-one correspondence between the verification nodes and variable nodes of the information source.

[0007] There is a set of length N s The statistical characteristics are (p0, p1) original source bits s, (1) after the source coding matrix is ​​calculated, a set of compressed bit sequences c is obtained, that is in(·) T (1) Perform the transpose of the matrix; (2) Concatenate s and c to obtain a new bit sequence [sc]; (3) Concatenate H L2 and H C , to obtain [H L2 H C After Gaussian elimination, we can obtain a form of [I]. C G C The system form of the generator matrix; (4) [sc] after channel coding G C The calculation yields a set of parity bit sequences p, i.e., p = [sc]·G C Therefore, the entire encoding process can be summarized as follows: Where u = [scp].

[0008] After removing the original source bits s from codeword u, [cp] is modulated and sent into the channel. At the receiver, the variable nodes corresponding to s are initialized using source statistical characteristics, and the variable nodes corresponding to c and p are initialized using channel information. Then, the Belief Propagation (BP) algorithm is used for iterative decoding. After several iterations, if the obtained... satisfy Alternatively, iteration will stop when the preset maximum number of iterations is reached, and extraction will proceed. In As the raw bit information for estimation. Here, H L2 It can be a zero matrix.

[0009] As mentioned earlier, the first type of connection matrix serves to connect the source coding matrix and the channel coding matrix, thereby enabling the exchange of source redundancy and channel state decoding information. Then, through H... I There are instances where a single connection relationship cannot fully utilize this decoded information.

[0010] In view of the above, this application is hereby submitted. Summary of the Invention

[0011] This invention discloses a coding optimization method, apparatus, device, and storage medium for dual-prototype LDPC codes, aiming to increase the possibility of fully utilizing source redundancy information and channel state information.

[0012] The first embodiment of the present invention provides an encoding optimization method for dual-prototype LDPC codes, comprising:

[0013] Construct a multi-connection basis matrix and optimize the multi-connection basis matrix;

[0014] A set of joint coding basis matrices based on dual-prototype graph LDPC codes is provided. The optimized multi-link basis matrix is ​​used to replace the single-side link basis matrix of the joint coding basis matrix to generate the joint coding matrix.

[0015] Each part of the joint coding matrix is ​​expanded to generate a channel coding matrix and a connection matrix of a predetermined size;

[0016] Source encoding is performed on the given raw source bits to generate source compressed information;

[0017] The joint coding matrix is ​​generalized and encoded based on the source compression information, the channel coding matrix, and the connection matrix of a predetermined size.

[0018] Preferably, by expanding each part of the joint coding matrix, a source coding matrix and a multiple connection coding matrix are also generated;

[0019] The source coding matrix, the multiple connection coding matrix, the channel coding matrix, and the connection matrix of a predetermined size are used to be concatenated into a joint coding matrix at the receiving end.

[0020] Preferably, the construction of a multi-linkage basis matrix and the optimization of the multi-linkage basis matrix specifically involves:

[0021] S1, Given an identity matrix, perform elementary transformation operations on the identity matrix and generate multi-linked basis matrices with different structures based on the operations;

[0022] S2, Based on the statistical characteristics of the information source and the signal-to-noise ratio, initialize the variance values ​​of different variable nodes of the multi-connection basis matrix;

[0023] S3 calls the traditional joint external information transmission algorithm to iteratively update the information transmission between variable nodes and verification nodes until the mutual information of variable nodes equals 1, or the maximum number of iterations is reached.

[0024] S4, change the initial signal-to-noise ratio, repeat S2 and S3 until the minimum signal-to-noise ratio is found, so that the mutual information of the variable nodes is equal to 1;

[0025] S5. Change the multi-connection basis matrix, and repeat S2, S3 and S4 to find the multi-connection basis matrix with the minimum signal-to-noise ratio.

[0026] Preferably, the generalized encoding of the joint coding matrix based on the source compression information, the channel coding matrix, and the connection matrix of a predetermined size specifically involves:

[0027] The source compressed information is subjected to multiple concatenation encoding to generate multiple encoded information;

[0028] The multi-encoded information and the original source bits are concatenated to generate a new bit sequence;

[0029] The channel coding matrix and the connection matrix of the predetermined size are concatenated to generate a concatenated result, and the concatenated result is subjected to Gaussian elimination to generate a generator matrix in the form of a system.

[0030] The generator matrix of the system form and the new bit sequence are multiplied to generate a combination of channel-coded source information and verification information.

[0031] The second embodiment of the present invention provides an encoding optimization device for dual-prototype LDPC codes, comprising:

[0032] An optimization unit is used to construct a multi-connection basis matrix and optimize the multi-connection basis matrix;

[0033] The replacement unit is used to provide a set of joint coding basis matrices based on dual-prototype graph LDPC codes, and replace the single-sided connection basis matrix of the joint coding basis matrix with the optimized multi-connection basis matrix to generate the joint coding matrix;

[0034] An expansion unit is used to expand each part of the joint coding matrix to generate a channel coding matrix and a connection matrix of a predetermined size.

[0035] The source compression information generation unit is used to perform source encoding on a given original source bit to generate source compression information;

[0036] The coding unit performs generalized coding on the joint coding matrix based on the source compression information, the channel coding matrix, and the connection matrix of a predetermined size.

[0037] Preferably, the extension unit also generates a source coding matrix and a multiple connection coding matrix;

[0038] The source coding matrix, the multiple connection coding matrix, the channel coding matrix, and the connection matrix of a predetermined size are used to be concatenated into a joint coding matrix at the receiving end.

[0039] Preferably, the optimization unit is specifically used for:

[0040] S1, Given an identity matrix, perform elementary transformation operations on the identity matrix and generate multi-linked basis matrices with different structures based on the operations;

[0041] S2, Based on the statistical characteristics of the information source and the signal-to-noise ratio, initialize the variance values ​​of different variable nodes of the multi-connection basis matrix;

[0042] S3 calls the traditional joint external information transmission algorithm to iteratively update the information transmission between variable nodes and verification nodes until the mutual information of variable nodes equals 1, or the maximum number of iterations is reached.

[0043] S4, change the initial signal-to-noise ratio, repeat S2 and S3 until the minimum signal-to-noise ratio is found, so that the mutual information of the variable nodes is equal to 1;

[0044] S5. Change the multi-connection basis matrix, and repeat S2, S3 and S4 to find the multi-connection basis matrix with the minimum signal-to-noise ratio.

[0045] Preferably, the encoding unit is specifically used for:

[0046] The source compressed information is subjected to multiple concatenation encoding to generate multiple encoded information;

[0047] The multi-encoded information and the original source bits are concatenated to generate a new bit sequence;

[0048] The channel coding matrix and the connection matrix of the predetermined size are concatenated to generate a concatenated result, and the concatenated result is subjected to Gaussian elimination to generate a generator matrix in the form of a system.

[0049] The generator matrix of the system form and the new bit sequence are multiplied to generate a combination of channel-coded source information and verification information.

[0050] The third embodiment of the present invention provides an encoding optimization device for dual-prototype LDPC codes, including a memory and a processor. The memory stores a computer program, which can be executed by the processor to implement the encoding optimization method for dual-prototype LDPC codes as described in any of the above embodiments.

[0051] The fourth embodiment of the present invention provides a storage medium storing a computer program, which can be executed by a processor of the device where the computer-readable storage medium is located, to implement an encoding optimization method for a dual-prototype LDPC code as described in any of the above claims.

[0052] Based on the coding optimization method, apparatus, device, and storage medium of dual-prototype LDPC codes provided by this invention, a joint coding matrix is ​​generated by replacing a set of single-sided connection basis matrices of a joint coding basis matrix based on dual-prototype LDPC codes with an optimized multiple connection basis matrix. Each part of the joint coding matrix is ​​then expanded to generate a channel coding matrix and a connection matrix of a predetermined size. Source coding is performed on a given original source bit to generate source compression information. The joint coding matrix is ​​then generalized based on the source compression information, the channel coding matrix, and the connection matrix of the predetermined size, increasing the possibility that source redundancy information and channel state information can be fully utilized. Attached Figure Description

[0053] Figure 1 This is a flowchart illustrating an encoding optimization method for a dual-prototype LDPC code provided in the first embodiment of the present invention;

[0054] Figure 2 This is an example diagram illustrating the construction of a multiple-linkage basis matrix using elementary matrix transformations provided in an embodiment of the present invention.

[0055] Figure 3 When the statistical characteristics provided in this embodiment of the invention are (0.96, 0.04) and (0.94, 0.06), and AWGN performance simulation diagram;

[0056] Figure 4 This is a schematic diagram of a coding optimization device for a dual-prototype LDPC code provided in the first embodiment of the present invention; Detailed Implementation

[0057] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0058] To better understand the technical solution of the present invention, the embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0059] It should be understood that the described embodiments are merely some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0060] The terminology used in the embodiments of this invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms “a,” “the,” and “the” as used in the embodiments of this invention and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.

[0061] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0062] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0063] The terms "first" and "second" used in the embodiments are merely to distinguish similar objects and do not represent a specific ordering of objects. It is understood that "first" and "second" can be interchanged in a specific order or sequence where permissible. It should be understood that the objects distinguished by "first" and "second" can be interchanged where appropriate so that the embodiments described herein can be implemented in orders other than those illustrated or described herein.

[0064] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0065] This invention discloses a coding optimization method, apparatus, device, and storage medium for dual-prototype LDPC codes, aiming to increase the possibility of fully utilizing source redundancy information and channel state information.

[0066] Please see Figure 1 The first embodiment of the present invention provides an encoding optimization method for dual-prototype LDPC codes, which can be executed by a dual-prototype LDPC code encoding optimization device (hereinafter referred to as the optimization device), specifically, by one or more processors within the optimization device, to at least implement the following steps:

[0067] S101, Construct a multi-connection basis matrix and optimize the multi-connection basis matrix;

[0068] In this embodiment, the optimization device can be a desktop computer, laptop computer, server, workstation, or other terminal with data processing and analysis capabilities. The evaluation device can be equipped with a corresponding operating system and application software, and the functions required in this embodiment are achieved through the combination of the operating system and application software.

[0069] Specifically, in this embodiment, multiple connection edge relationships will be introduced, using a multiple connection encoding matrix H. P The inventors stated that they discovered H P It is usually a very large matrix, and H is directly optimized. P The complexity would be very high, therefore, a protograph LDPC code with a special structure is introduced for optimization. A protograph LDPC code is an irregular code that can be constructed from a small-sized fundamental matrix B = [b...]. ij The code is expanded into a large LDPC matrix H using the PEG algorithm. Therefore, the performance of the original LDPC code can be determined by the fundamental matrix B. This embodiment will focus on the fundamental matrix B, utilizing its structured characteristics, and will address different source statistical characteristics and source coding fundamental matrix B. S Channel coding fundamental matrix B C and the second connection matrix B L2 Design a multi-connection coding matrix H P Corresponding B P The obtained B P And given B S and B C By using the PEG algorithm for expansion, a coding matrix H of a certain code length can be obtained. P H S H C and H L2 A generalized encoding algorithm is then implemented.

[0070] S1, Given an identity matrix, perform elementary transformation operations on the identity matrix and generate multi-linked basis matrices with different structures based on the operations;

[0071] It should be noted that elementary transformations include: a. transforming the identity matrix B... E Multiply a column of by an integer greater than 1;

[0072] b. The identity matrix B E Multiply one column by an integer greater than 1 and add it to the other columns;

[0073] c. Change B E The positions of any two columns;

[0074] In this embodiment:

[0075] First, matrix operations are used to construct different multi-link matrices, given an initial joint encoding basis matrix.

[0076]

[0077] Its original model structure is as follows Figure 2 As shown in (a). If matrix operations are performed using c, we can obtain...

[0078]

[0079] The original model result is as follows Figure 2 As shown in (b). If the matrix operation in a is performed, we can obtain...

[0080]

[0081] The original model result is as follows Figure 2 As shown in (c). If matrix operations are performed on a and b, we can obtain...

[0082]

[0083] The original model result is as follows Figure 2 As shown in (d).

[0084] S2, based on the source statistical characteristics (p0, p1) and the signal-to-noise ratio E b / N0, initialize the variance values ​​of different variable nodes in the multi-connection basis matrix, where the variance of the source variable nodes is ln(p1 / p0), and the variance of the channel variable nodes is... If there is a pruned structure, the variance of its corresponding variable node is 0;

[0085] S3 calls the traditional joint external information transmission algorithm to iteratively update the information transmission between variable nodes and verification nodes until the mutual information of variable nodes equals 1, or the maximum number of iterations is reached.

[0086] S4, change the initial signal-to-noise ratio E b / N0, repeat S2 and S3 until the minimum signal-to-noise ratio E is found. b / N0 makes the mutual information of the variable nodes equal to 1;

[0087] S5. Change the fundamental matrix of the multiple connections, and repeat S2, S3, and S4 to find the signal-to-noise ratio E with the minimum. b The multi-connection basis matrix of / N0.

[0088] It should be noted that the complexity of optimizing the multi-linking matrix depends on the matrix size and the maximum value of its elements. The matrix size is determined by the source coding matrix and the channel coding matrix. For optimizing the multi-linking matrix, combined with the joint external information transfer algorithm, the following example is given, where the traditional joint coding matrix is:

[0089]

[0090] Therefore, the size of this multi-connection basis matrix is ​​4×4. Setting the candidate element values ​​to {0,1,2} and the source statistical properties to (0.96,0.04), its decoding threshold is -1.666dB. Through optimization in steps S2-S5, the optimal multi-connection basis matrix can be obtained as follows:

[0091]

[0092] The corresponding joint fundamental matrix is ​​represented as Its decoding threshold is -2.605dB.

[0093] If we assume the source statistical characteristics are (0.94, 0.06), the traditional The corresponding decoding threshold is -0.481 dB. Through optimization steps S2-S5, the optimal multi-link fundamental matrix can be obtained as follows:

[0094]

[0095] The corresponding joint fundamental matrix is ​​represented as Its decoding threshold is -1.149 dB.

[0096] S102, provide a set of joint coding basis matrices based on dual original model graph LDPC codes, and replace the single-sided connection basis matrix of the joint coding basis matrix with the optimized multi-connection basis matrix to generate the joint coding matrix;

[0097] It should be noted that a multiple connection matrix is ​​introduced into both the source coding matrix and the channel coding matrix. This multiple connection coding between the traditional source and channel coding increases the possibility of more fully utilizing source redundancy and channel state information. This is done to enhance the coding gain brought by the multiple connection matrix. Although a multiple connection matrix is ​​introduced, the decoding method remains unchanged, and the complexity does not increase.

[0098] S103, expand each part of the joint coding matrix to generate a channel coding matrix and a connection matrix of a predetermined size;

[0099] In one possible embodiment of the invention, the PEG algorithm (not limited thereto) can be used to expand the various parts of the joint coding matrix, and a source coding matrix and a multiple connection coding matrix can also be generated.

[0100] Wherein, the source coding matrix H S Multiple connection coding matrix H P Channel coding matrix H C and the connection matrix H of a predetermined size. L2 Used to be concatenated into a joint coding matrix at the receiving end. At the receiving end, a confidence propagation algorithm can be used for decoding.

[0101] S104, perform source encoding on the given original source bits to generate source compressed information;

[0102] In this embodiment, the encoding can be performed according to the following model:

[0103] Where s represents the original source bits, and c represents the compressed source information. T This is the transpose operation for a matrix.

[0104] S105, the joint coding matrix is ​​generalized and encoded according to the source compression information, the channel coding matrix, and the connection matrix of a predetermined size.

[0105] Specifically, in this embodiment:

[0106] The source compressed information c is subjected to multiple concatenation encoding to generate multiple encoded information d, where d = c·H P ;

[0107] The multi-encoded information d and the original source bits s are concatenated to generate a new bit sequence [sd].

[0108] For the channel coding matrix H C and the connection matrix H of the predetermined size L2 The concatenation is performed to generate the concatenation result [H_L2 H_C], and the concatenation result is subjected to Gaussian elimination to generate a generator matrix in the form of a system [I]. C G C ];

[0109] For the generating matrix of the system form [I] C G C Multiplying the new bit sequence [sd] with the given information generates a combination u = [sd][I] of channel-coded source information and verification information. C G C ], where u=[sdp];

[0110] Based on a generalized encoding and decoding algorithm, taking the source statistical characteristics (0.96, 0.04) as an example, we will make an example comparison of the above optimization results.

[0111] Step 1: According to Introducing the optimized multi-connection basis matrix Replace the existing one-sided connection basis matrix B I ;

[0112] Step 2: Expand each part of the joint coding matrix using the PEG algorithm with an expansion factor of 200 to obtain the coding matrix corresponding to the original source bits with a code length of 3200. H S-0.04 and H C-0.04 H here L2 =0;

[0113] Step 3: Perform source coding on the given original source bits s, i.e., c = s·H S-0.04 ;

[0114] Step 4: Perform multi-linking encoding on the obtained 'c', that is...

[0115] Step 5: Concatenate s and d to obtain a new bit sequence [sd], H C-0.04 Gaussian elimination yields a form of [I] C-0.04 G C-0.04 The system form of the generator matrix;

[0116] Step 6: Combine the spliced ​​[sd] and the obtained [I] C G C Multiplying them together, we get u = [sd][I] C- 0.04 G C-0.04 ], where u=[sdp];

[0117] Step 7: At the receiving end, send H P H S H C and H L2 Concatenate them into a joint encoding matrix.

[0118]

[0119] Decoding is performed using the confidence propagation algorithm, with a maximum number of iterations of 100.

[0120] For the source statistical properties (0.94, 0.06) The same parameters were also used for expansion to obtain... Then the encoding and decoding algorithm is performed. The same parameter extension is also performed to obtain... and The simulation results using traditional encoding and decoding algorithms are as follows: Figure 3 As shown, its simulated channel is an AWGN channel. When BER = 1 × 10 -6 , Compared to It has a coding gain of 0.62dB. Compared to It has a coding gain of 0.36dB. This gain is consistent with the analysis of the decoding threshold, and highlights the role of the multiple connection matrix.

[0121] The above provides a detailed description and explanation of the generalized coding optimization algorithm for dual-primitive graph LDPC codes based on a multiple connection matrix proposed in this invention. The specific implementation described above can help understand the core idea of ​​this invention. In traditional dual-primitive graph LDPC coding systems, source information and channel information interact only through a single-sided connection matrix. This invention introduces a multiple connection matrix, enabling more comprehensive information interaction. Furthermore, to enhance the effectiveness of information interaction, the multiple connection matrix is ​​constructed using matrix operations, and the decoding threshold is calculated using an external joint information transfer algorithm, ultimately obtaining a joint coding matrix with a low decoding threshold. Finally, a generalized encoding and decoding algorithm can also be used for the optimized joint coding matrix.

[0122] Please see Figure 4 The second embodiment of the present invention provides an encoding optimization device for dual-prototype LDPC codes, comprising:

[0123] The optimization unit 201 is used to construct a multi-connection basis matrix and optimize the multi-connection basis matrix;

[0124] Replacement unit 202 is used to provide a set of joint coding basis matrices based on dual-prototype graph LDPC codes, and replace the single-sided connection basis matrix of the joint coding basis matrix with the optimized multi-connection basis matrix to generate a joint coding matrix;

[0125] The expansion unit 203 is used to expand each part of the joint coding matrix to generate a channel coding matrix and a connection matrix of a predetermined size;

[0126] The source compression information generation unit 204 is used to perform source encoding on the given original source bits to generate source compression information;

[0127] The coding unit 205 performs generalized coding on the joint coding matrix based on the source compression information, the channel coding matrix, and the connection matrix of a predetermined size.

[0128] Preferably, the extension unit also generates a source coding matrix and a multiple connection coding matrix;

[0129] The source coding matrix, the multiple connection coding matrix, the channel coding matrix, and the connection matrix of a predetermined size are used to be concatenated into a joint coding matrix at the receiving end.

[0130] Preferably, the optimization unit is specifically used for:

[0131] S1, Given an identity matrix, perform elementary transformation operations on the identity matrix and generate multi-linked basis matrices with different structures based on the operations;

[0132] S2, Based on the statistical characteristics of the information source and the signal-to-noise ratio, initialize the variance values ​​of different variable nodes of the multi-connection basis matrix;

[0133] S3 calls the traditional joint external information transmission algorithm to iteratively update the information transmission between variable nodes and verification nodes until the mutual information of variable nodes equals 1, or the maximum number of iterations is reached.

[0134] S4, change the initial signal-to-noise ratio, repeat S2 and S3 until the minimum signal-to-noise ratio is found, so that the mutual information of the variable nodes is equal to 1;

[0135] S5. Change the multi-connection basis matrix, and repeat S2, S3 and S4 to find the multi-connection basis matrix with the minimum signal-to-noise ratio.

[0136] Preferably, the encoding unit is specifically used for:

[0137] The source compressed information is subjected to multiple concatenation encoding to generate multiple encoded information;

[0138] The multi-encoded information and the original source bits are concatenated to generate a new bit sequence;

[0139] The channel coding matrix and the connection matrix of the predetermined size are concatenated to generate a concatenated result, and the concatenated result is subjected to Gaussian elimination to generate a generator matrix in the form of a system.

[0140] The generator matrix of the system form and the new bit sequence are multiplied to generate a combination of channel-coded source information and verification information.

[0141] The third embodiment of the present invention provides an encoding optimization device for dual-prototype LDPC codes, including a memory and a processor. The memory stores a computer program, which can be executed by the processor to implement the encoding optimization method for dual-prototype LDPC codes as described in any of the above embodiments.

[0142] The fourth embodiment of the present invention provides a storage medium storing a computer program, which can be executed by a processor of the device where the computer-readable storage medium is located, to implement an encoding optimization method for a dual-prototype LDPC code as described in any of the above claims.

[0143] Based on the coding optimization method, apparatus, device, and storage medium of dual-prototype LDPC codes provided by this invention, a joint coding matrix is ​​generated by replacing a set of single-sided connection basis matrices of a joint coding basis matrix based on dual-prototype LDPC codes with an optimized multiple connection basis matrix. Each part of the joint coding matrix is ​​then expanded to generate a channel coding matrix and a connection matrix of a predetermined size. Source coding is performed on a given original source bit to generate source compression information. The joint coding matrix is ​​then generalized based on the source compression information, the channel coding matrix, and the connection matrix of the predetermined size, increasing the possibility that source redundancy information and channel state information can be fully utilized.

[0144] Exemplary examples show that the computer program described in the third and fourth embodiments of the present invention can be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules can be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program in the encoding optimization device implementing a dual-prototype LDPC code. For example, the apparatus described in the second embodiment of the present invention.

[0145] The processor referred to can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the proposed dual-prototype LDPC code encoding optimization method, connecting various parts of the proposed method using various interfaces and lines.

[0146] The memory can be used to store the computer program and / or modules. The processor, by running or executing the computer program and / or modules stored in the memory, and by calling the data stored in the memory, implements various functions based on a dual-prototype LDPC code encoding optimization method. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, text conversion function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, text message data, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0147] If the implemented module is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0148] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0149] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A coding optimization method for dual-prototype LDPC codes, characterized in that, include: Construct a multi-connection basis matrix and optimize it. Specifically: S1, given an identity matrix, perform elementary transformations on the identity matrix and generate multi-connection basis matrices with different structures based on the transformations. The elementary transformations include: a: multiplying a column of the identity matrix by an integer greater than 1; b: multiplying a column of the identity matrix by an integer greater than 1 and adding it to other columns; c: changing the positions of any two columns; S2, initializing the variance values ​​of different variable nodes in the multi-connection basis matrix based on the source statistical characteristics and signal-to-noise ratio (SNR); S3, calling a traditional joint external information transfer algorithm to iteratively update the information transfer between variable nodes and check nodes until the mutual information of the variable nodes equals 1, or the maximum number of iterations is reached; S4, changing the initial SNR, repeating S2 and S3 until the minimum SNR is found, making the mutual information of the variable nodes equal to 1; S5, changing the multi-connection basis matrix, repeating S2, S3, and S4, to find the multi-connection basis matrix with the minimum SNR. A set of joint coding basis matrices based on dual-prototype graph LDPC codes is provided. The optimized multi-link basis matrix is ​​used to replace the single-side link basis matrix of the joint coding basis matrix to generate the joint coding matrix. Each part of the joint coding matrix is ​​expanded to generate a channel coding matrix and a connection matrix of a predetermined size; Source encoding is performed on the given raw source bits to generate source compressed information; The joint coding matrix is ​​generalized and encoded based on the source compression information, the channel coding matrix, and the connection matrix of a predetermined size.

2. The encoding optimization method for a dual-prototype LDPC code according to claim 1, characterized in that, By expanding each part of the joint coding matrix, a source coding matrix and a multi-link coding matrix are also generated; The source coding matrix, the multiple connection coding matrix, the channel coding matrix, and the connection matrix of a predetermined size are used to be concatenated into a joint coding matrix at the receiving end.

3. The encoding optimization method for a dual-prototype LDPC code according to claim 1, characterized in that, The generalized encoding of the joint coding matrix based on the source compression information, the channel coding matrix, and the connection matrix of a predetermined size specifically involves: The source compressed information is subjected to multiple concatenation encoding to generate multiple encoded information; The multi-encoded information and the original source bits are concatenated to generate a new bit sequence; The channel coding matrix and the connection matrix of the predetermined size are concatenated to generate a concatenated result, and the concatenated result is subjected to Gaussian elimination to generate a generator matrix in the form of a system. The generator matrix of the system form and the new bit sequence are multiplied to generate a combination of channel-coded source information and verification information.

4. A coding optimization device for dual-prototype LDPC codes, characterized in that, include: An optimization unit is used to construct a multi-connection basis matrix and optimize the multi-connection basis matrix; The optimization unit is specifically used for: S1, given an identity matrix, performing elementary transformation operations on the identity matrix, and generating multi-link basis matrices with different structures according to the operations; wherein, the elementary transformations include: a: multiplying a column of the identity matrix by an integer greater than 1; b: multiplying a column of the identity matrix by an integer greater than 1 and adding it to other columns; c: changing the positions of any two columns; S2, initializing the variance values ​​of different variable nodes of the multi-link basis matrix according to the source statistical characteristics and signal-to-noise ratio; S3, calling the traditional joint external information transfer algorithm, iteratively updating the information transfer of variable nodes and check nodes until the mutual information of variable nodes equals 1, or reaching the maximum number of iterations; S4, changing the initial signal-to-noise ratio, repeating S2 and S3 until finding the minimum signal-to-noise ratio so that the mutual information of variable nodes equals 1; S5, changing the multi-link basis matrix, repeating S2, S3 and S4, to find the multi-link basis matrix with the minimum signal-to-noise ratio; The replacement unit is used to provide a set of joint coding basis matrices based on dual-prototype graph LDPC codes, and replace the single-sided connection basis matrix of the joint coding basis matrix with the optimized multi-connection basis matrix to generate the joint coding matrix; An expansion unit is used to expand each part of the joint coding matrix to generate a channel coding matrix and a connection matrix of a predetermined size. The source compression information generation unit is used to perform source encoding on a given original source bit to generate source compression information; The coding unit performs generalized coding on the joint coding matrix based on the source compression information, the channel coding matrix, and the connection matrix of a predetermined size.

5. The encoding optimization device for a dual-prototype LDPC code according to claim 4, characterized in that, The extended unit also generates a source coding matrix and a multi-link coding matrix; The source coding matrix, the multiple connection coding matrix, the channel coding matrix, and the connection matrix of a predetermined size are used to be concatenated into a joint coding matrix at the receiving end.

6. The encoding optimization device for a dual-prototype LDPC code according to claim 4, characterized in that, The encoding unit is specifically used for: The source compressed information is subjected to multiple concatenation encoding to generate multiple encoded information; The multi-encoded information and the original source bits are concatenated to generate a new bit sequence; The channel coding matrix and the connection matrix of the predetermined size are concatenated to generate a concatenated result, and the concatenated result is subjected to Gaussian elimination to generate a generator matrix in the form of a system. The generator matrix of the system form and the new bit sequence are multiplied to generate a combination of channel-coded source information and verification information.

7. An encoding optimization device for dual-prototype LDPC codes, characterized in that, The system includes a memory and a processor. The memory stores a computer program that can be executed by the processor to implement the encoding optimization method for a dual-prototype LDPC code as described in any one of claims 1 to 3.

8. A storage medium, characterized in that, The device contains a computer program that can be executed by a processor of the device where the computer-readable storage medium is located, to implement an encoding optimization method for a dual-prototype LDPC code as described in any one of claims 1 to 3.

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