Dual-prototype LDPC code coding optimization method, device and product for underwater acoustic communication

Abstract

The present invention provides a dual-protograph LDPC code encoding optimization method, device and product for underwater acoustic communication, the method comprising: setting the initial dimension of the protograph matrix to m rows and n columns for a source or a channel; setting the target number of connections of the total number of connections existing in the m×n nodes of the protograph matrix; randomly assigning values ​​so that the total number of connections is equal to, thereby determining the connection relationship of the nodes in the protograph; constraining the degree values ​​of the nodes according to the connection relationship of the nodes in the protograph matrix, and obtaining a first adjacent protograph matrix; optimizing the degree values ​​in the first adjacent protograph matrix using a differential evolution algorithm and a predetermined degree value optimization target, and obtaining an optimized second adjacent protograph matrix. The technical solution of the present invention uses the connection relationship to constrain the degree values ​​of the nodes, optimizes the degree distribution and connection structure of the protograph matrix, and reduces the complexity of the optimization design of the LDPC code protograph matrix.

Description

Technical Field

[0001] The present invention relates to the field of underwater acoustic digital signal processing and information source and channel coding technology, and in particular to a dual-prototype LDPC code coding optimization method, device and product for underwater acoustic communication. Background Art

[0002] With the rapid development of communication technology, LDPC codes have been widely used in wireless communications, satellite communications, deep space communications, underwater communications and other fields due to their coding capabilities close to the Shannon limit.

[0003] However, in complex transmission environments, such as non-Gaussian distribution channels (e.g., Rician distribution channels), traditional LDPC code design methods exhibit the following limitations:

[0004] First, the inadaptability of non-Gaussian channels. The design of LDPC codes is usually based on the Gaussian channel model assumption. However, non-Gaussian channels, such as the Rician distribution channel in underwater communications, have significantly different statistical characteristics, including multipath fading, scattering, and uneven power distribution. As a result, the existing coding structure cannot fully utilize the channel characteristics for optimization, reducing transmission efficiency and reliability.

[0005] Second, the contradiction between bit error rate and power consumption. In complex channel environments, the improvement of error correction capability often comes at the cost of higher power consumption. For scenarios such as underwater communication and long-distance wireless transmission, low-power transmission is a key requirement. Therefore, how to strike a balance between power consumption and bit error rate has become an important research direction.

[0006] Third, the complexity of coding structure optimization. Existing LDPC code design methods usually rely on highly complex encoding and decoding algorithms when optimizing for complex channels, which makes it difficult to effectively apply them in resource-constrained scenarios. Although Joint Source and Channel Coding (JSCC) technology has been proposed to improve transmission performance, its design and implementation still have the following problems in actual applications: insufficient coding structure design to adapt to complex channel distribution, lack of flexible, low-complexity optimization algorithms, and inability to take into account both low power consumption and high transmission efficiency at the same time. Summary of the invention

[0007] Embodiments of the present invention provide a dual-prototype LDPC code encoding optimization method, device and product for underwater acoustic communication to reduce the complexity of LDPC code prototype matrix optimization design in complex channel environments.

[0008] In order to achieve the above object, on the one hand, a dual-primary-graph LDPC code coding optimization method for underwater acoustic communication is provided, comprising:

[0009] For the source or channel, the initial dimension of the original model matrix is ​​set to m rows and n columns. The original model matrix contains m×n nodes, and m×n is represented as mn. The value of each of the mn nodes is the degree value of the corresponding node, and is expressed as It indicates the The degree of a node, and are all positive integers, The initial value of is 0; where m and n are both positive integers;

[0010] use Represents the edges of the mn nodes in the original model matrix, and the edges correspond to the connection relationship between the nodes. The mn nodes have at most A connection relationship,

[0011] when When , it means that there is a connection relationship between the corresponding two nodes. When , it means that there is no connection relationship between the corresponding two nodes. is a positive integer,

[0012] The initial value of is 0;

[0013] Set the target number of connections for the total number of connections in the mn nodes of the original model matrix in is a positive integer, ;

[0014] Random Assign a value so that the total number of connections is equal to the , thereby determining the connection relationship of the nodes in the original model graph; wherein, during the assignment process, if the total number of connections does not reach the , then let , otherwise, let ;

[0015] Constraining the degree values ​​of nodes according to the connection relationship of nodes in the original model graph matrix, and obtaining a first adjacency original model graph matrix; wherein constraining the degree values ​​of nodes includes:

[0016] The degree values ​​of nodes that are connected in the horizontal or vertical direction are set to be equal; and, for nodes that are not connected in the horizontal or vertical direction, it is further determined whether the nodes are connected in the diagonal direction, and the degree values ​​of the nodes that are connected in the diagonal direction are set to be equal;

[0017] The degree values ​​in the first adjacency protograph matrix are optimized using a differential evolution algorithm and a predetermined degree value optimization target to obtain an optimized second adjacency protograph matrix.

[0018] Preferably, in the dual-primary-graph LDPC code encoding optimization method for underwater acoustic communication, the optimization objectives include:

[0019] minimizing a decoding threshold; and,

[0020] in, In the original model matrix The degree value corresponding to the node of is the threshold of the predetermined degree value corresponding to the optimization target.

[0021] On the other hand, a dual-prototype LDPC code coding optimization device for underwater acoustic communication is provided, comprising a memory and a processor, wherein the memory stores at least one program, and the at least one program is executed by the processor to implement the steps of any of the dual-prototype LDPC code coding optimization methods for underwater acoustic communication described above.

[0022] On the other hand, a computer program product is provided, comprising a computer program, characterized in that when the computer program is executed by a processor, the steps of the dual-prototype LDPC code encoding optimization method for underwater acoustic communication as described in any of the above are implemented.

[0023] The above technical solution has the following technical effects:

[0024] The technical solution of the embodiment of the present invention designs an adjacency original pattern graph structure, which optimizes the degree distribution and connection structure of the original pattern graph matrix by defining the connection relationship of the nodes in the original pattern graph matrix and using the connection relationship to constrain the degree value of the node, thereby reducing the complexity of the optimization design of the original pattern graph matrix of the LDPC code, effectively improving the coding performance, and reducing power consumption, thereby providing an efficient solution for data transmission in complex channel environments such as underwater acoustic communication scenarios.

[0025] Compared with the prior art solution that requires the introduction of other matrices, the technical solution of the embodiment of the present invention has the following advantages:

[0026] In the specific implementation, only the source coding matrix and the channel coding matrix with smaller dimensions are constructed, and no other matrix designs are introduced, which has lower matrix design complexity;

[0027] By using structured design to constrain the degree distribution of the matrix, the search complexity of the degree value in the matrix can be further reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1The present invention is a flowchart of a dual-primary-graph LDPC code encoding optimization method for underwater acoustic communication according to an embodiment of the present invention. DETAILED DESCRIPTION

[0029] To further illustrate the various embodiments, the present invention provides drawings. These drawings are part of the disclosure of the present invention, which are mainly used to illustrate the embodiments and can be used in conjunction with the relevant descriptions in the specification to explain the operating principles of the embodiments. With reference to these contents, a person of ordinary skill in the art should be able to understand other possible implementations and advantages of the present invention. The components in the figures are not drawn to scale, and similar component symbols are generally used to represent similar components.

[0030] The present invention will now be further described with reference to the accompanying drawings and specific implementation methods.

[0031] Embodiment 1:

[0032] The embodiment of the present invention provides a double protograph LDPC (DP-LDPC, DoubleProtograph Low-density Parity-check) code coding optimization method for underwater acoustic communication, which is used to optimize the coding of the DP-LDPC code by constructing an adjacent protograph structure of the source and channel codes, and obtain a set of target protograph matrices of the source code and the channel code. The basic idea of ​​the method of the present invention is to define the adjacency relationship of the degree of the nodes in the protograph, assign the same degree value to the degrees of the nodes with connection relationships in the protograph matrix to perform structural optimization, thereby reducing the search complexity of the degree in the matrix and ensuring its low power consumption and high transmission efficiency. In brief, the embodiment of the present invention includes: randomly generating and initializing the planar graph structure, that is, the degree value and the number of connections in the protograph; calculating the number of connections and filling the connection value by a predetermined algorithm to ensure that the connection between each node meets the predetermined constraints, thereby achieving adjacency matching between different nodes; further optimizing the degree distribution of the nodes by a differential evolution algorithm, so that the protograph has a higher matching degree, so as to further improve the robustness and error correction capability of the channel.

[0033] Figure 1 FIG. 1 is a flow chart of a method for optimizing dual-primary-graph LDPC code encoding for underwater acoustic communication according to an embodiment of the present invention. Figure 1 The dual-primary-graph LDPC code encoding optimization method for underwater acoustic communication of this embodiment includes:

[0034] For the source or channel, the initial dimension of the original model matrix is ​​set to m rows and n columns. The original model matrix contains m×n nodes. m×n is simplified to mn. The values ​​of each mn node are the degrees of the corresponding nodes. It indicates the The degree of a node, and are all positive integers, The initial value of is 0; wherein m and n are both positive integers; in a specific implementation, according to the dimensional relationship between the source code and the channel code, m and n can be respectively expressed as the source code ms, ns and the channel code mc, nc. To simplify the description, they are simplified as m and n in the following steps;

[0035] use Represents the edges of mn nodes in the original model matrix. The edges correspond to the connection relationship between the nodes. There are at most mn nodes. A connection relationship,

[0036] When , it indicates that there is a connection relationship between the corresponding two nodes. When , it means that there is no connection relationship between the corresponding two nodes. is a positive integer,

[0037] The initial value of is 0;

[0038] Set the target number of connections for the total number of connections in the mn nodes of the original model matrix , in is a positive integer, that is , represents the set of positive integers, ;

[0039] Assign connection relationships, including: Random Assign a value so that the total number of connections is equal to , thereby determining the connection relationship of the nodes in the original model graph; in the assignment process, if the total number of connections does not reach , then let , otherwise, let ; where the total number of connections is equal to It can be done through the formula expression; among them, represents norm operation;

[0040] Constrain the degree of the node according to the connection relationship of the node in the original model graph matrix, and obtain the first adjacency original model graph matrix; wherein the degree of the constrained node includes:

[0041] The degree values ​​of nodes that are connected in the horizontal or vertical direction are set to be equal; and, for nodes that are not connected in the horizontal or vertical direction, it is further determined whether the nodes are connected in the diagonal direction, and the degree values ​​of the nodes that are connected in the diagonal direction are set to be equal;

[0042] In one specific implementation, if the value The corresponding node (located in the matrix i Line j Nodes on the column) and degree values Or degree value There is a connection between the corresponding nodes, that is, if the degree value Corresponding nodes and degrees Or degree value There is a horizontal (matrix row direction) or vertical (matrix column direction) connection between the corresponding nodes, that is, the connection between the two nodes corresponds to , then the corresponding degree values ​​of these two nodes have constraints: or ; If there is no horizontal or vertical connection, the corresponding node is checked along the diagonal direction to see if there is a connection. If so, the corresponding node on the diagonal is assigned Equality constraints: ;

[0043] The first adjacency protograph matrix is ​​obtained according to the constrained nodes and connection relationships. The first adjacency protograph matrix is ​​the adjacency protograph matrix B used for target search, wherein:

[0044] ;

[0045] The degree values ​​in the first adjacency protograph matrix are optimized using a differential evolution algorithm and a predetermined degree value optimization target to obtain an optimized second adjacency protograph matrix.

[0046] The embodiment of the present invention further optimizes the degree value distribution of nodes through the differential evolution algorithm, so that the original model graph has a higher matching degree, thereby improving the channel robustness and error correction capability. The differential evolution algorithm is an existing algorithm, and those skilled in the art know how to use it, which will not be repeated here.

[0047] In a specific implementation, the optimization objectives include: minimizing a decoding threshold; and, ,in, In the original model matrix The degree value corresponding to the node of is the threshold of the predetermined degree value corresponding to the optimization target. That is, the optimization target simultaneously considers the threshold convergence and minimizes the sum of the absolute differences between the degree value of the search and the degree value threshold. In a specific implementation, is the theoretical value of the optimization objective.

[0048] Embodiment 2:

[0049] The present invention also provides a dual-prototype LDPC code coding optimization device for underwater acoustic communication, comprising a memory and a processor, wherein the memory stores at least one program, and the at least one program is executed by the processor to implement the steps of the dual-prototype LDPC code coding optimization method for underwater acoustic communication as described in any of the above.

[0050] Further, as an executable solution, the dual-primary-mode LDPC code encoding optimization device for underwater acoustic communication can be a computer unit, which can be a computing device such as a desktop computer, a notebook, a PDA, and a cloud server. The computer unit may include, but is not limited to, a processor and a memory. Those skilled in the art will understand that the composition structure of the above-mentioned computer unit is only an example of a computer unit and does not constitute a limitation on the computer unit. It may include more or less components than the above, or a combination of certain components, or different components. For example, the computer unit may also include input and output devices, network access devices, buses, etc., which are not limited in the embodiments of the present invention.

[0051] Further, as an executable solution, the processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the computer unit, and various interfaces and lines are used to connect various parts of the entire computer unit.

[0052] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the computer unit by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system and at least one application required for a function; the data storage area can store data created according to the use of the mobile phone, etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0053] Embodiment three:

[0054] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above method in the embodiment of the present invention are implemented.

[0055] If the module / unit integrated in the computer unit is implemented in the form of 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, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory) and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction.

[0056] Embodiment 4:

[0057] The present invention also provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the steps of the method described above are implemented.

[0058] Although the present invention has been specifically shown and described in conjunction with the preferred embodiments, it should be understood by those skilled in the art that various changes may be made to the present invention in form and details without departing from the spirit and scope of the present invention as defined by the appended claims, all of which are within the scope of protection of the present invention.

Claims

1. A dual-prime LDPC code coding optimization method for underwater acoustic communication, characterized in that: include: For the source or channel, the initial dimension of the original model matrix is ​​set to m rows and n columns. The original model matrix contains m×n nodes, and m×n is represented as mn. The value of each of the mn nodes is the degree value of the corresponding node, and is expressed as Indicates The degree of a node, and are all positive integers, The initial value of is 0; where m and n are both positive integers; use Represents the edges of the mn nodes in the original model matrix, and the edges correspond to the connection relationship between the nodes. The mn nodes have at most A connection relationship, when When , it means that there is a connection relationship between the corresponding two nodes. When , it means that there is no connection relationship between the corresponding two nodes. is a positive integer, The initial value of is 0; Set the target number of connections for the total number of connections in the mn nodes of the original model matrix in is a positive integer, ; Random Assign a value so that the total number of connections is equal to the , thereby determining the connection relationship of the nodes in the original model matrix; wherein, during the assignment process, if the total number of connections does not reach the Then , otherwise, let ; Constraining the degree values ​​of nodes according to the connection relationship of nodes in the original model graph matrix, and obtaining a first adjacency original model graph matrix; wherein constraining the degree values ​​of nodes includes: The degree values ​​of nodes that are connected in the horizontal or vertical direction are set to be equal; and, for nodes that are not connected in the horizontal or vertical direction, it is further determined whether the nodes are connected in the diagonal direction, and the degree values ​​of the nodes that are connected in the diagonal direction are set to be equal; The degree values ​​in the first adjacency protograph matrix are optimized using a differential evolution algorithm and a predetermined degree value optimization target to obtain an optimized second adjacency protograph matrix.

2. The dual-prototype LDPC code encoding optimization method for underwater acoustic communication according to claim 1, characterized in that: The optimization objectives include: minimizing a decoding threshold; and, ,in, is the first i Line j The degree values ​​corresponding to the nodes in the column, is the threshold of the predetermined degree value corresponding to the optimization target.

3. A dual-primary-graph LDPC code coding optimization device for underwater acoustic communication, characterized in that: It includes a memory and a processor, the memory stores at least one program, and the at least one program is executed by the processor to implement the steps of the dual-primary graph LDPC code encoding optimization method for underwater acoustic communication as described in any one of claims 1 to 2.

4. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the dual-prototype LDPC code encoding optimization method for underwater acoustic communication as described in any one of claims 1 to 2 are implemented.

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