Random interleaving relationship identification method and device, electronic equipment and data identification method

Abstract

This application provides a method, apparatus, electronic device, and data identification method for identifying random interleaving relationships, relating to the field of communication technology. The method is applied to a signal receiver for receiving Turbo code data. The method includes: constructing an error-free target matrix based on the received Turbo code data; and identifying the random interleaving relationship of a third path of data in the Turbo code data from the target matrix based on the encoding principle of the recursive systematic convolutional code. The technical solution provided by this application utilizes the encoding principle of recursive systematic convolutional codes to identify the random interleaving relationship of the third path of data in the Turbo code data from an error-free Turbo code data matrix, thus achieving the identification of the random interleaving relationship of the third path of data in the received Turbo code data without knowing the interleaving parameters.

Description

Technical Field

[0001] This application relates to the field of communication technology, and more specifically, to a method for identifying random interleaving relationships, a device for identifying random interleaving relationships, an electronic device, and a data identification method. Background Technology

[0002] In non-cooperative communication fields such as intelligence analysis and electronic reconnaissance, as well as in the future field of intelligent communication, the signal receiver needs to deinterleave the received signal data with only a small amount of information or even no information about the interleaving parameters. Therefore, how to identify the interleaving relationship of the received signal without knowing the interleaving parameters is a technical problem that urgently needs to be solved. Summary of the Invention

[0003] The embodiments of this application provide a random interleaving relationship identification method, a random interleaving relationship identification device, an electronic device, and a data identification method to solve the technical problem that Turbo code data cannot be blindly identified. This method can identify the random interleaving relationship of received Turbo code data without knowing the interleaving parameters.

[0004] Other features and advantages of this application will become apparent from the following detailed description, or may be learned in part from practice of this application.

[0005] According to a first aspect of the embodiments of this application, a random interleaving relationship identification method is provided, applied to a signal receiver, the signal receiver being used to receive Turbo code data, the Turbo code data including a first channel of data, a second channel of data, and a third channel of data, wherein the first channel of data is original input data, the second channel of data is data output after processing the original input data through a recursive system convolutional code encoder, and the third channel of data is data output after processing the original input data sequentially through a random interleaver and a recursive system convolutional code encoder, the method comprising:

[0006] Construct an error-free target matrix based on the received Turbo code data;

[0007] Based on the encoding principle of the recursive system convolutional code, the random interleaving relationship of the third path data in the Turbo code data is identified from the target matrix.

[0008] In some embodiments of this application, based on the foregoing scheme, constructing an error-free target matrix based on the received Turbo code data includes:

[0009] Construct a first matrix based on the first path data and the third path data in the received Turbo code data;

[0010] Gaussian elimination is performed on the first matrix to obtain the second matrix;

[0011] Errors are identified and eliminated in the second matrix.

[0012] In some embodiments of this application, based on the foregoing scheme, identifying and removing errors in the second matrix includes:

[0013] In the second matrix, identify and remove rows that do not conform to the linear combination property of recursive system convolutional codes.

[0014] In some embodiments of this application, based on the foregoing scheme, the step of identifying the random interleaving relationship of the third path data in the Turbo code data from the target matrix based on the encoding principle of the recursive system convolutional code includes:

[0015] Select the target sequence from the target matrix;

[0016] Based on the aforementioned encoding principle, the target sequence is convolutionally encoded to obtain convolutionally encoded data.

[0017] The random interleaving relationship of the third-path data is determined by comparing the convolutionally encoded data with the third-path Turbo code data in the target matrix.

[0018] In some embodiments of this application, based on the foregoing scheme, the step of performing convolutional encoding on the target sequence based on the encoding principle to obtain convolutionally encoded data includes:

[0019] Initialize the recursive system convolutional code encoder used for convolutional coding;

[0020] The target sequence is input into the recursive system convolutional code encoder for convolutional encoding to obtain the convolutional encoded data.

[0021] In some embodiments of this application, based on the foregoing scheme, the step of comparing the third-path data based on the convolutional encoded data and the Turbo code data in the target matrix to determine the random interleaving relationship of the third-path data includes:

[0022] The column vectors of the convolutionally encoded data are compared with the column vectors of the columns in the target matrix that need to be determined for random interleaving. If the comparison is successful, the column number of the target column and the column number of the column corresponding to the random interleaving relationship to be determined are obtained.

[0023] The random interleaving relationship of the third data is determined based on the column number of the target sequence and the column number of the sequence corresponding to the random interleaving relationship that needs to be determined.

[0024] According to a second aspect of the embodiments of this application, a random interleaving relationship identification device is provided, comprising:

[0025] A matrix construction unit is used to construct an error-free target matrix based on the received Turbo code data;

[0026] The identification unit is used to identify the random interleaving relationship of the third path data in the Turbo code data from the target matrix based on the encoding principle of the recursive system convolutional code.

[0027] According to a third aspect of the embodiments of this application, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the method described in the first aspect.

[0028] According to a fourth aspect of the present application, an electronic device is provided, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor is configured to implement the method described in the first aspect above when executing the computer program stored in the memory.

[0029] According to a fifth aspect of the embodiments of this application, a data identification method is provided, comprising:

[0030] Receive Turbo code data;

[0031] Based on the method described in the first aspect, the random interleaving relationship of the third path data in the Turbo code data is identified;

[0032] The Turbo code data is decoded based on the random interleaving relationship of the third path data in the Turbo code data and the structure of the recursive system convolutional code encoder.

[0033] The technical solution of this application utilizes the encoding principle of recursive system convolutional codes to identify the random interleaving relationship of the third path data in the Turbo code from an error-free Turbo code data matrix, thereby realizing the identification of the random interleaving relationship of the third path data in the received Turbo code data without knowing the interleaving parameters.

[0034] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0035] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort. In the drawings:

[0036] Figure 1 A schematic flowchart of a random interleaving relationship identification method according to an embodiment of this application is shown;

[0037] Figure 2 A schematic diagram of a Turbo code encoder structure according to an embodiment of this application is shown;

[0038] Figure 3 This is a schematic diagram of a random interleaving relationship identification device provided in an embodiment of this application;

[0039] Figure 4 This is a schematic diagram of an electronic device structure provided in an embodiment of this application. Detailed Implementation

[0040] The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus. The technical solutions of the embodiments of this application will now be clearly and completely described in conjunction with the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them.

[0041] The following detailed description of some embodiments of this application will be provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0042] Please see Figure 1 This is a schematic diagram of a random interleaving relationship identification method provided in an embodiment of this application.

[0043] like Figure 1As shown, a method for identifying random intertwined relationships is illustrated, specifically including steps S100 to S200.

[0044] This method is applied to a signal receiver for receiving Turbo code data. The Turbo code data includes a first data path, a second data path, and a third data path. The first data path is the original input data; the second data path is the output data after processing the original input data through a recursive system convolutional code encoder; and the third data path is the output data after processing the original input data sequentially through a random interleaver and a recursive system convolutional code encoder. For an example, please refer to [link to example]. Figure 2 This is a Turbo code data processing model provided in an embodiment of this application. For example... Figure 2 As shown, information sequence m0 is the first data path, information sequence m0 is processed by recursive system convolutional code encoder 1 and output data m1 is the second data path, information sequence m0 is processed by random interleaver and recursive system convolutional code encoder 2 and output data m2 is the third data path; the first data path m0, the second data path m1 and the third data path m2 are processed by pruning, multiplexing and other processes to obtain Turbo code data c.

[0045] Based on the structure of Turbo code data, it can be seen that only the third data path has passed through a random interleaver. Therefore, the essence of this method in identifying the random interleaving relationship of Turbo code data is to identify the random interleaving relationship of the third data path.

[0046] Step S100: Construct an error-free target matrix based on the received Turbo code data.

[0047] It is understandable that the received Turbo code data includes the first channel data, the second channel data, and the third channel data.

[0048] In some feasible embodiments, step S100 specifically includes:

[0049] Construct a first matrix based on the first path data and the third path data in the received Turbo code data;

[0050] Gaussian elimination is performed on the first matrix to obtain the second matrix;

[0051] Errors are identified and eliminated in the second matrix.

[0052] For example, if the first data path in the received Turbo code data is y0 and the third data path is y2, a first matrix Y with dimension S×2L is constructed based on the received y0 and y2 data. i As shown below:

[0053]

[0054] Where L is the length of each frame of data y0 and y2, S is the number of data frames of data y0 and y2, and y 0,xL+j (0≤x≤S-1,1≤j≤L) represents the j-th bit of the (x+1)-th frame of data y0, and y 2,xL+j (0≤x≤S-1,1≤j≤L) is the j-th bit of the (x+1)-th frame of data y2, initialized with i=0, and the number of iterations is N.

[0055] For the first matrix Y i Gaussian elimination yields the second matrix Y. i,0 In the second matrix Y i,0 Errors are identified and removed to obtain the target matrix.

[0056] Understandably, the purpose of Gaussian column elimination on a matrix is ​​to eliminate bit errors.

[0057] In some feasible embodiments, identifying and removing errors in the second matrix includes:

[0058] In the second matrix, identify and remove rows that do not conform to the linear combination property of recursive system convolutional codes.

[0059] It is understandable that the third data is obtained by interleaving and convolutional coding of the first data. Therefore, the data in the second matrix should conform to the linear combination property of the recursive system convolutional code. Based on this principle, rows that do not conform to the linear combination property of the recursive system convolutional code can be found in the second matrix, thereby eliminating errors.

[0060] For example, based on the foregoing example, firstly, according to the linear combination property of convolutional codes in recursive systems, in the second matrix Y... i,0 Find those that do not participate in other linear operations or only participate in a very small number of operations (less than Y). i,0 The rows that are linearly operated on (one-tenth of the total number of rows) do not conform to the linear combination property of the convolutional codes of the recursive system, indicating that there are errors in the data in these rows. Therefore, after identifying these rows, they need to be removed to obtain a new matrix Y. i * Then, matrix Y i * Perform random row swaps to obtain matrix Y i+1 Then from matrix Y i+1 Rows that do not conform to the linear combination property of recursive system convolutional codes are removed from the matrix, and then random row swapping is performed until all rows in the matrix conform to the linear combination property of recursive system convolutional codes. Finally, the matrix is ​​output to obtain the error-free target matrix.

[0061] Continue to refer to Figure 1 Step S200: Based on the encoding principle of the recursive system convolutional code, the random interleaving relationship of the third path data in the Turbo code data is identified from the target matrix.

[0062] It is understandable that the third data is obtained by interleaving and convolving the first data with a recursive system convolutional code encoder. Therefore, after understanding the encoding principle of the recursive system convolutional code, this principle can be used to convolve the first data. By comparing the encoded data with the received third data, the random interleaving relationship of the third data can be determined.

[0063] It should be noted that in this application, the generator polynomial of the recursive system convolutional code encoder is known, and the generator polynomial can be identified or exhausted by existing algorithms; at the same time, there is no pruning in the Turbo code encoder structure; in addition, the length (interleaving depth) and starting point of the Turbo code data are known.

[0064] In some feasible embodiments, step S200 specifically includes:

[0065] Select the target sequence from the target matrix;

[0066] Based on the aforementioned encoding principle, the target sequence is convolutionally encoded to obtain convolutionally encoded data.

[0067] The random interleaving relationship of the third-path data is determined by comparing the convolutionally encoded data with the third-path Turbo code data in the target matrix.

[0068] It is understandable that, based on the interleaving property, the convolutionally encoded data obtained by convolutionally encoding the target sequence has a corresponding random interleaving relationship with the third path data of the Turbo code data in the target matrix. By comparing the convolutionally encoded data with the third path data of the Turbo code data in the target matrix, the random interleaving relationship can be obtained.

[0069] It should be noted that when selecting the target data sequence, one column should be randomly selected from the odd-numbered columns of the target matrix.

[0070] In some feasible embodiments, the step of performing convolutional encoding on the target sequence based on the encoding principle to obtain convolutionally encoded data includes:

[0071] Initialize the recursive system convolutional code encoder used for convolutional coding;

[0072] The target sequence is input into the recursive system convolutional code encoder for convolutional encoding to obtain the convolutional encoded data.

[0073] For example, the recursive system convolutional encoder state is first initialized, and then an unselected column is randomly selected from the odd columns of the target matrix and input into the recursive system convolutional encoder for convolutional encoding to obtain convolutional encoded data, and the column vector of the convolutional encoded data is recorded.

[0074] In some feasible embodiments, the step of comparing the third-path data based on the convolutional encoded data and the Turbo code data in the target matrix to determine the random interleaving relationship of the third-path data includes:

[0075] The column vectors of the convolutionally encoded data are compared with the column vectors of the columns in the target matrix that need to be determined for random interleaving. If the comparison is successful, the column number of the target column and the column number of the column corresponding to the random interleaving relationship to be determined are obtained.

[0076] The random interleaving relationship of the third data is determined based on the column number of the target sequence and the column number of the sequence corresponding to the random interleaving relationship that needs to be determined.

[0077] Understandably, based on the interleaving property, when the target sequence is an odd sequence, the sequence corresponding to the random interleaving relationship in the target matrix needs to be an even sequence.

[0078] For example, the column vectors of the convolutionally encoded data obtained after convolution of the odd-numbered columns are compared with the column vectors of the columns in the target matrix for which the random interleaving relationship needs to be determined. If the comparison is successful, the column indices of the odd-numbered and even-numbered columns are obtained, for example, column indices of the odd-numbered columns are 'a' and column indices of the even-numbered columns are 'b'. After a successful comparison, the column indices of the two columns are recorded. At this point, the random interleaving relationship π between the two is... Then, the comparison continues until the random interleaving relationship of all third-way data in the target matrix is ​​obtained; if the comparison fails, an odd column that has not yet been selected is selected again in the target matrix, and convolutional encoding and comparison are performed again.

[0079] In summary, the random interleaving relationship identification method provided in this application can blindly identify the random interleaving relationship of the third path data in Turbo code data without knowing the interleaving parameters.

[0080] The following describes an apparatus embodiment of this application, which can be used to execute a random interleaving relationship identification method as described in the above embodiments of this application. For details not disclosed in the apparatus embodiments of this application, please refer to the embodiments of the method described in the above applications.

[0081] Reference Figure 3As shown, a random interleaving relationship identification device 300 according to an embodiment of this application includes: a matrix construction unit 301 and an identification unit 302.

[0082] The matrix construction unit 301 is used to construct an error-free target matrix based on the received Turbo code data; the identification unit 302 is used to identify the random interleaving relationship of the third path data in the Turbo code data from the target matrix based on the encoding principle of the recursive system convolutional code.

[0083] In some feasible embodiments, the matrix construction unit 301 includes: a first matrix construction unit, configured to construct a first matrix based on the first path data and the third path data in the received Turbo code data; a Gaussian column elimination unit, configured to perform Gaussian column elimination on the first matrix to obtain a second matrix; and an error removal unit, configured to identify and remove errors in the second matrix.

[0084] In some feasible embodiments, the error rejection unit is configured to: identify and reject rows in the second matrix that do not conform to the linear combination property of recursive system convolutional codes.

[0085] In some feasible embodiments, the identification unit 302 includes: a filtering unit for selecting a target sequence in the target matrix; a convolutional coding unit for performing convolutional coding on the target sequence based on the coding principle to obtain convolutional coded data; and a determination unit for comparing the convolutional coded data with the third path data of the Turbo code data in the target matrix to determine the random interleaving relationship of the third path data.

[0086] In some feasible embodiments, the convolutional coding unit is configured as follows:

[0087] Initialize the recursive system convolutional code encoder used for convolutional coding;

[0088] The target sequence is input into the recursive system convolutional code encoder for convolutional encoding to obtain the convolutional encoded data.

[0089] In some feasible embodiments, the determining unit is configured as follows:

[0090] The column vectors of the convolutionally encoded data are compared with the column vectors of the columns in the target matrix that need to be determined for random interleaving. If the comparison is successful, the column number of the target column and the column number of the column corresponding to the random interleaving relationship to be determined are obtained.

[0091] The random interleaving relationship of the third data is determined based on the column number of the target sequence and the column number of the sequence corresponding to the random interleaving relationship that needs to be determined.

[0092] Based on the same inventive concept, embodiments of this application also provide a data identification method, including:

[0093] Receive Turbo code data;

[0094] Based on the method described in the above embodiments, the random interleaving relationship of the third path data in the Turbo code data is identified;

[0095] The Turbo code data is decoded based on the random interleaving relationship of the third path data in the Turbo code data and the structure of the recursive system convolutional code encoder.

[0096] It should be noted that the original data needs to undergo signal modulation and encoding during transmission. Therefore, after receiving Turbo code data, demodulation and decoding are required before data recognition can be achieved.

[0097] like Figure 4 As shown, this application embodiment also provides an electronic device 400, including a memory 410, a processor 420, and a computer program 411 stored in the memory 410 and executable on the processor. When the processor 420 executes the computer program 411, it implements the steps of any of the above-described Turbo code random interleaving relationship recognition methods.

[0098] Since the electronic device described in this embodiment is the device used to implement the random interleaving relationship identification device in the embodiments of this application, those skilled in the art can understand the specific implementation method and various variations of the electronic device in this embodiment based on the method described in the embodiments of this application. Therefore, how the electronic device implements the method in the embodiments of this application will not be described in detail here. Any device used by those skilled in the art to implement the method in the embodiments of this application falls within the scope of protection of this application.

[0099] In practice, when the computer program 411 is executed by the processor, it can implement any of the embodiments corresponding to the first aspect.

[0100] It should be noted that the descriptions of each embodiment in the above embodiments have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0101] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0102] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0103] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0104] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0105] This application also provides a computer program product, which includes computer software instructions that, when executed on a processing device, cause the processing device to perform the process of the Turbo code random interleaving relationship recognition method as described in the first aspect corresponding embodiment.

[0106] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between apparatuses or units, and may be electrical, mechanical, or other forms.

[0107] The units described as separate components may or may not be physically separate. 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 units can be selected to achieve the purpose of this embodiment according to actual needs.

[0108] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0109] If the integrated unit 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, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0110] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for identifying random interleaving relationships, applied to a signal receiver, the signal receiver being used to receive Turbo code data, the Turbo code data including a first data stream, a second data stream, and a third data stream, wherein, The first data path is the original input data; the second data path is the output data after the original input data has been processed by a recursive system convolutional code encoder; and the third data path is the output data after the original input data has been processed sequentially by a random interleaver and a recursive system convolutional code encoder. The method is characterized by comprising: Construct an error-free target matrix based on the received Turbo code data; Based on the encoding principle of the recursive system convolutional code, the random interleaving relationship of the third path data in the Turbo code data is identified from the target matrix; The construction of the error-free target matrix based on the received Turbo code data includes: Construct a first matrix based on the first path data and the third path data in the received Turbo code data; Gaussian elimination is performed on the first matrix to obtain the second matrix; Errors are identified and eliminated in the second matrix.

2. The method according to claim 1, characterized in that, The step of identifying and removing errors in the second matrix includes: In the second matrix, identify and remove rows that do not conform to the linear combination property of recursive system convolutional codes.

3. The method according to claim 1, characterized in that, The method of identifying the random interleaving relationship of the third path data in the Turbo code data from the target matrix based on the encoding principle of the recursive system convolutional code includes: Select the target sequence from the target matrix; Based on the aforementioned encoding principle, the target sequence is convolutionally encoded to obtain convolutionally encoded data. The random interleaving relationship of the third-path data is determined by comparing the convolutionally encoded data with the third-path Turbo code data in the target matrix.

4. The method according to claim 3, characterized in that, The step of performing convolutional encoding on the target sequence based on the aforementioned encoding principle to obtain convolutionally encoded data includes: Initialize the recursive system convolutional code encoder used for convolutional coding; The target sequence is input into the recursive system convolutional code encoder for convolutional encoding to obtain the convolutional encoded data.

5. The method according to claim 3, characterized in that, The step of comparing the third-path data based on the convolutionally encoded data and the Turbo code data in the target matrix to determine the random interleaving relationship of the third-path data includes: The column vectors of the convolutionally encoded data are compared with the column vectors of the columns in the target matrix that need to be determined for random interleaving. If the comparison is successful, the column number of the target column and the column number of the column corresponding to the random interleaving relationship to be determined are obtained. The random interleaving relationship of the third data is determined based on the column number of the target sequence and the column number of the sequence corresponding to the random interleaving relationship that needs to be determined.

6. A random interleaving relationship identification device, applied to the method as described in any one of claims 1-5, characterized in that, include: A matrix construction unit is used to construct an error-free target matrix based on the received Turbo code data; The identification unit is used to identify the random interleaving relationship of the third path data in the Turbo code data from the target matrix based on the encoding principle of the recursive system convolutional code.

7. An electronic device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the method as described in any one of claims 1-5 when executing the computer program stored in the memory.

8. A data recognition method, characterized in that, include: Receive Turbo code data; Identify the random interleaving relationship of the third path data in the Turbo code data based on the method of any one of claims 1-5; The Turbo code data is decoded based on the random interleaving relationship of the third path data in the Turbo code data and the structure of the recursive system convolutional code encoder.

Images