Self-adaptive quantization LDPC (Low Density Parity Check) decoding method and device

Abstract

The invention provides a self-adaptive quantization LDPC decoding method and device, and relates to the technical field of optical communication, and the method comprises the steps: obtaining original information data, carrying out the preprocessing of the original information data, and obtaining standard information data; data adjustment is carried out on the standard information data, adjustment information data is obtained, and frame data information in the adjustment information data is in Gaussian distribution; the adjustment information data is input to an LDPC decoding initial quantization unit, decoding initialization processing is carried out on the adjustment information data, quantization information data is obtained, and the LDPC decoding initial quantization unit iteratively generates variable node information according to the adjustment information data; inputting the quantized information data into a self-adaptive quantized decoding processing unit to perform main decoding processing on the quantized information data to obtain main decoded data, and performing self-adaptive quantized processing on variable node information when variable nodes of the self-adaptive quantized decoding processing unit are updated to obtain updated node information; and integrating the main decoding data and the updated node information to enable the adaptive quantization decoding processing unit to output a target decoding result. The method is beneficial for improving the sensitivity of LDPC decoding.

Description

Technical Field

[0001] The present invention relates to the field of optical communication technology, and in particular to an adaptive quantized LDPC decoding method and device. Background Art

[0002] Driven by high-bandwidth services such as 4k / 8k video, virtual and augmented reality, and industrial Internet, passive optical networks (PON) are ushering in a new round of accelerated development of data traffic. The development of PON must be compatible with the existing ODN (optical distribution network), which puts forward specific requirements for the PR30 (29dB) link budget of 50G-PON. However, it is a huge challenge to increase the speed of each wavelength by 50Gb / s while maintaining sufficient power budget. In order to meet the power budget requirements of 50G-PON, multiple technologies such as digital signal processing (DSP), optical amplification, and soft decision forward error correction (SD-FEC) are required.

[0003] The Chinese patent with announcement number CN105978578B discloses a non-uniform quantization method for low-density parity check code and product decoding operation value, the method comprising: first, selecting a set of uniform quantization parameters represented by multiple bits for the soft information of the i-th code element of the LDPC code, the external information output from the bit node i to the check node j, and the external information output from the check node j to the bit node i; then, based on the uniform quantization parameter, gradually reducing the number of quantization values ​​of the three variables in turn until the number of quantization values ​​required for the three variables that meet the decoding performance requirements is obtained, these quantization values ​​constitute three different sets of non-uniform quantization parameters, and the three non-uniform quantization parameters are used to quantize the three variable values ​​respectively. However, as the required processing line data rate increases, the above scheme occupies more and more resources, and the performance of the quantization method gradually fails to meet the sensitivity. Therefore, it is very necessary to provide an adaptive quantization LDPC decoding method and device to improve the sensitivity of LDPC decoding. Summary of the invention

[0004] In view of this, the present invention proposes an adaptive quantization LDPC decoding method and device. The LDPC decoding initial quantization unit performs decoding initialization processing on the adjusted information data, and the partially parallelized processing helps to improve the overall decoding efficiency. The adaptive quantization decoding processing unit performs adaptive quantization processing when the variable node is updated, thereby improving the sensitivity and robustness of the decoding.

[0005] The present invention provides an adaptive quantized LDPC decoding method, the method comprising:

[0006] Acquiring original information data, and preprocessing the original information data to obtain standard information data;

[0007] Performing data adjustment on the standard information data to obtain adjusted information data, wherein the frame data information in the adjusted information data presents a Gaussian distribution;

[0008] Inputting the adjustment information data into an LDPC decoding initial quantization unit to perform decoding initialization processing on the adjustment information data to obtain quantized information data, wherein the LDPC decoding initial quantization unit iteratively generates variable node information according to the adjustment information data;

[0009] Inputting the quantization information data into an adaptive quantization decoding processing unit to perform main decoding processing on the quantization information data to obtain main decoding data, and performing adaptive quantization processing on the variable node information when the variable node of the adaptive quantization decoding processing unit is updated to obtain updated node information;

[0010] The main decoding data and the update node information are integrated so that the adaptive quantization decoding processing unit outputs a target decoding result.

[0011] On the basis of the above technical solution, preferably, the preprocessing of the original information data to obtain the standard information data specifically includes:

[0012] Acquire a modulation format type corresponding to the original information data, and demodulate the original information data according to the modulation format type to acquire transition information data;

[0013] The transition information data is frame synchronized, a frame header of each frame in the transition information data after frame synchronization and frame data information corresponding to the frame header are collected, and all frame data information is integrated into the standard information data.

[0014] On the basis of the above technical solution, preferably, the step of adjusting the standard information data to obtain the adjusted information data specifically includes:

[0015] Performing mean calculation on the frame data information of each frame in the standard information data to obtain a first frame data information mean and a second frame data information mean, wherein the first frame data information mean represents the mean of the frame data information whose mean is greater than 0, and the second frame data information mean represents the mean of the frame data information whose mean is less than 0;

[0016] Based on the mean value of the first frame data information and the mean value of the second frame data information, respectively calculating the deviation of the frame data information located at the positive semi-axis and the deviation of the frame data information located at the negative semi-axis;

[0017] The positive half-axis frame data information is used as a reference value, and the negative half-axis frame data information is optimized according to the data information adjustment function to obtain the adjustment information data.

[0018] More preferably, the deviation of the positive semi-axis frame data information, the deviation of the negative semi-axis frame data information and the data information adjustment function are respectively expressed as:

[0019]

[0020] Among them, σ1 represents the deviation of the positive half-axis frame data information, σ2 represents the deviation of the negative half-axis frame data information, and X i Indicates the positive half axis frame data information, Y i Indicates the negative half-axis frame data information, V avg+ Represents the mean value of the first frame data information, V avg- Represents the mean value of the second frame data information, y i represents the data information adjustment function, and n represents the number of data points corresponding to the frame data information.

[0021] More preferably, the step of inputting the adjustment information data into an LDPC decoding initial quantization unit to perform decoding initialization processing on the adjustment information data to obtain quantized information data specifically includes:

[0022] Calculating the quantization step size of the LDPC decoding initial quantization unit according to the initial parameter quantization threshold and the number of quantization bits;

[0023] Based on the quantization step size and the quantization decoding function, quantization information data corresponding to the quantization step size is obtained.

[0024] More preferably, the expression of the quantization decoding function is:

[0025]

[0026] Wherein, Δ represents the quantization step size of the LDPC decoding unit, ω represents the initial parameter quantization threshold, q represents the number of quantization bits, and r i represents the quantization decoding function, sgn() represents the first sign function, y i Represents the data information adjustment function, Represents the floor function.

[0027] More preferably, the expression of the quantization decoding function is:

[0028]

[0029] Wherein, Δ represents the quantization step size of the LDPC decoding unit, ω represents the initial parameter quantization threshold, q represents the number of quantization bits, and ri represents the quantization decoding function, sgn() represents the first sign function, y i Represents the data information adjustment function, Represents the floor function.

[0030] More preferably, the expression of the check node update function is:

[0031]

[0032] Where l represents the number of iterations, m represents the number of decoding layers, i and j represent the node positions, sgn() represents the first symbol function, ω l represents the initial quantization upper limit threshold, q represents the number of quantization bits, Represents the variable node c i The variable node information is adaptively quantized to obtain the quantization result. represents the check node update function, Π represents the continuous product, sign() represents the second sign function, i' represents all variable nodes connected to node position j except node position i, N c represents the number of node positions, and α represents the correction coefficient.

[0033] A second aspect of the present invention provides an adaptive quantization LDPC decoding device, the adaptive quantization LDPC decoding device comprises a data processing module, a data adjustment module and a data processing module, wherein:

[0034] The data processing module is used to obtain original information data, and pre-process the original information data to obtain standard information data;

[0035] The data adjustment module is used to perform data adjustment on the standard information data to obtain adjustment information data, wherein the frame data information in the adjustment information data is Gaussian distributed;

[0036] The data processing module is used to input the adjustment information data into the LDPC decoding initial quantization unit to perform decoding initialization processing on the adjustment information data and obtain quantization information data, wherein the LDPC decoding initial quantization unit iteratively generates variable node information according to the adjustment information data, inputs the quantization information data into the adaptive quantization decoding processing unit to perform main decoding processing on the quantization information data and obtain main decoding data, and performs adaptive quantization processing on the variable node information when the variable node of the adaptive quantization decoding processing unit is updated to obtain updated node information, and integrates the main decoding data and the updated node information so that the adaptive quantization decoding processing unit outputs a target decoding result.

[0037] In the third aspect of the present invention, an electronic device is provided, characterized in that it includes a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the above method.

[0038] The adaptive quantization LDPC decoding method and device provided by the present invention have the following beneficial effects compared with the prior art:

[0039] (1) By preprocessing and adjusting the original information data, the frame data information is made to be Gaussian distributed, providing a better quality input for the subsequent LDPC decoding, avoiding the impact of non-Gaussian noise on the LDPC decoding performance, and enhancing the system's anti-noise ability in complex channel environments. The LDPC decoding initial quantization unit performs decoding initialization processing on the adjusted information data, and the partially parallelized processing helps to improve the overall decoding efficiency. The adaptive quantization decoding processing unit performs adaptive quantization processing when the variable node is updated, so that the decoding process can dynamically adapt to changes in signal characteristics. At the same time, it can reduce the hardware resources occupied by LDPC decoding and reduce power consumption, thereby improving the sensitivity and robustness of decoding.

[0040] (2) By using the preset initial a posteriori probability and check node update function, a good starting point is provided for the iterative process, which helps to accelerate convergence and improve decoding performance. By setting the maximum number of iterations, the complexity and time consumption of the decoding process can be effectively controlled, preventing it from falling into infinite iterations, ensuring that the decoding process is completed within an acceptable time. Through multiple iterations and outputting the corresponding node information, the decoding process can adapt to different channel conditions and signal characteristics, thereby improving the flexibility and adaptability of decoding. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0042] Figure 1 A schematic diagram of a flow chart of an adaptive quantized LDPC decoding method provided by the present invention;

[0043] Figure 2 A schematic diagram of the structure of an adaptive quantized LDPC decoding device provided in an embodiment of the present invention;

[0044] Figure 3 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention.

[0045] Explanation of the accompanying drawings: 1. Adaptive quantization LDPC decoding device; 11. Data processing module; 12. Data adjustment module; 13. Data processing module; 2. Electronic device; 21. Processor; 22. Communication bus; 23. User interface; 24. Network interface; 25. Memory. DETAILED DESCRIPTION

[0046] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] The following is combined with Figure 1-3 The present invention is described in further detail.

[0048] The present invention discloses a data processing method combining retention enhancement and mosaic enhancement, such as Figure 1 As shown, the method comprises steps S1 to S5.

[0049] Step S1, obtaining original information data, and preprocessing the original information data to obtain standard information data.

[0050] In this embodiment, step S1 also includes steps S11 to S12.

[0051] Step S11, obtaining a modulation format type corresponding to the original information data, and demodulating the original information data according to the modulation format type to obtain transition information data.

[0052] In this step, the modulation format (such as QPSK, 16QAM, etc.) is identified through preset signal characteristics or header information, and the modulation type is determined using signal processing techniques such as spectrum analysis, constellation diagram analysis, etc.

[0053] In one example, for QPSK, the received complex signal is mapped to the IQ plane, and the corresponding binary bits are determined according to the quadrant position of the signal in the IQ plane, and each symbol is demodulated into 2 bits; for 16QAM, the received signal is mapped to the 16QAM constellation diagram, and the corresponding 4 bits are determined according to the position of the signal point, and each symbol is demodulated into 4 bits.

[0054] Step S12, frame synchronization is performed on the transition information data, a frame header of each frame in the transition information data after frame synchronization and frame data information corresponding to the frame header are collected, and all frame data information is integrated into standard information data.

[0055] In this step, a sliding window technique is used to scan the transition information data to find a predefined frame synchronization sequence (usually a specific bit pattern). When a frame synchronization sequence is detected, the position is marked as the start of the frame header, and the position information of each frame header is recorded. Based on the known frame structure (such as frame length), the complete frame data is extracted from each frame header, and non-data parts such as the frame header and frame tail are removed. All extracted frame data are combined in sequence, possible duplicate data or padding data are removed, and the integrated data is organized into a predefined standard format.

[0056] Step S2, performing data adjustment on the standard information data to obtain adjusted information data, wherein the frame data information in the adjusted information data presents a Gaussian distribution.

[0057] In this embodiment, step S2 also includes steps S21 to S23.

[0058] Step S21, calculate the mean of the frame data information of each frame in the standard information data to obtain the first frame data information mean and the second frame data information mean, wherein the first frame data information mean represents the mean of the frame data information whose mean is greater than 0, and the second frame data information mean represents the mean of the frame data information whose mean is less than 0.

[0059] Step S22, based on the mean value of the first frame data information and the mean value of the second frame data information, respectively calculate the deviation of the positive half-axis frame data information and the deviation of the negative half-axis frame data information.

[0060] Step S23, taking the positive half-axis frame data information as a reference value, and optimizing the negative half-axis frame data information according to the data information adjustment function, to obtain adjustment information data.

[0061] The expressions of the deviation of the positive half-axis frame data information, the deviation of the negative half-axis frame data information and the data information adjustment function are:

[0062]

[0063] Among them, σ1 represents the deviation of the positive half-axis frame data information, σ2 represents the deviation of the negative half-axis frame data information, and X i Indicates the positive half axis frame data information, Y i Indicates the negative half-axis frame data information, V avg+ Represents the mean value of the first frame data information, V avg- Represents the mean value of the second frame data information, y i represents the data information adjustment function, and n represents the number of data points corresponding to the frame data information.

[0064] In one example, each frame of data in the standard information data is scanned, and the mean value V of the first frame of data information greater than 0 in the frame data information is calculated. avg+ and the second frame data information mean value V less than 0 avg- ; Calculate the deviation of the data information in the positive and negative half-axis frames respectively and Among them, X i Indicates the positive half axis frame data information, that is, the frame data information greater than 0, Y i Indicates the negative half-axis frame data information, that is, the frame data information less than 0; sets the positive half-axis frame data information Y i is a reference value. For the negative half-axis frame data information Y less than 0 i Modified to Frame data information X greater than 0 i No change, output y i Initialize the quantization decoding unit for LDPC.

[0065] Step S3, inputting the adjustment information data into the LDPC decoding initial quantization unit to perform decoding initialization processing on the adjustment information data to obtain quantized information data, wherein the LDPC decoding initial quantization unit iteratively generates variable node information according to the adjustment information data.

[0066] In this step, the quantization step size of the initial quantization unit of the LDPC decoding is calculated according to the initial parameter quantization threshold and the number of quantization bits; based on the quantization step size and the quantization decoding function, the quantization information data corresponding to the quantization step size is obtained.

[0067] The expression of the quantization decoding function is:

[0068]

[0069] Where Δ represents the quantization step size of the LDPC decoding unit, ω represents the initial parameter quantization threshold, q represents the number of quantization bits, and r i represents the quantization decoding function, sgn() represents the first sign function, y i Represents the data information adjustment function, Represents the floor function.

[0070] Furthermore, by setting two key parameters, the quantization threshold ω and the number of quantization bits q, the quantization threshold ω determines the range of quantization, while the number of quantization bits q determines the accuracy of quantization. The quantization step size is the interval that divides the continuous signal into discrete levels. Calculation formula The effect of the quantization threshold and the number of bits on the step size is reflected. The advantage of this non-uniform quantization method is that it can better retain the important information of the signal under a limited number of bits. By adjusting the quantization threshold ω and the number of quantization bits q, a balance can be found between accuracy and resource consumption. This enables the LDPC decoding initial quantization unit to provide sufficient information to support an efficient decoding process while maintaining low hardware complexity.

[0071] Step S4, inputting the quantization information data into the adaptive quantization decoding processing unit to perform main decoding processing on the quantization information data to obtain main decoding data, and performing adaptive quantization processing on the variable node information when the variable node of the adaptive quantization decoding processing unit is updated to obtain updated node information.

[0072] In this embodiment, step S4 also includes steps S41 to S43.

[0073] Step S41, setting preset iteration parameters and initial input parameters, wherein the preset iteration parameters include a maximum number of iterations, and the initial input parameters include an initial posterior probability and a check node update function.

[0074] The expression of the check node update function is:

[0075]

[0076] Where l represents the number of iterations, m represents the number of decoding layers, i and j represent the node positions, sgn() represents the first symbol function, ω l represents the initial quantization upper limit threshold, q represents the number of quantization bits, Represents the variable node c i The variable node information is adaptively quantized to obtain the quantization result. represents the check node update function, Π represents the continuous product, sign() represents the second sign function, i' represents all variable nodes connected to node position j except node position i, N c represents the number of node positions, and α represents the correction coefficient.

[0077] Step S42, based on the initial posterior probability and the check node update function, iterate the initial posterior probability for multiple times, and output node information corresponding to the number of iterations.

[0078] Step S43: when the number of iterations reaches the maximum number of iterations, update node information is obtained.

[0079] By using the preset initial a posteriori probability and check node update function, a good starting point is provided for the iterative process, which helps to accelerate convergence and improve decoding performance. By setting the maximum number of iterations, the complexity and time consumption of the decoding process can be effectively controlled, preventing it from falling into infinite iterations, ensuring that the decoding process is completed within an acceptable time. Through multiple iterations and outputting the corresponding node information, the decoding process can adapt to different channel conditions and signal characteristics, thereby improving the flexibility and adaptability of decoding.

[0080] In one example, the number of layers of the check matrix is ​​set to M, the number of initialization iterations is set to l = 1, and the maximum number of iterations is set to l max , adaptive quantization upper threshold ω max , initial quantization upper limit threshold ω l =ω, upper threshold growth rate τ, where,

[0081] set up The initial posterior probability of the first layer is:

[0082] set up To update as the initial check node:

[0083] Subsequent decoding block updates:

[0084] set up is the variable node c of the mth layer of the check matrix H i Information:

[0085] Where l is the number of iterations, m is the number of decoding layers, and i and j are node positions;

[0086] For variable node c i Information Perform adaptive quantization and quantize the results in, is the floor rounding function;

[0087] set up To update the verification node:

[0088]

[0089] set up For the lth iteration, the probability of the posterior information of the mth layer is:

[0090]

[0091] Set the quantization parameter ω l renew,

[0092] The number of iterations l = l + 1, when the number of iterations l = l max , complete the last iteration process, and output the data judgment; otherwise, repeat the subsequent decoding word block update;

[0093] Setting z i The output codeword information: z i =1, if z i =0, other cases.

[0094] Step S5, integrating the main decoding data and the update node information so that the adaptive quantization decoding processing unit outputs a target decoding result.

[0095] In one example, the posterior probability of each bit is extracted from the last iteration These values ​​represent the probability of each bit being 0 or 1, creating an array or matrix to store the final states of all variable nodes, including their posterior probabilities and the associated check node information. i Combined into a bit sequence z = [z1, z2, ..., z n ], where n is the length of the LDPC codeword. Use the LDPC code check matrix H and the hard decision result z to check: s = Hz T (modulo 2 addition), if s is an all-zero vector, the decoding is considered successful; otherwise, it is marked that there are uncorrected errors. Compare the original received sequence and the decoded sequence, calculate the number of corrected error bits, and if the check fails, estimate the number of remaining error bits. The number of failures in the check equation can be used as an estimate.

[0096] By preprocessing and adjusting the original information data, the frame data information is made to be Gaussian distributed, providing a better quality input for subsequent LDPC decoding, avoiding the impact of non-Gaussian noise on the LDPC decoding performance, and enhancing the system's anti-noise ability in complex channel environments. The LDPC decoding initial quantization unit performs decoding initialization processing on the adjusted information data, and partially parallel processing helps to improve the overall decoding efficiency. The adaptive quantization decoding processing unit performs adaptive quantization processing when the variable node is updated, so that the decoding process can dynamically adapt to changes in signal characteristics. At the same time, it can reduce the hardware resources occupied by the LDPC decoder and reduce power consumption, thereby improving the sensitivity and robustness of the decoding.

[0097] Based on the above method, the embodiment of the present application discloses an adaptive quantized LDPC decoding device, referring to Figure 2 The adaptive quantization LDPC decoding device 1 includes a data processing module 11, a data adjustment module 12 and a data processing module 13, wherein:

[0098] The data processing module 11 is used to obtain original information data, and pre-process the original information data to obtain standard information data;

[0099] The data adjustment module 12 is used to adjust the standard information data to obtain the adjusted information data, wherein the frame data information in the adjusted information data is Gaussian distributed;

[0100] The data processing module 13 is used to input the adjustment information data into the LDPC decoding initial quantization unit to perform decoding initialization processing on the adjustment information data and obtain quantization information data, wherein the LDPC decoding initial quantization unit iteratively generates variable node information according to the adjustment information data, inputs the quantization information data into the adaptive quantization decoding processing unit to perform main decoding processing on the quantization information data and obtain main decoding data, and performs adaptive quantization processing on the variable node information when the variable node of the adaptive quantization decoding processing unit is updated to obtain updated node information, and integrates the main decoding data and the updated node information so that the adaptive quantization decoding processing unit outputs the target decoding result.

[0101] In one example, the data processing module 11 is used to obtain the modulation format type corresponding to the original information data, and demodulate the original information data according to the modulation format type to obtain transition information data; perform frame synchronization on the transition information data, collect the frame header of each frame in the transition information data after frame synchronization and the frame data information corresponding to the frame header, and integrate all frame data information into standard information data.

[0102] In one example, the data adjustment module 12 is used to perform mean calculation on the frame data information of each frame in the standard information data to obtain the first frame data information mean and the second frame data information mean, wherein the first frame data information mean represents the frame data information mean whose mean is greater than 0, and the second frame data information mean represents the frame data information mean whose mean is less than 0; based on the first frame data information mean and the second frame data information mean, the deviation of the positive half-axis frame data information and the deviation of the negative half-axis frame data information are calculated respectively; the positive half-axis frame data information is used as a reference value, and the negative half-axis frame data information is optimized according to the data information adjustment function to obtain adjustment information data.

[0103] In one example, the deviation of the positive half-axis frame data information, the deviation of the negative half-axis frame data information, and the expressions of the data information adjustment function are respectively:

[0104]

[0105] Among them, σ1 represents the deviation of the positive half-axis frame data information, σ2 represents the deviation of the negative half-axis frame data information, and X i Indicates the positive half axis frame data information, Y iIndicates the negative half-axis frame data information, V avg+ Represents the mean value of the first frame data information, V avg- Represents the mean value of the second frame data information, y i represents the data information adjustment function, and n represents the number of data points corresponding to the frame data information.

[0106] In one example, the data processing module 13 is used to calculate the quantization step of the initial quantization unit of LDPC decoding according to the initial parameter quantization threshold and the number of quantization bits; based on the quantization step and the quantization decoding function, obtain the quantization information data corresponding to the quantization step.

[0107] In one example, the data processing module 12 is used to perform a derivative operation according to the label logic value corresponding to each pixel in the second feature map to obtain a single importance score, and the data processing module sums the single importance scores to obtain the importance score.

[0108] In one example, the expression of the quantization decoding function is:

[0109]

[0110] Where Δ represents the quantization step size of the LDPC decoding unit, ω represents the initial parameter quantization threshold, q represents the number of quantization bits, and r i represents the quantization decoding function, sgn() represents the first sign function, y i Represents the data information adjustment function, Represents the floor function.

[0111] In one example, the data processing module 13 is used to set preset iteration parameters and initial input parameters, wherein the preset iteration parameters include the maximum number of iterations, and the initial input parameters include the initial posterior probability and the check node update function; based on the initial posterior probability and the check node update function, the initial posterior probability is iterated multiple times, and the node information corresponding to the number of iterations is output; when the number of iterations reaches the maximum number of iterations, the updated node information is obtained.

[0112] In one example, the expression of the check node update function is:

[0113]

[0114] Where l represents the number of iterations, m represents the number of decoding layers, i and j represent the node positions, sgn() represents the first symbol function, ω l represents the initial quantization upper limit threshold, q represents the number of quantization bits, Represents the variable node c i The variable node information is adaptively quantized to obtain the quantization result. represents the check node update function, Π represents the continuous product, sign() represents the second sign function, i' represents all variable nodes connected to node position j except node position i, N c represents the number of node positions, and α represents the correction coefficient.

[0115] See also Figure 3 , is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 3 As shown, the electronic device 2 may include: at least one processor 21 , at least one network interface 24 , a user interface 23 , a memory 25 , and at least one communication bus 22 .

[0116] The communication bus 22 is used to realize the connection and communication between these components.

[0117] The user interface 23 may include a display screen (Display) and a camera (Camera), and the optional user interface 23 may also include a standard wired interface and a wireless interface.

[0118] The network interface 24 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0119] Among them, the processor 21 may include one or more processing cores. The processor 21 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 25, and calling data stored in the memory 25. Optionally, the processor 21 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 21 can integrate one or more combinations of a central processing unit (Central Processing Unit, CPU), a graphics processor (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 21, and it can be implemented separately through a chip.

[0120] Among them, the memory 25 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 25 includes a non-transitory computer-readable storage medium. The memory 25 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 25 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned method embodiments, etc.; the data storage area may store data involved in the above-mentioned method embodiments, etc. The memory 25 may also be optionally at least one storage device located away from the aforementioned processor 21. As Figure 3 As shown, the memory 25 as a computer storage medium may include an operating system, a network communication module, a user interface module, and an application program of an adaptive quantization LDPC decoding method.

[0121] exist Figure 3 In the electronic device 2 shown, the user interface 23 is mainly used to provide an input interface for the user and obtain data input by the user; and the processor 21 can be used to call the application program storing the adaptive quantization LDPC decoding method in the memory 25. When executed by one or more processors, the electronic device executes one or more methods in the above-mentioned embodiments.

[0122] A computer-readable storage medium stores instructions, which, when executed by one or more processors, enable the computer to execute one or more methods in the above-mentioned embodiments.

[0123] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the order of the actions described, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0124] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0125] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0126] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0127] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0128] If the integrated 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 memory. Based on this understanding, the technical solution of the present application, 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, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0129] The above are only exemplary embodiments of the present disclosure, and the scope of the present disclosure cannot be limited thereto. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and the truth of practice, it will be easy for those skilled in the art to think of other embodiments of the present disclosure. This application is intended to cover any variation, use or adaptive change of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary techniques in the technical field that are not recorded in the present disclosure.

[0130] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An adaptive quantization LDPC decoding method, applied to a signal receiving end, It is characterized in that The method comprises: Acquiring original information data, and preprocessing the original information data to obtain standard information data; Performing data adjustment on the standard information data to obtain adjusted information data, wherein the frame data information in the adjusted information data presents a Gaussian distribution; Inputting the adjustment information data into an LDPC decoding initial quantization unit to perform decoding initialization processing on the adjustment information data to obtain quantized information data, wherein the LDPC decoding initial quantization unit iteratively generates variable node information according to the adjustment information data; Inputting the quantization information data into an adaptive quantization decoding processing unit to perform main decoding processing on the quantization information data to obtain main decoding data, and performing adaptive quantization processing on the variable node information when the variable node of the adaptive quantization decoding processing unit is updated to obtain updated node information; The main decoding data and the update node information are integrated so that the adaptive quantization decoding processing unit outputs a target decoding result.

2. The method according to claim 1, It is characterized in that The preprocessing of the original information data to obtain standard information data specifically includes: Acquire a modulation format type corresponding to the original information data, and demodulate the original information data according to the modulation format type to acquire transition information data; The transition information data is frame synchronized, a frame header of each frame in the transition information data after frame synchronization and frame data information corresponding to the frame header are collected, and all frame data information is integrated into the standard information data.

3. The method according to claim 1, It is characterized in that The step of adjusting the standard information data to obtain the adjusted information data specifically includes: Performing mean calculation on the frame data information of each frame in the standard information data to obtain a first frame data information mean and a second frame data information mean, wherein the first frame data information mean represents the mean of the frame data information whose mean is greater than 0, and the second frame data information mean represents the mean of the frame data information whose mean is less than 0; Based on the mean value of the first frame data information and the mean value of the second frame data information, respectively calculating the deviation of the frame data information located at the positive semi-axis and the deviation of the frame data information located at the negative semi-axis; The positive half-axis frame data information is used as a reference value, and the negative half-axis frame data information is optimized according to the data information adjustment function to obtain the adjustment information data.

4. The method according to claim 3, It is characterized in that The expressions of the deviation of the positive semi-axis frame data information, the deviation of the negative semi-axis frame data information and the data information adjustment function are respectively: Among them, σ 1 Indicates the deviation of the positive half-axis frame data information, σ 2 Indicates the deviation of the negative half-axis frame data information, X i Indicates the positive half axis frame data information, Y i Indicates the negative half-axis frame data information, V avg+ Represents the mean value of the first frame data information, V avg- Represents the mean value of the second frame data information, y i represents the data information adjustment function, and n represents the number of data points corresponding to the frame data information.

5. The method according to claim 1, It is characterized in that The step of inputting the adjustment information data into an LDPC decoding initial quantization unit to perform decoding initialization processing on the adjustment information data to obtain quantized information data specifically includes: Calculating the quantization step size of the LDPC decoding initial quantization unit according to the initial parameter quantization threshold and the number of quantization bits; Based on the quantization step size and the quantization decoding function, quantization information data corresponding to the quantization step size is obtained.

6. The method according to claim 5, It is characterized in that The expression of the quantization decoding function is: Wherein, Δ represents the quantization step size of the LDPC decoding unit, ω represents the initial parameter quantization threshold, q represents the number of quantization bits, and r i represents the quantization decoding function, sgn() represents the first sign function, y i Represents the data information adjustment function, Represents the floor function.

7. The method according to claim 1, It is characterized in that The step of performing adaptive quantization processing on the variable node information when the variable node of the adaptive quantization decoding processing unit is updated to obtain the updated node information specifically includes: Setting preset iteration parameters and initial input parameters, wherein the preset iteration parameters include a maximum number of iterations, and the initial input parameters include an initial posterior probability and a check node update function; Based on the initial posterior probability and the check node update function, the initial posterior probability is iterated multiple times, and node information corresponding to the number of iterations is output; When the number of iterations reaches the maximum number of iterations, the updated node information is obtained.

8. The method according to claim 7, It is characterized in that The expression of the check node update function is: Where l represents the number of iterations, m represents the number of decoding layers, i and j represent the node positions, sgn() represents the first symbol function, ω l represents the initial quantization upper limit threshold, q represents the number of quantization bits, Represents the variable node c i The variable node information is adaptively quantized to obtain the quantization result. represents the check node update function, Π represents the continuous product, sign() represents the second sign function, i' represents all variable nodes connected to node position j except node position i, N c represents the number of node positions, and α represents the correction coefficient.

9. An adaptive quantized LDPC decoding device, It is characterized in that The adaptive quantization LDPC decoding device (1) comprises a data processing module (11), a data adjustment module (12) and a data processing module (13), wherein: The data processing module (11) is used to obtain original information data and pre-process the original information data to obtain standard information data; The data adjustment module (12) is used to perform data adjustment on the standard information data to obtain adjustment information data, wherein the frame data information in the adjustment information data presents a Gaussian distribution; The data processing module (13) is used to input the adjustment information data into the LDPC decoding initial quantization unit to perform decoding initialization processing on the adjustment information data and obtain quantization information data, wherein the LDPC decoding initial quantization unit iteratively generates variable node information according to the adjustment information data, inputs the quantization information data into the adaptive quantization decoding processing unit to perform main decoding processing on the quantization information data and obtain main decoding data, and when the variable nodes of the adaptive quantization decoding processing unit are updated, the variable node information is adaptively quantized to obtain updated node information, and the main decoding data and the updated node information are integrated so that the adaptive quantization decoding processing unit outputs a target decoding result.

10. An electronic device, It is characterized in that The electronic device (2) comprises a processor (21), a memory (25), a user interface (23) and a network interface (24), wherein the memory (25) is used to store instructions, the user interface (23) and the network interface (24) are used to communicate with other devices, and the processor (21) is used to execute the instructions stored in the memory (25) so that the electronic device (2) executes the method according to any one of claims 1 to 8.

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